This section of the Kubernetes documentation contains pages that show how to do individual tasks. A task page shows how to do a single thing, typically by giving a short sequence of steps.

If you would like to write a task page, see Creating a Documentation Pull Request.

1 - Install Tools

Set up Kubernetes tools on your computer.

kubectl

The Kubernetes command-line tool, kubectl, allows you to run commands against Kubernetes clusters. You can use kubectl to deploy applications, inspect and manage cluster resources, and view logs. For more information including a complete list of kubectl operations, see the kubectl reference documentation.

kubectl is installable on a variety of Linux platforms, macOS and Windows. Find your preferred operating system below.

kind

kind lets you run Kubernetes on your local computer. This tool requires that you have either Docker or Podman installed.

The kind Quick Start page shows you what you need to do to get up and running with kind.

View kind Quick Start Guide

minikube

Like kind, minikube is a tool that lets you run Kubernetes locally. minikube runs an all-in-one or a multi-node local Kubernetes cluster on your personal computer (including Windows, macOS and Linux PCs) so that you can try out Kubernetes, or for daily development work.

You can follow the official Get Started! guide if your focus is on getting the tool installed.

View minikube Get Started! Guide

Once you have minikube working, you can use it to run a sample application.

kubeadm

You can use the kubeadm tool to create and manage Kubernetes clusters. It performs the actions necessary to get a minimum viable, secure cluster up and running in a user friendly way.

Installing kubeadm shows you how to install kubeadm. Once installed, you can use it to create a cluster.

View kubeadm Install Guide

1.1 - Install and Set Up kubectl on Linux

Before you begin

You must use a kubectl version that is within one minor version difference of your cluster. For example, a v1.30 client can communicate with v1.29, v1.30, and v1.31 control planes. Using the latest compatible version of kubectl helps avoid unforeseen issues.

Install kubectl on Linux

The following methods exist for installing kubectl on Linux:

Install kubectl binary with curl on Linux
Install using native package management
Install using other package management

Install kubectl binary with curl on Linux

Download the latest release with the command:

x86-64
ARM64


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/arm64/kubectl"

Note:

To download a specific version, replace the $(curl -L -s https://dl.k8s.io/release/stable.txt) portion of the command with the specific version.

For example, to download version 1.30.0 on Linux x86-64, type:

curl -LO https://dl.k8s.io/release/v1.30.0/bin/linux/amd64/kubectl

And for Linux ARM64, type:

curl -LO https://dl.k8s.io/release/v1.30.0/bin/linux/arm64/kubectl

Validate the binary (optional)

Download the kubectl checksum file:

x86-64
ARM64


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl.sha256"


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/arm64/kubectl.sha256"

Validate the kubectl binary against the checksum file:

echo "$(cat kubectl.sha256)  kubectl" | sha256sum --check

If valid, the output is:

kubectl: OK

If the check fails, sha256 exits with nonzero status and prints output similar to:

kubectl: FAILED
sha256sum: WARNING: 1 computed checksum did NOT match

Note:

Download the same version of the binary and checksum.

Install kubectl

sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl

Note:

If you do not have root access on the target system, you can still install kubectl to the ~/.local/bin directory:

chmod +x kubectl
mkdir -p ~/.local/bin
mv ./kubectl ~/.local/bin/kubectl
# and then append (or prepend) ~/.local/bin to $PATH

Test to ensure the version you installed is up-to-date:
```
kubectl version --client
```
Or use this for detailed view of version:
```
kubectl version --client --output=yaml
```

Install using native package management

Update the apt package index and install packages needed to use the Kubernetes apt repository:

sudo apt-get update
# apt-transport-https may be a dummy package; if so, you can skip that package
sudo apt-get install -y apt-transport-https ca-certificates curl gnupg

Download the public signing key for the Kubernetes package repositories. The same signing key is used for all repositories so you can disregard the version in the URL:

# If the folder `/etc/apt/keyrings` does not exist, it should be created before the curl command, read the note below.
# sudo mkdir -p -m 755 /etc/apt/keyrings
curl -fsSL https://pkgs.k8s.io/core:/stable:/v1.30/deb/Release.key | sudo gpg --dearmor -o /etc/apt/keyrings/kubernetes-apt-keyring.gpg
sudo chmod 644 /etc/apt/keyrings/kubernetes-apt-keyring.gpg # allow unprivileged APT programs to read this keyring

Note:

In releases older than Debian 12 and Ubuntu 22.04, folder /etc/apt/keyrings does not exist by default, and it should be created before the curl command.

Add the appropriate Kubernetes apt repository. If you want to use Kubernetes version different than v1.30, replace v1.30 with the desired minor version in the command below:

# This overwrites any existing configuration in /etc/apt/sources.list.d/kubernetes.list
echo 'deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.30/deb/ /' | sudo tee /etc/apt/sources.list.d/kubernetes.list
sudo chmod 644 /etc/apt/sources.list.d/kubernetes.list   # helps tools such as command-not-found to work correctly

Note:

To upgrade kubectl to another minor release, you'll need to bump the version in /etc/apt/sources.list.d/kubernetes.list before running apt-get update and apt-get upgrade. This procedure is described in more detail in Changing The Kubernetes Package Repository.

Update apt package index, then install kubectl:

sudo apt-get update
sudo apt-get install -y kubectl

Add the Kubernetes yum repository. If you want to use Kubernetes version different than v1.30, replace v1.30 with the desired minor version in the command below.

# This overwrites any existing configuration in /etc/yum.repos.d/kubernetes.repo
cat <<EOF | sudo tee /etc/yum.repos.d/kubernetes.repo
[kubernetes]
name=Kubernetes
baseurl=https://pkgs.k8s.io/core:/stable:/v1.30/rpm/
enabled=1
gpgcheck=1
gpgkey=https://pkgs.k8s.io/core:/stable:/v1.30/rpm/repodata/repomd.xml.key
EOF

Note:

To upgrade kubectl to another minor release, you'll need to bump the version in /etc/yum.repos.d/kubernetes.repo before running yum update. This procedure is described in more detail in Changing The Kubernetes Package Repository.

Install kubectl using yum:
```
sudo yum install -y kubectl
```

Add the Kubernetes zypper repository. If you want to use Kubernetes version different than v1.30, replace v1.30 with the desired minor version in the command below.

# This overwrites any existing configuration in /etc/zypp/repos.d/kubernetes.repo
cat <<EOF | sudo tee /etc/zypp/repos.d/kubernetes.repo
[kubernetes]
name=Kubernetes
baseurl=https://pkgs.k8s.io/core:/stable:/v1.30/rpm/
enabled=1
gpgcheck=1
gpgkey=https://pkgs.k8s.io/core:/stable:/v1.30/rpm/repodata/repomd.xml.key
EOF

Note:

To upgrade kubectl to another minor release, you'll need to bump the version in /etc/zypp/repos.d/kubernetes.repo before running zypper update. This procedure is described in more detail in Changing The Kubernetes Package Repository.

Install kubectl using zypper:
```
sudo zypper install -y kubectl
```

If you are on Ubuntu or another Linux distribution that supports the snap package manager, kubectl is available as a snap application.

snap install kubectl --classic
kubectl version --client

If you are on Linux and using Homebrew package manager, kubectl is available for installation.

brew install kubectl
kubectl version --client

Verify kubectl configuration

In order for kubectl to find and access a Kubernetes cluster, it needs a kubeconfig file, which is created automatically when you create a cluster using kube-up.sh or successfully deploy a Minikube cluster. By default, kubectl configuration is located at ~/.kube/config.

Check that kubectl is properly configured by getting the cluster state:

kubectl cluster-info

If you see a URL response, kubectl is correctly configured to access your cluster.

If you see a message similar to the following, kubectl is not configured correctly or is not able to connect to a Kubernetes cluster.

The connection to the server <server-name:port> was refused - did you specify the right host or port?

For example, if you are intending to run a Kubernetes cluster on your laptop (locally), you will need a tool like Minikube to be installed first and then re-run the commands stated above.

If kubectl cluster-info returns the url response but you can't access your cluster, to check whether it is configured properly, use:

kubectl cluster-info dump

Troubleshooting the 'No Auth Provider Found' error message

In Kubernetes 1.26, kubectl removed the built-in authentication for the following cloud providers' managed Kubernetes offerings. These providers have released kubectl plugins to provide the cloud-specific authentication. For instructions, refer to the following provider documentation:

Azure AKS: kubelogin plugin
Google Kubernetes Engine: gke-gcloud-auth-plugin

(There could also be other reasons to see the same error message, unrelated to that change.)

Optional kubectl configurations and plugins

Enable shell autocompletion

kubectl provides autocompletion support for Bash, Zsh, Fish, and PowerShell, which can save you a lot of typing.

Below are the procedures to set up autocompletion for Bash, Fish, and Zsh.

Introduction

The kubectl completion script for Bash can be generated with the command kubectl completion bash. Sourcing the completion script in your shell enables kubectl autocompletion.

However, the completion script depends on bash-completion, which means that you have to install this software first (you can test if you have bash-completion already installed by running type _init_completion).

Install bash-completion

bash-completion is provided by many package managers (see here). You can install it with apt-get install bash-completion or yum install bash-completion, etc.

The above commands create /usr/share/bash-completion/bash_completion, which is the main script of bash-completion. Depending on your package manager, you have to manually source this file in your ~/.bashrc file.

To find out, reload your shell and run type _init_completion. If the command succeeds, you're already set, otherwise add the following to your ~/.bashrc file:

source /usr/share/bash-completion/bash_completion

Reload your shell and verify that bash-completion is correctly installed by typing type _init_completion.

Enable kubectl autocompletion

Bash

You now need to ensure that the kubectl completion script gets sourced in all your shell sessions. There are two ways in which you can do this:

User
System


echo 'source <(kubectl completion bash)' >>~/.bashrc


kubectl completion bash | sudo tee /etc/bash_completion.d/kubectl > /dev/null
sudo chmod a+r /etc/bash_completion.d/kubectl

If you have an alias for kubectl, you can extend shell completion to work with that alias:

echo 'alias k=kubectl' >>~/.bashrc
echo 'complete -o default -F __start_kubectl k' >>~/.bashrc

Note:

bash-completion sources all completion scripts in /etc/bash_completion.d.

Both approaches are equivalent. After reloading your shell, kubectl autocompletion should be working. To enable bash autocompletion in current session of shell, source the ~/.bashrc file:

source ~/.bashrc

Note:

Autocomplete for Fish requires kubectl 1.23 or later.

The kubectl completion script for Fish can be generated with the command kubectl completion fish. Sourcing the completion script in your shell enables kubectl autocompletion.

To do so in all your shell sessions, add the following line to your ~/.config/fish/config.fish file:

kubectl completion fish | source

After reloading your shell, kubectl autocompletion should be working.

The kubectl completion script for Zsh can be generated with the command kubectl completion zsh. Sourcing the completion script in your shell enables kubectl autocompletion.

To do so in all your shell sessions, add the following to your ~/.zshrc file:

source <(kubectl completion zsh)

If you have an alias for kubectl, kubectl autocompletion will automatically work with it.

After reloading your shell, kubectl autocompletion should be working.

If you get an error like 2: command not found: compdef, then add the following to the beginning of your ~/.zshrc file:

autoload -Uz compinit
compinit

Install `kubectl convert` plugin

A plugin for Kubernetes command-line tool kubectl, which allows you to convert manifests between different API versions. This can be particularly helpful to migrate manifests to a non-deprecated api version with newer Kubernetes release. For more info, visit migrate to non deprecated apis

Download the latest release with the command:

x86-64
ARM64


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl-convert"


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/arm64/kubectl-convert"

Validate the binary (optional)

Download the kubectl-convert checksum file:

x86-64
ARM64


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl-convert.sha256"


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/arm64/kubectl-convert.sha256"

Validate the kubectl-convert binary against the checksum file:

echo "$(cat kubectl-convert.sha256) kubectl-convert" | sha256sum --check

If valid, the output is:

kubectl-convert: OK

If the check fails, sha256 exits with nonzero status and prints output similar to:

kubectl-convert: FAILED
sha256sum: WARNING: 1 computed checksum did NOT match

Note:

Download the same version of the binary and checksum.

Install kubectl-convert

sudo install -o root -g root -m 0755 kubectl-convert /usr/local/bin/kubectl-convert

Verify plugin is successfully installed
```
kubectl convert --help
```
If you do not see an error, it means the plugin is successfully installed.
After installing the plugin, clean up the installation files:
```
rm kubectl-convert kubectl-convert.sha256
```

What's next

Install Minikube
See the getting started guides for more about creating clusters.
Learn how to launch and expose your application.
If you need access to a cluster you didn't create, see the Sharing Cluster Access document.
Read the kubectl reference docs

1.2 - Install and Set Up kubectl on macOS

Before you begin

Install kubectl on macOS

The following methods exist for installing kubectl on macOS:

Install kubectl on macOS
Verify kubectl configuration
Optional kubectl configurations and plugins
- Enable shell autocompletion
- Install kubectl convert plugin

Install kubectl binary with curl on macOS

Download the latest release:

Intel
Apple Silicon


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/amd64/kubectl"


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/arm64/kubectl"

Note:

To download a specific version, replace the $(curl -L -s https://dl.k8s.io/release/stable.txt) portion of the command with the specific version.

For example, to download version 1.30.0 on Intel macOS, type:

curl -LO "https://dl.k8s.io/release/v1.30.0/bin/darwin/amd64/kubectl"

And for macOS on Apple Silicon, type:

curl -LO "https://dl.k8s.io/release/v1.30.0/bin/darwin/arm64/kubectl"

Validate the binary (optional)

Download the kubectl checksum file:

Intel
Apple Silicon


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/amd64/kubectl.sha256"


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/arm64/kubectl.sha256"

Validate the kubectl binary against the checksum file:

echo "$(cat kubectl.sha256)  kubectl" | shasum -a 256 --check

If valid, the output is:

kubectl: OK

If the check fails, shasum exits with nonzero status and prints output similar to:

kubectl: FAILED
shasum: WARNING: 1 computed checksum did NOT match

Note:

Download the same version of the binary and checksum.

Make the kubectl binary executable.
```
chmod +x ./kubectl
```
Move the kubectl binary to a file location on your system PATH.
```
sudo mv ./kubectl /usr/local/bin/kubectl
sudo chown root: /usr/local/bin/kubectl
```
Note:
Make sure /usr/local/bin is in your PATH environment variable.
Test to ensure the version you installed is up-to-date:
```
kubectl version --client
```
Or use this for detailed view of version:
```
kubectl version --client --output=yaml
```
After installing and validating kubectl, delete the checksum file:
```
rm kubectl.sha256
```

Install with Homebrew on macOS

If you are on macOS and using Homebrew package manager, you can install kubectl with Homebrew.

Run the installation command:

brew install kubectl

brew install kubernetes-cli

Test to ensure the version you installed is up-to-date:
```
kubectl version --client
```

Install with Macports on macOS

If you are on macOS and using Macports package manager, you can install kubectl with Macports.

Run the installation command:

sudo port selfupdate
sudo port install kubectl

Test to ensure the version you installed is up-to-date:
```
kubectl version --client
```

Verify kubectl configuration

Check that kubectl is properly configured by getting the cluster state:

kubectl cluster-info

If you see a URL response, kubectl is correctly configured to access your cluster.

If you see a message similar to the following, kubectl is not configured correctly or is not able to connect to a Kubernetes cluster.

The connection to the server <server-name:port> was refused - did you specify the right host or port?

For example, if you are intending to run a Kubernetes cluster on your laptop (locally), you will need a tool like Minikube to be installed first and then re-run the commands stated above.

If kubectl cluster-info returns the url response but you can't access your cluster, to check whether it is configured properly, use:

kubectl cluster-info dump

Troubleshooting the 'No Auth Provider Found' error message

Azure AKS: kubelogin plugin
Google Kubernetes Engine: gke-gcloud-auth-plugin

(There could also be other reasons to see the same error message, unrelated to that change.)

Optional kubectl configurations and plugins

Enable shell autocompletion

kubectl provides autocompletion support for Bash, Zsh, Fish, and PowerShell which can save you a lot of typing.

Below are the procedures to set up autocompletion for Bash, Fish, and Zsh.

Introduction

The kubectl completion script for Bash can be generated with kubectl completion bash. Sourcing this script in your shell enables kubectl completion.

However, the kubectl completion script depends on bash-completion which you thus have to previously install.

Warning:

There are two versions of bash-completion, v1 and v2. V1 is for Bash 3.2 (which is the default on macOS), and v2 is for Bash 4.1+. The kubectl completion script doesn't work correctly with bash-completion v1 and Bash 3.2. It requires bash-completion v2 and Bash 4.1+. Thus, to be able to correctly use kubectl completion on macOS, you have to install and use Bash 4.1+ (instructions). The following instructions assume that you use Bash 4.1+ (that is, any Bash version of 4.1 or newer).

Upgrade Bash

The instructions here assume you use Bash 4.1+. You can check your Bash's version by running:

echo $BASH_VERSION

If it is too old, you can install/upgrade it using Homebrew:

brew install bash

Reload your shell and verify that the desired version is being used:

echo $BASH_VERSION $SHELL

Homebrew usually installs it at /usr/local/bin/bash.

Install bash-completion

Note:

As mentioned, these instructions assume you use Bash 4.1+, which means you will install bash-completion v2 (in contrast to Bash 3.2 and bash-completion v1, in which case kubectl completion won't work).

You can test if you have bash-completion v2 already installed with type _init_completion. If not, you can install it with Homebrew:

brew install bash-completion@2

As stated in the output of this command, add the following to your ~/.bash_profile file:

brew_etc="$(brew --prefix)/etc" && [[ -r "${brew_etc}/profile.d/bash_completion.sh" ]] && . "${brew_etc}/profile.d/bash_completion.sh"

Reload your shell and verify that bash-completion v2 is correctly installed with type _init_completion.

Enable kubectl autocompletion

You now have to ensure that the kubectl completion script gets sourced in all your shell sessions. There are multiple ways to achieve this:

Source the completion script in your ~/.bash_profile file:

echo 'source <(kubectl completion bash)' >>~/.bash_profile

Add the completion script to the /usr/local/etc/bash_completion.d directory:
```
kubectl completion bash >/usr/local/etc/bash_completion.d/kubectl
```

If you have an alias for kubectl, you can extend shell completion to work with that alias:

echo 'alias k=kubectl' >>~/.bash_profile
echo 'complete -o default -F __start_kubectl k' >>~/.bash_profile

If you installed kubectl with Homebrew (as explained here), then the kubectl completion script should already be in /usr/local/etc/bash_completion.d/kubectl. In that case, you don't need to do anything.

Note:
The Homebrew installation of bash-completion v2 sources all the files in the BASH_COMPLETION_COMPAT_DIR directory, that's why the latter two methods work.

In any case, after reloading your shell, kubectl completion should be working.

Note:

Autocomplete for Fish requires kubectl 1.23 or later.

The kubectl completion script for Fish can be generated with the command kubectl completion fish. Sourcing the completion script in your shell enables kubectl autocompletion.

To do so in all your shell sessions, add the following line to your ~/.config/fish/config.fish file:

kubectl completion fish | source

After reloading your shell, kubectl autocompletion should be working.

The kubectl completion script for Zsh can be generated with the command kubectl completion zsh. Sourcing the completion script in your shell enables kubectl autocompletion.

To do so in all your shell sessions, add the following to your ~/.zshrc file:

source <(kubectl completion zsh)

If you have an alias for kubectl, kubectl autocompletion will automatically work with it.

After reloading your shell, kubectl autocompletion should be working.

If you get an error like 2: command not found: compdef, then add the following to the beginning of your ~/.zshrc file:

autoload -Uz compinit
compinit

Install `kubectl convert` plugin

Download the latest release with the command:

Intel
Apple Silicon


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/amd64/kubectl-convert"


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/arm64/kubectl-convert"

Validate the binary (optional)

Download the kubectl-convert checksum file:

Intel
Apple Silicon


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/amd64/kubectl-convert.sha256"


   curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/arm64/kubectl-convert.sha256"

Validate the kubectl-convert binary against the checksum file:

echo "$(cat kubectl-convert.sha256)  kubectl-convert" | shasum -a 256 --check

If valid, the output is:

kubectl-convert: OK

If the check fails, shasum exits with nonzero status and prints output similar to:

kubectl-convert: FAILED
shasum: WARNING: 1 computed checksum did NOT match

Note:

Download the same version of the binary and checksum.

Make kubectl-convert binary executable
```
chmod +x ./kubectl-convert
```
Move the kubectl-convert binary to a file location on your system PATH.
```
sudo mv ./kubectl-convert /usr/local/bin/kubectl-convert
sudo chown root: /usr/local/bin/kubectl-convert
```
Note:
Make sure /usr/local/bin is in your PATH environment variable.
Verify plugin is successfully installed
```
kubectl convert --help
```
If you do not see an error, it means the plugin is successfully installed.
After installing the plugin, clean up the installation files:
```
rm kubectl-convert kubectl-convert.sha256
```

Uninstall kubectl on macOS

Depending on how you installed kubectl, use one of the following methods.

Uninstall kubectl using the command-line

Locate the kubectl binary on your system:
```
which kubectl
```
Remove the kubectl binary:
```
sudo rm <path>
```
Replace <path> with the path to the kubectl binary from the previous step. For example, sudo rm /usr/local/bin/kubectl.

Uninstall kubectl using homebrew

If you installed kubectl using Homebrew, run the following command:

brew remove kubectl

What's next

Install Minikube
See the getting started guides for more about creating clusters.
Learn how to launch and expose your application.
If you need access to a cluster you didn't create, see the Sharing Cluster Access document.
Read the kubectl reference docs

1.3 - Install and Set Up kubectl on Windows

Before you begin

Install kubectl on Windows

The following methods exist for installing kubectl on Windows:

Install kubectl binary with curl on Windows
Install on Windows using Chocolatey, Scoop, or winget

Install kubectl binary with curl on Windows

Download the latest 1.30 patch release: kubectl 1.30.0.

Or if you have curl installed, use this command:
```
curl.exe -LO "https://dl.k8s.io/release/v1.30.0/bin/windows/amd64/kubectl.exe"
```
Note:
To find out the latest stable version (for example, for scripting), take a look at https://dl.k8s.io/release/stable.txt.
Validate the binary (optional)

Download the kubectl checksum file:
```
curl.exe -LO "https://dl.k8s.io/v1.30.0/bin/windows/amd64/kubectl.exe.sha256"
```
Validate the kubectl binary against the checksum file:
- Using Command Prompt to manually compare CertUtil's output to the checksum file downloaded:
```
CertUtil -hashfile kubectl.exe SHA256
type kubectl.exe.sha256
```
- Using PowerShell to automate the verification using the -eq operator to get a True or False result:
```
 $(Get-FileHash -Algorithm SHA256 .\kubectl.exe).Hash -eq $(Get-Content .\kubectl.exe.sha256)
```
Append or prepend the kubectl binary folder to your PATH environment variable.
Test to ensure the version of kubectl is the same as downloaded:
```
kubectl version --client
```
Or use this for detailed view of version:
```
kubectl version --client --output=yaml
```

Note:

Docker Desktop for Windows adds its own version of kubectl to PATH. If you have installed Docker Desktop before, you may need to place your PATH entry before the one added by the Docker Desktop installer or remove the Docker Desktop's kubectl.

Install on Windows using Chocolatey, Scoop, or winget

To install kubectl on Windows you can use either Chocolatey package manager, Scoop command-line installer, or winget package manager.
- choco
- scoop
- winget
```
choco install kubernetes-cli
```
```
scoop install kubectl
```
```
winget install -e --id Kubernetes.kubectl
```
Test to ensure the version you installed is up-to-date:
```
kubectl version --client
```

Navigate to your home directory:

# If you're using cmd.exe, run: cd %USERPROFILE%
cd ~

Create the .kube directory:
```
mkdir .kube
```
Change to the .kube directory you just created:
```
cd .kube
```
Configure kubectl to use a remote Kubernetes cluster:
```
New-Item config -type file
```

Note:

Edit the config file with a text editor of your choice, such as Notepad.

Verify kubectl configuration

Check that kubectl is properly configured by getting the cluster state:

kubectl cluster-info

If you see a URL response, kubectl is correctly configured to access your cluster.

If you see a message similar to the following, kubectl is not configured correctly or is not able to connect to a Kubernetes cluster.

The connection to the server <server-name:port> was refused - did you specify the right host or port?

For example, if you are intending to run a Kubernetes cluster on your laptop (locally), you will need a tool like Minikube to be installed first and then re-run the commands stated above.

If kubectl cluster-info returns the url response but you can't access your cluster, to check whether it is configured properly, use:

kubectl cluster-info dump

Troubleshooting the 'No Auth Provider Found' error message

Azure AKS: kubelogin plugin
Google Kubernetes Engine: gke-gcloud-auth-plugin

(There could also be other reasons to see the same error message, unrelated to that change.)

Optional kubectl configurations and plugins

Enable shell autocompletion

kubectl provides autocompletion support for Bash, Zsh, Fish, and PowerShell, which can save you a lot of typing.

Below are the procedures to set up autocompletion for PowerShell.

The kubectl completion script for PowerShell can be generated with the command kubectl completion powershell.

To do so in all your shell sessions, add the following line to your $PROFILE file:

kubectl completion powershell | Out-String | Invoke-Expression

This command will regenerate the auto-completion script on every PowerShell start up. You can also add the generated script directly to your $PROFILE file.

To add the generated script to your $PROFILE file, run the following line in your powershell prompt:

kubectl completion powershell >> $PROFILE

After reloading your shell, kubectl autocompletion should be working.

Install `kubectl convert` plugin

Download the latest release with the command:

curl.exe -LO "https://dl.k8s.io/release/v1.30.0/bin/windows/amd64/kubectl-convert.exe"

Validate the binary (optional).

Download the kubectl-convert checksum file:
```
curl.exe -LO "https://dl.k8s.io/v1.30.0/bin/windows/amd64/kubectl-convert.exe.sha256"
```
Validate the kubectl-convert binary against the checksum file:
- Using Command Prompt to manually compare CertUtil's output to the checksum file downloaded:
```
CertUtil -hashfile kubectl-convert.exe SHA256
type kubectl-convert.exe.sha256
```
- Using PowerShell to automate the verification using the -eq operator to get a True or False result:
```
$($(CertUtil -hashfile .\kubectl-convert.exe SHA256)[1] -replace " ", "") -eq $(type .\kubectl-convert.exe.sha256)
```
Append or prepend the kubectl-convert binary folder to your PATH environment variable.
Verify the plugin is successfully installed.
```
kubectl convert --help
```
If you do not see an error, it means the plugin is successfully installed.
After installing the plugin, clean up the installation files:
```
del kubectl-convert.exe
del kubectl-convert.exe.sha256
```

What's next

Install Minikube
See the getting started guides for more about creating clusters.
Learn how to launch and expose your application.
If you need access to a cluster you didn't create, see the Sharing Cluster Access document.
Read the kubectl reference docs

2 - Administer a Cluster

Learn common tasks for administering a cluster.

2.1 - Administration with kubeadm

2.1.1 - Certificate Management with kubeadm

FEATURE STATE: Kubernetes v1.15 [stable]

Client certificates generated by kubeadm expire after 1 year. This page explains how to manage certificate renewals with kubeadm. It also covers other tasks related to kubeadm certificate management.

Before you begin

You should be familiar with PKI certificates and requirements in Kubernetes.

Using custom certificates

By default, kubeadm generates all the certificates needed for a cluster to run. You can override this behavior by providing your own certificates.

To do so, you must place them in whatever directory is specified by the --cert-dir flag or the certificatesDir field of kubeadm's ClusterConfiguration. By default this is /etc/kubernetes/pki.

If a given certificate and private key pair exists before running kubeadm init, kubeadm does not overwrite them. This means you can, for example, copy an existing CA into /etc/kubernetes/pki/ca.crt and /etc/kubernetes/pki/ca.key, and kubeadm will use this CA for signing the rest of the certificates.

External CA mode

It is also possible to provide only the ca.crt file and not the ca.key file (this is only available for the root CA file, not other cert pairs). If all other certificates and kubeconfig files are in place, kubeadm recognizes this condition and activates the "External CA" mode. kubeadm will proceed without the CA key on disk.

Instead, run the controller-manager standalone with --controllers=csrsigner and point to the CA certificate and key.

There are various ways to prepare the component credentials when using external CA mode.

Manual preparation of component credentials

PKI certificates and requirements includes information on how to prepare all the required by kubeadm component credentials manually.

Preparation of credentials by signing CSRs generated by kubeadm

kubeadm can generate CSR files that you can sign manually with tools like openssl and your external CA. These CSR files will include all the specification for credentials that components deployed by kubeadm require.

Automated preparation of component credentials by using kubeadm phases

Alternatively, it is possible to use kubeadm phase commands to automate this process.

Go to a host that you want to prepare as a kubeadm control plane node with external CA.
Copy the external CA files ca.crt and ca.key that you have into /etc/kubernetes/pki on the node.
Prepare a temporary kubeadm configuration file called config.yaml that can be used with kubeadm init. Make sure that this file includes any relevant cluster wide or host-specific information that could be included in certificates, such as, ClusterConfiguration.controlPlaneEndpoint, ClusterConfiguration.certSANs and InitConfiguration.APIEndpoint.
On the same host execute the commands kubeadm init phase kubeconfig all --config config.yaml and kubeadm init phase certs all --config config.yaml. This will generate all required kubeconfig files and certificates under /etc/kubernetes/ and its pki sub directory.
Inspect the generated files. Delete /etc/kubernetes/pki/ca.key, delete or move to a safe location the file /etc/kubernetes/super-admin.conf.
On nodes where kubeadm join will be called also delete /etc/kubernetes/kubelet.conf. This file is only required on the first node where kubeadm init will be called.
Note that some files such pki/sa.*, pki/front-proxy-ca.* and pki/etc/ca.* are shared between control plane nodes, You can generate them once and distribute them manually to nodes where kubeadm join will be called, or you can use the --upload-certs functionality of kubeadm init and --certificate-key of kubeadm join to automate this distribution.

Once the credentials are prepared on all nodes, call kubeadm init and kubeadm join for these nodes to join the cluster. kubeadm will use the existing kubeconfig and certificate files under /etc/kubernetes/ and its pki sub directory.

Check certificate expiration

You can use the check-expiration subcommand to check when certificates expire:

kubeadm certs check-expiration

The output is similar to this:

CERTIFICATE                EXPIRES                  RESIDUAL TIME   CERTIFICATE AUTHORITY   EXTERNALLY MANAGED
admin.conf                 Dec 30, 2020 23:36 UTC   364d                                    no
apiserver                  Dec 30, 2020 23:36 UTC   364d            ca                      no
apiserver-etcd-client      Dec 30, 2020 23:36 UTC   364d            etcd-ca                 no
apiserver-kubelet-client   Dec 30, 2020 23:36 UTC   364d            ca                      no
controller-manager.conf    Dec 30, 2020 23:36 UTC   364d                                    no
etcd-healthcheck-client    Dec 30, 2020 23:36 UTC   364d            etcd-ca                 no
etcd-peer                  Dec 30, 2020 23:36 UTC   364d            etcd-ca                 no
etcd-server                Dec 30, 2020 23:36 UTC   364d            etcd-ca                 no
front-proxy-client         Dec 30, 2020 23:36 UTC   364d            front-proxy-ca          no
scheduler.conf             Dec 30, 2020 23:36 UTC   364d                                    no

CERTIFICATE AUTHORITY   EXPIRES                  RESIDUAL TIME   EXTERNALLY MANAGED
ca                      Dec 28, 2029 23:36 UTC   9y              no
etcd-ca                 Dec 28, 2029 23:36 UTC   9y              no
front-proxy-ca          Dec 28, 2029 23:36 UTC   9y              no

The command shows expiration/residual time for the client certificates in the /etc/kubernetes/pki folder and for the client certificate embedded in the kubeconfig files used by kubeadm (admin.conf, controller-manager.conf and scheduler.conf).

Additionally, kubeadm informs the user if the certificate is externally managed; in this case, the user should take care of managing certificate renewal manually/using other tools.

Warning:

kubeadm cannot manage certificates signed by an external CA.

Note:

kubelet.conf is not included in the list above because kubeadm configures kubelet for automatic certificate renewal with rotatable certificates under /var/lib/kubelet/pki. To repair an expired kubelet client certificate see Kubelet client certificate rotation fails.

Warning:

On nodes created with kubeadm init, prior to kubeadm version 1.17, there is a bug where you manually have to modify the contents of kubelet.conf. After kubeadm init finishes, you should update kubelet.conf to point to the rotated kubelet client certificates, by replacing client-certificate-data and client-key-data with:

client-certificate: /var/lib/kubelet/pki/kubelet-client-current.pem
client-key: /var/lib/kubelet/pki/kubelet-client-current.pem

Automatic certificate renewal

kubeadm renews all the certificates during control plane upgrade.

This feature is designed for addressing the simplest use cases; if you don't have specific requirements on certificate renewal and perform Kubernetes version upgrades regularly (less than 1 year in between each upgrade), kubeadm will take care of keeping your cluster up to date and reasonably secure.

Note:

It is a best practice to upgrade your cluster frequently in order to stay secure.

If you have more complex requirements for certificate renewal, you can opt out from the default behavior by passing --certificate-renewal=false to kubeadm upgrade apply or to kubeadm upgrade node.

Warning:

Prior to kubeadm version 1.17 there is a bug where the default value for --certificate-renewal is false for the kubeadm upgrade node command. In that case, you should explicitly set --certificate-renewal=true.

Manual certificate renewal

You can renew your certificates manually at any time with the kubeadm certs renew command, with the appropriate command line options.

This command performs the renewal using CA (or front-proxy-CA) certificate and key stored in /etc/kubernetes/pki.

After running the command you should restart the control plane Pods. This is required since dynamic certificate reload is currently not supported for all components and certificates. Static Pods are managed by the local kubelet and not by the API Server, thus kubectl cannot be used to delete and restart them. To restart a static Pod you can temporarily remove its manifest file from /etc/kubernetes/manifests/ and wait for 20 seconds (see the fileCheckFrequency value in KubeletConfiguration struct. The kubelet will terminate the Pod if it's no longer in the manifest directory. You can then move the file back and after another fileCheckFrequency period, the kubelet will recreate the Pod and the certificate renewal for the component can complete.

Warning:

If you are running an HA cluster, this command needs to be executed on all the control-plane nodes.

Note:

certs renew uses the existing certificates as the authoritative source for attributes (Common Name, Organization, SAN, etc.) instead of the kubeadm-config ConfigMap. It is strongly recommended to keep them both in sync.

kubeadm certs renew can renew any specific certificate or, with the subcommand all, it can renew all of them, as shown below:

kubeadm certs renew all

Note:

Clusters built with kubeadm often copy the admin.conf certificate into $HOME/.kube/config, as instructed in Creating a cluster with kubeadm. On such a system, to update the contents of $HOME/.kube/config after renewing the admin.conf, you must run the following commands:

sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
sudo chown $(id -u):$(id -g) $HOME/.kube/config

Renew certificates with the Kubernetes certificates API

This section provides more details about how to execute manual certificate renewal using the Kubernetes certificates API.

Caution:

These are advanced topics for users who need to integrate their organization's certificate infrastructure into a kubeadm-built cluster. If the default kubeadm configuration satisfies your needs, you should let kubeadm manage certificates instead.

Set up a signer

The Kubernetes Certificate Authority does not work out of the box. You can configure an external signer such as cert-manager, or you can use the built-in signer.

The built-in signer is part of kube-controller-manager.

To activate the built-in signer, you must pass the --cluster-signing-cert-file and --cluster-signing-key-file flags.

If you're creating a new cluster, you can use a kubeadm configuration file:

apiVersion: kubeadm.k8s.io/v1beta3
kind: ClusterConfiguration
controllerManager:
  extraArgs:
    cluster-signing-cert-file: /etc/kubernetes/pki/ca.crt
    cluster-signing-key-file: /etc/kubernetes/pki/ca.key

Create certificate signing requests (CSR)

See Create CertificateSigningRequest for creating CSRs with the Kubernetes API.

Renew certificates with external CA

This section provide more details about how to execute manual certificate renewal using an external CA.

To better integrate with external CAs, kubeadm can also produce certificate signing requests (CSRs). A CSR represents a request to a CA for a signed certificate for a client. In kubeadm terms, any certificate that would normally be signed by an on-disk CA can be produced as a CSR instead. A CA, however, cannot be produced as a CSR.

Renewal by using certificate signing requests (CSR)

Renewal of ceritficates is possible by generating new CSRs and signing them with the external CA. For more details about working with CSRs generated by kubeadm see the section Signing certificate signing requests (CSR) generated by kubeadm.

Certificate authority (CA) rotation

Kubeadm does not support rotation or replacement of CA certificates out of the box.

For more information about manual rotation or replacement of CA, see manual rotation of CA certificates.

Enabling signed kubelet serving certificates

By default the kubelet serving certificate deployed by kubeadm is self-signed. This means a connection from external services like the metrics-server to a kubelet cannot be secured with TLS.

To configure the kubelets in a new kubeadm cluster to obtain properly signed serving certificates you must pass the following minimal configuration to kubeadm init:

apiVersion: kubeadm.k8s.io/v1beta3
kind: ClusterConfiguration
---
apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
serverTLSBootstrap: true

If you have already created the cluster you must adapt it by doing the following:

Find and edit the kubelet-config-1.30 ConfigMap in the kube-system namespace. In that ConfigMap, the kubelet key has a KubeletConfiguration document as its value. Edit the KubeletConfiguration document to set serverTLSBootstrap: true.
On each node, add the serverTLSBootstrap: true field in /var/lib/kubelet/config.yaml and restart the kubelet with systemctl restart kubelet

The field serverTLSBootstrap: true will enable the bootstrap of kubelet serving certificates by requesting them from the certificates.k8s.io API. One known limitation is that the CSRs (Certificate Signing Requests) for these certificates cannot be automatically approved by the default signer in the kube-controller-manager - kubernetes.io/kubelet-serving. This will require action from the user or a third party controller.

These CSRs can be viewed using:

kubectl get csr

NAME        AGE     SIGNERNAME                        REQUESTOR                      CONDITION
csr-9wvgt   112s    kubernetes.io/kubelet-serving     system:node:worker-1           Pending
csr-lz97v   1m58s   kubernetes.io/kubelet-serving     system:node:control-plane-1    Pending

To approve them you can do the following:

kubectl certificate approve <CSR-name>

By default, these serving certificate will expire after one year. Kubeadm sets the KubeletConfiguration field rotateCertificates to true, which means that close to expiration a new set of CSRs for the serving certificates will be created and must be approved to complete the rotation. To understand more see Certificate Rotation.

If you are looking for a solution for automatic approval of these CSRs it is recommended that you contact your cloud provider and ask if they have a CSR signer that verifies the node identity with an out of band mechanism.

Note: This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.

Third party custom controllers can be used:

kubelet-csr-approver

Such a controller is not a secure mechanism unless it not only verifies the CommonName in the CSR but also verifies the requested IPs and domain names. This would prevent a malicious actor that has access to a kubelet client certificate to create CSRs requesting serving certificates for any IP or domain name.

Generating kubeconfig files for additional users

During cluster creation, kubeadm signs the certificate in the admin.conf to have Subject: O = system:masters, CN = kubernetes-admin. system:masters is a break-glass, super user group that bypasses the authorization layer (for example, RBAC). Sharing the admin.conf with additional users is not recommended!

Instead, you can use the kubeadm kubeconfig user command to generate kubeconfig files for additional users. The command accepts a mixture of command line flags and kubeadm configuration options. The generated kubeconfig will be written to stdout and can be piped to a file using kubeadm kubeconfig user ... > somefile.conf.

Example configuration file that can be used with --config:

# example.yaml
apiVersion: kubeadm.k8s.io/v1beta3
kind: ClusterConfiguration
# Will be used as the target "cluster" in the kubeconfig
clusterName: "kubernetes"
# Will be used as the "server" (IP or DNS name) of this cluster in the kubeconfig
controlPlaneEndpoint: "some-dns-address:6443"
# The cluster CA key and certificate will be loaded from this local directory
certificatesDir: "/etc/kubernetes/pki"

Make sure that these settings match the desired target cluster settings. To see the settings of an existing cluster use:

kubectl get cm kubeadm-config -n kube-system -o=jsonpath="{.data.ClusterConfiguration}"

The following example will generate a kubeconfig file with credentials valid for 24 hours for a new user johndoe that is part of the appdevs group:

kubeadm kubeconfig user --config example.yaml --org appdevs --client-name johndoe --validity-period 24h

The following example will generate a kubeconfig file with administrator credentials valid for 1 week:

kubeadm kubeconfig user --config example.yaml --client-name admin --validity-period 168h

Signing certificate signing requests (CSR) generated by kubeadm

You can create certificate signing requests with kubeadm certs generate-csr. Calling this command will generate .csr / .key file pairs for regular certificates. For certificates embedded in kubeconfig files, the command will generate a .csr / .conf pair where the key is already embedded in the .conf file.

A CSR file contains all relevant information for a CA to sign a certificate. kubeadm uses a well defined specification for all its certificates and CSRs.

The default certificate directory is /etc/kubernetes/pki, while the default directory for kubeconfig files is /etc/kubernetes. These defaults can be overridden with the flags --cert-dir and --kubeconfig-dir, respectively.

To pass custom options to kubeadm certs generate-csr use the --config flag, which accepts a kubeadm configuration file, similarly to commands such as kubeadm init. Any specification such as extra SANs and custom IP addresses must be stored in the same configuration file and used for all relevant kubeadm commands by passing it as --config.

Note:

This guide will cover the usage of the openssl command for singing the CSRs, but you can use your preferred tools.

Note:

This guide will use the default Kubernetes directory /etc/kubernetes, which requires a super user. If you are following this guide with permissive directories (by passing --cert-dir and --kubeconfig-dir) you can omit the sudo command). But note that the resulted files must be copied to the /etc/kubernetes tree, so that kubeadm init or kubeadm join will find them.

Preparing CA and service account files

On the primary control plane node, where kubeadm init will be executed, call the following commands:

sudo kubeadm init phase certs ca
sudo kubeadm init phase certs etcd-ca
sudo kubeadm init phase certs front-proxy-ca
sudo kubeadm init phase certs sa

This will populate the folders /etc/kubernetes/pki and /etc/kubernetes/pki/etcd with all self-signed CA files (certificates and keys) and service account (public and private keys) that kubeadm needs for a control plane node.

Note:

If you are using an external CA, you must generate the same files out of band and manually copy them to the primary control plane node in /etc/kubernetes. Once all CSRs are signed, you can delete the root CA key (ca.key) as noted in the External CA mode section.

For secondary control plane nodes (kubeadm join --control-plane) there is no need to call the above commands. Depending on how you setup the High Availability cluster, you either have to manually copy the same files from the primary control plane node, or use the automated --upload-certs functionality of kubeadm init.

Generate CSRs

The kubeadm certs generate-csr command generates CSRs for all known certificates managed by kubeadm. Once the command is done you must manually delete .csr, .conf or .key files that you don't need.

Considerations for kubelet.conf

This section applies to both control plane and worker nodes.

If you have deleted the ca.key file from control plane nodes (External CA mode), the active kube-controller-manager in this cluster will not be able to sign kubelet client certificates. If no external method for signing these certificates exists in your setup (such as an external signer, you could manually sign the kubelet.conf.csr as explained in this guide.

Note that this also means that the automatic kubelet client certificate rotation will be disabled. If so, close to certificate expiration, you must generate a new kubelet.conf.csr, sign the certificate, embed it in kubelet.conf and restart the kubelet.

If this does not apply to your setup, you can skip processing the kubelet.conf.csr on secondary control plane and on workers nodes (all nodes that call kubeadm join ...). That is because the active kube-controller-manager will be responsible for signing new kubelet client certificates.

Note:

Processing the kubelet.conf.csr on the primary control plane node (kubeadm init) is required, because that is considered the node that bootstraps the cluster and a pre-populated kubelet.conf is needed.

Control plane nodes

Execute the following command on primary (kubeadm init) and secondary (kubeadm join --control-plane) control plane nodes to generate all CSR files:

sudo kubeadm certs generate-csr

If external etcd is to be used, follow the External etcd with kubeadm guide to understand what CSR files are needed on the kubeadm and etcd nodes. Other .csr and .key files under /etc/kubernetes/pki/etcd can be removed.

Based on the explanation in Considerations for kubelet.conf keep or delete the kubelet.conf and kubelet.conf.csr files.

Worker nodes

Based on the explanation in Considerations for kubelet.conf, optionally call:

sudo kubeadm certs generate-csr

and keep only the kubelet.conf and kubelet.conf.csr files. Alternatively skip the steps for worker nodes entirely.

Signing CSRs for all certificates

Note:

If you are using external CA and already have CA serial number files (.srl) for openssl you can copy such files to a kubeadm node where CSRs will be processed. .srl files to copy are /etc/kubernetes/pki/ca.srl, /etc/kubernetes/pki/front-proxy-ca.srl and /etc/kubernetes/pki/etcd/ca.srl. The files can be then moved to a new node where CSR files will be processed.

If a .srl file is missing for a CA on a node, the script below will generate a new SRL file with a random starting serial number.

To read more about .srl files see the openssl documentation for the --CAserial flag.

Repeat this step for all nodes that have CSR files.

Write the following script in the /etc/kubernetes directory, navigate to the directory and execute the script. The script will generate certificates for all CSR files that are present in the /etc/kubernetes tree.

#!/bin/bash

# Set certificate expiration time in days
DAYS=365

# Process all CSR files except those for front-proxy and etcd
find ./ -name "*.csr" | grep -v "pki/etcd" | grep -v "front-proxy" | while read -r FILE;
do
    echo "* Processing ${FILE} ..."
    FILE=${FILE%.*} # Trim the extension
    if [ -f "./pki/ca.srl" ]; then
        SERIAL_FLAG="-CAserial ./pki/ca.srl"
    else
        SERIAL_FLAG="-CAcreateserial"
    fi
    openssl x509 -req -days "${DAYS}" -CA ./pki/ca.crt -CAkey ./pki/ca.key ${SERIAL_FLAG} \
        -in "${FILE}.csr" -out "${FILE}.crt"
    sleep 2
done

# Process all etcd CSRs
find ./pki/etcd -name "*.csr" | while read -r FILE;
do
    echo "* Processing ${FILE} ..."
    FILE=${FILE%.*} # Trim the extension
    if [ -f "./pki/etcd/ca.srl" ]; then
        SERIAL_FLAG=-CAserial ./pki/etcd/ca.srl
    else
        SERIAL_FLAG=-CAcreateserial
    fi
    openssl x509 -req -days "${DAYS}" -CA ./pki/etcd/ca.crt -CAkey ./pki/etcd/ca.key ${SERIAL_FLAG} \
        -in "${FILE}.csr" -out "${FILE}.crt"
done

# Process front-proxy CSRs
echo "* Processing ./pki/front-proxy-client.csr ..."
openssl x509 -req -days "${DAYS}" -CA ./pki/front-proxy-ca.crt -CAkey ./pki/front-proxy-ca.key -CAcreateserial \
    -in ./pki/front-proxy-client.csr -out ./pki/front-proxy-client.crt

Embedding certificates in kubeconfig files

Repeat this step for all nodes that have CSR files.

Write the following script in the /etc/kubernetes directory, navigate to the directory and execute the script. The script will take the .crt files that were signed for kubeconfig files from CSRs in the previous step and will embed them in the kubeconfig files.

#!/bin/bash

CLUSTER=kubernetes
find ./ -name "*.conf" | while read -r FILE;
do
    echo "* Processing ${FILE} ..."
    KUBECONFIG="${FILE}" kubectl config set-cluster "${CLUSTER}" --certificate-authority ./pki/ca.crt --embed-certs
    USER=$(KUBECONFIG="${FILE}" kubectl config view -o jsonpath='{.users[0].name}')
    KUBECONFIG="${FILE}" kubectl config set-credentials "${USER}" --client-certificate "${FILE}.crt" --embed-certs
done

Performing cleanup

Perform this step on all nodes that have CSR files.

Write the following script in the /etc/kubernetes directory, navigate to the directory and execute the script.

#!/bin/bash

# Cleanup CSR files
rm -f ./*.csr ./pki/*.csr ./pki/etcd/*.csr # Clean all CSR files

# Cleanup CRT files that were already embedded in kubeconfig files
rm -f ./*.crt

Optionally, move .srl files to the next node to be processed.

Optionally, if using external CA remove the /etc/kubernetes/pki/ca.key file, as explained in the External CA node section.

kubeadm node initialization

Once CSR files have been signed and required certificates are in place on the hosts you want to use as nodes, you can use the commands kubeadm init and kubeadm join to create a Kubernetes cluster from these nodes. During init and join, kubeadm uses existing certificates, encryption keys and kubeconfig files that it finds in the /etc/kubernetes tree on the host's local filesystem.

2.1.2 - Configuring a cgroup driver

This page explains how to configure the kubelet's cgroup driver to match the container runtime cgroup driver for kubeadm clusters.

Before you begin

You should be familiar with the Kubernetes container runtime requirements.

Configuring the container runtime cgroup driver

The Container runtimes page explains that the systemd driver is recommended for kubeadm based setups instead of the kubelet's default cgroupfs driver, because kubeadm manages the kubelet as a systemd service.

The page also provides details on how to set up a number of different container runtimes with the systemd driver by default.

Configuring the kubelet cgroup driver

kubeadm allows you to pass a KubeletConfiguration structure during kubeadm init. This KubeletConfiguration can include the cgroupDriver field which controls the cgroup driver of the kubelet.

Note:

In v1.22 and later, if the user does not set the cgroupDriver field under KubeletConfiguration, kubeadm defaults it to systemd.

In Kubernetes v1.28, you can enable automatic detection of the cgroup driver as an alpha feature. See systemd cgroup driver for more details.

A minimal example of configuring the field explicitly:

# kubeadm-config.yaml
kind: ClusterConfiguration
apiVersion: kubeadm.k8s.io/v1beta3
kubernetesVersion: v1.21.0
---
kind: KubeletConfiguration
apiVersion: kubelet.config.k8s.io/v1beta1
cgroupDriver: systemd

Such a configuration file can then be passed to the kubeadm command:

kubeadm init --config kubeadm-config.yaml

Note:

Kubeadm uses the same KubeletConfiguration for all nodes in the cluster. The KubeletConfiguration is stored in a ConfigMap object under the kube-system namespace.

Executing the sub commands init, join and upgrade would result in kubeadm writing the KubeletConfiguration as a file under /var/lib/kubelet/config.yaml and passing it to the local node kubelet.

Using the `cgroupfs` driver

To use cgroupfs and to prevent kubeadm upgrade from modifying the KubeletConfiguration cgroup driver on existing setups, you must be explicit about its value. This applies to a case where you do not wish future versions of kubeadm to apply the systemd driver by default.

See the below section on "Modify the kubelet ConfigMap" for details on how to be explicit about the value.

If you wish to configure a container runtime to use the cgroupfs driver, you must refer to the documentation of the container runtime of your choice.

Migrating to the `systemd` driver

To change the cgroup driver of an existing kubeadm cluster from cgroupfs to systemd in-place, a similar procedure to a kubelet upgrade is required. This must include both steps outlined below.

Note:

Alternatively, it is possible to replace the old nodes in the cluster with new ones that use the systemd driver. This requires executing only the first step below before joining the new nodes and ensuring the workloads can safely move to the new nodes before deleting the old nodes.

Modify the kubelet ConfigMap

Call kubectl edit cm kubelet-config -n kube-system.
Either modify the existing cgroupDriver value or add a new field that looks like this:
```
cgroupDriver: systemd
```
This field must be present under the kubelet: section of the ConfigMap.

Update the cgroup driver on all nodes

For each node in the cluster:

Drain the node using kubectl drain <node-name> --ignore-daemonsets
Stop the kubelet using systemctl stop kubelet
Stop the container runtime
Modify the container runtime cgroup driver to systemd
Set cgroupDriver: systemd in /var/lib/kubelet/config.yaml
Start the container runtime
Start the kubelet using systemctl start kubelet
Uncordon the node using kubectl uncordon <node-name>

Execute these steps on nodes one at a time to ensure workloads have sufficient time to schedule on different nodes.

Once the process is complete ensure that all nodes and workloads are healthy.

2.1.3 - Reconfiguring a kubeadm cluster

kubeadm does not support automated ways of reconfiguring components that were deployed on managed nodes. One way of automating this would be by using a custom operator.

To modify the components configuration you must manually edit associated cluster objects and files on disk.

This guide shows the correct sequence of steps that need to be performed to achieve kubeadm cluster reconfiguration.

Before you begin

You need a cluster that was deployed using kubeadm
Have administrator credentials (/etc/kubernetes/admin.conf) and network connectivity to a running kube-apiserver in the cluster from a host that has kubectl installed
Have a text editor installed on all hosts

Reconfiguring the cluster

kubeadm writes a set of cluster wide component configuration options in ConfigMaps and other objects. These objects must be manually edited. The command kubectl edit can be used for that.

The kubectl edit command will open a text editor where you can edit and save the object directly.

You can use the environment variables KUBECONFIG and KUBE_EDITOR to specify the location of the kubectl consumed kubeconfig file and preferred text editor.

For example:

KUBECONFIG=/etc/kubernetes/admin.conf KUBE_EDITOR=nano kubectl edit <parameters>

Note:

Upon saving any changes to these cluster objects, components running on nodes may not be automatically updated. The steps below instruct you on how to perform that manually.

Warning:

Component configuration in ConfigMaps is stored as unstructured data (YAML string). This means that validation will not be performed upon updating the contents of a ConfigMap. You have to be careful to follow the documented API format for a particular component configuration and avoid introducing typos and YAML indentation mistakes.

Applying cluster configuration changes

Updating the `ClusterConfiguration`

During cluster creation and upgrade, kubeadm writes its ClusterConfiguration in a ConfigMap called kubeadm-config in the kube-system namespace.

To change a particular option in the ClusterConfiguration you can edit the ConfigMap with this command:

kubectl edit cm -n kube-system kubeadm-config

The configuration is located under the data.ClusterConfiguration key.

Note:

The ClusterConfiguration includes a variety of options that affect the configuration of individual components such as kube-apiserver, kube-scheduler, kube-controller-manager, CoreDNS, etcd and kube-proxy. Changes to the configuration must be reflected on node components manually.

Reflecting `ClusterConfiguration` changes on control plane nodes

kubeadm manages the control plane components as static Pod manifests located in the directory /etc/kubernetes/manifests. Any changes to the ClusterConfiguration under the apiServer, controllerManager, scheduler or etcd keys must be reflected in the associated files in the manifests directory on a control plane node.

Such changes may include:

extraArgs - requires updating the list of flags passed to a component container
extraVolumes - requires updating the volume mounts for a component container
*SANs - requires writing new certificates with updated Subject Alternative Names

Before proceeding with these changes, make sure you have backed up the directory /etc/kubernetes/.

To write new certificates you can use:

kubeadm init phase certs <component-name> --config <config-file>

To write new manifest files in /etc/kubernetes/manifests you can use:

# For Kubernetes control plane components
kubeadm init phase control-plane <component-name> --config <config-file>
# For local etcd
kubeadm init phase etcd local --config <config-file>

The <config-file> contents must match the updated ClusterConfiguration. The <component-name> value must be a name of a Kubernetes control plane component (apiserver, controller-manager or scheduler).

Note:

Updating a file in /etc/kubernetes/manifests will tell the kubelet to restart the static Pod for the corresponding component. Try doing these changes one node at a time to leave the cluster without downtime.

Applying kubelet configuration changes

Updating the `KubeletConfiguration`

During cluster creation and upgrade, kubeadm writes its KubeletConfiguration in a ConfigMap called kubelet-config in the kube-system namespace.

You can edit the ConfigMap with this command:

kubectl edit cm -n kube-system kubelet-config

The configuration is located under the data.kubelet key.

Reflecting the kubelet changes

To reflect the change on kubeadm nodes you must do the following:

Log in to a kubeadm node
Run kubeadm upgrade node phase kubelet-config to download the latest kubelet-config ConfigMap contents into the local file /var/lib/kubelet/config.yaml
Edit the file /var/lib/kubelet/kubeadm-flags.env to apply additional configuration with flags
Restart the kubelet service with systemctl restart kubelet

Note:

Do these changes one node at a time to allow workloads to be rescheduled properly.

Note:

During kubeadm upgrade, kubeadm downloads the KubeletConfiguration from the kubelet-config ConfigMap and overwrite the contents of /var/lib/kubelet/config.yaml. This means that node local configuration must be applied either by flags in /var/lib/kubelet/kubeadm-flags.env or by manually updating the contents of /var/lib/kubelet/config.yaml after kubeadm upgrade, and then restarting the kubelet.

Applying kube-proxy configuration changes

Updating the `KubeProxyConfiguration`

During cluster creation and upgrade, kubeadm writes its KubeProxyConfiguration in a ConfigMap in the kube-system namespace called kube-proxy.

This ConfigMap is used by the kube-proxy DaemonSet in the kube-system namespace.

To change a particular option in the KubeProxyConfiguration, you can edit the ConfigMap with this command:

kubectl edit cm -n kube-system kube-proxy

The configuration is located under the data.config.conf key.

Reflecting the kube-proxy changes

Once the kube-proxy ConfigMap is updated, you can restart all kube-proxy Pods:

Obtain the Pod names:

kubectl get po -n kube-system | grep kube-proxy

Delete a Pod with:

kubectl delete po -n kube-system <pod-name>

New Pods that use the updated ConfigMap will be created.

Note:

Because kubeadm deploys kube-proxy as a DaemonSet, node specific configuration is unsupported.

Applying CoreDNS configuration changes

Updating the CoreDNS Deployment and Service

kubeadm deploys CoreDNS as a Deployment called coredns and with a Service kube-dns, both in the kube-system namespace.

To update any of the CoreDNS settings, you can edit the Deployment and Service objects:

kubectl edit deployment -n kube-system coredns
kubectl edit service -n kube-system kube-dns

Reflecting the CoreDNS changes

Once the CoreDNS changes are applied you can delete the CoreDNS Pods:

Obtain the Pod names:

kubectl get po -n kube-system | grep coredns

Delete a Pod with:

kubectl delete po -n kube-system <pod-name>

New Pods with the updated CoreDNS configuration will be created.

Note:

kubeadm does not allow CoreDNS configuration during cluster creation and upgrade. This means that if you execute kubeadm upgrade apply, your changes to the CoreDNS objects will be lost and must be reapplied.

Persisting the reconfiguration

During the execution of kubeadm upgrade on a managed node, kubeadm might overwrite configuration that was applied after the cluster was created (reconfiguration).

Persisting Node object reconfiguration

kubeadm writes Labels, Taints, CRI socket and other information on the Node object for a particular Kubernetes node. To change any of the contents of this Node object you can use:

kubectl edit no <node-name>

During kubeadm upgrade the contents of such a Node might get overwritten. If you would like to persist your modifications to the Node object after upgrade, you can prepare a kubectl patch and apply it to the Node object:

kubectl patch no <node-name> --patch-file <patch-file>

Persisting control plane component reconfiguration

The main source of control plane configuration is the ClusterConfiguration object stored in the cluster. To extend the static Pod manifests configuration, patches can be used.

These patch files must remain as files on the control plane nodes to ensure that they can be used by the kubeadm upgrade ... --patches <directory>.

If reconfiguration is done to the ClusterConfiguration and static Pod manifests on disk, the set of node specific patches must be updated accordingly.

Persisting kubelet reconfiguration

Any changes to the KubeletConfiguration stored in /var/lib/kubelet/config.yaml will be overwritten on kubeadm upgrade by downloading the contents of the cluster wide kubelet-config ConfigMap. To persist kubelet node specific configuration either the file /var/lib/kubelet/config.yaml has to be updated manually post-upgrade or the file /var/lib/kubelet/kubeadm-flags.env can include flags. The kubelet flags override the associated KubeletConfiguration options, but note that some of the flags are deprecated.

A kubelet restart will be required after changing /var/lib/kubelet/config.yaml or /var/lib/kubelet/kubeadm-flags.env.

What's next

2.1.4 - Upgrading kubeadm clusters

This page explains how to upgrade a Kubernetes cluster created with kubeadm from version 1.29.x to version 1.30.x, and from version 1.30.x to 1.30.y (where y > x). Skipping MINOR versions when upgrading is unsupported. For more details, please visit Version Skew Policy.

To see information about upgrading clusters created using older versions of kubeadm, please refer to following pages instead:

The upgrade workflow at high level is the following:

Upgrade a primary control plane node.
Upgrade additional control plane nodes.
Upgrade worker nodes.

Before you begin

Make sure you read the release notes carefully.
The cluster should use a static control plane and etcd pods or external etcd.
Make sure to back up any important components, such as app-level state stored in a database. kubeadm upgrade does not touch your workloads, only components internal to Kubernetes, but backups are always a best practice.
Swap must be disabled.

Additional information

The instructions below outline when to drain each node during the upgrade process. If you are performing a minor version upgrade for any kubelet, you must first drain the node (or nodes) that you are upgrading. In the case of control plane nodes, they could be running CoreDNS Pods or other critical workloads. For more information see Draining nodes.
The Kubernetes project recommends that you match your kubelet and kubeadm versions. You can instead use a version of kubelet that is older than kubeadm, provided it is within the range of supported versions. For more details, please visit kubeadm's skew against the kubelet.
All containers are restarted after upgrade, because the container spec hash value is changed.
To verify that the kubelet service has successfully restarted after the kubelet has been upgraded, you can execute systemctl status kubelet or view the service logs with journalctl -xeu kubelet.
Usage of the --config flag of kubeadm upgrade with kubeadm configuration API types with the purpose of reconfiguring the cluster is not recommended and can have unexpected results. Follow the steps in Reconfiguring a kubeadm cluster instead.

Considerations when upgrading etcd

Because the kube-apiserver static pod is running at all times (even if you have drained the node), when you perform a kubeadm upgrade which includes an etcd upgrade, in-flight requests to the server will stall while the new etcd static pod is restarting. As a workaround, it is possible to actively stop the kube-apiserver process a few seconds before starting the kubeadm upgrade apply command. This permits to complete in-flight requests and close existing connections, and minimizes the consequence of the etcd downtime. This can be done as follows on control plane nodes:

killall -s SIGTERM kube-apiserver # trigger a graceful kube-apiserver shutdown
sleep 20 # wait a little bit to permit completing in-flight requests
kubeadm upgrade ... # execute a kubeadm upgrade command

Changing the package repository

If you're using the community-owned package repositories (pkgs.k8s.io), you need to enable the package repository for the desired Kubernetes minor release. This is explained in Changing the Kubernetes package repository document.

Note: The legacy package repositories (apt.kubernetes.io and yum.kubernetes.io) have been deprecated and frozen starting from September 13, 2023. Using the new package repositories hosted at pkgs.k8s.io is strongly recommended and required in order to install Kubernetes versions released after September 13, 2023. The deprecated legacy repositories, and their contents, might be removed at any time in the future and without a further notice period. The new package repositories provide downloads for Kubernetes versions starting with v1.24.0.

Determine which version to upgrade to

Find the latest patch release for Kubernetes 1.30 using the OS package manager:

Ubuntu, Debian or HypriotOS
CentOS, RHEL or Fedora

# Find the latest 1.30 version in the list.
# It should look like 1.30.x-*, where x is the latest patch.
sudo apt update
sudo apt-cache madison kubeadm

# Find the latest 1.30 version in the list.
# It should look like 1.30.x-*, where x is the latest patch.
sudo yum list --showduplicates kubeadm --disableexcludes=kubernetes

Upgrading control plane nodes

The upgrade procedure on control plane nodes should be executed one node at a time. Pick a control plane node that you wish to upgrade first. It must have the /etc/kubernetes/admin.conf file.

Call "kubeadm upgrade"

For the first control plane node

Upgrade kubeadm:

Ubuntu, Debian or HypriotOS
CentOS, RHEL or Fedora

# replace x in 1.30.x-* with the latest patch version
sudo apt-mark unhold kubeadm && \
sudo apt-get update && sudo apt-get install -y kubeadm='1.30.x-*' && \
sudo apt-mark hold kubeadm

# replace x in 1.30.x-* with the latest patch version
sudo yum install -y kubeadm-'1.30.x-*' --disableexcludes=kubernetes

Verify that the download works and has the expected version:
```
kubeadm version
```
Verify the upgrade plan:
```
sudo kubeadm upgrade plan
```
This command checks that your cluster can be upgraded, and fetches the versions you can upgrade to. It also shows a table with the component config version states.

Note:
kubeadm upgrade also automatically renews the certificates that it manages on this node. To opt-out of certificate renewal the flag --certificate-renewal=false can be used. For more information see the certificate management guide.

Note:
If kubeadm upgrade plan shows any component configs that require manual upgrade, users must provide a config file with replacement configs to kubeadm upgrade apply via the --config command line flag. Failing to do so will cause kubeadm upgrade apply to exit with an error and not perform an upgrade.
Choose a version to upgrade to, and run the appropriate command. For example:
```
# replace x with the patch version you picked for this upgrade
sudo kubeadm upgrade apply v1.30.x
```
Once the command finishes you should see:
```
[upgrade/successful] SUCCESS! Your cluster was upgraded to "v1.30.x". Enjoy!

[upgrade/kubelet] Now that your control plane is upgraded, please proceed with upgrading your kubelets if you haven't already done so.
```
Note:
For versions earlier than v1.28, kubeadm defaulted to a mode that upgrades the addons (including CoreDNS and kube-proxy) immediately during kubeadm upgrade apply, regardless of whether there are other control plane instances that have not been upgraded. This may cause compatibility problems. Since v1.28, kubeadm defaults to a mode that checks whether all the control plane instances have been upgraded before starting to upgrade the addons. You must perform control plane instances upgrade sequentially or at least ensure that the last control plane instance upgrade is not started until all the other control plane instances have been upgraded completely, and the addons upgrade will be performed after the last control plane instance is upgraded. If you want to keep the old upgrade behavior, please enable the UpgradeAddonsBeforeControlPlane feature gate by kubeadm upgrade apply --feature-gates=UpgradeAddonsBeforeControlPlane=true. The Kubernetes project does not in general recommend enabling this feature gate, you should instead change your upgrade process or cluster addons so that you do not need to enable the legacy behavior. The UpgradeAddonsBeforeControlPlane feature gate will be removed in a future release.
Manually upgrade your CNI provider plugin.

Your Container Network Interface (CNI) provider may have its own upgrade instructions to follow. Check the addons page to find your CNI provider and see whether additional upgrade steps are required.

This step is not required on additional control plane nodes if the CNI provider runs as a DaemonSet.

For the other control plane nodes

Same as the first control plane node but use:

sudo kubeadm upgrade node

instead of:

sudo kubeadm upgrade apply

Also calling kubeadm upgrade plan and upgrading the CNI provider plugin is no longer needed.

Drain the node

Prepare the node for maintenance by marking it unschedulable and evicting the workloads:

# replace <node-to-drain> with the name of your node you are draining
kubectl drain <node-to-drain> --ignore-daemonsets

Upgrade kubelet and kubectl

Upgrade the kubelet and kubectl:

Ubuntu, Debian or HypriotOS
CentOS, RHEL or Fedora

# replace x in 1.30.x-* with the latest patch version
sudo apt-mark unhold kubelet kubectl && \
sudo apt-get update && sudo apt-get install -y kubelet='1.30.x-*' kubectl='1.30.x-*' && \
sudo apt-mark hold kubelet kubectl

# replace x in 1.30.x-* with the latest patch version
sudo yum install -y kubelet-'1.30.x-*' kubectl-'1.30.x-*' --disableexcludes=kubernetes

Restart the kubelet:

sudo systemctl daemon-reload
sudo systemctl restart kubelet

Uncordon the node

Bring the node back online by marking it schedulable:

# replace <node-to-uncordon> with the name of your node
kubectl uncordon <node-to-uncordon>

Upgrade worker nodes

The upgrade procedure on worker nodes should be executed one node at a time or few nodes at a time, without compromising the minimum required capacity for running your workloads.

The following pages show how to upgrade Linux and Windows worker nodes:

Verify the status of the cluster

After the kubelet is upgraded on all nodes verify that all nodes are available again by running the following command from anywhere kubectl can access the cluster:

kubectl get nodes

The STATUS column should show Ready for all your nodes, and the version number should be updated.

Recovering from a failure state

If kubeadm upgrade fails and does not roll back, for example because of an unexpected shutdown during execution, you can run kubeadm upgrade again. This command is idempotent and eventually makes sure that the actual state is the desired state you declare.

To recover from a bad state, you can also run sudo kubeadm upgrade apply --force without changing the version that your cluster is running.

During upgrade kubeadm writes the following backup folders under /etc/kubernetes/tmp:

kubeadm-backup-etcd-<date>-<time>
kubeadm-backup-manifests-<date>-<time>

kubeadm-backup-etcd contains a backup of the local etcd member data for this control plane Node. In case of an etcd upgrade failure and if the automatic rollback does not work, the contents of this folder can be manually restored in /var/lib/etcd. In case external etcd is used this backup folder will be empty.

kubeadm-backup-manifests contains a backup of the static Pod manifest files for this control plane Node. In case of a upgrade failure and if the automatic rollback does not work, the contents of this folder can be manually restored in /etc/kubernetes/manifests. If for some reason there is no difference between a pre-upgrade and post-upgrade manifest file for a certain component, a backup file for it will not be written.

Note:

After the cluster upgrade using kubeadm, the backup directory /etc/kubernetes/tmp will remain and these backup files will need to be cleared manually.

How it works

kubeadm upgrade apply does the following:

Checks that your cluster is in an upgradeable state:
- The API server is reachable
- All nodes are in the Ready state
- The control plane is healthy
Enforces the version skew policies.
Makes sure the control plane images are available or available to pull to the machine.
Generates replacements and/or uses user supplied overwrites if component configs require version upgrades.
Upgrades the control plane components or rollbacks if any of them fails to come up.
Applies the new CoreDNS and kube-proxy manifests and makes sure that all necessary RBAC rules are created.
Creates new certificate and key files of the API server and backs up old files if they're about to expire in 180 days.

kubeadm upgrade node does the following on additional control plane nodes:

Fetches the kubeadm ClusterConfiguration from the cluster.
Optionally backups the kube-apiserver certificate.
Upgrades the static Pod manifests for the control plane components.
Upgrades the kubelet configuration for this node.

kubeadm upgrade node does the following on worker nodes:

Fetches the kubeadm ClusterConfiguration from the cluster.
Upgrades the kubelet configuration for this node.

2.1.5 - Upgrading Linux nodes

This page explains how to upgrade a Linux Worker Nodes created with kubeadm.

Before you begin

You need to have shell access to all the nodes, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts.

To check the version, enter kubectl version.

Familiarize yourself with the process for upgrading the rest of your kubeadm cluster. You will want to upgrade the control plane nodes before upgrading your Linux Worker nodes.

Changing the package repository

Upgrading worker nodes

Upgrade kubeadm

Upgrade kubeadm:

Ubuntu, Debian or HypriotOS
CentOS, RHEL or Fedora

# replace x in 1.30.x-* with the latest patch version
sudo apt-mark unhold kubeadm && \
sudo apt-get update && sudo apt-get install -y kubeadm='1.30.x-*' && \
sudo apt-mark hold kubeadm

# replace x in 1.30.x-* with the latest patch version
sudo yum install -y kubeadm-'1.30.x-*' --disableexcludes=kubernetes

Call "kubeadm upgrade"

For worker nodes this upgrades the local kubelet configuration:

sudo kubeadm upgrade node

Drain the node

Prepare the node for maintenance by marking it unschedulable and evicting the workloads:

# execute this command on a control plane node
# replace <node-to-drain> with the name of your node you are draining
kubectl drain <node-to-drain> --ignore-daemonsets

Upgrade kubelet and kubectl

Upgrade the kubelet and kubectl:

Ubuntu, Debian or HypriotOS
CentOS, RHEL or Fedora

# replace x in 1.30.x-* with the latest patch version
sudo apt-mark unhold kubelet kubectl && \
sudo apt-get update && sudo apt-get install -y kubelet='1.30.x-*' kubectl='1.30.x-*' && \
sudo apt-mark hold kubelet kubectl

# replace x in 1.30.x-* with the latest patch version
sudo yum install -y kubelet-'1.30.x-*' kubectl-'1.30.x-*' --disableexcludes=kubernetes

Restart the kubelet:

sudo systemctl daemon-reload
sudo systemctl restart kubelet

Uncordon the node

Bring the node back online by marking it schedulable:

# execute this command on a control plane node
# replace <node-to-uncordon> with the name of your node
kubectl uncordon <node-to-uncordon>

What's next

See how to Upgrade Windows nodes.

2.1.6 - Upgrading Windows nodes

FEATURE STATE: Kubernetes v1.18 [beta]

This page explains how to upgrade a Windows node created with kubeadm.

Before you begin

Your Kubernetes server must be at or later than version 1.17. To check the version, enter kubectl version.

Familiarize yourself with the process for upgrading the rest of your kubeadm cluster. You will want to upgrade the control plane nodes before upgrading your Windows nodes.

Upgrading worker nodes

Upgrade kubeadm

From the Windows node, upgrade kubeadm:

# replace 1.30.0 with your desired version
curl.exe -Lo <path-to-kubeadm.exe>  "https://dl.k8s.io/v1.30.0/bin/windows/amd64/kubeadm.exe"

Drain the node

From a machine with access to the Kubernetes API, prepare the node for maintenance by marking it unschedulable and evicting the workloads:

# replace <node-to-drain> with the name of your node you are draining
kubectl drain <node-to-drain> --ignore-daemonsets

You should see output similar to this:

node/ip-172-31-85-18 cordoned
node/ip-172-31-85-18 drained

Upgrade the kubelet configuration

From the Windows node, call the following command to sync new kubelet configuration:
```
kubeadm upgrade node
```

Upgrade kubelet and kube-proxy

From the Windows node, upgrade and restart the kubelet:

stop-service kubelet
curl.exe -Lo <path-to-kubelet.exe> "https://dl.k8s.io/v1.30.0/bin/windows/amd64/kubelet.exe"
restart-service kubelet

From the Windows node, upgrade and restart the kube-proxy.

stop-service kube-proxy
curl.exe -Lo <path-to-kube-proxy.exe> "https://dl.k8s.io/v1.30.0/bin/windows/amd64/kube-proxy.exe"
restart-service kube-proxy

Note:

If you are running kube-proxy in a HostProcess container within a Pod, and not as a Windows Service, you can upgrade kube-proxy by applying a newer version of your kube-proxy manifests.

Uncordon the node

From a machine with access to the Kubernetes API, bring the node back online by marking it schedulable:
```
# replace <node-to-drain> with the name of your node
kubectl uncordon <node-to-drain>
```

What's next

See how to Upgrade Linux nodes.

2.1.7 - Changing The Kubernetes Package Repository

This page explains how to enable a package repository for the desired Kubernetes minor release upon upgrading a cluster. This is only needed for users of the community-owned package repositories hosted at pkgs.k8s.io. Unlike the legacy package repositories, the community-owned package repositories are structured in a way that there's a dedicated package repository for each Kubernetes minor version.

Note:

This guide only covers a part of the Kubernetes upgrade process. Please see the upgrade guide for more information about upgrading Kubernetes clusters.

Note:

This step is only needed upon upgrading a cluster to another minor release. If you're upgrading to another patch release within the same minor release (e.g. v1.30.5 to v1.30.7), you don't need to follow this guide. However, if you're still using the legacy package repositories, you'll need to migrate to the new community-owned package repositories before upgrading (see the next section for more details on how to do this).

Before you begin

This document assumes that you're already using the community-owned package repositories (pkgs.k8s.io). If that's not the case, it's strongly recommended to migrate to the community-owned package repositories as described in the official announcement.

Verifying if the Kubernetes package repositories are used

If you're unsure whether you're using the community-owned package repositories or the legacy package repositories, take the following steps to verify:

Print the contents of the file that defines the Kubernetes apt repository:

# On your system, this configuration file could have a different name
pager /etc/apt/sources.list.d/kubernetes.list

If you see a line similar to:

deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.29/deb/ /

You're using the Kubernetes package repositories and this guide applies to you. Otherwise, it's strongly recommended to migrate to the Kubernetes package repositories as described in the official announcement.

Print the contents of the file that defines the Kubernetes yum repository:

# On your system, this configuration file could have a different name
cat /etc/yum.repos.d/kubernetes.repo

If you see a baseurl similar to the baseurl in the output below:

[kubernetes]
name=Kubernetes
baseurl=https://pkgs.k8s.io/core:/stable:/v1.29/rpm/
enabled=1
gpgcheck=1
gpgkey=https://pkgs.k8s.io/core:/stable:/v1.29/rpm/repodata/repomd.xml.key
exclude=kubelet kubeadm kubectl

Print the contents of the file that defines the Kubernetes zypper repository:

# On your system, this configuration file could have a different name
cat /etc/zypp/repos.d/kubernetes.repo

If you see a baseurl similar to the baseurl in the output below:

[kubernetes]
name=Kubernetes
baseurl=https://pkgs.k8s.io/core:/stable:/v1.29/rpm/
enabled=1
gpgcheck=1
gpgkey=https://pkgs.k8s.io/core:/stable:/v1.29/rpm/repodata/repomd.xml.key
exclude=kubelet kubeadm kubectl

Note:

The URL used for the Kubernetes package repositories is not limited to pkgs.k8s.io, it can also be one of:

pkgs.k8s.io
pkgs.kubernetes.io
packages.kubernetes.io

Switching to another Kubernetes package repository

This step should be done upon upgrading from one to another Kubernetes minor release in order to get access to the packages of the desired Kubernetes minor version.

Ubuntu, Debian or HypriotOS
CentOS, RHEL or Fedora

Open the file that defines the Kubernetes apt repository using a text editor of your choice:
```
nano /etc/apt/sources.list.d/kubernetes.list
```
You should see a single line with the URL that contains your current Kubernetes minor version. For example, if you're using v1.29, you should see this:
```
deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.29/deb/ /
```

Change the version in the URL to the next available minor release, for example:

deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.30/deb/ /

Save the file and exit your text editor. Continue following the relevant upgrade instructions.

Open the file that defines the Kubernetes yum repository using a text editor of your choice:

nano /etc/yum.repos.d/kubernetes.repo

You should see a file with two URLs that contain your current Kubernetes minor version. For example, if you're using v1.29, you should see this:

[kubernetes]
name=Kubernetes
baseurl=https://pkgs.k8s.io/core:/stable:/v1.29/rpm/
enabled=1
gpgcheck=1
gpgkey=https://pkgs.k8s.io/core:/stable:/v1.29/rpm/repodata/repomd.xml.key
exclude=kubelet kubeadm kubectl cri-tools kubernetes-cni

Change the version in these URLs to the next available minor release, for example:

[kubernetes]
name=Kubernetes
baseurl=https://pkgs.k8s.io/core:/stable:/v1.30/rpm/
enabled=1
gpgcheck=1
gpgkey=https://pkgs.k8s.io/core:/stable:/v1.30/rpm/repodata/repomd.xml.key
exclude=kubelet kubeadm kubectl cri-tools kubernetes-cni

Save the file and exit your text editor. Continue following the relevant upgrade instructions.

What's next

See how to Upgrade Linux nodes.
See how to Upgrade Windows nodes.

2.2 - Migrating from dockershim

This section presents information you need to know when migrating from dockershim to other container runtimes.

Since the announcement of dockershim deprecation in Kubernetes 1.20, there were questions on how this will affect various workloads and Kubernetes installations. Our Dockershim Removal FAQ is there to help you to understand the problem better.

Dockershim was removed from Kubernetes with the release of v1.24. If you use Docker Engine via dockershim as your container runtime and wish to upgrade to v1.24, it is recommended that you either migrate to another runtime or find an alternative means to obtain Docker Engine support. Check out the container runtimes section to know your options.

The version of Kubernetes with dockershim (1.23) is out of support and the v1.24 will run out of support soon. Make sure to report issues you encountered with the migration so the issues can be fixed in a timely manner and your cluster would be ready for dockershim removal. After v1.24 running out of support, you will need to contact your Kubernetes provider for support or upgrade multiple versions at a time if there are critical issues affecting your cluster.

Your cluster might have more than one kind of node, although this is not a common configuration.

These tasks will help you to migrate:

What's next

Check out container runtimes to understand your options for an alternative.
If you find a defect or other technical concern relating to migrating away from dockershim, you can report an issue to the Kubernetes project.

2.2.1 - Changing the Container Runtime on a Node from Docker Engine to containerd

This task outlines the steps needed to update your container runtime to containerd from Docker. It is applicable for cluster operators running Kubernetes 1.23 or earlier. This also covers an example scenario for migrating from dockershim to containerd. Alternative container runtimes can be picked from this page.

Before you begin

Install containerd. For more information see containerd's installation documentation and for specific prerequisite follow the containerd guide.

Drain the node

kubectl drain <node-to-drain> --ignore-daemonsets

Replace <node-to-drain> with the name of your node you are draining.

Stop the Docker daemon

systemctl stop kubelet
systemctl disable docker.service --now

Install Containerd

Follow the guide for detailed steps to install containerd.

Linux
Windows (PowerShell)

Install the containerd.io package from the official Docker repositories. Instructions for setting up the Docker repository for your respective Linux distribution and installing the containerd.io package can be found at Getting started with containerd.

Configure containerd:

sudo mkdir -p /etc/containerd
containerd config default | sudo tee /etc/containerd/config.toml

Restart containerd:
```
sudo systemctl restart containerd
```

Start a Powershell session, set $Version to the desired version (ex: $Version="1.4.3"), and then run the following commands:

Download containerd:

curl.exe -L https://github.com/containerd/containerd/releases/download/v$Version/containerd-$Version-windows-amd64.tar.gz -o containerd-windows-amd64.tar.gz
tar.exe xvf .\containerd-windows-amd64.tar.gz

Extract and configure:

Copy-Item -Path ".\bin\" -Destination "$Env:ProgramFiles\containerd" -Recurse -Force
cd $Env:ProgramFiles\containerd\
.\containerd.exe config default | Out-File config.toml -Encoding ascii

# Review the configuration. Depending on setup you may want to adjust:
# - the sandbox_image (Kubernetes pause image)
# - cni bin_dir and conf_dir locations
Get-Content config.toml

# (Optional - but highly recommended) Exclude containerd from Windows Defender Scans
Add-MpPreference -ExclusionProcess "$Env:ProgramFiles\containerd\containerd.exe"

Start containerd:

.\containerd.exe --register-service
Start-Service containerd

Configure the kubelet to use containerd as its container runtime

Edit the file /var/lib/kubelet/kubeadm-flags.env and add the containerd runtime to the flags; --container-runtime-endpoint=unix:///run/containerd/containerd.sock.

Users using kubeadm should be aware that the kubeadm tool stores the CRI socket for each host as an annotation in the Node object for that host. To change it you can execute the following command on a machine that has the kubeadm /etc/kubernetes/admin.conf file.

kubectl edit no <node-name>

This will start a text editor where you can edit the Node object. To choose a text editor you can set the KUBE_EDITOR environment variable.

Change the value of kubeadm.alpha.kubernetes.io/cri-socket from /var/run/dockershim.sock to the CRI socket path of your choice (for example unix:///run/containerd/containerd.sock).

Note that new CRI socket paths must be prefixed with unix:// ideally.
Save the changes in the text editor, which will update the Node object.

Restart the kubelet

systemctl start kubelet

Verify that the node is healthy

Run kubectl get nodes -o wide and containerd appears as the runtime for the node we just changed.

Remove Docker Engine

If the node appears healthy, remove Docker.

sudo yum remove docker-ce docker-ce-cli

sudo apt-get purge docker-ce docker-ce-cli

sudo dnf remove docker-ce docker-ce-cli

sudo apt-get purge docker-ce docker-ce-cli

The preceding commands don't remove images, containers, volumes, or customized configuration files on your host. To delete them, follow Docker's instructions to Uninstall Docker Engine.

Caution:

Docker's instructions for uninstalling Docker Engine create a risk of deleting containerd. Be careful when executing commands.

Uncordon the node

kubectl uncordon <node-to-uncordon>

Replace <node-to-uncordon> with the name of your node you previously drained.

2.2.2 - Migrate Docker Engine nodes from dockershim to cri-dockerd

This page shows you how to migrate your Docker Engine nodes to use cri-dockerd instead of dockershim. You should follow these steps in these scenarios:

You want to switch away from dockershim and still use Docker Engine to run containers in Kubernetes.
You want to upgrade to Kubernetes v1.30 and your existing cluster relies on dockershim, in which case you must migrate from dockershim and cri-dockerd is one of your options.

To learn more about the removal of dockershim, read the FAQ page.

What is cri-dockerd?

In Kubernetes 1.23 and earlier, you could use Docker Engine with Kubernetes, relying on a built-in component of Kubernetes named dockershim. The dockershim component was removed in the Kubernetes 1.24 release; however, a third-party replacement, cri-dockerd, is available. The cri-dockerd adapter lets you use Docker Engine through the Container Runtime Interface.

Note:

If you already use cri-dockerd, you aren't affected by the dockershim removal. Before you begin, Check whether your nodes use the dockershim.

If you want to migrate to cri-dockerd so that you can continue using Docker Engine as your container runtime, you should do the following for each affected node:

Install cri-dockerd.
Cordon and drain the node.
Configure the kubelet to use cri-dockerd.
Restart the kubelet.
Verify that the node is healthy.

Test the migration on non-critical nodes first.

You should perform the following steps for each node that you want to migrate to cri-dockerd.

Before you begin

cri-dockerd installed and started on each node.
A network plugin.

Cordon and drain the node

Cordon the node to stop new Pods scheduling on it:
```
kubectl cordon <NODE_NAME>
```
Replace <NODE_NAME> with the name of the node.

Drain the node to safely evict running Pods:

kubectl drain <NODE_NAME> \
    --ignore-daemonsets

Configure the kubelet to use cri-dockerd

The following steps apply to clusters set up using the kubeadm tool. If you use a different tool, you should modify the kubelet using the configuration instructions for that tool.

Open /var/lib/kubelet/kubeadm-flags.env on each affected node.
Modify the --container-runtime-endpoint flag to unix:///var/run/cri-dockerd.sock.
Modify the --container-runtime flag to remote (unavailable in Kubernetes v1.27 and later).

The kubeadm tool stores the node's socket as an annotation on the Node object in the control plane. To modify this socket for each affected node:

Edit the YAML representation of the Node object:
```
KUBECONFIG=/path/to/admin.conf kubectl edit no <NODE_NAME>
```
Replace the following:
- /path/to/admin.conf: the path to the kubectl configuration file, admin.conf.
- <NODE_NAME>: the name of the node you want to modify.
Change kubeadm.alpha.kubernetes.io/cri-socket from /var/run/dockershim.sock to unix:///var/run/cri-dockerd.sock.
Save the changes. The Node object is updated on save.

Restart the kubelet

systemctl restart kubelet

Verify that the node is healthy

To check whether the node uses the cri-dockerd endpoint, follow the instructions in Find out which runtime you use. The --container-runtime-endpoint flag for the kubelet should be unix:///var/run/cri-dockerd.sock.

Uncordon the node

Uncordon the node to let Pods schedule on it:

kubectl uncordon <NODE_NAME>

What's next

2.2.3 - Find Out What Container Runtime is Used on a Node

This page outlines steps to find out what container runtime the nodes in your cluster use.

Depending on the way you run your cluster, the container runtime for the nodes may have been pre-configured or you need to configure it. If you're using a managed Kubernetes service, there might be vendor-specific ways to check what container runtime is configured for the nodes. The method described on this page should work whenever the execution of kubectl is allowed.

Before you begin

Install and configure kubectl. See Install Tools section for details.

Find out the container runtime used on a Node

Use kubectl to fetch and show node information:

kubectl get nodes -o wide

The output is similar to the following. The column CONTAINER-RUNTIME outputs the runtime and its version.

For Docker Engine, the output is similar to this:

NAME         STATUS   VERSION    CONTAINER-RUNTIME
node-1       Ready    v1.16.15   docker://19.3.1
node-2       Ready    v1.16.15   docker://19.3.1
node-3       Ready    v1.16.15   docker://19.3.1

If your runtime shows as Docker Engine, you still might not be affected by the removal of dockershim in Kubernetes v1.24. Check the runtime endpoint to see if you use dockershim. If you don't use dockershim, you aren't affected.

For containerd, the output is similar to this:

NAME         STATUS   VERSION   CONTAINER-RUNTIME
node-1       Ready    v1.19.6   containerd://1.4.1
node-2       Ready    v1.19.6   containerd://1.4.1
node-3       Ready    v1.19.6   containerd://1.4.1

Find out more information about container runtimes on Container Runtimes page.

Find out what container runtime endpoint you use

The container runtime talks to the kubelet over a Unix socket using the CRI protocol, which is based on the gRPC framework. The kubelet acts as a client, and the runtime acts as the server. In some cases, you might find it useful to know which socket your nodes use. For example, with the removal of dockershim in Kubernetes v1.24 and later, you might want to know whether you use Docker Engine with dockershim.

Note:

If you currently use Docker Engine in your nodes with cri-dockerd, you aren't affected by the dockershim removal.

You can check which socket you use by checking the kubelet configuration on your nodes.

Read the starting commands for the kubelet process:
```
tr \\0 ' ' < /proc/"$(pgrep kubelet)"/cmdline
```
If you don't have tr or pgrep, check the command line for the kubelet process manually.
In the output, look for the --container-runtime flag and the --container-runtime-endpoint flag.
- If your nodes use Kubernetes v1.23 and earlier and these flags aren't present or if the --container-runtime flag is not remote, you use the dockershim socket with Docker Engine. The --container-runtime command line argument is not available in Kubernetes v1.27 and later.
- If the --container-runtime-endpoint flag is present, check the socket name to find out which runtime you use. For example, unix:///run/containerd/containerd.sock is the containerd endpoint.

If you want to change the Container Runtime on a Node from Docker Engine to containerd, you can find out more information on migrating from Docker Engine to containerd, or, if you want to continue using Docker Engine in Kubernetes v1.24 and later, migrate to a CRI-compatible adapter like cri-dockerd.

2.2.4 - Troubleshooting CNI plugin-related errors

To avoid CNI plugin-related errors, verify that you are using or upgrading to a container runtime that has been tested to work correctly with your version of Kubernetes.

About the "Incompatible CNI versions" and "Failed to destroy network for sandbox" errors

Service issues exist for pod CNI network setup and tear down in containerd v1.6.0-v1.6.3 when the CNI plugins have not been upgraded and/or the CNI config version is not declared in the CNI config files. The containerd team reports, "these issues are resolved in containerd v1.6.4."

With containerd v1.6.0-v1.6.3, if you do not upgrade the CNI plugins and/or declare the CNI config version, you might encounter the following "Incompatible CNI versions" or "Failed to destroy network for sandbox" error conditions.

Incompatible CNI versions error

If the version of your CNI plugin does not correctly match the plugin version in the config because the config version is later than the plugin version, the containerd log will likely show an error message on startup of a pod similar to:

incompatible CNI versions; config is \"1.0.0\", plugin supports [\"0.1.0\" \"0.2.0\" \"0.3.0\" \"0.3.1\" \"0.4.0\"]"

To fix this issue, update your CNI plugins and CNI config files.

Failed to destroy network for sandbox error

If the version of the plugin is missing in the CNI plugin config, the pod may run. However, stopping the pod generates an error similar to:

ERROR[2022-04-26T00:43:24.518165483Z] StopPodSandbox for "b" failed
error="failed to destroy network for sandbox \"bbc85f891eaf060c5a879e27bba9b6b06450210161dfdecfbb2732959fb6500a\": invalid version \"\": the version is empty"

This error leaves the pod in the not-ready state with a network namespace still attached. To recover from this problem, edit the CNI config file to add the missing version information. The next attempt to stop the pod should be successful.

Updating your CNI plugins and CNI config files

If you're using containerd v1.6.0-v1.6.3 and encountered "Incompatible CNI versions" or "Failed to destroy network for sandbox" errors, consider updating your CNI plugins and editing the CNI config files.

Here's an overview of the typical steps for each node:

Safely drain and cordon the node.
After stopping your container runtime and kubelet services, perform the following upgrade operations:

If you're running CNI plugins, upgrade them to the latest version.
If you're using non-CNI plugins, replace them with CNI plugins. Use the latest version of the plugins.
Update the plugin configuration file to specify or match a version of the CNI specification that the plugin supports, as shown in the following "An example containerd configuration file" section.
For containerd, ensure that you have installed the latest version (v1.0.0 or later) of the CNI loopback plugin.
Upgrade node components (for example, the kubelet) to Kubernetes v1.24
Upgrade to or install the most current version of the container runtime.

Bring the node back into your cluster by restarting your container runtime and kubelet. Uncordon the node (kubectl uncordon <nodename>).

An example containerd configuration file

The following example shows a configuration for containerd runtime v1.6.x, which supports a recent version of the CNI specification (v1.0.0).

Please see the documentation from your plugin and networking provider for further instructions on configuring your system.

On Kubernetes, containerd runtime adds a loopback interface, lo, to pods as a default behavior. The containerd runtime configures the loopback interface via a CNI plugin, loopback. The loopback plugin is distributed as part of the containerd release packages that have the cni designation. containerd v1.6.0 and later includes a CNI v1.0.0-compatible loopback plugin as well as other default CNI plugins. The configuration for the loopback plugin is done internally by containerd, and is set to use CNI v1.0.0. This also means that the version of the loopback plugin must be v1.0.0 or later when this newer version containerd is started.

The following bash command generates an example CNI config. Here, the 1.0.0 value for the config version is assigned to the cniVersion field for use when containerd invokes the CNI bridge plugin.

cat << EOF | tee /etc/cni/net.d/10-containerd-net.conflist
{
 "cniVersion": "1.0.0",
 "name": "containerd-net",
 "plugins": [
   {
     "type": "bridge",
     "bridge": "cni0",
     "isGateway": true,
     "ipMasq": true,
     "promiscMode": true,
     "ipam": {
       "type": "host-local",
       "ranges": [
         [{
           "subnet": "10.88.0.0/16"
         }],
         [{
           "subnet": "2001:db8:4860::/64"
         }]
       ],
       "routes": [
         { "dst": "0.0.0.0/0" },
         { "dst": "::/0" }
       ]
     }
   },
   {
     "type": "portmap",
     "capabilities": {"portMappings": true},
     "externalSetMarkChain": "KUBE-MARK-MASQ"
   }
 ]
}
EOF

Update the IP address ranges in the preceding example with ones that are based on your use case and network addressing plan.

2.2.5 - Check whether dockershim removal affects you

The dockershim component of Kubernetes allows the use of Docker as a Kubernetes's container runtime. Kubernetes' built-in dockershim component was removed in release v1.24.

This page explains how your cluster could be using Docker as a container runtime, provides details on the role that dockershim plays when in use, and shows steps you can take to check whether any workloads could be affected by dockershim removal.

Finding if your app has a dependencies on Docker

If you are using Docker for building your application containers, you can still run these containers on any container runtime. This use of Docker does not count as a dependency on Docker as a container runtime.

When alternative container runtime is used, executing Docker commands may either not work or yield unexpected output. This is how you can find whether you have a dependency on Docker:

Make sure no privileged Pods execute Docker commands (like docker ps), restart the Docker service (commands such as systemctl restart docker.service), or modify Docker-specific files such as /etc/docker/daemon.json.
Check for any private registries or image mirror settings in the Docker configuration file (like /etc/docker/daemon.json). Those typically need to be reconfigured for another container runtime.
Check that scripts and apps running on nodes outside of your Kubernetes infrastructure do not execute Docker commands. It might be:
- SSH to nodes to troubleshoot;
- Node startup scripts;
- Monitoring and security agents installed on nodes directly.
Third-party tools that perform above mentioned privileged operations. See Migrating telemetry and security agents from dockershim for more information.
Make sure there are no indirect dependencies on dockershim behavior. This is an edge case and unlikely to affect your application. Some tooling may be configured to react to Docker-specific behaviors, for example, raise alert on specific metrics or search for a specific log message as part of troubleshooting instructions. If you have such tooling configured, test the behavior on a test cluster before migration.

Dependency on Docker explained

A container runtime is software that can execute the containers that make up a Kubernetes pod. Kubernetes is responsible for orchestration and scheduling of Pods; on each node, the kubelet uses the container runtime interface as an abstraction so that you can use any compatible container runtime.

In its earliest releases, Kubernetes offered compatibility with one container runtime: Docker. Later in the Kubernetes project's history, cluster operators wanted to adopt additional container runtimes. The CRI was designed to allow this kind of flexibility - and the kubelet began supporting CRI. However, because Docker existed before the CRI specification was invented, the Kubernetes project created an adapter component, dockershim. The dockershim adapter allows the kubelet to interact with Docker as if Docker were a CRI compatible runtime.

You can read about it in Kubernetes Containerd integration goes GA blog post.

Dockershim vs. CRI with Containerd

Switching to Containerd as a container runtime eliminates the middleman. All the same containers can be run by container runtimes like Containerd as before. But now, since containers schedule directly with the container runtime, they are not visible to Docker. So any Docker tooling or fancy UI you might have used before to check on these containers is no longer available.

You cannot get container information using docker ps or docker inspect commands. As you cannot list containers, you cannot get logs, stop containers, or execute something inside a container using docker exec.

Note:

If you're running workloads via Kubernetes, the best way to stop a container is through the Kubernetes API rather than directly through the container runtime (this advice applies for all container runtimes, not only Docker).

You can still pull images or build them using docker build command. But images built or pulled by Docker would not be visible to container runtime and Kubernetes. They needed to be pushed to some registry to allow them to be used by Kubernetes.

Known issues

Some filesystem metrics are missing and the metrics format is different

The Kubelet /metrics/cadvisor endpoint provides Prometheus metrics, as documented in Metrics for Kubernetes system components. If you install a metrics collector that depends on that endpoint, you might see the following issues:

The metrics format on the Docker node is k8s_<container-name>_<pod-name>_<namespace>_<pod-uid>_<restart-count> but the format on other runtime is different. For example, on containerd node it is <container-id>.

Some filesystem metrics are missing, as follows:

container_fs_inodes_free
container_fs_inodes_total
container_fs_io_current
container_fs_io_time_seconds_total
container_fs_io_time_weighted_seconds_total
container_fs_limit_bytes
container_fs_read_seconds_total
container_fs_reads_merged_total
container_fs_sector_reads_total
container_fs_sector_writes_total
container_fs_usage_bytes
container_fs_write_seconds_total
container_fs_writes_merged_total

Workaround

You can mitigate this issue by using cAdvisor as a standalone daemonset.

Find the latest cAdvisor release with the name pattern vX.Y.Z-containerd-cri (for example, v0.42.0-containerd-cri).
Follow the steps in cAdvisor Kubernetes Daemonset to create the daemonset.
Point the installed metrics collector to use the cAdvisor /metrics endpoint which provides the full set of Prometheus container metrics.

Alternatives:

Use alternative third party metrics collection solution.
Collect metrics from the Kubelet summary API that is served at /stats/summary.

What's next

Read Migrating from dockershim to understand your next steps
Read the dockershim deprecation FAQ article for more information.

2.2.6 - Migrating telemetry and security agents from dockershim

Kubernetes' support for direct integration with Docker Engine is deprecated and has been removed. Most apps do not have a direct dependency on runtime hosting containers. However, there are still a lot of telemetry and monitoring agents that have a dependency on Docker to collect containers metadata, logs, and metrics. This document aggregates information on how to detect these dependencies as well as links on how to migrate these agents to use generic tools or alternative runtimes.

Telemetry and security agents

Within a Kubernetes cluster there are a few different ways to run telemetry or security agents. Some agents have a direct dependency on Docker Engine when they run as DaemonSets or directly on nodes.

Why do some telemetry agents communicate with Docker Engine?

Historically, Kubernetes was written to work specifically with Docker Engine. Kubernetes took care of networking and scheduling, relying on Docker Engine for launching and running containers (within Pods) on a node. Some information that is relevant to telemetry, such as a pod name, is only available from Kubernetes components. Other data, such as container metrics, is not the responsibility of the container runtime. Early telemetry agents needed to query the container runtime and Kubernetes to report an accurate picture. Over time, Kubernetes gained the ability to support multiple runtimes, and now supports any runtime that is compatible with the container runtime interface.

Some telemetry agents rely specifically on Docker Engine tooling. For example, an agent might run a command such as docker ps or docker top to list containers and processes or docker logs to receive streamed logs. If nodes in your existing cluster use Docker Engine, and you switch to a different container runtime, these commands will not work any longer.

Identify DaemonSets that depend on Docker Engine

If a pod wants to make calls to the dockerd running on the node, the pod must either:

mount the filesystem containing the Docker daemon's privileged socket, as a volume; or
mount the specific path of the Docker daemon's privileged socket directly, also as a volume.

For example: on COS images, Docker exposes its Unix domain socket at /var/run/docker.sock This means that the pod spec will include a hostPath volume mount of /var/run/docker.sock.

Here's a sample shell script to find Pods that have a mount directly mapping the Docker socket. This script outputs the namespace and name of the pod. You can remove the grep '/var/run/docker.sock' to review other mounts.

kubectl get pods --all-namespaces \
-o=jsonpath='{range .items[*]}{"\n"}{.metadata.namespace}{":\t"}{.metadata.name}{":\t"}{range .spec.volumes[*]}{.hostPath.path}{", "}{end}{end}' \
| sort \
| grep '/var/run/docker.sock'

Note:

There are alternative ways for a pod to access Docker on the host. For instance, the parent directory /var/run may be mounted instead of the full path (like in this example). The script above only detects the most common uses.

Detecting Docker dependency from node agents

If your cluster nodes are customized and install additional security and telemetry agents on the node, check with the agent vendor to verify whether it has any dependency on Docker.

Telemetry and security agent vendors

This section is intended to aggregate information about various telemetry and security agents that may have a dependency on container runtimes.

We keep the work in progress version of migration instructions for various telemetry and security agent vendors in Google doc. Please contact the vendor to get up to date instructions for migrating from dockershim.

Migration from dockershim

Aqua

No changes are needed: everything should work seamlessly on the runtime switch.

Datadog

How to migrate: Docker deprecation in Kubernetes The pod that accesses Docker Engine may have a name containing any of:

datadog-agent
datadog
dd-agent

Dynatrace

How to migrate: Migrating from Docker-only to generic container metrics in Dynatrace

Containerd support announcement: Get automated full-stack visibility into containerd-based Kubernetes environments

CRI-O support announcement: Get automated full-stack visibility into your CRI-O Kubernetes containers (Beta)

The pod accessing Docker may have name containing:

dynatrace-oneagent

Falco

How to migrate:

Migrate Falco from dockershim Falco supports any CRI-compatible runtime (containerd is used in the default configuration); the documentation explains all details. The pod accessing Docker may have name containing:

falco

Prisma Cloud Compute

Check documentation for Prisma Cloud, under the "Install Prisma Cloud on a CRI (non-Docker) cluster" section. The pod accessing Docker may be named like:

twistlock-defender-ds

SignalFx (Splunk)

The SignalFx Smart Agent (deprecated) uses several different monitors for Kubernetes including kubernetes-cluster, kubelet-stats/kubelet-metrics, and docker-container-stats. The kubelet-stats monitor was previously deprecated by the vendor, in favor of kubelet-metrics. The docker-container-stats monitor is the one affected by dockershim removal. Do not use the docker-container-stats with container runtimes other than Docker Engine.

How to migrate from dockershim-dependent agent:

Remove docker-container-stats from the list of configured monitors. Note, keeping this monitor enabled with non-dockershim runtime will result in incorrect metrics being reported when docker is installed on node and no metrics when docker is not installed.
Enable and configure kubelet-metrics monitor.

Note:

The set of collected metrics will change. Review your alerting rules and dashboards.

The Pod accessing Docker may be named something like:

signalfx-agent

Yahoo Kubectl Flame

Flame does not support container runtimes other than Docker. See https://github.com/yahoo/kubectl-flame/issues/51

2.3 - Generate Certificates Manually

When using client certificate authentication, you can generate certificates manually through easyrsa, openssl or cfssl.

easyrsa

easyrsa can manually generate certificates for your cluster.

Download, unpack, and initialize the patched version of easyrsa3.

curl -LO https://dl.k8s.io/easy-rsa/easy-rsa.tar.gz
tar xzf easy-rsa.tar.gz
cd easy-rsa-master/easyrsa3
./easyrsa init-pki

Generate a new certificate authority (CA). --batch sets automatic mode; --req-cn specifies the Common Name (CN) for the CA's new root certificate.
```
./easyrsa --batch "--req-cn=${MASTER_IP}@`date +%s`" build-ca nopass
```
Generate server certificate and key.

The argument --subject-alt-name sets the possible IPs and DNS names the API server will be accessed with. The MASTER_CLUSTER_IP is usually the first IP from the service CIDR that is specified as the --service-cluster-ip-range argument for both the API server and the controller manager component. The argument --days is used to set the number of days after which the certificate expires. The sample below also assumes that you are using cluster.local as the default DNS domain name.
```
./easyrsa --subject-alt-name="IP:${MASTER_IP},"\
"IP:${MASTER_CLUSTER_IP},"\
"DNS:kubernetes,"\
"DNS:kubernetes.default,"\
"DNS:kubernetes.default.svc,"\
"DNS:kubernetes.default.svc.cluster,"\
"DNS:kubernetes.default.svc.cluster.local" \
--days=10000 \
build-server-full server nopass
```
Copy pki/ca.crt, pki/issued/server.crt, and pki/private/server.key to your directory.

Fill in and add the following parameters into the API server start parameters:

--client-ca-file=/yourdirectory/ca.crt
--tls-cert-file=/yourdirectory/server.crt
--tls-private-key-file=/yourdirectory/server.key

openssl

openssl can manually generate certificates for your cluster.

Generate a ca.key with 2048bit:
```
openssl genrsa -out ca.key 2048
```

According to the ca.key generate a ca.crt (use -days to set the certificate effective time):

openssl req -x509 -new -nodes -key ca.key -subj "/CN=${MASTER_IP}" -days 10000 -out ca.crt

Generate a server.key with 2048bit:
```
openssl genrsa -out server.key 2048
```

Create a config file for generating a Certificate Signing Request (CSR).

Be sure to substitute the values marked with angle brackets (e.g. <MASTER_IP>) with real values before saving this to a file (e.g. csr.conf). Note that the value for MASTER_CLUSTER_IP is the service cluster IP for the API server as described in previous subsection. The sample below also assumes that you are using cluster.local as the default DNS domain name.

[ req ]
default_bits = 2048
prompt = no
default_md = sha256
req_extensions = req_ext
distinguished_name = dn

[ dn ]
C = <country>
ST = <state>
L = <city>
O = <organization>
OU = <organization unit>
CN = <MASTER_IP>

[ req_ext ]
subjectAltName = @alt_names

[ alt_names ]
DNS.1 = kubernetes
DNS.2 = kubernetes.default
DNS.3 = kubernetes.default.svc
DNS.4 = kubernetes.default.svc.cluster
DNS.5 = kubernetes.default.svc.cluster.local
IP.1 = <MASTER_IP>
IP.2 = <MASTER_CLUSTER_IP>

[ v3_ext ]
authorityKeyIdentifier=keyid,issuer:always
basicConstraints=CA:FALSE
keyUsage=keyEncipherment,dataEncipherment
extendedKeyUsage=serverAuth,clientAuth
subjectAltName=@alt_names

Generate the certificate signing request based on the config file:

openssl req -new -key server.key -out server.csr -config csr.conf

Generate the server certificate using the ca.key, ca.crt and server.csr:

openssl x509 -req -in server.csr -CA ca.crt -CAkey ca.key \
    -CAcreateserial -out server.crt -days 10000 \
    -extensions v3_ext -extfile csr.conf -sha256

View the certificate signing request:

openssl req  -noout -text -in ./server.csr

View the certificate:

openssl x509  -noout -text -in ./server.crt

Finally, add the same parameters into the API server start parameters.

cfssl

cfssl is another tool for certificate generation.

Download, unpack and prepare the command line tools as shown below.

Note that you may need to adapt the sample commands based on the hardware architecture and cfssl version you are using.

curl -L https://github.com/cloudflare/cfssl/releases/download/v1.5.0/cfssl_1.5.0_linux_amd64 -o cfssl
chmod +x cfssl
curl -L https://github.com/cloudflare/cfssl/releases/download/v1.5.0/cfssljson_1.5.0_linux_amd64 -o cfssljson
chmod +x cfssljson
curl -L https://github.com/cloudflare/cfssl/releases/download/v1.5.0/cfssl-certinfo_1.5.0_linux_amd64 -o cfssl-certinfo
chmod +x cfssl-certinfo

Create a directory to hold the artifacts and initialize cfssl:

mkdir cert
cd cert
../cfssl print-defaults config > config.json
../cfssl print-defaults csr > csr.json

Create a JSON config file for generating the CA file, for example, ca-config.json:

{
  "signing": {
    "default": {
      "expiry": "8760h"
    },
    "profiles": {
      "kubernetes": {
        "usages": [
          "signing",
          "key encipherment",
          "server auth",
          "client auth"
        ],
        "expiry": "8760h"
      }
    }
  }
}

Create a JSON config file for CA certificate signing request (CSR), for example, ca-csr.json. Be sure to replace the values marked with angle brackets with real values you want to use.

{
  "CN": "kubernetes",
  "key": {
    "algo": "rsa",
    "size": 2048
  },
  "names":[{
    "C": "<country>",
    "ST": "<state>",
    "L": "<city>",
    "O": "<organization>",
    "OU": "<organization unit>"
  }]
}

Generate CA key (ca-key.pem) and certificate (ca.pem):

../cfssl gencert -initca ca-csr.json | ../cfssljson -bare ca

Create a JSON config file for generating keys and certificates for the API server, for example, server-csr.json. Be sure to replace the values in angle brackets with real values you want to use. The <MASTER_CLUSTER_IP> is the service cluster IP for the API server as described in previous subsection. The sample below also assumes that you are using cluster.local as the default DNS domain name.

{
  "CN": "kubernetes",
  "hosts": [
    "127.0.0.1",
    "<MASTER_IP>",
    "<MASTER_CLUSTER_IP>",
    "kubernetes",
    "kubernetes.default",
    "kubernetes.default.svc",
    "kubernetes.default.svc.cluster",
    "kubernetes.default.svc.cluster.local"
  ],
  "key": {
    "algo": "rsa",
    "size": 2048
  },
  "names": [{
    "C": "<country>",
    "ST": "<state>",
    "L": "<city>",
    "O": "<organization>",
    "OU": "<organization unit>"
  }]
}

Generate the key and certificate for the API server, which are by default saved into file server-key.pem and server.pem respectively:

../cfssl gencert -ca=ca.pem -ca-key=ca-key.pem \
     --config=ca-config.json -profile=kubernetes \
     server-csr.json | ../cfssljson -bare server

Distributing Self-Signed CA Certificate

A client node may refuse to recognize a self-signed CA certificate as valid. For a non-production deployment, or for a deployment that runs behind a company firewall, you can distribute a self-signed CA certificate to all clients and refresh the local list for valid certificates.

On each client, perform the following operations:

sudo cp ca.crt /usr/local/share/ca-certificates/kubernetes.crt
sudo update-ca-certificates

Updating certificates in /etc/ssl/certs...
1 added, 0 removed; done.
Running hooks in /etc/ca-certificates/update.d....
done.

Certificates API

You can use the certificates.k8s.io API to provision x509 certificates to use for authentication as documented in the Managing TLS in a cluster task page.

2.4 - Manage Memory, CPU, and API Resources

2.4.1 - Configure Default Memory Requests and Limits for a Namespace

Define a default memory resource limit for a namespace, so that every new Pod in that namespace has a memory resource limit configured.

This page shows how to configure default memory requests and limits for a namespace.

A Kubernetes cluster can be divided into namespaces. Once you have a namespace that has a default memory limit, and you then try to create a Pod with a container that does not specify its own memory limit, then the control plane assigns the default memory limit to that container.

Kubernetes assigns a default memory request under certain conditions that are explained later in this topic.

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:

You must have access to create namespaces in your cluster.

Each node in your cluster must have at least 2 GiB of memory.

Create a namespace

Create a namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace default-mem-example

Create a LimitRange and a Pod

Here's a manifest for an example LimitRange. The manifest specifies a default memory request and a default memory limit.

admin/resource/memory-defaults.yaml

apiVersion: v1
kind: LimitRange
metadata:
  name: mem-limit-range
spec:
  limits:
  - default:
      memory: 512Mi
    defaultRequest:
      memory: 256Mi
    type: Container

Create the LimitRange in the default-mem-example namespace:

kubectl apply -f https://k8s.io/examples/admin/resource/memory-defaults.yaml --namespace=default-mem-example

Now if you create a Pod in the default-mem-example namespace, and any container within that Pod does not specify its own values for memory request and memory limit, then the control plane applies default values: a memory request of 256MiB and a memory limit of 512MiB.

Here's an example manifest for a Pod that has one container. The container does not specify a memory request and limit.

admin/resource/memory-defaults-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: default-mem-demo
spec:
  containers:
  - name: default-mem-demo-ctr
    image: nginx

Create the Pod.

kubectl apply -f https://k8s.io/examples/admin/resource/memory-defaults-pod.yaml --namespace=default-mem-example

View detailed information about the Pod:

kubectl get pod default-mem-demo --output=yaml --namespace=default-mem-example

The output shows that the Pod's container has a memory request of 256 MiB and a memory limit of 512 MiB. These are the default values specified by the LimitRange.

containers:
- image: nginx
  imagePullPolicy: Always
  name: default-mem-demo-ctr
  resources:
    limits:
      memory: 512Mi
    requests:
      memory: 256Mi

Delete your Pod:

kubectl delete pod default-mem-demo --namespace=default-mem-example

What if you specify a container's limit, but not its request?

Here's a manifest for a Pod that has one container. The container specifies a memory limit, but not a request:

admin/resource/memory-defaults-pod-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: default-mem-demo-2
spec:
  containers:
  - name: default-mem-demo-2-ctr
    image: nginx
    resources:
      limits:
        memory: "1Gi"

Create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/memory-defaults-pod-2.yaml --namespace=default-mem-example

View detailed information about the Pod:

kubectl get pod default-mem-demo-2 --output=yaml --namespace=default-mem-example

The output shows that the container's memory request is set to match its memory limit. Notice that the container was not assigned the default memory request value of 256Mi.

resources:
  limits:
    memory: 1Gi
  requests:
    memory: 1Gi

What if you specify a container's request, but not its limit?

Here's a manifest for a Pod that has one container. The container specifies a memory request, but not a limit:

admin/resource/memory-defaults-pod-3.yaml

apiVersion: v1
kind: Pod
metadata:
  name: default-mem-demo-3
spec:
  containers:
  - name: default-mem-demo-3-ctr
    image: nginx
    resources:
      requests:
        memory: "128Mi"

Create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/memory-defaults-pod-3.yaml --namespace=default-mem-example

View the Pod's specification:

kubectl get pod default-mem-demo-3 --output=yaml --namespace=default-mem-example

The output shows that the container's memory request is set to the value specified in the container's manifest. The container is limited to use no more than 512MiB of memory, which matches the default memory limit for the namespace.

resources:
  limits:
    memory: 512Mi
  requests:
    memory: 128Mi

Note:

A LimitRange does not check the consistency of the default values it applies. This means that a default value for the limit that is set by LimitRange may be less than the request value specified for the container in the spec that a client submits to the API server. If that happens, the final Pod will not be scheduleable. See Constraints on resource limits and requests for more details.

Motivation for default memory limits and requests

If your namespace has a memory resource quota configured, it is helpful to have a default value in place for memory limit. Here are three of the restrictions that a resource quota imposes on a namespace:

For every Pod that runs in the namespace, the Pod and each of its containers must have a memory limit. (If you specify a memory limit for every container in a Pod, Kubernetes can infer the Pod-level memory limit by adding up the limits for its containers).
Memory limits apply a resource reservation on the node where the Pod in question is scheduled. The total amount of memory reserved for all Pods in the namespace must not exceed a specified limit.
The total amount of memory actually used by all Pods in the namespace must also not exceed a specified limit.

When you add a LimitRange:

If any Pod in that namespace that includes a container does not specify its own memory limit, the control plane applies the default memory limit to that container, and the Pod can be allowed to run in a namespace that is restricted by a memory ResourceQuota.

Clean up

Delete your namespace:

kubectl delete namespace default-mem-example

What's next

For cluster administrators

For app developers

2.4.2 - Configure Default CPU Requests and Limits for a Namespace

Define a default CPU resource limits for a namespace, so that every new Pod in that namespace has a CPU resource limit configured.

This page shows how to configure default CPU requests and limits for a namespace.

A Kubernetes cluster can be divided into namespaces. If you create a Pod within a namespace that has a default CPU limit, and any container in that Pod does not specify its own CPU limit, then the control plane assigns the default CPU limit to that container.

Kubernetes assigns a default CPU request, but only under certain conditions that are explained later in this page.

Before you begin

You must have access to create namespaces in your cluster.

If you're not already familiar with what Kubernetes means by 1.0 CPU, read meaning of CPU.

Create a namespace

Create a namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace default-cpu-example

Create a LimitRange and a Pod

Here's a manifest for an example LimitRange. The manifest specifies a default CPU request and a default CPU limit.

admin/resource/cpu-defaults.yaml

apiVersion: v1
kind: LimitRange
metadata:
  name: cpu-limit-range
spec:
  limits:
  - default:
      cpu: 1
    defaultRequest:
      cpu: 0.5
    type: Container

Create the LimitRange in the default-cpu-example namespace:

kubectl apply -f https://k8s.io/examples/admin/resource/cpu-defaults.yaml --namespace=default-cpu-example

Now if you create a Pod in the default-cpu-example namespace, and any container in that Pod does not specify its own values for CPU request and CPU limit, then the control plane applies default values: a CPU request of 0.5 and a default CPU limit of 1.

Here's a manifest for a Pod that has one container. The container does not specify a CPU request and limit.

admin/resource/cpu-defaults-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: default-cpu-demo
spec:
  containers:
  - name: default-cpu-demo-ctr
    image: nginx

Create the Pod.

kubectl apply -f https://k8s.io/examples/admin/resource/cpu-defaults-pod.yaml --namespace=default-cpu-example

View the Pod's specification:

kubectl get pod default-cpu-demo --output=yaml --namespace=default-cpu-example

The output shows that the Pod's only container has a CPU request of 500m cpu (which you can read as “500 millicpu”), and a CPU limit of 1 cpu. These are the default values specified by the LimitRange.

containers:
- image: nginx
  imagePullPolicy: Always
  name: default-cpu-demo-ctr
  resources:
    limits:
      cpu: "1"
    requests:
      cpu: 500m

What if you specify a container's limit, but not its request?

Here's a manifest for a Pod that has one container. The container specifies a CPU limit, but not a request:

admin/resource/cpu-defaults-pod-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: default-cpu-demo-2
spec:
  containers:
  - name: default-cpu-demo-2-ctr
    image: nginx
    resources:
      limits:
        cpu: "1"

Create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/cpu-defaults-pod-2.yaml --namespace=default-cpu-example

View the specification of the Pod that you created:

kubectl get pod default-cpu-demo-2 --output=yaml --namespace=default-cpu-example

The output shows that the container's CPU request is set to match its CPU limit. Notice that the container was not assigned the default CPU request value of 0.5 cpu:

resources:
  limits:
    cpu: "1"
  requests:
    cpu: "1"

What if you specify a container's request, but not its limit?

Here's an example manifest for a Pod that has one container. The container specifies a CPU request, but not a limit:

admin/resource/cpu-defaults-pod-3.yaml

apiVersion: v1
kind: Pod
metadata:
  name: default-cpu-demo-3
spec:
  containers:
  - name: default-cpu-demo-3-ctr
    image: nginx
    resources:
      requests:
        cpu: "0.75"

Create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/cpu-defaults-pod-3.yaml --namespace=default-cpu-example

View the specification of the Pod that you created:

kubectl get pod default-cpu-demo-3 --output=yaml --namespace=default-cpu-example

The output shows that the container's CPU request is set to the value you specified at the time you created the Pod (in other words: it matches the manifest). However, the same container's CPU limit is set to 1 cpu, which is the default CPU limit for that namespace.

resources:
  limits:
    cpu: "1"
  requests:
    cpu: 750m

Motivation for default CPU limits and requests

If your namespace has a CPU resource quota configured, it is helpful to have a default value in place for CPU limit. Here are two of the restrictions that a CPU resource quota imposes on a namespace:

For every Pod that runs in the namespace, each of its containers must have a CPU limit.
CPU limits apply a resource reservation on the node where the Pod in question is scheduled. The total amount of CPU that is reserved for use by all Pods in the namespace must not exceed a specified limit.

When you add a LimitRange:

If any Pod in that namespace that includes a container does not specify its own CPU limit, the control plane applies the default CPU limit to that container, and the Pod can be allowed to run in a namespace that is restricted by a CPU ResourceQuota.

Clean up

Delete your namespace:

kubectl delete namespace default-cpu-example

What's next

For cluster administrators

For app developers

2.4.3 - Configure Minimum and Maximum Memory Constraints for a Namespace

Define a range of valid memory resource limits for a namespace, so that every new Pod in that namespace falls within the range you configure.

This page shows how to set minimum and maximum values for memory used by containers running in a namespace. You specify minimum and maximum memory values in a LimitRange object. If a Pod does not meet the constraints imposed by the LimitRange, it cannot be created in the namespace.

Before you begin

You must have access to create namespaces in your cluster.

Each node in your cluster must have at least 1 GiB of memory available for Pods.

Create a namespace

Create a namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace constraints-mem-example

Create a LimitRange and a Pod

Here's an example manifest for a LimitRange:

admin/resource/memory-constraints.yaml

apiVersion: v1
kind: LimitRange
metadata:
  name: mem-min-max-demo-lr
spec:
  limits:
  - max:
      memory: 1Gi
    min:
      memory: 500Mi
    type: Container

Create the LimitRange:

kubectl apply -f https://k8s.io/examples/admin/resource/memory-constraints.yaml --namespace=constraints-mem-example

View detailed information about the LimitRange:

kubectl get limitrange mem-min-max-demo-lr --namespace=constraints-mem-example --output=yaml

The output shows the minimum and maximum memory constraints as expected. But notice that even though you didn't specify default values in the configuration file for the LimitRange, they were created automatically.

  limits:
  - default:
      memory: 1Gi
    defaultRequest:
      memory: 1Gi
    max:
      memory: 1Gi
    min:
      memory: 500Mi
    type: Container

Now whenever you define a Pod within the constraints-mem-example namespace, Kubernetes performs these steps:

If any container in that Pod does not specify its own memory request and limit, the control plane assigns the default memory request and limit to that container.
Verify that every container in that Pod requests at least 500 MiB of memory.
Verify that every container in that Pod requests no more than 1024 MiB (1 GiB) of memory.

Here's a manifest for a Pod that has one container. Within the Pod spec, the sole container specifies a memory request of 600 MiB and a memory limit of 800 MiB. These satisfy the minimum and maximum memory constraints imposed by the LimitRange.

admin/resource/memory-constraints-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: constraints-mem-demo
spec:
  containers:
  - name: constraints-mem-demo-ctr
    image: nginx
    resources:
      limits:
        memory: "800Mi"
      requests:
        memory: "600Mi"

Create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/memory-constraints-pod.yaml --namespace=constraints-mem-example

Verify that the Pod is running and that its container is healthy:

kubectl get pod constraints-mem-demo --namespace=constraints-mem-example

View detailed information about the Pod:

kubectl get pod constraints-mem-demo --output=yaml --namespace=constraints-mem-example

The output shows that the container within that Pod has a memory request of 600 MiB and a memory limit of 800 MiB. These satisfy the constraints imposed by the LimitRange for this namespace:

resources:
  limits:
     memory: 800Mi
  requests:
    memory: 600Mi

Delete your Pod:

kubectl delete pod constraints-mem-demo --namespace=constraints-mem-example

Attempt to create a Pod that exceeds the maximum memory constraint

Here's a manifest for a Pod that has one container. The container specifies a memory request of 800 MiB and a memory limit of 1.5 GiB.

admin/resource/memory-constraints-pod-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: constraints-mem-demo-2
spec:
  containers:
  - name: constraints-mem-demo-2-ctr
    image: nginx
    resources:
      limits:
        memory: "1.5Gi"
      requests:
        memory: "800Mi"

Attempt to create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/memory-constraints-pod-2.yaml --namespace=constraints-mem-example

The output shows that the Pod does not get created, because it defines a container that requests more memory than is allowed:

Error from server (Forbidden): error when creating "examples/admin/resource/memory-constraints-pod-2.yaml":
pods "constraints-mem-demo-2" is forbidden: maximum memory usage per Container is 1Gi, but limit is 1536Mi.

Attempt to create a Pod that does not meet the minimum memory request

Here's a manifest for a Pod that has one container. That container specifies a memory request of 100 MiB and a memory limit of 800 MiB.

admin/resource/memory-constraints-pod-3.yaml

apiVersion: v1
kind: Pod
metadata:
  name: constraints-mem-demo-3
spec:
  containers:
  - name: constraints-mem-demo-3-ctr
    image: nginx
    resources:
      limits:
        memory: "800Mi"
      requests:
        memory: "100Mi"

Attempt to create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/memory-constraints-pod-3.yaml --namespace=constraints-mem-example

The output shows that the Pod does not get created, because it defines a container that requests less memory than the enforced minimum:

Error from server (Forbidden): error when creating "examples/admin/resource/memory-constraints-pod-3.yaml":
pods "constraints-mem-demo-3" is forbidden: minimum memory usage per Container is 500Mi, but request is 100Mi.

Create a Pod that does not specify any memory request or limit

Here's a manifest for a Pod that has one container. The container does not specify a memory request, and it does not specify a memory limit.

admin/resource/memory-constraints-pod-4.yaml

apiVersion: v1
kind: Pod
metadata:
  name: constraints-mem-demo-4
spec:
  containers:
  - name: constraints-mem-demo-4-ctr
    image: nginx

Create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/memory-constraints-pod-4.yaml --namespace=constraints-mem-example

View detailed information about the Pod:

kubectl get pod constraints-mem-demo-4 --namespace=constraints-mem-example --output=yaml

The output shows that the Pod's only container has a memory request of 1 GiB and a memory limit of 1 GiB. How did that container get those values?

resources:
  limits:
    memory: 1Gi
  requests:
    memory: 1Gi

Because your Pod did not define any memory request and limit for that container, the cluster applied a default memory request and limit from the LimitRange.

This means that the definition of that Pod shows those values. You can check it using kubectl describe:

# Look for the "Requests:" section of the output
kubectl describe pod constraints-mem-demo-4 --namespace=constraints-mem-example

At this point, your Pod might be running or it might not be running. Recall that a prerequisite for this task is that your Nodes have at least 1 GiB of memory. If each of your Nodes has only 1 GiB of memory, then there is not enough allocatable memory on any Node to accommodate a memory request of 1 GiB. If you happen to be using Nodes with 2 GiB of memory, then you probably have enough space to accommodate the 1 GiB request.

Delete your Pod:

kubectl delete pod constraints-mem-demo-4 --namespace=constraints-mem-example

Enforcement of minimum and maximum memory constraints

The maximum and minimum memory constraints imposed on a namespace by a LimitRange are enforced only when a Pod is created or updated. If you change the LimitRange, it does not affect Pods that were created previously.

Motivation for minimum and maximum memory constraints

As a cluster administrator, you might want to impose restrictions on the amount of memory that Pods can use. For example:

Each Node in a cluster has 2 GiB of memory. You do not want to accept any Pod that requests more than 2 GiB of memory, because no Node in the cluster can support the request.
A cluster is shared by your production and development departments. You want to allow production workloads to consume up to 8 GiB of memory, but you want development workloads to be limited to 512 MiB. You create separate namespaces for production and development, and you apply memory constraints to each namespace.

Clean up

Delete your namespace:

kubectl delete namespace constraints-mem-example

What's next

For cluster administrators

For app developers

2.4.4 - Configure Minimum and Maximum CPU Constraints for a Namespace

Define a range of valid CPU resource limits for a namespace, so that every new Pod in that namespace falls within the range you configure.

This page shows how to set minimum and maximum values for the CPU resources used by containers and Pods in a namespace. You specify minimum and maximum CPU values in a LimitRange object. If a Pod does not meet the constraints imposed by the LimitRange, it cannot be created in the namespace.

Before you begin

You must have access to create namespaces in your cluster.

Each node in your cluster must have at least 1.0 CPU available for Pods. See meaning of CPU to learn what Kubernetes means by “1 CPU”.

Create a namespace

Create a namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace constraints-cpu-example

Create a LimitRange and a Pod

Here's a manifest for an example LimitRange:

admin/resource/cpu-constraints.yaml

apiVersion: v1
kind: LimitRange
metadata:
  name: cpu-min-max-demo-lr
spec:
  limits:
  - max:
      cpu: "800m"
    min:
      cpu: "200m"
    type: Container

Create the LimitRange:

kubectl apply -f https://k8s.io/examples/admin/resource/cpu-constraints.yaml --namespace=constraints-cpu-example

View detailed information about the LimitRange:

kubectl get limitrange cpu-min-max-demo-lr --output=yaml --namespace=constraints-cpu-example

The output shows the minimum and maximum CPU constraints as expected. But notice that even though you didn't specify default values in the configuration file for the LimitRange, they were created automatically.

limits:
- default:
    cpu: 800m
  defaultRequest:
    cpu: 800m
  max:
    cpu: 800m
  min:
    cpu: 200m
  type: Container

Now whenever you create a Pod in the constraints-cpu-example namespace (or some other client of the Kubernetes API creates an equivalent Pod), Kubernetes performs these steps:

If any container in that Pod does not specify its own CPU request and limit, the control plane assigns the default CPU request and limit to that container.
Verify that every container in that Pod specifies a CPU request that is greater than or equal to 200 millicpu.
Verify that every container in that Pod specifies a CPU limit that is less than or equal to 800 millicpu.

Note:

When creating a LimitRange object, you can specify limits on huge-pages or GPUs as well. However, when both default and defaultRequest are specified on these resources, the two values must be the same.

Here's a manifest for a Pod that has one container. The container manifest specifies a CPU request of 500 millicpu and a CPU limit of 800 millicpu. These satisfy the minimum and maximum CPU constraints imposed by the LimitRange for this namespace.

admin/resource/cpu-constraints-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: constraints-cpu-demo
spec:
  containers:
  - name: constraints-cpu-demo-ctr
    image: nginx
    resources:
      limits:
        cpu: "800m"
      requests:
        cpu: "500m"

Create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/cpu-constraints-pod.yaml --namespace=constraints-cpu-example

Verify that the Pod is running and that its container is healthy:

kubectl get pod constraints-cpu-demo --namespace=constraints-cpu-example

View detailed information about the Pod:

kubectl get pod constraints-cpu-demo --output=yaml --namespace=constraints-cpu-example

The output shows that the Pod's only container has a CPU request of 500 millicpu and CPU limit of 800 millicpu. These satisfy the constraints imposed by the LimitRange.

resources:
  limits:
    cpu: 800m
  requests:
    cpu: 500m

Delete the Pod

kubectl delete pod constraints-cpu-demo --namespace=constraints-cpu-example

Attempt to create a Pod that exceeds the maximum CPU constraint

Here's a manifest for a Pod that has one container. The container specifies a CPU request of 500 millicpu and a cpu limit of 1.5 cpu.

admin/resource/cpu-constraints-pod-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: constraints-cpu-demo-2
spec:
  containers:
  - name: constraints-cpu-demo-2-ctr
    image: nginx
    resources:
      limits:
        cpu: "1.5"
      requests:
        cpu: "500m"

Attempt to create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/cpu-constraints-pod-2.yaml --namespace=constraints-cpu-example

The output shows that the Pod does not get created, because it defines an unacceptable container. That container is not acceptable because it specifies a CPU limit that is too large:

Error from server (Forbidden): error when creating "examples/admin/resource/cpu-constraints-pod-2.yaml":
pods "constraints-cpu-demo-2" is forbidden: maximum cpu usage per Container is 800m, but limit is 1500m.

Attempt to create a Pod that does not meet the minimum CPU request

Here's a manifest for a Pod that has one container. The container specifies a CPU request of 100 millicpu and a CPU limit of 800 millicpu.

admin/resource/cpu-constraints-pod-3.yaml

apiVersion: v1
kind: Pod
metadata:
  name: constraints-cpu-demo-3
spec:
  containers:
  - name: constraints-cpu-demo-3-ctr
    image: nginx
    resources:
      limits:
        cpu: "800m"
      requests:
        cpu: "100m"

Attempt to create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/cpu-constraints-pod-3.yaml --namespace=constraints-cpu-example

The output shows that the Pod does not get created, because it defines an unacceptable container. That container is not acceptable because it specifies a CPU request that is lower than the enforced minimum:

Error from server (Forbidden): error when creating "examples/admin/resource/cpu-constraints-pod-3.yaml":
pods "constraints-cpu-demo-3" is forbidden: minimum cpu usage per Container is 200m, but request is 100m.

Create a Pod that does not specify any CPU request or limit

Here's a manifest for a Pod that has one container. The container does not specify a CPU request, nor does it specify a CPU limit.

admin/resource/cpu-constraints-pod-4.yaml

apiVersion: v1
kind: Pod
metadata:
  name: constraints-cpu-demo-4
spec:
  containers:
  - name: constraints-cpu-demo-4-ctr
    image: vish/stress

Create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/cpu-constraints-pod-4.yaml --namespace=constraints-cpu-example

View detailed information about the Pod:

kubectl get pod constraints-cpu-demo-4 --namespace=constraints-cpu-example --output=yaml

The output shows that the Pod's single container has a CPU request of 800 millicpu and a CPU limit of 800 millicpu. How did that container get those values?

resources:
  limits:
    cpu: 800m
  requests:
    cpu: 800m

Because that container did not specify its own CPU request and limit, the control plane applied the default CPU request and limit from the LimitRange for this namespace.

At this point, your Pod may or may not be running. Recall that a prerequisite for this task is that your Nodes must have at least 1 CPU available for use. If each of your Nodes has only 1 CPU, then there might not be enough allocatable CPU on any Node to accommodate a request of 800 millicpu. If you happen to be using Nodes with 2 CPU, then you probably have enough CPU to accommodate the 800 millicpu request.

Delete your Pod:

kubectl delete pod constraints-cpu-demo-4 --namespace=constraints-cpu-example

Enforcement of minimum and maximum CPU constraints

The maximum and minimum CPU constraints imposed on a namespace by a LimitRange are enforced only when a Pod is created or updated. If you change the LimitRange, it does not affect Pods that were created previously.

Motivation for minimum and maximum CPU constraints

As a cluster administrator, you might want to impose restrictions on the CPU resources that Pods can use. For example:

Each Node in a cluster has 2 CPU. You do not want to accept any Pod that requests more than 2 CPU, because no Node in the cluster can support the request.
A cluster is shared by your production and development departments. You want to allow production workloads to consume up to 3 CPU, but you want development workloads to be limited to 1 CPU. You create separate namespaces for production and development, and you apply CPU constraints to each namespace.

Clean up

Delete your namespace:

kubectl delete namespace constraints-cpu-example

What's next

For cluster administrators

For app developers

2.4.5 - Configure Memory and CPU Quotas for a Namespace

Define overall memory and CPU resource limits for a namespace.

This page shows how to set quotas for the total amount memory and CPU that can be used by all Pods running in a namespace. You specify quotas in a ResourceQuota object.

Before you begin

You must have access to create namespaces in your cluster.

Each node in your cluster must have at least 1 GiB of memory.

Create a namespace

Create a namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace quota-mem-cpu-example

Create a ResourceQuota

Here is a manifest for an example ResourceQuota:

admin/resource/quota-mem-cpu.yaml

apiVersion: v1
kind: ResourceQuota
metadata:
  name: mem-cpu-demo
spec:
  hard:
    requests.cpu: "1"
    requests.memory: 1Gi
    limits.cpu: "2"
    limits.memory: 2Gi

Create the ResourceQuota:

kubectl apply -f https://k8s.io/examples/admin/resource/quota-mem-cpu.yaml --namespace=quota-mem-cpu-example

View detailed information about the ResourceQuota:

kubectl get resourcequota mem-cpu-demo --namespace=quota-mem-cpu-example --output=yaml

The ResourceQuota places these requirements on the quota-mem-cpu-example namespace:

For every Pod in the namespace, each container must have a memory request, memory limit, cpu request, and cpu limit.
The memory request total for all Pods in that namespace must not exceed 1 GiB.
The memory limit total for all Pods in that namespace must not exceed 2 GiB.
The CPU request total for all Pods in that namespace must not exceed 1 cpu.
The CPU limit total for all Pods in that namespace must not exceed 2 cpu.

See meaning of CPU to learn what Kubernetes means by “1 CPU”.

Create a Pod

Here is a manifest for an example Pod:

admin/resource/quota-mem-cpu-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: quota-mem-cpu-demo
spec:
  containers:
  - name: quota-mem-cpu-demo-ctr
    image: nginx
    resources:
      limits:
        memory: "800Mi"
        cpu: "800m"
      requests:
        memory: "600Mi"
        cpu: "400m"

Create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/quota-mem-cpu-pod.yaml --namespace=quota-mem-cpu-example

Verify that the Pod is running and that its (only) container is healthy:

kubectl get pod quota-mem-cpu-demo --namespace=quota-mem-cpu-example

Once again, view detailed information about the ResourceQuota:

kubectl get resourcequota mem-cpu-demo --namespace=quota-mem-cpu-example --output=yaml

The output shows the quota along with how much of the quota has been used. You can see that the memory and CPU requests and limits for your Pod do not exceed the quota.

status:
  hard:
    limits.cpu: "2"
    limits.memory: 2Gi
    requests.cpu: "1"
    requests.memory: 1Gi
  used:
    limits.cpu: 800m
    limits.memory: 800Mi
    requests.cpu: 400m
    requests.memory: 600Mi

If you have the jq tool, you can also query (using JSONPath) for just the used values, and pretty-print that that of the output. For example:

kubectl get resourcequota mem-cpu-demo --namespace=quota-mem-cpu-example -o jsonpath='{ .status.used }' | jq .

Attempt to create a second Pod

Here is a manifest for a second Pod:

admin/resource/quota-mem-cpu-pod-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: quota-mem-cpu-demo-2
spec:
  containers:
  - name: quota-mem-cpu-demo-2-ctr
    image: redis
    resources:
      limits:
        memory: "1Gi"
        cpu: "800m"
      requests:
        memory: "700Mi"
        cpu: "400m"

In the manifest, you can see that the Pod has a memory request of 700 MiB. Notice that the sum of the used memory request and this new memory request exceeds the memory request quota: 600 MiB + 700 MiB > 1 GiB.

Attempt to create the Pod:

kubectl apply -f https://k8s.io/examples/admin/resource/quota-mem-cpu-pod-2.yaml --namespace=quota-mem-cpu-example

The second Pod does not get created. The output shows that creating the second Pod would cause the memory request total to exceed the memory request quota.

Error from server (Forbidden): error when creating "examples/admin/resource/quota-mem-cpu-pod-2.yaml":
pods "quota-mem-cpu-demo-2" is forbidden: exceeded quota: mem-cpu-demo,
requested: requests.memory=700Mi,used: requests.memory=600Mi, limited: requests.memory=1Gi

Discussion

As you have seen in this exercise, you can use a ResourceQuota to restrict the memory request total for all Pods running in a namespace. You can also restrict the totals for memory limit, cpu request, and cpu limit.

Instead of managing total resource use within a namespace, you might want to restrict individual Pods, or the containers in those Pods. To achieve that kind of limiting, use a LimitRange.

Clean up

Delete your namespace:

kubectl delete namespace quota-mem-cpu-example

What's next

For cluster administrators

For app developers

2.4.6 - Configure a Pod Quota for a Namespace

Restrict how many Pods you can create within a namespace.

This page shows how to set a quota for the total number of Pods that can run in a Namespace. You specify quotas in a ResourceQuota object.

Before you begin

You must have access to create namespaces in your cluster.

Create a namespace

Create a namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace quota-pod-example

Create a ResourceQuota

Here is an example manifest for a ResourceQuota:

admin/resource/quota-pod.yaml

apiVersion: v1
kind: ResourceQuota
metadata:
  name: pod-demo
spec:
  hard:
    pods: "2"

Create the ResourceQuota:

kubectl apply -f https://k8s.io/examples/admin/resource/quota-pod.yaml --namespace=quota-pod-example

View detailed information about the ResourceQuota:

kubectl get resourcequota pod-demo --namespace=quota-pod-example --output=yaml

The output shows that the namespace has a quota of two Pods, and that currently there are no Pods; that is, none of the quota is used.

spec:
  hard:
    pods: "2"
status:
  hard:
    pods: "2"
  used:
    pods: "0"

Here is an example manifest for a Deployment:

admin/resource/quota-pod-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: pod-quota-demo
spec:
  selector:
    matchLabels:
      purpose: quota-demo
  replicas: 3
  template:
    metadata:
      labels:
        purpose: quota-demo
    spec:
      containers:
      - name: pod-quota-demo
        image: nginx

In that manifest, replicas: 3 tells Kubernetes to attempt to create three new Pods, all running the same application.

Create the Deployment:

kubectl apply -f https://k8s.io/examples/admin/resource/quota-pod-deployment.yaml --namespace=quota-pod-example

View detailed information about the Deployment:

kubectl get deployment pod-quota-demo --namespace=quota-pod-example --output=yaml

The output shows that even though the Deployment specifies three replicas, only two Pods were created because of the quota you defined earlier:

spec:
  ...
  replicas: 3
...
status:
  availableReplicas: 2
...
lastUpdateTime: 2021-04-02T20:57:05Z
    message: 'unable to create pods: pods "pod-quota-demo-1650323038-" is forbidden:
      exceeded quota: pod-demo, requested: pods=1, used: pods=2, limited: pods=2'

Choice of resource

In this task you have defined a ResourceQuota that limited the total number of Pods, but you could also limit the total number of other kinds of object. For example, you might decide to limit how many CronJobs that can live in a single namespace.

Clean up

Delete your namespace:

kubectl delete namespace quota-pod-example

What's next

For cluster administrators

For app developers

2.5 - Install a Network Policy Provider

2.5.1 - Use Antrea for NetworkPolicy

This page shows how to install and use Antrea CNI plugin on Kubernetes. For background on Project Antrea, read the Introduction to Antrea.

Before you begin

You need to have a Kubernetes cluster. Follow the kubeadm getting started guide to bootstrap one.

Deploying Antrea with kubeadm

Follow Getting Started guide to deploy Antrea for kubeadm.

What's next

Once your cluster is running, you can follow the Declare Network Policy to try out Kubernetes NetworkPolicy.

2.5.2 - Use Calico for NetworkPolicy

This page shows a couple of quick ways to create a Calico cluster on Kubernetes.

Before you begin

Decide whether you want to deploy a cloud or local cluster.

Creating a Calico cluster with Google Kubernetes Engine (GKE)

Prerequisite: gcloud.

To launch a GKE cluster with Calico, include the --enable-network-policy flag.

Syntax

gcloud container clusters create [CLUSTER_NAME] --enable-network-policy

Example

gcloud container clusters create my-calico-cluster --enable-network-policy

To verify the deployment, use the following command.
```
kubectl get pods --namespace=kube-system
```
The Calico pods begin with calico. Check to make sure each one has a status of Running.

Creating a local Calico cluster with kubeadm

To get a local single-host Calico cluster in fifteen minutes using kubeadm, refer to the Calico Quickstart.

What's next

Once your cluster is running, you can follow the Declare Network Policy to try out Kubernetes NetworkPolicy.

2.5.3 - Use Cilium for NetworkPolicy

This page shows how to use Cilium for NetworkPolicy.

For background on Cilium, read the Introduction to Cilium.

Before you begin

To check the version, enter kubectl version.

Deploying Cilium on Minikube for Basic Testing

To get familiar with Cilium easily you can follow the Cilium Kubernetes Getting Started Guide to perform a basic DaemonSet installation of Cilium in minikube.

To start minikube, minimal version required is >= v1.5.2, run the with the following arguments:

minikube version

minikube version: v1.5.2

minikube start --network-plugin=cni

For minikube you can install Cilium using its CLI tool. To do so, first download the latest version of the CLI with the following command:

curl -LO https://github.com/cilium/cilium-cli/releases/latest/download/cilium-linux-amd64.tar.gz

Then extract the downloaded file to your /usr/local/bin directory with the following command:

sudo tar xzvfC cilium-linux-amd64.tar.gz /usr/local/bin
rm cilium-linux-amd64.tar.gz

After running the above commands, you can now install Cilium with the following command:

cilium install

Cilium will then automatically detect the cluster configuration and create and install the appropriate components for a successful installation. The components are:

Certificate Authority (CA) in Secret cilium-ca and certificates for Hubble (Cilium's observability layer).
Service accounts.
Cluster roles.
ConfigMap.
Agent DaemonSet and an Operator Deployment.

After the installation, you can view the overall status of the Cilium deployment with the cilium status command. See the expected output of the status command here.

The remainder of the Getting Started Guide explains how to enforce both L3/L4 (i.e., IP address + port) security policies, as well as L7 (e.g., HTTP) security policies using an example application.

Deploying Cilium for Production Use

For detailed instructions around deploying Cilium for production, see: Cilium Kubernetes Installation Guide This documentation includes detailed requirements, instructions and example production DaemonSet files.

Understanding Cilium components

Deploying a cluster with Cilium adds Pods to the kube-system namespace. To see this list of Pods run:

kubectl get pods --namespace=kube-system -l k8s-app=cilium

You'll see a list of Pods similar to this:

NAME           READY   STATUS    RESTARTS   AGE
cilium-kkdhz   1/1     Running   0          3m23s
...

A cilium Pod runs on each node in your cluster and enforces network policy on the traffic to/from Pods on that node using Linux BPF.

What's next

Once your cluster is running, you can follow the Declare Network Policy to try out Kubernetes NetworkPolicy with Cilium. Have fun, and if you have questions, contact us using the Cilium Slack Channel.

2.5.4 - Use Kube-router for NetworkPolicy

This page shows how to use Kube-router for NetworkPolicy.

Before you begin

You need to have a Kubernetes cluster running. If you do not already have a cluster, you can create one by using any of the cluster installers like Kops, Bootkube, Kubeadm etc.

Installing Kube-router addon

The Kube-router Addon comes with a Network Policy Controller that watches Kubernetes API server for any NetworkPolicy and pods updated and configures iptables rules and ipsets to allow or block traffic as directed by the policies. Please follow the trying Kube-router with cluster installers guide to install Kube-router addon.

What's next

Once you have installed the Kube-router addon, you can follow the Declare Network Policy to try out Kubernetes NetworkPolicy.

2.5.5 - Romana for NetworkPolicy

This page shows how to use Romana for NetworkPolicy.

Before you begin

Complete steps 1, 2, and 3 of the kubeadm getting started guide.

Installing Romana with kubeadm

Follow the containerized installation guide for kubeadm.

Applying network policies

To apply network policies use one of the following:

Romana network policies.
- Example of Romana network policy.
The NetworkPolicy API.

What's next

Once you have installed Romana, you can follow the Declare Network Policy to try out Kubernetes NetworkPolicy.

2.5.6 - Weave Net for NetworkPolicy

This page shows how to use Weave Net for NetworkPolicy.

Before you begin

You need to have a Kubernetes cluster. Follow the kubeadm getting started guide to bootstrap one.

Install the Weave Net addon

Follow the Integrating Kubernetes via the Addon guide.

The Weave Net addon for Kubernetes comes with a Network Policy Controller that automatically monitors Kubernetes for any NetworkPolicy annotations on all namespaces and configures iptables rules to allow or block traffic as directed by the policies.

Test the installation

Verify that the weave works.

Enter the following command:

kubectl get pods -n kube-system -o wide

The output is similar to this:

NAME                                    READY     STATUS    RESTARTS   AGE       IP              NODE
weave-net-1t1qg                         2/2       Running   0          9d        192.168.2.10    worknode3
weave-net-231d7                         2/2       Running   1          7d        10.2.0.17       worknodegpu
weave-net-7nmwt                         2/2       Running   3          9d        192.168.2.131   masternode
weave-net-pmw8w                         2/2       Running   0          9d        192.168.2.216   worknode2

Each Node has a weave Pod, and all Pods are Running and 2/2 READY. (2/2 means that each Pod has weave and weave-npc.)

What's next

Once you have installed the Weave Net addon, you can follow the Declare Network Policy to try out Kubernetes NetworkPolicy. If you have any question, contact us at #weave-community on Slack or Weave User Group.

2.6 - Access Clusters Using the Kubernetes API

This page shows how to access clusters using the Kubernetes API.

Before you begin

To check the version, enter kubectl version.

Accessing the Kubernetes API

Accessing for the first time with kubectl

When accessing the Kubernetes API for the first time, use the Kubernetes command-line tool, kubectl.

To access a cluster, you need to know the location of the cluster and have credentials to access it. Typically, this is automatically set-up when you work through a Getting started guide, or someone else set up the cluster and provided you with credentials and a location.

Check the location and credentials that kubectl knows about with this command:

kubectl config view

Many of the examples provide an introduction to using kubectl. Complete documentation is found in the kubectl manual.

Directly accessing the REST API

kubectl handles locating and authenticating to the API server. If you want to directly access the REST API with an http client like curl or wget, or a browser, there are multiple ways you can locate and authenticate against the API server:

Run kubectl in proxy mode (recommended). This method is recommended, since it uses the stored API server location and verifies the identity of the API server using a self-signed certificate. No man-in-the-middle (MITM) attack is possible using this method.
Alternatively, you can provide the location and credentials directly to the http client. This works with client code that is confused by proxies. To protect against man in the middle attacks, you'll need to import a root cert into your browser.

Using the Go or Python client libraries provides accessing kubectl in proxy mode.

Using kubectl proxy

The following command runs kubectl in a mode where it acts as a reverse proxy. It handles locating the API server and authenticating.

Run it like this:

kubectl proxy --port=8080 &

See kubectl proxy for more details.

Then you can explore the API with curl, wget, or a browser, like so:

curl http://localhost:8080/api/

The output is similar to this:

{
  "versions": [
    "v1"
  ],
  "serverAddressByClientCIDRs": [
    {
      "clientCIDR": "0.0.0.0/0",
      "serverAddress": "10.0.1.149:443"
    }
  ]
}

Without kubectl proxy

It is possible to avoid using kubectl proxy by passing an authentication token directly to the API server, like this:

Using grep/cut approach:

# Check all possible clusters, as your .KUBECONFIG may have multiple contexts:
kubectl config view -o jsonpath='{"Cluster name\tServer\n"}{range .clusters[*]}{.name}{"\t"}{.cluster.server}{"\n"}{end}'

# Select name of cluster you want to interact with from above output:
export CLUSTER_NAME="some_server_name"

# Point to the API server referring the cluster name
APISERVER=$(kubectl config view -o jsonpath="{.clusters[?(@.name==\"$CLUSTER_NAME\")].cluster.server}")

# Create a secret to hold a token for the default service account
kubectl apply -f - <<EOF
apiVersion: v1
kind: Secret
metadata:
  name: default-token
  annotations:
    kubernetes.io/service-account.name: default
type: kubernetes.io/service-account-token
EOF

# Wait for the token controller to populate the secret with a token:
while ! kubectl describe secret default-token | grep -E '^token' >/dev/null; do
  echo "waiting for token..." >&2
  sleep 1
done

# Get the token value
TOKEN=$(kubectl get secret default-token -o jsonpath='{.data.token}' | base64 --decode)

# Explore the API with TOKEN
curl -X GET $APISERVER/api --header "Authorization: Bearer $TOKEN" --insecure

The output is similar to this:

{
  "kind": "APIVersions",
  "versions": [
    "v1"
  ],
  "serverAddressByClientCIDRs": [
    {
      "clientCIDR": "0.0.0.0/0",
      "serverAddress": "10.0.1.149:443"
    }
  ]
}

The above example uses the --insecure flag. This leaves it subject to MITM attacks. When kubectl accesses the cluster it uses a stored root certificate and client certificates to access the server. (These are installed in the ~/.kube directory). Since cluster certificates are typically self-signed, it may take special configuration to get your http client to use root certificate.

On some clusters, the API server does not require authentication; it may serve on localhost, or be protected by a firewall. There is not a standard for this. Controlling Access to the Kubernetes API describes how you can configure this as a cluster administrator.

Programmatic access to the API

Kubernetes officially supports client libraries for Go, Python, Java, dotnet, JavaScript, and Haskell. There are other client libraries that are provided and maintained by their authors, not the Kubernetes team. See client libraries for accessing the API from other languages and how they authenticate.

Go client

To get the library, run the following command: go get k8s.io/client-go@kubernetes-<kubernetes-version-number> See https://github.com/kubernetes/client-go/releases to see which versions are supported.
Write an application atop of the client-go clients.

Note:

client-go defines its own API objects, so if needed, import API definitions from client-go rather than from the main repository. For example, import "k8s.io/client-go/kubernetes" is correct.

The Go client can use the same kubeconfig file as the kubectl CLI does to locate and authenticate to the API server. See this example:

package main

import (
  "context"
  "fmt"
  "k8s.io/apimachinery/pkg/apis/meta/v1"
  "k8s.io/client-go/kubernetes"
  "k8s.io/client-go/tools/clientcmd"
)

func main() {
  // uses the current context in kubeconfig
  // path-to-kubeconfig -- for example, /root/.kube/config
  config, _ := clientcmd.BuildConfigFromFlags("", "<path-to-kubeconfig>")
  // creates the clientset
  clientset, _ := kubernetes.NewForConfig(config)
  // access the API to list pods
  pods, _ := clientset.CoreV1().Pods("").List(context.TODO(), v1.ListOptions{})
  fmt.Printf("There are %d pods in the cluster\n", len(pods.Items))
}

If the application is deployed as a Pod in the cluster, see Accessing the API from within a Pod.

Python client

To use Python client, run the following command: pip install kubernetes. See Python Client Library page for more installation options.

The Python client can use the same kubeconfig file as the kubectl CLI does to locate and authenticate to the API server. See this example:

from kubernetes import client, config

config.load_kube_config()

v1=client.CoreV1Api()
print("Listing pods with their IPs:")
ret = v1.list_pod_for_all_namespaces(watch=False)
for i in ret.items:
    print("%s\t%s\t%s" % (i.status.pod_ip, i.metadata.namespace, i.metadata.name))

Java client

To install the Java Client, run:

# Clone java library
git clone --recursive https://github.com/kubernetes-client/java

# Installing project artifacts, POM etc:
cd java
mvn install

See https://github.com/kubernetes-client/java/releases to see which versions are supported.

The Java client can use the same kubeconfig file as the kubectl CLI does to locate and authenticate to the API server. See this example:

package io.kubernetes.client.examples;

import io.kubernetes.client.ApiClient;
import io.kubernetes.client.ApiException;
import io.kubernetes.client.Configuration;
import io.kubernetes.client.apis.CoreV1Api;
import io.kubernetes.client.models.V1Pod;
import io.kubernetes.client.models.V1PodList;
import io.kubernetes.client.util.ClientBuilder;
import io.kubernetes.client.util.KubeConfig;
import java.io.FileReader;
import java.io.IOException;

/**
 * A simple example of how to use the Java API from an application outside a kubernetes cluster
 *
 * <p>Easiest way to run this: mvn exec:java
 * -Dexec.mainClass="io.kubernetes.client.examples.KubeConfigFileClientExample"
 *
 */
public class KubeConfigFileClientExample {
  public static void main(String[] args) throws IOException, ApiException {

    // file path to your KubeConfig
    String kubeConfigPath = "~/.kube/config";

    // loading the out-of-cluster config, a kubeconfig from file-system
    ApiClient client =
        ClientBuilder.kubeconfig(KubeConfig.loadKubeConfig(new FileReader(kubeConfigPath))).build();

    // set the global default api-client to the in-cluster one from above
    Configuration.setDefaultApiClient(client);

    // the CoreV1Api loads default api-client from global configuration.
    CoreV1Api api = new CoreV1Api();

    // invokes the CoreV1Api client
    V1PodList list = api.listPodForAllNamespaces(null, null, null, null, null, null, null, null, null);
    System.out.println("Listing all pods: ");
    for (V1Pod item : list.getItems()) {
      System.out.println(item.getMetadata().getName());
    }
  }
}

dotnet client

To use dotnet client, run the following command: dotnet add package KubernetesClient --version 1.6.1. See dotnet Client Library page for more installation options. See https://github.com/kubernetes-client/csharp/releases to see which versions are supported.

The dotnet client can use the same kubeconfig file as the kubectl CLI does to locate and authenticate to the API server. See this example:

using System;
using k8s;

namespace simple
{
    internal class PodList
    {
        private static void Main(string[] args)
        {
            var config = KubernetesClientConfiguration.BuildDefaultConfig();
            IKubernetes client = new Kubernetes(config);
            Console.WriteLine("Starting Request!");

            var list = client.ListNamespacedPod("default");
            foreach (var item in list.Items)
            {
                Console.WriteLine(item.Metadata.Name);
            }
            if (list.Items.Count == 0)
            {
                Console.WriteLine("Empty!");
            }
        }
    }
}

JavaScript client

To install JavaScript client, run the following command: npm install @kubernetes/client-node. See https://github.com/kubernetes-client/javascript/releases to see which versions are supported.

The JavaScript client can use the same kubeconfig file as the kubectl CLI does to locate and authenticate to the API server. See this example:

const k8s = require('@kubernetes/client-node');

const kc = new k8s.KubeConfig();
kc.loadFromDefault();

const k8sApi = kc.makeApiClient(k8s.CoreV1Api);

k8sApi.listNamespacedPod('default').then((res) => {
    console.log(res.body);
});

Haskell client

See https://github.com/kubernetes-client/haskell/releases to see which versions are supported.

The Haskell client can use the same kubeconfig file as the kubectl CLI does to locate and authenticate to the API server. See this example:

exampleWithKubeConfig :: IO ()
exampleWithKubeConfig = do
    oidcCache <- atomically $ newTVar $ Map.fromList []
    (mgr, kcfg) <- mkKubeClientConfig oidcCache $ KubeConfigFile "/path/to/kubeconfig"
    dispatchMime
            mgr
            kcfg
            (CoreV1.listPodForAllNamespaces (Accept MimeJSON))
        >>= print

What's next

Accessing the Kubernetes API from a Pod

2.7 - Advertise Extended Resources for a Node

This page shows how to specify extended resources for a Node. Extended resources allow cluster administrators to advertise node-level resources that would otherwise be unknown to Kubernetes.

Before you begin

To check the version, enter kubectl version.

Get the names of your Nodes

kubectl get nodes

Choose one of your Nodes to use for this exercise.

Advertise a new extended resource on one of your Nodes

To advertise a new extended resource on a Node, send an HTTP PATCH request to the Kubernetes API server. For example, suppose one of your Nodes has four dongles attached. Here's an example of a PATCH request that advertises four dongle resources for your Node.

PATCH /api/v1/nodes/<your-node-name>/status HTTP/1.1
Accept: application/json
Content-Type: application/json-patch+json
Host: k8s-master:8080

[
  {
    "op": "add",
    "path": "/status/capacity/example.com~1dongle",
    "value": "4"
  }
]

Note that Kubernetes does not need to know what a dongle is or what a dongle is for. The preceding PATCH request tells Kubernetes that your Node has four things that you call dongles.

Start a proxy, so that you can easily send requests to the Kubernetes API server:

kubectl proxy

In another command window, send the HTTP PATCH request. Replace <your-node-name> with the name of your Node:

curl --header "Content-Type: application/json-patch+json" \
  --request PATCH \
  --data '[{"op": "add", "path": "/status/capacity/example.com~1dongle", "value": "4"}]' \
  http://localhost:8001/api/v1/nodes/<your-node-name>/status

Note:

In the preceding request, ~1 is the encoding for the character / in the patch path. The operation path value in JSON-Patch is interpreted as a JSON-Pointer. For more details, see IETF RFC 6901, section 3.

The output shows that the Node has a capacity of 4 dongles:

"capacity": {
  "cpu": "2",
  "memory": "2049008Ki",
  "example.com/dongle": "4",

Describe your Node:

kubectl describe node <your-node-name>

Once again, the output shows the dongle resource:

Capacity:
  cpu: 2
  memory: 2049008Ki
  example.com/dongle: 4

Now, application developers can create Pods that request a certain number of dongles. See Assign Extended Resources to a Container.

Discussion

Extended resources are similar to memory and CPU resources. For example, just as a Node has a certain amount of memory and CPU to be shared by all components running on the Node, it can have a certain number of dongles to be shared by all components running on the Node. And just as application developers can create Pods that request a certain amount of memory and CPU, they can create Pods that request a certain number of dongles.

Extended resources are opaque to Kubernetes; Kubernetes does not know anything about what they are. Kubernetes knows only that a Node has a certain number of them. Extended resources must be advertised in integer amounts. For example, a Node can advertise four dongles, but not 4.5 dongles.

Storage example

Suppose a Node has 800 GiB of a special kind of disk storage. You could create a name for the special storage, say example.com/special-storage. Then you could advertise it in chunks of a certain size, say 100 GiB. In that case, your Node would advertise that it has eight resources of type example.com/special-storage.

Capacity:
 ...
 example.com/special-storage: 8

If you want to allow arbitrary requests for special storage, you could advertise special storage in chunks of size 1 byte. In that case, you would advertise 800Gi resources of type example.com/special-storage.

Capacity:
 ...
 example.com/special-storage:  800Gi

Then a Container could request any number of bytes of special storage, up to 800Gi.

Clean up

Here is a PATCH request that removes the dongle advertisement from a Node.

PATCH /api/v1/nodes/<your-node-name>/status HTTP/1.1
Accept: application/json
Content-Type: application/json-patch+json
Host: k8s-master:8080

[
  {
    "op": "remove",
    "path": "/status/capacity/example.com~1dongle",
  }
]

Start a proxy, so that you can easily send requests to the Kubernetes API server:

kubectl proxy

In another command window, send the HTTP PATCH request. Replace <your-node-name> with the name of your Node:

curl --header "Content-Type: application/json-patch+json" \
  --request PATCH \
  --data '[{"op": "remove", "path": "/status/capacity/example.com~1dongle"}]' \
  http://localhost:8001/api/v1/nodes/<your-node-name>/status

Verify that the dongle advertisement has been removed:

kubectl describe node <your-node-name> | grep dongle

(you should not see any output)

What's next

For application developers

Assign Extended Resources to a Container

For cluster administrators

2.8 - Autoscale the DNS Service in a Cluster

This page shows how to enable and configure autoscaling of the DNS service in your Kubernetes cluster.

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes
To check the version, enter kubectl version.
This guide assumes your nodes use the AMD64 or Intel 64 CPU architecture.
Make sure Kubernetes DNS is enabled.

Determine whether DNS horizontal autoscaling is already enabled

List the Deployments in your cluster in the kube-system namespace:

kubectl get deployment --namespace=kube-system

The output is similar to this:

NAME                   READY   UP-TO-DATE   AVAILABLE   AGE
...
kube-dns-autoscaler    1/1     1            1           ...
...

If you see "kube-dns-autoscaler" in the output, DNS horizontal autoscaling is already enabled, and you can skip to Tuning autoscaling parameters.

Get the name of your DNS Deployment

List the DNS deployments in your cluster in the kube-system namespace:

kubectl get deployment -l k8s-app=kube-dns --namespace=kube-system

The output is similar to this:

NAME      READY   UP-TO-DATE   AVAILABLE   AGE
...
coredns   2/2     2            2           ...
...

If you don't see a Deployment for DNS services, you can also look for it by name:

kubectl get deployment --namespace=kube-system

and look for a deployment named coredns or kube-dns.

Your scale target is

Deployment/<your-deployment-name>

where <your-deployment-name> is the name of your DNS Deployment. For example, if the name of your Deployment for DNS is coredns, your scale target is Deployment/coredns.

Note:

CoreDNS is the default DNS service for Kubernetes. CoreDNS sets the label k8s-app=kube-dns so that it can work in clusters that originally used kube-dns.

Enable DNS horizontal autoscaling

In this section, you create a new Deployment. The Pods in the Deployment run a container based on the cluster-proportional-autoscaler-amd64 image.

Create a file named dns-horizontal-autoscaler.yaml with this content:

admin/dns/dns-horizontal-autoscaler.yaml

kind: ServiceAccount
apiVersion: v1
metadata:
  name: kube-dns-autoscaler
  namespace: kube-system
---
kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: system:kube-dns-autoscaler
rules:
  - apiGroups: [""]
    resources: ["nodes"]
    verbs: ["list", "watch"]
  - apiGroups: [""]
    resources: ["replicationcontrollers/scale"]
    verbs: ["get", "update"]
  - apiGroups: ["apps"]
    resources: ["deployments/scale", "replicasets/scale"]
    verbs: ["get", "update"]
# Remove the configmaps rule once below issue is fixed:
# kubernetes-incubator/cluster-proportional-autoscaler#16
  - apiGroups: [""]
    resources: ["configmaps"]
    verbs: ["get", "create"]
---
kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: system:kube-dns-autoscaler
subjects:
  - kind: ServiceAccount
    name: kube-dns-autoscaler
    namespace: kube-system
roleRef:
  kind: ClusterRole
  name: system:kube-dns-autoscaler
  apiGroup: rbac.authorization.k8s.io

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: kube-dns-autoscaler
  namespace: kube-system
  labels:
    k8s-app: kube-dns-autoscaler
    kubernetes.io/cluster-service: "true"
spec:
  selector:
    matchLabels:
      k8s-app: kube-dns-autoscaler
  template:
    metadata:
      labels:
        k8s-app: kube-dns-autoscaler
    spec:
      priorityClassName: system-cluster-critical
      securityContext:
        seccompProfile:
          type: RuntimeDefault
        supplementalGroups: [ 65534 ]
        fsGroup: 65534
      nodeSelector:
        kubernetes.io/os: linux
      containers:
      - name: autoscaler
        image: registry.k8s.io/cpa/cluster-proportional-autoscaler:1.8.4
        resources:
            requests:
                cpu: "20m"
                memory: "10Mi"
        command:
          - /cluster-proportional-autoscaler
          - --namespace=kube-system
          - --configmap=kube-dns-autoscaler
          # Should keep target in sync with cluster/addons/dns/kube-dns.yaml.base
          - --target=<SCALE_TARGET>
          # When cluster is using large nodes(with more cores), "coresPerReplica" should dominate.
          # If using small nodes, "nodesPerReplica" should dominate.
          - --default-params={"linear":{"coresPerReplica":256,"nodesPerReplica":16,"preventSinglePointFailure":true,"includeUnschedulableNodes":true}}
          - --logtostderr=true
          - --v=2
      tolerations:
      - key: "CriticalAddonsOnly"
        operator: "Exists"
      serviceAccountName: kube-dns-autoscaler

In the file, replace <SCALE_TARGET> with your scale target.

Go to the directory that contains your configuration file, and enter this command to create the Deployment:

kubectl apply -f dns-horizontal-autoscaler.yaml

The output of a successful command is:

deployment.apps/kube-dns-autoscaler created

DNS horizontal autoscaling is now enabled.

Tune DNS autoscaling parameters

Verify that the kube-dns-autoscaler ConfigMap exists:

kubectl get configmap --namespace=kube-system

The output is similar to this:

NAME                  DATA      AGE
...
kube-dns-autoscaler   1         ...
...

Modify the data in the ConfigMap:

kubectl edit configmap kube-dns-autoscaler --namespace=kube-system

Look for this line:

linear: '{"coresPerReplica":256,"min":1,"nodesPerReplica":16}'

Modify the fields according to your needs. The "min" field indicates the minimal number of DNS backends. The actual number of backends is calculated using this equation:

replicas = max( ceil( cores × 1/coresPerReplica ) , ceil( nodes × 1/nodesPerReplica ) )

Note that the values of both coresPerReplica and nodesPerReplica are floats.

The idea is that when a cluster is using nodes that have many cores, coresPerReplica dominates. When a cluster is using nodes that have fewer cores, nodesPerReplica dominates.

There are other supported scaling patterns. For details, see cluster-proportional-autoscaler.

Disable DNS horizontal autoscaling

There are a few options for tuning DNS horizontal autoscaling. Which option to use depends on different conditions.

Option 1: Scale down the kube-dns-autoscaler deployment to 0 replicas

This option works for all situations. Enter this command:

kubectl scale deployment --replicas=0 kube-dns-autoscaler --namespace=kube-system

The output is:

deployment.apps/kube-dns-autoscaler scaled

Verify that the replica count is zero:

kubectl get rs --namespace=kube-system

The output displays 0 in the DESIRED and CURRENT columns:

NAME                                  DESIRED   CURRENT   READY   AGE
...
kube-dns-autoscaler-6b59789fc8        0         0         0       ...
...

Option 2: Delete the kube-dns-autoscaler deployment

This option works if kube-dns-autoscaler is under your own control, which means no one will re-create it:

kubectl delete deployment kube-dns-autoscaler --namespace=kube-system

The output is:

deployment.apps "kube-dns-autoscaler" deleted

Option 3: Delete the kube-dns-autoscaler manifest file from the master node

This option works if kube-dns-autoscaler is under control of the (deprecated) Addon Manager, and you have write access to the master node.

/etc/kubernetes/addons/dns-horizontal-autoscaler/dns-horizontal-autoscaler.yaml

After the manifest file is deleted, the Addon Manager will delete the kube-dns-autoscaler Deployment.

Understanding how DNS horizontal autoscaling works

The cluster-proportional-autoscaler application is deployed separately from the DNS service.
An autoscaler Pod runs a client that polls the Kubernetes API server for the number of nodes and cores in the cluster.
A desired replica count is calculated and applied to the DNS backends based on the current schedulable nodes and cores and the given scaling parameters.
The scaling parameters and data points are provided via a ConfigMap to the autoscaler, and it refreshes its parameters table every poll interval to be up to date with the latest desired scaling parameters.
Changes to the scaling parameters are allowed without rebuilding or restarting the autoscaler Pod.
The autoscaler provides a controller interface to support two control patterns: linear and ladder.

What's next

Read about Guaranteed Scheduling For Critical Add-On Pods.
Learn more about the implementation of cluster-proportional-autoscaler.

2.9 - Change the Access Mode of a PersistentVolume to ReadWriteOncePod

This page shows how to change the access mode on an existing PersistentVolume to use ReadWriteOncePod.

Before you begin

Your Kubernetes server must be at or later than version v1.22. To check the version, enter kubectl version.

Note:

The ReadWriteOncePod access mode graduated to stable in the Kubernetes v1.29 release. If you are running a version of Kubernetes older than v1.29, you might need to enable a feature gate. Check the documentation for your version of Kubernetes.

Note:

The ReadWriteOncePod access mode is only supported for CSI volumes. To use this volume access mode you will need to update the following CSI sidecars to these versions or greater:

Why should I use `ReadWriteOncePod`?

Prior to Kubernetes v1.22, the ReadWriteOnce access mode was commonly used to restrict PersistentVolume access for workloads that required single-writer access to storage. However, this access mode had a limitation: it restricted volume access to a single node, allowing multiple pods on the same node to read from and write to the same volume simultaneously. This could pose a risk for applications that demand strict single-writer access for data safety.

If ensuring single-writer access is critical for your workloads, consider migrating your volumes to ReadWriteOncePod.

Migrating existing PersistentVolumes

If you have existing PersistentVolumes, they can be migrated to use ReadWriteOncePod. Only migrations from ReadWriteOnce to ReadWriteOncePod are supported.

In this example, there is already a ReadWriteOnce "cat-pictures-pvc" PersistentVolumeClaim that is bound to a "cat-pictures-pv" PersistentVolume, and a "cat-pictures-writer" Deployment that uses this PersistentVolumeClaim.

Note:

If your storage plugin supports Dynamic provisioning, the "cat-picutres-pv" will be created for you, but its name may differ. To get your PersistentVolume's name run:

kubectl get pvc cat-pictures-pvc -o jsonpath='{.spec.volumeName}'

And you can view the PVC before you make changes. Either view the manifest locally, or run kubectl get pvc <name-of-pvc> -o yaml. The output is similar to:

# cat-pictures-pvc.yaml
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
  name: cat-pictures-pvc
spec:
  accessModes:
  - ReadWriteOnce
  resources:
    requests:
      storage: 1Gi

Here's an example Deployment that relies on that PersistentVolumeClaim:

# cat-pictures-writer-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: cat-pictures-writer
spec:
  replicas: 3
  selector:
    matchLabels:
      app: cat-pictures-writer
  template:
    metadata:
      labels:
        app: cat-pictures-writer
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80
        volumeMounts:
        - name: cat-pictures
          mountPath: /mnt
      volumes:
      - name: cat-pictures
        persistentVolumeClaim:
          claimName: cat-pictures-pvc
          readOnly: false

As a first step, you need to edit your PersistentVolume's spec.persistentVolumeReclaimPolicy and set it to Retain. This ensures your PersistentVolume will not be deleted when you delete the corresponding PersistentVolumeClaim:

kubectl patch pv cat-pictures-pv -p '{"spec":{"persistentVolumeReclaimPolicy":"Retain"}}'

Next you need to stop any workloads that are using the PersistentVolumeClaim bound to the PersistentVolume you want to migrate, and then delete the PersistentVolumeClaim. Avoid making any other changes to the PersistentVolumeClaim, such as volume resizes, until after the migration is complete.

Once that is done, you need to clear your PersistentVolume's spec.claimRef.uid to ensure PersistentVolumeClaims can bind to it upon recreation:

kubectl scale --replicas=0 deployment cat-pictures-writer
kubectl delete pvc cat-pictures-pvc
kubectl patch pv cat-pictures-pv -p '{"spec":{"claimRef":{"uid":""}}}'

After that, replace the PersistentVolume's list of valid access modes to be (only) ReadWriteOncePod:

kubectl patch pv cat-pictures-pv -p '{"spec":{"accessModes":["ReadWriteOncePod"]}}'

Note:

The ReadWriteOncePod access mode cannot be combined with other access modes. Make sure ReadWriteOncePod is the only access mode on the PersistentVolume when updating, otherwise the request will fail.

Next you need to modify your PersistentVolumeClaim to set ReadWriteOncePod as the only access mode. You should also set the PersistentVolumeClaim's spec.volumeName to the name of your PersistentVolume to ensure it binds to this specific PersistentVolume.

Once this is done, you can recreate your PersistentVolumeClaim and start up your workloads:

# IMPORTANT: Make sure to edit your PVC in cat-pictures-pvc.yaml before applying. You need to:
# - Set ReadWriteOncePod as the only access mode
# - Set spec.volumeName to "cat-pictures-pv"

kubectl apply -f cat-pictures-pvc.yaml
kubectl apply -f cat-pictures-writer-deployment.yaml

Lastly you may edit your PersistentVolume's spec.persistentVolumeReclaimPolicy and set to it back to Delete if you previously changed it.

kubectl patch pv cat-pictures-pv -p '{"spec":{"persistentVolumeReclaimPolicy":"Delete"}}'

What's next

Learn more about PersistentVolumes.
Learn more about PersistentVolumeClaims.
Learn more about Configuring a Pod to Use a PersistentVolume for Storage

2.10 - Change the default StorageClass

This page shows how to change the default Storage Class that is used to provision volumes for PersistentVolumeClaims that have no special requirements.

Before you begin

To check the version, enter kubectl version.

Why change the default storage class?

Depending on the installation method, your Kubernetes cluster may be deployed with an existing StorageClass that is marked as default. This default StorageClass is then used to dynamically provision storage for PersistentVolumeClaims that do not require any specific storage class. See PersistentVolumeClaim documentation for details.

The pre-installed default StorageClass may not fit well with your expected workload; for example, it might provision storage that is too expensive. If this is the case, you can either change the default StorageClass or disable it completely to avoid dynamic provisioning of storage.

Deleting the default StorageClass may not work, as it may be re-created automatically by the addon manager running in your cluster. Please consult the docs for your installation for details about addon manager and how to disable individual addons.

Changing the default StorageClass

List the StorageClasses in your cluster:

kubectl get storageclass

The output is similar to this:

NAME                 PROVISIONER               AGE
standard (default)   kubernetes.io/gce-pd      1d
gold                 kubernetes.io/gce-pd      1d

The default StorageClass is marked by (default).

Mark the default StorageClass as non-default:

The default StorageClass has an annotation storageclass.kubernetes.io/is-default-class set to true. Any other value or absence of the annotation is interpreted as false.

To mark a StorageClass as non-default, you need to change its value to false:
```
kubectl patch storageclass standard -p '{"metadata": {"annotations":{"storageclass.kubernetes.io/is-default-class":"false"}}}'
```
where standard is the name of your chosen StorageClass.
Mark a StorageClass as default:

Similar to the previous step, you need to add/set the annotation storageclass.kubernetes.io/is-default-class=true.
```
kubectl patch storageclass gold -p '{"metadata": {"annotations":{"storageclass.kubernetes.io/is-default-class":"true"}}}'
```
Please note that at most one StorageClass can be marked as default. If two or more of them are marked as default, a PersistentVolumeClaim without storageClassName explicitly specified cannot be created.

Verify that your chosen StorageClass is default:

kubectl get storageclass

The output is similar to this:

NAME             PROVISIONER               AGE
standard         kubernetes.io/gce-pd      1d
gold (default)   kubernetes.io/gce-pd      1d

What's next

Learn more about PersistentVolumes.

2.11 - Switching from Polling to CRI Event-based Updates to Container Status

FEATURE STATE: Kubernetes v1.25 [alpha]

This page shows how to migrate nodes to use event based updates for container status. The event-based implementation reduces node resource consumption by the kubelet, compared to the legacy approach that relies on polling. You may know this feature as evented Pod lifecycle event generator (PLEG). That's the name used internally within the Kubernetes project for a key implementation detail.

The polling based approach is referred to as generic PLEG.

Before you begin

You need to run a version of Kubernetes that provides this feature. Kubernetes v1.27 includes beta support for event-based container status updates. The feature is beta but is disabled by default because it requires support from the container runtime.
Your Kubernetes server must be at or later than version 1.26. To check the version, enter kubectl version. If you are running a different version of Kubernetes, check the documentation for that release.
The container runtime in use must support container lifecycle events. The kubelet automatically switches back to the legacy generic PLEG mechanism if the container runtime does not announce support for container lifecycle events, even if you have this feature gate enabled.

Why switch to Evented PLEG?

The Generic PLEG incurs non-negligible overhead due to frequent polling of container statuses.
This overhead is exacerbated by Kubelet's parallelized polling of container states, thus limiting its scalability and causing poor performance and reliability problems.
The goal of Evented PLEG is to reduce unnecessary work during inactivity by replacing periodic polling.

Switching to Evented PLEG

Start the Kubelet with the feature gate EventedPLEG enabled. You can manage the kubelet feature gates editing the kubelet config file and restarting the kubelet service. You need to do this on each node where you are using this feature.
Make sure the node is drained before proceeding.
Start the container runtime with the container event generation enabled.
- Containerd
- CRI-O
Version 1.7+
Version 1.26+

Check if the CRI-O is already configured to emit CRI events by verifying the configuration,
```
crio config | grep enable_pod_events
```
If it is enabled, the output should be similar to the following:
```
enable_pod_events = true
```
To enable it, start the CRI-O daemon with the flag --enable-pod-events=true or use a dropin config with the following lines:
```
[crio.runtime]
enable_pod_events: true
```
Your Kubernetes server must be at or later than version 1.26. To check the version, enter kubectl version.

Verify that the kubelet is using event-based container stage change monitoring. To check, look for the term EventedPLEG in the kubelet logs.

The output should be similar to this:

I0314 11:10:13.909915 1105457 feature_gate.go:249] feature gates: &{map[EventedPLEG:true]}

If you have set --v to 4 and above, you might see more entries that indicate that the kubelet is using event-based container state monitoring.

I0314 11:12:42.009542 1110177 evented.go:238] "Evented PLEG: Generated pod status from the received event" podUID=3b2c6172-b112-447a-ba96-94e7022912dc
I0314 11:12:44.623326 1110177 evented.go:238] "Evented PLEG: Generated pod status from the received event" podUID=b3fba5ea-a8c5-4b76-8f43-481e17e8ec40
I0314 11:12:44.714564 1110177 evented.go:238] "Evented PLEG: Generated pod status from the received event" podUID=b3fba5ea-a8c5-4b76-8f43-481e17e8ec40

What's next

Learn more about the design in the Kubernetes Enhancement Proposal (KEP): Kubelet Evented PLEG for Better Performance.

2.12 - Change the Reclaim Policy of a PersistentVolume

This page shows how to change the reclaim policy of a Kubernetes PersistentVolume.

Before you begin

To check the version, enter kubectl version.

Why change reclaim policy of a PersistentVolume

PersistentVolumes can have various reclaim policies, including "Retain", "Recycle", and "Delete". For dynamically provisioned PersistentVolumes, the default reclaim policy is "Delete". This means that a dynamically provisioned volume is automatically deleted when a user deletes the corresponding PersistentVolumeClaim. This automatic behavior might be inappropriate if the volume contains precious data. In that case, it is more appropriate to use the "Retain" policy. With the "Retain" policy, if a user deletes a PersistentVolumeClaim, the corresponding PersistentVolume will not be deleted. Instead, it is moved to the Released phase, where all of its data can be manually recovered.

Changing the reclaim policy of a PersistentVolume

List the PersistentVolumes in your cluster:

kubectl get pv

The output is similar to this:

NAME                                       CAPACITY   ACCESSMODES   RECLAIMPOLICY   STATUS    CLAIM             STORAGECLASS     REASON    AGE
pvc-b6efd8da-b7b5-11e6-9d58-0ed433a7dd94   4Gi        RWO           Delete          Bound     default/claim1    manual                     10s
pvc-b95650f8-b7b5-11e6-9d58-0ed433a7dd94   4Gi        RWO           Delete          Bound     default/claim2    manual                     6s
pvc-bb3ca71d-b7b5-11e6-9d58-0ed433a7dd94   4Gi        RWO           Delete          Bound     default/claim3    manual                     3s

This list also includes the name of the claims that are bound to each volume for easier identification of dynamically provisioned volumes.

Choose one of your PersistentVolumes and change its reclaim policy:
```
kubectl patch pv <your-pv-name> -p '{"spec":{"persistentVolumeReclaimPolicy":"Retain"}}'
```
where <your-pv-name> is the name of your chosen PersistentVolume.
Note:
On Windows, you must double quote any JSONPath template that contains spaces (not single quote as shown above for bash). This in turn means that you must use a single quote or escaped double quote around any literals in the template. For example:
```
kubectl patch pv <your-pv-name> -p "{\"spec\":{\"persistentVolumeReclaimPolicy\":\"Retain\"}}"
```

Verify that your chosen PersistentVolume has the right policy:

kubectl get pv

The output is similar to this:

NAME                                       CAPACITY   ACCESSMODES   RECLAIMPOLICY   STATUS    CLAIM             STORAGECLASS     REASON    AGE
pvc-b6efd8da-b7b5-11e6-9d58-0ed433a7dd94   4Gi        RWO           Delete          Bound     default/claim1    manual                     40s
pvc-b95650f8-b7b5-11e6-9d58-0ed433a7dd94   4Gi        RWO           Delete          Bound     default/claim2    manual                     36s
pvc-bb3ca71d-b7b5-11e6-9d58-0ed433a7dd94   4Gi        RWO           Retain          Bound     default/claim3    manual                     33s

In the preceding output, you can see that the volume bound to claim default/claim3 has reclaim policy Retain. It will not be automatically deleted when a user deletes claim default/claim3.

What's next

Learn more about PersistentVolumes.
Learn more about PersistentVolumeClaims.

References

PersistentVolume
- Pay attention to the .spec.persistentVolumeReclaimPolicy field of PersistentVolume.
PersistentVolumeClaim

2.13 - Cloud Controller Manager Administration

FEATURE STATE: Kubernetes v1.11 [beta]

Since cloud providers develop and release at a different pace compared to the Kubernetes project, abstracting the provider-specific code to the cloud-controller-manager binary allows cloud vendors to evolve independently from the core Kubernetes code.

The cloud-controller-manager can be linked to any cloud provider that satisfies cloudprovider.Interface. For backwards compatibility, the cloud-controller-manager provided in the core Kubernetes project uses the same cloud libraries as kube-controller-manager. Cloud providers already supported in Kubernetes core are expected to use the in-tree cloud-controller-manager to transition out of Kubernetes core.

Administration

Requirements

Every cloud has their own set of requirements for running their own cloud provider integration, it should not be too different from the requirements when running kube-controller-manager. As a general rule of thumb you'll need:

cloud authentication/authorization: your cloud may require a token or IAM rules to allow access to their APIs
kubernetes authentication/authorization: cloud-controller-manager may need RBAC rules set to speak to the kubernetes apiserver
high availability: like kube-controller-manager, you may want a high available setup for cloud controller manager using leader election (on by default).

Running cloud-controller-manager

Successfully running cloud-controller-manager requires some changes to your cluster configuration.

kubelet, kube-apiserver, and kube-controller-manager must be set according to the user's usage of external CCM. If the user has an external CCM (not the internal cloud controller loops in the Kubernetes Controller Manager), then --cloud-provider=external must be specified. Otherwise, it should not be specified.

Keep in mind that setting up your cluster to use cloud controller manager will change your cluster behaviour in a few ways:

Components that specify --cloud-provider=external will add a taint node.cloudprovider.kubernetes.io/uninitialized with an effect NoSchedule during initialization. This marks the node as needing a second initialization from an external controller before it can be scheduled work. Note that in the event that cloud controller manager is not available, new nodes in the cluster will be left unschedulable. The taint is important since the scheduler may require cloud specific information about nodes such as their region or type (high cpu, gpu, high memory, spot instance, etc).
cloud information about nodes in the cluster will no longer be retrieved using local metadata, but instead all API calls to retrieve node information will go through cloud controller manager. This may mean you can restrict access to your cloud API on the kubelets for better security. For larger clusters you may want to consider if cloud controller manager will hit rate limits since it is now responsible for almost all API calls to your cloud from within the cluster.

The cloud controller manager can implement:

Node controller - responsible for updating kubernetes nodes using cloud APIs and deleting kubernetes nodes that were deleted on your cloud.
Service controller - responsible for loadbalancers on your cloud against services of type LoadBalancer.
Route controller - responsible for setting up network routes on your cloud
any other features you would like to implement if you are running an out-of-tree provider.

Examples

If you are using a cloud that is currently supported in Kubernetes core and would like to adopt cloud controller manager, see the cloud controller manager in kubernetes core.

For cloud controller managers not in Kubernetes core, you can find the respective projects in repositories maintained by cloud vendors or by SIGs.

For providers already in Kubernetes core, you can run the in-tree cloud controller manager as a DaemonSet in your cluster, use the following as a guideline:

admin/cloud/ccm-example.yaml

# This is an example of how to set up cloud-controller-manager as a Daemonset in your cluster.
# It assumes that your masters can run pods and has the role node-role.kubernetes.io/master
# Note that this Daemonset will not work straight out of the box for your cloud, this is
# meant to be a guideline.

---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: cloud-controller-manager
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: system:cloud-controller-manager
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: cluster-admin
subjects:
- kind: ServiceAccount
  name: cloud-controller-manager
  namespace: kube-system
---
apiVersion: apps/v1
kind: DaemonSet
metadata:
  labels:
    k8s-app: cloud-controller-manager
  name: cloud-controller-manager
  namespace: kube-system
spec:
  selector:
    matchLabels:
      k8s-app: cloud-controller-manager
  template:
    metadata:
      labels:
        k8s-app: cloud-controller-manager
    spec:
      serviceAccountName: cloud-controller-manager
      containers:
      - name: cloud-controller-manager
        # for in-tree providers we use registry.k8s.io/cloud-controller-manager
        # this can be replaced with any other image for out-of-tree providers
        image: registry.k8s.io/cloud-controller-manager:v1.8.0
        command:
        - /usr/local/bin/cloud-controller-manager
        - --cloud-provider=[YOUR_CLOUD_PROVIDER]  # Add your own cloud provider here!
        - --leader-elect=true
        - --use-service-account-credentials
        # these flags will vary for every cloud provider
        - --allocate-node-cidrs=true
        - --configure-cloud-routes=true
        - --cluster-cidr=172.17.0.0/16
      tolerations:
      # this is required so CCM can bootstrap itself
      - key: node.cloudprovider.kubernetes.io/uninitialized
        value: "true"
        effect: NoSchedule
      # these tolerations are to have the daemonset runnable on control plane nodes
      # remove them if your control plane nodes should not run pods
      - key: node-role.kubernetes.io/control-plane
        operator: Exists
        effect: NoSchedule
      - key: node-role.kubernetes.io/master
        operator: Exists
        effect: NoSchedule
      # this is to restrict CCM to only run on master nodes
      # the node selector may vary depending on your cluster setup
      nodeSelector:
        node-role.kubernetes.io/master: ""

Limitations

Running cloud controller manager comes with a few possible limitations. Although these limitations are being addressed in upcoming releases, it's important that you are aware of these limitations for production workloads.

Support for Volumes

Cloud controller manager does not implement any of the volume controllers found in kube-controller-manager as the volume integrations also require coordination with kubelets. As we evolve CSI (container storage interface) and add stronger support for flex volume plugins, necessary support will be added to cloud controller manager so that clouds can fully integrate with volumes. Learn more about out-of-tree CSI volume plugins here.

Scalability

The cloud-controller-manager queries your cloud provider's APIs to retrieve information for all nodes. For very large clusters, consider possible bottlenecks such as resource requirements and API rate limiting.

Chicken and Egg

The goal of the cloud controller manager project is to decouple development of cloud features from the core Kubernetes project. Unfortunately, many aspects of the Kubernetes project has assumptions that cloud provider features are tightly integrated into the project. As a result, adopting this new architecture can create several situations where a request is being made for information from a cloud provider, but the cloud controller manager may not be able to return that information without the original request being complete.

A good example of this is the TLS bootstrapping feature in the Kubelet. TLS bootstrapping assumes that the Kubelet has the ability to ask the cloud provider (or a local metadata service) for all its address types (private, public, etc) but cloud controller manager cannot set a node's address types without being initialized in the first place which requires that the kubelet has TLS certificates to communicate with the apiserver.

As this initiative evolves, changes will be made to address these issues in upcoming releases.

What's next

To build and develop your own cloud controller manager, read Developing Cloud Controller Manager.

2.14 - Configure a kubelet image credential provider

FEATURE STATE: Kubernetes v1.26 [stable]

Starting from Kubernetes v1.20, the kubelet can dynamically retrieve credentials for a container image registry using exec plugins. The kubelet and the exec plugin communicate through stdio (stdin, stdout, and stderr) using Kubernetes versioned APIs. These plugins allow the kubelet to request credentials for a container registry dynamically as opposed to storing static credentials on disk. For example, the plugin may talk to a local metadata server to retrieve short-lived credentials for an image that is being pulled by the kubelet.

You may be interested in using this capability if any of the below are true:

API calls to a cloud provider service are required to retrieve authentication information for a registry.
Credentials have short expiration times and requesting new credentials frequently is required.
Storing registry credentials on disk or in imagePullSecrets is not acceptable.

This guide demonstrates how to configure the kubelet's image credential provider plugin mechanism.

Before you begin

You need a Kubernetes cluster with nodes that support kubelet credential provider plugins. This support is available in Kubernetes 1.30; Kubernetes v1.24 and v1.25 included this as a beta feature, enabled by default.
A working implementation of a credential provider exec plugin. You can build your own plugin or use one provided by cloud providers.

Your Kubernetes server must be at or later than version v1.26. To check the version, enter kubectl version.

Installing Plugins on Nodes

A credential provider plugin is an executable binary that will be run by the kubelet. Ensure that the plugin binary exists on every node in your cluster and stored in a known directory. The directory will be required later when configuring kubelet flags.

Configuring the Kubelet

In order to use this feature, the kubelet expects two flags to be set:

--image-credential-provider-config - the path to the credential provider plugin config file.
--image-credential-provider-bin-dir - the path to the directory where credential provider plugin binaries are located.

Configure a kubelet credential provider

The configuration file passed into --image-credential-provider-config is read by the kubelet to determine which exec plugins should be invoked for which container images. Here's an example configuration file you may end up using if you are using the ECR-based plugin:

apiVersion: kubelet.config.k8s.io/v1
kind: CredentialProviderConfig
# providers is a list of credential provider helper plugins that will be enabled by the kubelet.
# Multiple providers may match against a single image, in which case credentials
# from all providers will be returned to the kubelet. If multiple providers are called
# for a single image, the results are combined. If providers return overlapping
# auth keys, the value from the provider earlier in this list is used.
providers:
  # name is the required name of the credential provider. It must match the name of the
  # provider executable as seen by the kubelet. The executable must be in the kubelet's
  # bin directory (set by the --image-credential-provider-bin-dir flag).
  - name: ecr-credential-provider
    # matchImages is a required list of strings used to match against images in order to
    # determine if this provider should be invoked. If one of the strings matches the
    # requested image from the kubelet, the plugin will be invoked and given a chance
    # to provide credentials. Images are expected to contain the registry domain
    # and URL path.
    #
    # Each entry in matchImages is a pattern which can optionally contain a port and a path.
    # Globs can be used in the domain, but not in the port or the path. Globs are supported
    # as subdomains like '*.k8s.io' or 'k8s.*.io', and top-level-domains such as 'k8s.*'.
    # Matching partial subdomains like 'app*.k8s.io' is also supported. Each glob can only match
    # a single subdomain segment, so `*.io` does **not** match `*.k8s.io`.
    #
    # A match exists between an image and a matchImage when all of the below are true:
    # - Both contain the same number of domain parts and each part matches.
    # - The URL path of an matchImages must be a prefix of the target image URL path.
    # - If the matchImages contains a port, then the port must match in the image as well.
    #
    # Example values of matchImages:
    # - 123456789.dkr.ecr.us-east-1.amazonaws.com
    # - *.azurecr.io
    # - gcr.io
    # - *.*.registry.io
    # - registry.io:8080/path
    matchImages:
      - "*.dkr.ecr.*.amazonaws.com"
      - "*.dkr.ecr.*.amazonaws.com.cn"
      - "*.dkr.ecr-fips.*.amazonaws.com"
      - "*.dkr.ecr.us-iso-east-1.c2s.ic.gov"
      - "*.dkr.ecr.us-isob-east-1.sc2s.sgov.gov"
    # defaultCacheDuration is the default duration the plugin will cache credentials in-memory
    # if a cache duration is not provided in the plugin response. This field is required.
    defaultCacheDuration: "12h"
    # Required input version of the exec CredentialProviderRequest. The returned CredentialProviderResponse
    # MUST use the same encoding version as the input. Current supported values are:
    # - credentialprovider.kubelet.k8s.io/v1
    apiVersion: credentialprovider.kubelet.k8s.io/v1
    # Arguments to pass to the command when executing it.
    # +optional
    # args:
    #   - --example-argument
    # Env defines additional environment variables to expose to the process. These
    # are unioned with the host's environment, as well as variables client-go uses
    # to pass argument to the plugin.
    # +optional
    env:
      - name: AWS_PROFILE
        value: example_profile

The providers field is a list of enabled plugins used by the kubelet. Each entry has a few required fields:

name: the name of the plugin which MUST match the name of the executable binary that exists in the directory passed into --image-credential-provider-bin-dir.
matchImages: a list of strings used to match against images in order to determine if this provider should be invoked. More on this below.
defaultCacheDuration: the default duration the kubelet will cache credentials in-memory if a cache duration was not specified by the plugin.
apiVersion: the API version that the kubelet and the exec plugin will use when communicating.

Each credential provider can also be given optional args and environment variables as well. Consult the plugin implementors to determine what set of arguments and environment variables are required for a given plugin.

Configure image matching

The matchImages field for each credential provider is used by the kubelet to determine whether a plugin should be invoked for a given image that a Pod is using. Each entry in matchImages is an image pattern which can optionally contain a port and a path. Globs can be used in the domain, but not in the port or the path. Globs are supported as subdomains like *.k8s.io or k8s.*.io, and top-level domains such as k8s.*. Matching partial subdomains like app*.k8s.io is also supported. Each glob can only match a single subdomain segment, so *.io does NOT match *.k8s.io.

A match exists between an image name and a matchImage entry when all of the below are true:

Both contain the same number of domain parts and each part matches.
The URL path of match image must be a prefix of the target image URL path.
If the matchImages contains a port, then the port must match in the image as well.

Some example values of matchImages patterns are:

123456789.dkr.ecr.us-east-1.amazonaws.com
*.azurecr.io
gcr.io
*.*.registry.io
foo.registry.io:8080/path

What's next

Read the details about CredentialProviderConfig in the kubelet configuration API (v1) reference.
Read the kubelet credential provider API reference (v1).

2.15 - Configure Quotas for API Objects

This page shows how to configure quotas for API objects, including PersistentVolumeClaims and Services. A quota restricts the number of objects, of a particular type, that can be created in a namespace. You specify quotas in a ResourceQuota object.

Before you begin

To check the version, enter kubectl version.

Create a namespace

Create a namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace quota-object-example

Create a ResourceQuota

Here is the configuration file for a ResourceQuota object:

admin/resource/quota-objects.yaml

apiVersion: v1
kind: ResourceQuota
metadata:
  name: object-quota-demo
spec:
  hard:
    persistentvolumeclaims: "1"
    services.loadbalancers: "2"
    services.nodeports: "0"

Create the ResourceQuota:

kubectl apply -f https://k8s.io/examples/admin/resource/quota-objects.yaml --namespace=quota-object-example

View detailed information about the ResourceQuota:

kubectl get resourcequota object-quota-demo --namespace=quota-object-example --output=yaml

The output shows that in the quota-object-example namespace, there can be at most one PersistentVolumeClaim, at most two Services of type LoadBalancer, and no Services of type NodePort.

status:
  hard:
    persistentvolumeclaims: "1"
    services.loadbalancers: "2"
    services.nodeports: "0"
  used:
    persistentvolumeclaims: "0"
    services.loadbalancers: "0"
    services.nodeports: "0"

Create a PersistentVolumeClaim

Here is the configuration file for a PersistentVolumeClaim object:

admin/resource/quota-objects-pvc.yaml

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: pvc-quota-demo
spec:
  storageClassName: manual
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 3Gi

Create the PersistentVolumeClaim:

kubectl apply -f https://k8s.io/examples/admin/resource/quota-objects-pvc.yaml --namespace=quota-object-example

Verify that the PersistentVolumeClaim was created:

kubectl get persistentvolumeclaims --namespace=quota-object-example

The output shows that the PersistentVolumeClaim exists and has status Pending:

NAME             STATUS
pvc-quota-demo   Pending

Attempt to create a second PersistentVolumeClaim

Here is the configuration file for a second PersistentVolumeClaim:

admin/resource/quota-objects-pvc-2.yaml

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: pvc-quota-demo-2
spec:
  storageClassName: manual
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 4Gi

Attempt to create the second PersistentVolumeClaim:

kubectl apply -f https://k8s.io/examples/admin/resource/quota-objects-pvc-2.yaml --namespace=quota-object-example

The output shows that the second PersistentVolumeClaim was not created, because it would have exceeded the quota for the namespace.

persistentvolumeclaims "pvc-quota-demo-2" is forbidden:
exceeded quota: object-quota-demo, requested: persistentvolumeclaims=1,
used: persistentvolumeclaims=1, limited: persistentvolumeclaims=1

Notes

These are the strings used to identify API resources that can be constrained by quotas:

String	API Object
"pods"	Pod
"services"	Service
"replicationcontrollers"	ReplicationController
"resourcequotas"	ResourceQuota
"secrets"	Secret
"configmaps"	ConfigMap
"persistentvolumeclaims"	PersistentVolumeClaim
"services.nodeports"	Service of type NodePort
"services.loadbalancers"	Service of type LoadBalancer

Clean up

Delete your namespace:

kubectl delete namespace quota-object-example

What's next

For cluster administrators

For app developers

2.16 - Control CPU Management Policies on the Node

FEATURE STATE: Kubernetes v1.26 [stable]

Kubernetes keeps many aspects of how pods execute on nodes abstracted from the user. This is by design. However, some workloads require stronger guarantees in terms of latency and/or performance in order to operate acceptably. The kubelet provides methods to enable more complex workload placement policies while keeping the abstraction free from explicit placement directives.

For detailed information on resource management, please refer to the Resource Management for Pods and Containers documentation.

Before you begin

Your Kubernetes server must be at or later than version v1.26. To check the version, enter kubectl version.

If you are running an older version of Kubernetes, please look at the documentation for the version you are actually running.

CPU Management Policies

By default, the kubelet uses CFS quota to enforce pod CPU limits. When the node runs many CPU-bound pods, the workload can move to different CPU cores depending on whether the pod is throttled and which CPU cores are available at scheduling time. Many workloads are not sensitive to this migration and thus work fine without any intervention.

However, in workloads where CPU cache affinity and scheduling latency significantly affect workload performance, the kubelet allows alternative CPU management policies to determine some placement preferences on the node.

Configuration

The CPU Manager policy is set with the --cpu-manager-policy kubelet flag or the cpuManagerPolicy field in KubeletConfiguration. There are two supported policies:

none: the default policy.
static: allows pods with certain resource characteristics to be granted increased CPU affinity and exclusivity on the node.

The CPU manager periodically writes resource updates through the CRI in order to reconcile in-memory CPU assignments with cgroupfs. The reconcile frequency is set through a new Kubelet configuration value --cpu-manager-reconcile-period. If not specified, it defaults to the same duration as --node-status-update-frequency.

The behavior of the static policy can be fine-tuned using the --cpu-manager-policy-options flag. The flag takes a comma-separated list of key=value policy options. If you disable the CPUManagerPolicyOptions feature gate then you cannot fine-tune CPU manager policies. In that case, the CPU manager operates only using its default settings.

In addition to the top-level CPUManagerPolicyOptions feature gate, the policy options are split into two groups: alpha quality (hidden by default) and beta quality (visible by default). The groups are guarded respectively by the CPUManagerPolicyAlphaOptions and CPUManagerPolicyBetaOptions feature gates. Diverging from the Kubernetes standard, these feature gates guard groups of options, because it would have been too cumbersome to add a feature gate for each individual option.

Changing the CPU Manager Policy

Since the CPU manager policy can only be applied when kubelet spawns new pods, simply changing from "none" to "static" won't apply to existing pods. So in order to properly change the CPU manager policy on a node, perform the following steps:

Drain the node.
Stop kubelet.
Remove the old CPU manager state file. The path to this file is /var/lib/kubelet/cpu_manager_state by default. This clears the state maintained by the CPUManager so that the cpu-sets set up by the new policy won’t conflict with it.
Edit the kubelet configuration to change the CPU manager policy to the desired value.
Start kubelet.

Repeat this process for every node that needs its CPU manager policy changed. Skipping this process will result in kubelet crashlooping with the following error:

could not restore state from checkpoint: configured policy "static" differs from state checkpoint policy "none", please drain this node and delete the CPU manager checkpoint file "/var/lib/kubelet/cpu_manager_state" before restarting Kubelet

None policy

The none policy explicitly enables the existing default CPU affinity scheme, providing no affinity beyond what the OS scheduler does automatically. Limits on CPU usage for Guaranteed pods and Burstable pods are enforced using CFS quota.

Static policy

The static policy allows containers in Guaranteed pods with integer CPU requests access to exclusive CPUs on the node. This exclusivity is enforced using the cpuset cgroup controller.

Note:

System services such as the container runtime and the kubelet itself can continue to run on these exclusive CPUs. The exclusivity only extends to other pods.

Note:

CPU Manager doesn't support offlining and onlining of CPUs at runtime. Also, if the set of online CPUs changes on the node, the node must be drained and CPU manager manually reset by deleting the state file cpu_manager_state in the kubelet root directory.

This policy manages a shared pool of CPUs that initially contains all CPUs in the node. The amount of exclusively allocatable CPUs is equal to the total number of CPUs in the node minus any CPU reservations by the kubelet --kube-reserved or --system-reserved options. From 1.17, the CPU reservation list can be specified explicitly by kubelet --reserved-cpus option. The explicit CPU list specified by --reserved-cpus takes precedence over the CPU reservation specified by --kube-reserved and --system-reserved. CPUs reserved by these options are taken, in integer quantity, from the initial shared pool in ascending order by physical core ID. This shared pool is the set of CPUs on which any containers in BestEffort and Burstable pods run. Containers in Guaranteed pods with fractional CPU requests also run on CPUs in the shared pool. Only containers that are both part of a Guaranteed pod and have integer CPU requests are assigned exclusive CPUs.

Note:

The kubelet requires a CPU reservation greater than zero be made using either --kube-reserved and/or --system-reserved or --reserved-cpus when the static policy is enabled. This is because zero CPU reservation would allow the shared pool to become empty.

As Guaranteed pods whose containers fit the requirements for being statically assigned are scheduled to the node, CPUs are removed from the shared pool and placed in the cpuset for the container. CFS quota is not used to bound the CPU usage of these containers as their usage is bound by the scheduling domain itself. In others words, the number of CPUs in the container cpuset is equal to the integer CPU limit specified in the pod spec. This static assignment increases CPU affinity and decreases context switches due to throttling for the CPU-bound workload.

Consider the containers in the following pod specs:

spec:
  containers:
  - name: nginx
    image: nginx

The pod above runs in the BestEffort QoS class because no resource requests or limits are specified. It runs in the shared pool.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
      requests:
        memory: "100Mi"

The pod above runs in the Burstable QoS class because resource requests do not equal limits and the cpu quantity is not specified. It runs in the shared pool.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "2"
      requests:
        memory: "100Mi"
        cpu: "1"

The pod above runs in the Burstable QoS class because resource requests do not equal limits. It runs in the shared pool.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "2"
      requests:
        memory: "200Mi"
        cpu: "2"

The pod above runs in the Guaranteed QoS class because requests are equal to limits. And the container's resource limit for the CPU resource is an integer greater than or equal to one. The nginx container is granted 2 exclusive CPUs.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "1.5"
      requests:
        memory: "200Mi"
        cpu: "1.5"

The pod above runs in the Guaranteed QoS class because requests are equal to limits. But the container's resource limit for the CPU resource is a fraction. It runs in the shared pool.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "2"

The pod above runs in the Guaranteed QoS class because only limits are specified and requests are set equal to limits when not explicitly specified. And the container's resource limit for the CPU resource is an integer greater than or equal to one. The nginx container is granted 2 exclusive CPUs.

Static policy options

You can toggle groups of options on and off based upon their maturity level using the following feature gates:

CPUManagerPolicyBetaOptions default enabled. Disable to hide beta-level options.
CPUManagerPolicyAlphaOptions default disabled. Enable to show alpha-level options. You will still have to enable each option using the CPUManagerPolicyOptions kubelet option.

The following policy options exist for the static CPUManager policy:

full-pcpus-only (beta, visible by default) (1.22 or higher)
distribute-cpus-across-numa (alpha, hidden by default) (1.23 or higher)
align-by-socket (alpha, hidden by default) (1.25 or higher)

If the full-pcpus-only policy option is specified, the static policy will always allocate full physical cores. By default, without this option, the static policy allocates CPUs using a topology-aware best-fit allocation. On SMT enabled systems, the policy can allocate individual virtual cores, which correspond to hardware threads. This can lead to different containers sharing the same physical cores; this behaviour in turn contributes to the noisy neighbours problem. With the option enabled, the pod will be admitted by the kubelet only if the CPU request of all its containers can be fulfilled by allocating full physical cores. If the pod does not pass the admission, it will be put in Failed state with the message SMTAlignmentError.

If the distribute-cpus-across-numapolicy option is specified, the static policy will evenly distribute CPUs across NUMA nodes in cases where more than one NUMA node is required to satisfy the allocation. By default, the CPUManager will pack CPUs onto one NUMA node until it is filled, with any remaining CPUs simply spilling over to the next NUMA node. This can cause undesired bottlenecks in parallel code relying on barriers (and similar synchronization primitives), as this type of code tends to run only as fast as its slowest worker (which is slowed down by the fact that fewer CPUs are available on at least one NUMA node). By distributing CPUs evenly across NUMA nodes, application developers can more easily ensure that no single worker suffers from NUMA effects more than any other, improving the overall performance of these types of applications.

If the align-by-socket policy option is specified, CPUs will be considered aligned at the socket boundary when deciding how to allocate CPUs to a container. By default, the CPUManager aligns CPU allocations at the NUMA boundary, which could result in performance degradation if CPUs need to be pulled from more than one NUMA node to satisfy the allocation. Although it tries to ensure that all CPUs are allocated from the minimum number of NUMA nodes, there is no guarantee that those NUMA nodes will be on the same socket. By directing the CPUManager to explicitly align CPUs at the socket boundary rather than the NUMA boundary, we are able to avoid such issues. Note, this policy option is not compatible with TopologyManager single-numa-node policy and does not apply to hardware where the number of sockets is greater than number of NUMA nodes.

The full-pcpus-only option can be enabled by adding full-pcpus-only=true to the CPUManager policy options. Likewise, the distribute-cpus-across-numa option can be enabled by adding distribute-cpus-across-numa=true to the CPUManager policy options. When both are set, they are "additive" in the sense that CPUs will be distributed across NUMA nodes in chunks of full-pcpus rather than individual cores. The align-by-socket policy option can be enabled by adding align-by-socket=true to the CPUManager policy options. It is also additive to the full-pcpus-only and distribute-cpus-across-numa policy options.

2.17 - Control Topology Management Policies on a node

FEATURE STATE: Kubernetes v1.27 [stable]

An increasing number of systems leverage a combination of CPUs and hardware accelerators to support latency-critical execution and high-throughput parallel computation. These include workloads in fields such as telecommunications, scientific computing, machine learning, financial services and data analytics. Such hybrid systems comprise a high performance environment.

In order to extract the best performance, optimizations related to CPU isolation, memory and device locality are required. However, in Kubernetes, these optimizations are handled by a disjoint set of components.

Topology Manager is a Kubelet component that aims to coordinate the set of components that are responsible for these optimizations.

Before you begin

Your Kubernetes server must be at or later than version v1.18. To check the version, enter kubectl version.

How Topology Manager Works

Prior to the introduction of Topology Manager, the CPU and Device Manager in Kubernetes make resource allocation decisions independently of each other. This can result in undesirable allocations on multiple-socketed systems, performance/latency sensitive applications will suffer due to these undesirable allocations. Undesirable in this case meaning for example, CPUs and devices being allocated from different NUMA Nodes thus, incurring additional latency.

The Topology Manager is a Kubelet component, which acts as a source of truth so that other Kubelet components can make topology aligned resource allocation choices.

The Topology Manager provides an interface for components, called Hint Providers, to send and receive topology information. Topology Manager has a set of node level policies which are explained below.

The Topology manager receives Topology information from the Hint Providers as a bitmask denoting NUMA Nodes available and a preferred allocation indication. The Topology Manager policies perform a set of operations on the hints provided and converge on the hint determined by the policy to give the optimal result, if an undesirable hint is stored the preferred field for the hint will be set to false. In the current policies preferred is the narrowest preferred mask. The selected hint is stored as part of the Topology Manager. Depending on the policy configured the pod can be accepted or rejected from the node based on the selected hint. The hint is then stored in the Topology Manager for use by the Hint Providers when making the resource allocation decisions.

Topology Manager Scopes and Policies

The Topology Manager currently:

Aligns Pods of all QoS classes.
Aligns the requested resources that Hint Provider provides topology hints for.

If these conditions are met, the Topology Manager will align the requested resources.

In order to customise how this alignment is carried out, the Topology Manager provides two distinct knobs: scope and policy.

The scope defines the granularity at which you would like resource alignment to be performed (e.g. at the pod or container level). And the policy defines the actual strategy used to carry out the alignment (e.g. best-effort, restricted, single-numa-node, etc.). Details on the various scopes and policies available today can be found below.

Note:

To align CPU resources with other requested resources in a Pod Spec, the CPU Manager should be enabled and proper CPU Manager policy should be configured on a Node. See control CPU Management Policies.

Note:

To align memory (and hugepages) resources with other requested resources in a Pod Spec, the Memory Manager should be enabled and proper Memory Manager policy should be configured on a Node. Examine Memory Manager documentation.

Topology Manager Scopes

The Topology Manager can deal with the alignment of resources in a couple of distinct scopes:

container (default)
pod

Either option can be selected at a time of the kubelet startup, with --topology-manager-scope flag.

container scope

The container scope is used by default.

Within this scope, the Topology Manager performs a number of sequential resource alignments, i.e., for each container (in a pod) a separate alignment is computed. In other words, there is no notion of grouping the containers to a specific set of NUMA nodes, for this particular scope. In effect, the Topology Manager performs an arbitrary alignment of individual containers to NUMA nodes.

The notion of grouping the containers was endorsed and implemented on purpose in the following scope, for example the pod scope.

pod scope

To select the pod scope, start the kubelet with the command line option --topology-manager-scope=pod.

This scope allows for grouping all containers in a pod to a common set of NUMA nodes. That is, the Topology Manager treats a pod as a whole and attempts to allocate the entire pod (all containers) to either a single NUMA node or a common set of NUMA nodes. The following examples illustrate the alignments produced by the Topology Manager on different occasions:

all containers can be and are allocated to a single NUMA node;
all containers can be and are allocated to a shared set of NUMA nodes.

The total amount of particular resource demanded for the entire pod is calculated according to effective requests/limits formula, and thus, this total value is equal to the maximum of:

the sum of all app container requests,
the maximum of init container requests,

for a resource.

Using the pod scope in tandem with single-numa-node Topology Manager policy is specifically valuable for workloads that are latency sensitive or for high-throughput applications that perform IPC. By combining both options, you are able to place all containers in a pod onto a single NUMA node; hence, the inter-NUMA communication overhead can be eliminated for that pod.

In the case of single-numa-node policy, a pod is accepted only if a suitable set of NUMA nodes is present among possible allocations. Reconsider the example above:

a set containing only a single NUMA node - it leads to pod being admitted,
whereas a set containing more NUMA nodes - it results in pod rejection (because instead of one NUMA node, two or more NUMA nodes are required to satisfy the allocation).

To recap, Topology Manager first computes a set of NUMA nodes and then tests it against Topology Manager policy, which either leads to the rejection or admission of the pod.

Topology Manager Policies

Topology Manager supports four allocation policies. You can set a policy via a Kubelet flag, --topology-manager-policy. There are four supported policies:

none (default)
best-effort
restricted
single-numa-node

Note:

If Topology Manager is configured with the pod scope, the container, which is considered by the policy, is reflecting requirements of the entire pod, and thus each container from the pod will result with the same topology alignment decision.

none policy

This is the default policy and does not perform any topology alignment.

best-effort policy

For each container in a Pod, the kubelet, with best-effort topology management policy, calls each Hint Provider to discover their resource availability. Using this information, the Topology Manager stores the preferred NUMA Node affinity for that container. If the affinity is not preferred, Topology Manager will store this and admit the pod to the node anyway.

The Hint Providers can then use this information when making the resource allocation decision.

restricted policy

For each container in a Pod, the kubelet, with restricted topology management policy, calls each Hint Provider to discover their resource availability. Using this information, the Topology Manager stores the preferred NUMA Node affinity for that container. If the affinity is not preferred, Topology Manager will reject this pod from the node. This will result in a pod in a Terminated state with a pod admission failure.

Once the pod is in a Terminated state, the Kubernetes scheduler will not attempt to reschedule the pod. It is recommended to use a ReplicaSet or Deployment to trigger a redeploy of the pod. An external control loop could be also implemented to trigger a redeployment of pods that have the Topology Affinity error.

If the pod is admitted, the Hint Providers can then use this information when making the resource allocation decision.

single-numa-node policy

For each container in a Pod, the kubelet, with single-numa-node topology management policy, calls each Hint Provider to discover their resource availability. Using this information, the Topology Manager determines if a single NUMA Node affinity is possible. If it is, Topology Manager will store this and the Hint Providers can then use this information when making the resource allocation decision. If, however, this is not possible then the Topology Manager will reject the pod from the node. This will result in a pod in a Terminated state with a pod admission failure.

Once the pod is in a Terminated state, the Kubernetes scheduler will not attempt to reschedule the pod. It is recommended to use a Deployment with replicas to trigger a redeploy of the Pod.An external control loop could be also implemented to trigger a redeployment of pods that have the Topology Affinity error.

Topology manager policy options

Support for the Topology Manager policy options requires TopologyManagerPolicyOptions feature gate to be enabled (it is enabled by default).

You can toggle groups of options on and off based upon their maturity level using the following feature gates:

TopologyManagerPolicyBetaOptions default enabled. Enable to show beta-level options.
TopologyManagerPolicyAlphaOptions default disabled. Enable to show alpha-level options.

You will still have to enable each option using the TopologyManagerPolicyOptions kubelet option.

The following policy options exists:

prefer-closest-numa-nodes (beta, visible by default; TopologyManagerPolicyOptions and TopologyManagerPolicyBetaOptions feature gates have to be enabled). The prefer-closest-numa-nodes policy option is beta in Kubernetes 1.30.

If the prefer-closest-numa-nodes policy option is specified, the best-effort and restricted policies will favor sets of NUMA nodes with shorter distance between them when making admission decisions. You can enable this option by adding prefer-closest-numa-nodes=true to the Topology Manager policy options. By default, without this option, Topology Manager aligns resources on either a single NUMA node or the minimum number of NUMA nodes (in cases where more than one NUMA node is required). However, the TopologyManager is not aware of NUMA distances and does not take them into account when making admission decisions. This limitation surfaces in multi-socket, as well as single-socket multi NUMA systems, and can cause significant performance degradation in latency-critical execution and high-throughput applications if the Topology Manager decides to align resources on non-adjacent NUMA nodes.

Pod Interactions with Topology Manager Policies

Consider the containers in the following pod specs:

spec:
  containers:
  - name: nginx
    image: nginx

This pod runs in the BestEffort QoS class because no resource requests or limits are specified.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
      requests:
        memory: "100Mi"

This pod runs in the Burstable QoS class because requests are less than limits.

If the selected policy is anything other than none, Topology Manager would consider these Pod specifications. The Topology Manager would consult the Hint Providers to get topology hints. In the case of the static, the CPU Manager policy would return default topology hint, because these Pods do not have explicitly request CPU resources.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "2"
        example.com/device: "1"
      requests:
        memory: "200Mi"
        cpu: "2"
        example.com/device: "1"

This pod with integer CPU request runs in the Guaranteed QoS class because requests are equal to limits.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "300m"
        example.com/device: "1"
      requests:
        memory: "200Mi"
        cpu: "300m"
        example.com/device: "1"

This pod with sharing CPU request runs in the Guaranteed QoS class because requests are equal to limits.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        example.com/deviceA: "1"
        example.com/deviceB: "1"
      requests:
        example.com/deviceA: "1"
        example.com/deviceB: "1"

This pod runs in the BestEffort QoS class because there are no CPU and memory requests.

The Topology Manager would consider the above pods. The Topology Manager would consult the Hint Providers, which are CPU and Device Manager to get topology hints for the pods.

In the case of the Guaranteed pod with integer CPU request, the static CPU Manager policy would return topology hints relating to the exclusive CPU and the Device Manager would send back hints for the requested device.

In the case of the Guaranteed pod with sharing CPU request, the static CPU Manager policy would return default topology hint as there is no exclusive CPU request and the Device Manager would send back hints for the requested device.

In the above two cases of the Guaranteed pod, the none CPU Manager policy would return default topology hint.

In the case of the BestEffort pod, the static CPU Manager policy would send back the default topology hint as there is no CPU request and the Device Manager would send back the hints for each of the requested devices.

Using this information the Topology Manager calculates the optimal hint for the pod and stores this information, which will be used by the Hint Providers when they are making their resource assignments.

Known Limitations

The maximum number of NUMA nodes that Topology Manager allows is 8. With more than 8 NUMA nodes there will be a state explosion when trying to enumerate the possible NUMA affinities and generating their hints.
The scheduler is not topology-aware, so it is possible to be scheduled on a node and then fail on the node due to the Topology Manager.

2.18 - Customizing DNS Service

This page explains how to configure your DNS Pod(s) and customize the DNS resolution process in your cluster.

Before you begin

Your cluster must be running the CoreDNS add-on.

Your Kubernetes server must be at or later than version v1.12. To check the version, enter kubectl version.

Introduction

DNS is a built-in Kubernetes service launched automatically using the addon manager cluster add-on.

Note:

The CoreDNS Service is named kube-dns in the metadata.name field.
The intent is to ensure greater interoperability with workloads that relied on the legacy kube-dns Service name to resolve addresses internal to the cluster. Using a Service named kube-dns abstracts away the implementation detail of which DNS provider is running behind that common name.

If you are running CoreDNS as a Deployment, it will typically be exposed as a Kubernetes Service with a static IP address. The kubelet passes DNS resolver information to each container with the --cluster-dns=<dns-service-ip> flag.

DNS names also need domains. You configure the local domain in the kubelet with the flag --cluster-domain=<default-local-domain>.

The DNS server supports forward lookups (A and AAAA records), port lookups (SRV records), reverse IP address lookups (PTR records), and more. For more information, see DNS for Services and Pods.

If a Pod's dnsPolicy is set to default, it inherits the name resolution configuration from the node that the Pod runs on. The Pod's DNS resolution should behave the same as the node. But see Known issues.

If you don't want this, or if you want a different DNS config for pods, you can use the kubelet's --resolv-conf flag. Set this flag to "" to prevent Pods from inheriting DNS. Set it to a valid file path to specify a file other than /etc/resolv.conf for DNS inheritance.

CoreDNS

CoreDNS is a general-purpose authoritative DNS server that can serve as cluster DNS, complying with the DNS specifications.

CoreDNS ConfigMap options

CoreDNS is a DNS server that is modular and pluggable, with plugins adding new functionalities. The CoreDNS server can be configured by maintaining a Corefile, which is the CoreDNS configuration file. As a cluster administrator, you can modify the ConfigMap for the CoreDNS Corefile to change how DNS service discovery behaves for that cluster.

In Kubernetes, CoreDNS is installed with the following default Corefile configuration:

apiVersion: v1
kind: ConfigMap
metadata:
  name: coredns
  namespace: kube-system
data:
  Corefile: |
    .:53 {
        errors
        health {
            lameduck 5s
        }
        ready
        kubernetes cluster.local in-addr.arpa ip6.arpa {
            pods insecure
            fallthrough in-addr.arpa ip6.arpa
            ttl 30
        }
        prometheus :9153
        forward . /etc/resolv.conf
        cache 30
        loop
        reload
        loadbalance
    }

The Corefile configuration includes the following plugins of CoreDNS:

errors: Errors are logged to stdout.
health: Health of CoreDNS is reported to http://localhost:8080/health. In this extended syntax lameduck will make the process unhealthy then wait for 5 seconds before the process is shut down.
ready: An HTTP endpoint on port 8181 will return 200 OK, when all plugins that are able to signal readiness have done so.
kubernetes: CoreDNS will reply to DNS queries based on IP of the Services and Pods. You can find more details about this plugin on the CoreDNS website.
- ttl allows you to set a custom TTL for responses. The default is 5 seconds. The minimum TTL allowed is 0 seconds, and the maximum is capped at 3600 seconds. Setting TTL to 0 will prevent records from being cached.
- The pods insecure option is provided for backward compatibility with kube-dns.
- You can use the pods verified option, which returns an A record only if there exists a pod in the same namespace with a matching IP.
- The pods disabled option can be used if you don't use pod records.
prometheus: Metrics of CoreDNS are available at http://localhost:9153/metrics in the Prometheus format (also known as OpenMetrics).
forward: Any queries that are not within the Kubernetes cluster domain are forwarded to predefined resolvers (/etc/resolv.conf).
cache: This enables a frontend cache.
loop: Detects simple forwarding loops and halts the CoreDNS process if a loop is found.
reload: Allows automatic reload of a changed Corefile. After you edit the ConfigMap configuration, allow two minutes for your changes to take effect.
loadbalance: This is a round-robin DNS loadbalancer that randomizes the order of A, AAAA, and MX records in the answer.

You can modify the default CoreDNS behavior by modifying the ConfigMap.

Configuration of Stub-domain and upstream nameserver using CoreDNS

CoreDNS has the ability to configure stub-domains and upstream nameservers using the forward plugin.

Example

If a cluster operator has a Consul domain server located at "10.150.0.1", and all Consul names have the suffix ".consul.local". To configure it in CoreDNS, the cluster administrator creates the following stanza in the CoreDNS ConfigMap.

consul.local:53 {
    errors
    cache 30
    forward . 10.150.0.1
}

To explicitly force all non-cluster DNS lookups to go through a specific nameserver at 172.16.0.1, point the forward to the nameserver instead of /etc/resolv.conf

forward .  172.16.0.1

The final ConfigMap along with the default Corefile configuration looks like:

apiVersion: v1
kind: ConfigMap
metadata:
  name: coredns
  namespace: kube-system
data:
  Corefile: |
    .:53 {
        errors
        health
        kubernetes cluster.local in-addr.arpa ip6.arpa {
           pods insecure
           fallthrough in-addr.arpa ip6.arpa
        }
        prometheus :9153
        forward . 172.16.0.1
        cache 30
        loop
        reload
        loadbalance
    }
    consul.local:53 {
        errors
        cache 30
        forward . 10.150.0.1
    }

Note:

CoreDNS does not support FQDNs for stub-domains and nameservers (eg: "ns.foo.com"). During translation, all FQDN nameservers will be omitted from the CoreDNS config.

What's next

Read Debugging DNS Resolution

2.19 - Debugging DNS Resolution

This page provides hints on diagnosing DNS problems.

Before you begin

Your cluster must be configured to use the CoreDNS or its precursor, kube-dns.

Your Kubernetes server must be at or later than version v1.6. To check the version, enter kubectl version.

Create a simple Pod to use as a test environment

admin/dns/dnsutils.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dnsutils
  namespace: default
spec:
  containers:
  - name: dnsutils
    image: registry.k8s.io/e2e-test-images/jessie-dnsutils:1.3
    command:
      - sleep
      - "infinity"
    imagePullPolicy: IfNotPresent
  restartPolicy: Always

Note:

This example creates a pod in the default namespace. DNS name resolution for services depends on the namespace of the pod. For more information, review DNS for Services and Pods.

Use that manifest to create a Pod:

kubectl apply -f https://k8s.io/examples/admin/dns/dnsutils.yaml

pod/dnsutils created

…and verify its status:

kubectl get pods dnsutils

NAME       READY     STATUS    RESTARTS   AGE
dnsutils   1/1       Running   0          <some-time>

Once that Pod is running, you can exec nslookup in that environment. If you see something like the following, DNS is working correctly.

kubectl exec -i -t dnsutils -- nslookup kubernetes.default

Server:    10.0.0.10
Address 1: 10.0.0.10

Name:      kubernetes.default
Address 1: 10.0.0.1

If the nslookup command fails, check the following:

Check the local DNS configuration first

Take a look inside the resolv.conf file. (See Customizing DNS Service and Known issues below for more information)

kubectl exec -ti dnsutils -- cat /etc/resolv.conf

Verify that the search path and name server are set up like the following (note that search path may vary for different cloud providers):

search default.svc.cluster.local svc.cluster.local cluster.local google.internal c.gce_project_id.internal
nameserver 10.0.0.10
options ndots:5

Errors such as the following indicate a problem with the CoreDNS (or kube-dns) add-on or with associated Services:

kubectl exec -i -t dnsutils -- nslookup kubernetes.default

Server:    10.0.0.10
Address 1: 10.0.0.10

nslookup: can't resolve 'kubernetes.default'

kubectl exec -i -t dnsutils -- nslookup kubernetes.default

Server:    10.0.0.10
Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local

nslookup: can't resolve 'kubernetes.default'

Check if the DNS pod is running

Use the kubectl get pods command to verify that the DNS pod is running.

kubectl get pods --namespace=kube-system -l k8s-app=kube-dns

NAME                       READY     STATUS    RESTARTS   AGE
...
coredns-7b96bf9f76-5hsxb   1/1       Running   0           1h
coredns-7b96bf9f76-mvmmt   1/1       Running   0           1h
...

Note:

The value for label k8s-app is kube-dns for both CoreDNS and kube-dns deployments.

If you see that no CoreDNS Pod is running or that the Pod has failed/completed, the DNS add-on may not be deployed by default in your current environment and you will have to deploy it manually.

Check for errors in the DNS pod

Use the kubectl logs command to see logs for the DNS containers.

For CoreDNS:

kubectl logs --namespace=kube-system -l k8s-app=kube-dns

Here is an example of a healthy CoreDNS log:

.:53
2018/08/15 14:37:17 [INFO] CoreDNS-1.2.2
2018/08/15 14:37:17 [INFO] linux/amd64, go1.10.3, 2e322f6
CoreDNS-1.2.2
linux/amd64, go1.10.3, 2e322f6
2018/08/15 14:37:17 [INFO] plugin/reload: Running configuration MD5 = 24e6c59e83ce706f07bcc82c31b1ea1c

See if there are any suspicious or unexpected messages in the logs.

Is DNS service up?

Verify that the DNS service is up by using the kubectl get service command.

kubectl get svc --namespace=kube-system

NAME         TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)             AGE
...
kube-dns     ClusterIP   10.0.0.10      <none>        53/UDP,53/TCP        1h
...

Note:

The service name is kube-dns for both CoreDNS and kube-dns deployments.

If you have created the Service or in the case it should be created by default but it does not appear, see debugging Services for more information.

Are DNS endpoints exposed?

You can verify that DNS endpoints are exposed by using the kubectl get endpoints command.

kubectl get endpoints kube-dns --namespace=kube-system

NAME       ENDPOINTS                       AGE
kube-dns   10.180.3.17:53,10.180.3.17:53    1h

If you do not see the endpoints, see the endpoints section in the debugging Services documentation.

For additional Kubernetes DNS examples, see the cluster-dns examples in the Kubernetes GitHub repository.

Are DNS queries being received/processed?

You can verify if queries are being received by CoreDNS by adding the log plugin to the CoreDNS configuration (aka Corefile). The CoreDNS Corefile is held in a ConfigMap named coredns. To edit it, use the command:

kubectl -n kube-system edit configmap coredns

Then add log in the Corefile section per the example below:

apiVersion: v1
kind: ConfigMap
metadata:
  name: coredns
  namespace: kube-system
data:
  Corefile: |
    .:53 {
        log
        errors
        health
        kubernetes cluster.local in-addr.arpa ip6.arpa {
          pods insecure
          upstream
          fallthrough in-addr.arpa ip6.arpa
        }
        prometheus :9153
        forward . /etc/resolv.conf
        cache 30
        loop
        reload
        loadbalance
    }

After saving the changes, it may take up to minute or two for Kubernetes to propagate these changes to the CoreDNS pods.

Next, make some queries and view the logs per the sections above in this document. If CoreDNS pods are receiving the queries, you should see them in the logs.

Here is an example of a query in the log:

.:53
2018/08/15 14:37:15 [INFO] CoreDNS-1.2.0
2018/08/15 14:37:15 [INFO] linux/amd64, go1.10.3, 2e322f6
CoreDNS-1.2.0
linux/amd64, go1.10.3, 2e322f6
2018/09/07 15:29:04 [INFO] plugin/reload: Running configuration MD5 = 162475cdf272d8aa601e6fe67a6ad42f
2018/09/07 15:29:04 [INFO] Reloading complete
172.17.0.18:41675 - [07/Sep/2018:15:29:11 +0000] 59925 "A IN kubernetes.default.svc.cluster.local. udp 54 false 512" NOERROR qr,aa,rd,ra 106 0.000066649s

Does CoreDNS have sufficient permissions?

CoreDNS must be able to list service and endpoint related resources to properly resolve service names.

Sample error message:

2022-03-18T07:12:15.699431183Z [INFO] 10.96.144.227:52299 - 3686 "A IN serverproxy.contoso.net.cluster.local. udp 52 false 512" SERVFAIL qr,aa,rd 145 0.000091221s

First, get the current ClusterRole of system:coredns:

kubectl describe clusterrole system:coredns -n kube-system

Expected output:

PolicyRule:
  Resources                        Non-Resource URLs  Resource Names  Verbs
  ---------                        -----------------  --------------  -----
  endpoints                        []                 []              [list watch]
  namespaces                       []                 []              [list watch]
  pods                             []                 []              [list watch]
  services                         []                 []              [list watch]
  endpointslices.discovery.k8s.io  []                 []              [list watch]

If any permissions are missing, edit the ClusterRole to add them:

kubectl edit clusterrole system:coredns -n kube-system

Example insertion of EndpointSlices permissions:

...
- apiGroups:
  - discovery.k8s.io
  resources:
  - endpointslices
  verbs:
  - list
  - watch
...

Are you in the right namespace for the service?

DNS queries that don't specify a namespace are limited to the pod's namespace.

If the namespace of the pod and service differ, the DNS query must include the namespace of the service.

This query is limited to the pod's namespace:

kubectl exec -i -t dnsutils -- nslookup <service-name>

This query specifies the namespace:

kubectl exec -i -t dnsutils -- nslookup <service-name>.<namespace>

To learn more about name resolution, see DNS for Services and Pods.

Known issues

Some Linux distributions (e.g. Ubuntu) use a local DNS resolver by default (systemd-resolved). Systemd-resolved moves and replaces /etc/resolv.conf with a stub file that can cause a fatal forwarding loop when resolving names in upstream servers. This can be fixed manually by using kubelet's --resolv-conf flag to point to the correct resolv.conf (With systemd-resolved, this is /run/systemd/resolve/resolv.conf). kubeadm automatically detects systemd-resolved, and adjusts the kubelet flags accordingly.

Kubernetes installs do not configure the nodes' resolv.conf files to use the cluster DNS by default, because that process is inherently distribution-specific. This should probably be implemented eventually.

Linux's libc (a.k.a. glibc) has a limit for the DNS nameserver records to 3 by default and Kubernetes needs to consume 1 nameserver record. This means that if a local installation already uses 3 nameservers, some of those entries will be lost. To work around this limit, the node can run dnsmasq, which will provide more nameserver entries. You can also use kubelet's --resolv-conf flag.

If you are using Alpine version 3.17 or earlier as your base image, DNS may not work properly due to a design issue with Alpine. Until musl version 1.24 didn't include TCP fallback to the DNS stub resolver meaning any DNS call above 512 bytes would fail. Please upgrade your images to Alpine version 3.18 or above.

What's next

See Autoscaling the DNS Service in a Cluster.
Read DNS for Services and Pods

2.20 - Declare Network Policy

This document helps you get started using the Kubernetes NetworkPolicy API to declare network policies that govern how pods communicate with each other.

Before you begin

Your Kubernetes server must be at or later than version v1.8. To check the version, enter kubectl version.

Make sure you've configured a network provider with network policy support. There are a number of network providers that support NetworkPolicy, including:

Create an `nginx` deployment and expose it via a service

To see how Kubernetes network policy works, start off by creating an nginx Deployment.

kubectl create deployment nginx --image=nginx

deployment.apps/nginx created

Expose the Deployment through a Service called nginx.

kubectl expose deployment nginx --port=80

service/nginx exposed

The above commands create a Deployment with an nginx Pod and expose the Deployment through a Service named nginx. The nginx Pod and Deployment are found in the default namespace.

kubectl get svc,pod

NAME                        CLUSTER-IP    EXTERNAL-IP   PORT(S)    AGE
service/kubernetes          10.100.0.1    <none>        443/TCP    46m
service/nginx               10.100.0.16   <none>        80/TCP     33s

NAME                        READY         STATUS        RESTARTS   AGE
pod/nginx-701339712-e0qfq   1/1           Running       0          35s

Test the service by accessing it from another Pod

You should be able to access the new nginx service from other Pods. To access the nginx Service from another Pod in the default namespace, start a busybox container:

kubectl run busybox --rm -ti --image=busybox:1.28 -- /bin/sh

In your shell, run the following command:

wget --spider --timeout=1 nginx

Connecting to nginx (10.100.0.16:80)
remote file exists

Limit access to the `nginx` service

To limit the access to the nginx service so that only Pods with the label access: true can query it, create a NetworkPolicy object as follows:

service/networking/nginx-policy.yaml

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: access-nginx
spec:
  podSelector:
    matchLabels:
      app: nginx
  ingress:
  - from:
    - podSelector:
        matchLabels:
          access: "true"

The name of a NetworkPolicy object must be a valid DNS subdomain name.

Note:

NetworkPolicy includes a podSelector which selects the grouping of Pods to which the policy applies. You can see this policy selects Pods with the label app=nginx. The label was automatically added to the Pod in the nginx Deployment. An empty podSelector selects all pods in the namespace.

Assign the policy to the service

Use kubectl to create a NetworkPolicy from the above nginx-policy.yaml file:

kubectl apply -f https://k8s.io/examples/service/networking/nginx-policy.yaml

networkpolicy.networking.k8s.io/access-nginx created

Test access to the service when access label is not defined

When you attempt to access the nginx Service from a Pod without the correct labels, the request times out:

kubectl run busybox --rm -ti --image=busybox:1.28 -- /bin/sh

In your shell, run the command:

wget --spider --timeout=1 nginx

Connecting to nginx (10.100.0.16:80)
wget: download timed out

Define access label and test again

You can create a Pod with the correct labels to see that the request is allowed:

kubectl run busybox --rm -ti --labels="access=true" --image=busybox:1.28 -- /bin/sh

In your shell, run the command:

wget --spider --timeout=1 nginx

Connecting to nginx (10.100.0.16:80)
remote file exists

2.21 - Developing Cloud Controller Manager

FEATURE STATE: Kubernetes v1.11 [beta]

The cloud-controller-manager is a Kubernetes control plane component that embeds cloud-specific control logic. The cloud controller manager lets you link your cluster into your cloud provider's API, and separates out the components that interact with that cloud platform from components that only interact with your cluster.

By decoupling the interoperability logic between Kubernetes and the underlying cloud infrastructure, the cloud-controller-manager component enables cloud providers to release features at a different pace compared to the main Kubernetes project.

Background

The Kubernetes project provides skeleton cloud-controller-manager code with Go interfaces to allow you (or your cloud provider) to plug in your own implementations. This means that a cloud provider can implement a cloud-controller-manager by importing packages from Kubernetes core; each cloudprovider will register their own code by calling cloudprovider.RegisterCloudProvider to update a global variable of available cloud providers.

Developing

Out of tree

To build an out-of-tree cloud-controller-manager for your cloud:

Create a go package with an implementation that satisfies cloudprovider.Interface.
Use main.go in cloud-controller-manager from Kubernetes core as a template for your main.go. As mentioned above, the only difference should be the cloud package that will be imported.
Import your cloud package in main.go, ensure your package has an init block to run cloudprovider.RegisterCloudProvider.

Many cloud providers publish their controller manager code as open source. If you are creating a new cloud-controller-manager from scratch, you could take an existing out-of-tree cloud controller manager as your starting point.

In tree

For in-tree cloud providers, you can run the in-tree cloud controller manager as a DaemonSet in your cluster. See Cloud Controller Manager Administration for more details.

2.22 - Enable Or Disable A Kubernetes API

This page shows how to enable or disable an API version from your cluster's control plane.

Specific API versions can be turned on or off by passing --runtime-config=api/<version> as a command line argument to the API server. The values for this argument are a comma-separated list of API versions. Later values override earlier values.

The runtime-config command line argument also supports 2 special keys:

api/all, representing all known APIs
api/legacy, representing only legacy APIs. Legacy APIs are any APIs that have been explicitly deprecated.

For example, to turn off all API versions except v1, pass --runtime-config=api/all=false,api/v1=true to the kube-apiserver.

What's next

Read the full documentation for the kube-apiserver component.

2.23 - Encrypting Confidential Data at Rest

All of the APIs in Kubernetes that let you write persistent API resource data support at-rest encryption. For example, you can enable at-rest encryption for Secrets. This at-rest encryption is additional to any system-level encryption for the etcd cluster or for the filesystem(s) on hosts where you are running the kube-apiserver.

This page shows how to enable and configure encryption of API data at rest.

Note:

This task covers encryption for resource data stored using the Kubernetes API. For example, you can encrypt Secret objects, including the key-value data they contain.

If you want to encrypt data in filesystems that are mounted into containers, you instead need to either:

use a storage integration that provides encrypted volumes
encrypt the data within your own application

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes
This task assumes that you are running the Kubernetes API server as a static pod on each control plane node.
Your cluster's control plane must use etcd v3.x (major version 3, any minor version).
To encrypt a custom resource, your cluster must be running Kubernetes v1.26 or newer.
To use a wildcard to match resources, your cluster must be running Kubernetes v1.27 or newer.

To check the version, enter kubectl version.

Determine whether encryption at rest is already enabled

By default, the API server stores plain-text representations of resources into etcd, with no at-rest encryption.

The kube-apiserver process accepts an argument --encryption-provider-config that specifies a path to a configuration file. The contents of that file, if you specify one, control how Kubernetes API data is encrypted in etcd. If you are running the kube-apiserver without the --encryption-provider-config command line argument, you do not have encryption at rest enabled. If you are running the kube-apiserver with the --encryption-provider-config command line argument, and the file that it references specifies the identity provider as the first encryption provider in the list, then you do not have at-rest encryption enabled (the default identity provider does not provide any confidentiality protection.)

If you are running the kube-apiserver with the --encryption-provider-config command line argument, and the file that it references specifies a provider other than identity as the first encryption provider in the list, then you already have at-rest encryption enabled. However, that check does not tell you whether a previous migration to encrypted storage has succeeded. If you are not sure, see ensure all relevant data are encrypted.

Understanding the encryption at rest configuration

---
#
# CAUTION: this is an example configuration.
#          Do not use this for your own cluster!
#
apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:
  - resources:
      - secrets
      - configmaps
      - pandas.awesome.bears.example # a custom resource API
    providers:
      # This configuration does not provide data confidentiality. The first
      # configured provider is specifying the "identity" mechanism, which
      # stores resources as plain text.
      #
      - identity: {} # plain text, in other words NO encryption
      - aesgcm:
          keys:
            - name: key1
              secret: c2VjcmV0IGlzIHNlY3VyZQ==
            - name: key2
              secret: dGhpcyBpcyBwYXNzd29yZA==
      - aescbc:
          keys:
            - name: key1
              secret: c2VjcmV0IGlzIHNlY3VyZQ==
            - name: key2
              secret: dGhpcyBpcyBwYXNzd29yZA==
      - secretbox:
          keys:
            - name: key1
              secret: YWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXoxMjM0NTY=
  - resources:
      - events
    providers:
      - identity: {} # do not encrypt Events even though *.* is specified below
  - resources:
      - '*.apps' # wildcard match requires Kubernetes 1.27 or later
    providers:
      - aescbc:
          keys:
          - name: key2
            secret: c2VjcmV0IGlzIHNlY3VyZSwgb3IgaXMgaXQ/Cg==
  - resources:
      - '*.*' # wildcard match requires Kubernetes 1.27 or later
    providers:
      - aescbc:
          keys:
          - name: key3
            secret: c2VjcmV0IGlzIHNlY3VyZSwgSSB0aGluaw==

Each resources array item is a separate config and contains a complete configuration. The resources.resources field is an array of Kubernetes resource names (resource or resource.group) that should be encrypted like Secrets, ConfigMaps, or other resources.

If custom resources are added to EncryptionConfiguration and the cluster version is 1.26 or newer, any newly created custom resources mentioned in the EncryptionConfiguration will be encrypted. Any custom resources that existed in etcd prior to that version and configuration will be unencrypted until they are next written to storage. This is the same behavior as built-in resources. See the Ensure all secrets are encrypted section.

The providers array is an ordered list of the possible encryption providers to use for the APIs that you listed. Each provider supports multiple keys - the keys are tried in order for decryption, and if the provider is the first provider, the first key is used for encryption.

Only one provider type may be specified per entry (identity or aescbc may be provided, but not both in the same item). The first provider in the list is used to encrypt resources written into the storage. When reading resources from storage, each provider that matches the stored data attempts in order to decrypt the data. If no provider can read the stored data due to a mismatch in format or secret key, an error is returned which prevents clients from accessing that resource.

EncryptionConfiguration supports the use of wildcards to specify the resources that should be encrypted. Use '*.<group>' to encrypt all resources within a group (for eg '*.apps' in above example) or '*.*' to encrypt all resources. '*.' can be used to encrypt all resource in the core group. '*.*' will encrypt all resources, even custom resources that are added after API server start.

Note:

Use of wildcards that overlap within the same resource list or across multiple entries are not allowed since part of the configuration would be ineffective. The resources list's processing order and precedence are determined by the order it's listed in the configuration.

If you have a wildcard covering resources and want to opt out of at-rest encryption for a particular kind of resource, you achieve that by adding a separate resources array item with the name of the resource that you want to exempt, followed by a providers array item where you specify the identity provider. You add this item to the list so that it appears earlier than the configuration where you do specify encryption (a provider that is not identity).

For example, if '*.*' is enabled and you want to opt out of encryption for Events and ConfigMaps, add a new earlier item to the resources, followed by the providers array item with identity as the provider. The more specific entry must come before the wildcard entry.

The new item would look similar to:

  ...
  - resources:
      - configmaps. # specifically from the core API group,
                    # because of trailing "."
      - events
    providers:
      - identity: {}
  # and then other entries in resources

Ensure that the exemption is listed before the wildcard '*.*' item in the resources array to give it precedence.

For more detailed information about the EncryptionConfiguration struct, please refer to the encryption configuration API.

Caution:

If any resource is not readable via the encryption configuration (because keys were changed), and you cannot restore a working configuration, your only recourse is to delete that entry from the underlying etcd directly.

Any calls to the Kubernetes API that attempt to read that resource will fail until it is deleted or a valid decryption key is provided.

Available providers

Before you configure encryption-at-rest for data in your cluster's Kubernetes API, you need to select which provider(s) you will use.

The following table describes each available provider.

Providers for Kubernetes encryption at rest
Name	Encryption	Strength	Speed	Key length
`identity`	None	N/A	N/A	N/A
`identity`	Resources written as-is without encryption. When set as the first provider, the resource will be decrypted as new values are written. Existing encrypted resources are not automatically overwritten with the plaintext data. The `identity` provider is the default if you do not specify otherwise.
`aescbc`	AES-CBC with PKCS#7 padding	Weak	Fast	32-byte
`aescbc`	Not recommended due to CBC's vulnerability to padding oracle attacks. Key material accessible from control plane host.
`aesgcm`	AES-GCM with random nonce	Must be rotated every 200,000 writes	Fastest	16, 24, or 32-byte
`aesgcm`	Not recommended for use except when an automated key rotation scheme is implemented. Key material accessible from control plane host.
`kms` v1 (deprecated since Kubernetes v1.28)	Uses envelope encryption scheme with DEK per resource.	Strongest	Slow (compared to `kms` version 2)	32-bytes
`kms` v1 (deprecated since Kubernetes v1.28)	Data is encrypted by data encryption keys (DEKs) using AES-GCM; DEKs are encrypted by key encryption keys (KEKs) according to configuration in Key Management Service (KMS). Simple key rotation, with a new DEK generated for each encryption, and KEK rotation controlled by the user. Read how to configure the KMS V1 provider.
`kms` v2	Uses envelope encryption scheme with DEK per API server.	Strongest	Fast	32-bytes
`kms` v2	Data is encrypted by data encryption keys (DEKs) using AES-GCM; DEKs are encrypted by key encryption keys (KEKs) according to configuration in Key Management Service (KMS). Kubernetes generates a new DEK per encryption from a secret seed. The seed is rotated whenever the KEK is rotated. A good choice if using a third party tool for key management. Available as stable from Kubernetes v1.29. Read how to configure the KMS V2 provider.
`secretbox`	XSalsa20 and Poly1305	Strong	Faster	32-byte
`secretbox`	Uses relatively new encryption technologies that may not be considered acceptable in environments that require high levels of review. Key material accessible from control plane host.

The identity provider is the default if you do not specify otherwise. The identity provider does not encrypt stored data and provides no additional confidentiality protection.

Key storage

Local key storage

Encrypting secret data with a locally managed key protects against an etcd compromise, but it fails to protect against a host compromise. Since the encryption keys are stored on the host in the EncryptionConfiguration YAML file, a skilled attacker can access that file and extract the encryption keys.

Managed (KMS) key storage

The KMS provider uses envelope encryption: Kubernetes encrypts resources using a data key, and then encrypts that data key using the managed encryption service. Kubernetes generates a unique data key for each resource. The API server stores an encrypted version of the data key in etcd alongside the ciphertext; when reading the resource, the API server calls the managed encryption service and provides both the ciphertext and the (encrypted) data key. Within the managed encryption service, the provider use a key encryption key to decipher the data key, deciphers the data key, and finally recovers the plain text. Communication between the control plane and the KMS requires in-transit protection, such as TLS.

Using envelope encryption creates dependence on the key encryption key, which is not stored in Kubernetes. In the KMS case, an attacker who intends to get unauthorised access to the plaintext values would need to compromise etcd and the third-party KMS provider.

Protection for encryption keys

You should take appropriate measures to protect the confidential information that allows decryption, whether that is a local encryption key, or an authentication token that allows the API server to call KMS.

Even when you rely on a provider to manage the use and lifecycle of the main encryption key (or keys), you are still responsible for making sure that access controls and other security measures for the managed encryption service are appropriate for your security needs.

Encrypt your data

Generate the encryption key

The following steps assume that you are not using KMS, and therefore the steps also assume that you need to generate an encryption key. If you already have an encryption key, skip to Write an encryption configuration file.

Caution:

Storing the raw encryption key in the EncryptionConfig only moderately improves your security posture, compared to no encryption.

For additional secrecy, consider using the kms provider as this relies on keys held outside your Kubernetes cluster. Implementations of kms can work with hardware security modules or with encryption services managed by your cloud provider.

To learn about setting up encryption at rest using KMS, see Using a KMS provider for data encryption. The KMS provider plugin that you use may also come with additional specific documentation.

Start by generating a new encryption key, and then encode it using base64:

Generate a 32-byte random key and base64 encode it. You can use this command:

head -c 32 /dev/urandom | base64

You can use /dev/hwrng instead of /dev/urandom if you want to use your PC's built-in hardware entropy source. Not all Linux devices provide a hardware random generator.

Generate a 32-byte random key and base64 encode it. You can use this command:

head -c 32 /dev/urandom | base64

Generate a 32-byte random key and base64 encode it. You can use this command:

# Do not run this in a session where you have set a random number
# generator seed.
[Convert]::ToBase64String((1..32|%{[byte](Get-Random -Max 256)}))

Note:

Keep the encryption key confidential, including while you generate it and ideally even after you are no longer actively using it.

Replicate the encryption key

Using a secure mechanism for file transfer, make a copy of that encryption key available to every other control plane host.

At a minimum, use encryption in transit - for example, secure shell (SSH). For more security, use asymmetric encryption between hosts, or change the approach you are using so that you're relying on KMS encryption.

Write an encryption configuration file

Caution:

The encryption configuration file may contain keys that can decrypt content in etcd. If the configuration file contains any key material, you must properly restrict permissions on all your control plane hosts so only the user who runs the kube-apiserver can read this configuration.

Create a new encryption configuration file. The contents should be similar to:

---
apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:
  - resources:
      - secrets
      - configmaps
      - pandas.awesome.bears.example
    providers:
      - aescbc:
          keys:
            - name: key1
              # See the following text for more details about the secret value
              secret: <BASE 64 ENCODED SECRET>
      - identity: {} # this fallback allows reading unencrypted secrets;
                     # for example, during initial migration

To create a new encryption key (that does not use KMS), see Generate the encryption key.

Use the new encryption configuration file

You will need to mount the new encryption config file to the kube-apiserver static pod. Here is an example on how to do that:

Save the new encryption config file to /etc/kubernetes/enc/enc.yaml on the control-plane node.

Edit the manifest for the kube-apiserver static pod: /etc/kubernetes/manifests/kube-apiserver.yaml so that it is similar to:

---
#
# This is a fragment of a manifest for a static Pod.
# Check whether this is correct for your cluster and for your API server.
#
apiVersion: v1
kind: Pod
metadata:
  annotations:
    kubeadm.kubernetes.io/kube-apiserver.advertise-address.endpoint: 10.20.30.40:443
  creationTimestamp: null
  labels:
    app.kubernetes.io/component: kube-apiserver
    tier: control-plane
  name: kube-apiserver
  namespace: kube-system
spec:
  containers:
  - command:
    - kube-apiserver
    ...
    - --encryption-provider-config=/etc/kubernetes/enc/enc.yaml  # add this line
    volumeMounts:
    ...
    - name: enc                           # add this line
      mountPath: /etc/kubernetes/enc      # add this line
      readOnly: true                      # add this line
    ...
  volumes:
  ...
  - name: enc                             # add this line
    hostPath:                             # add this line
      path: /etc/kubernetes/enc           # add this line
      type: DirectoryOrCreate             # add this line
  ...

Restart your API server.

Caution:

Your config file contains keys that can decrypt the contents in etcd, so you must properly restrict permissions on your control-plane nodes so only the user who runs the kube-apiserver can read it.

You now have encryption in place for one control plane host. A typical Kubernetes cluster has multiple control plane hosts, so there is more to do.

Reconfigure other control plane hosts

If you have multiple API servers in your cluster, you should deploy the changes in turn to each API server.

Caution:

For cluster configurations with two or more control plane nodes, the encryption configuration should be identical across each control plane node.

If there is a difference in the encryption provider configuration between control plane nodes, this difference may mean that the kube-apiserver can't decrypt data.

When you are planning to update the encryption configuration of your cluster, plan this so that the API servers in your control plane can always decrypt the stored data (even part way through rolling out the change).

Make sure that you use the same encryption configuration on each control plane host.

Verify that newly written data is encrypted

Data is encrypted when written to etcd. After restarting your kube-apiserver, any newly created or updated Secret (or other resource kinds configured in EncryptionConfiguration) should be encrypted when stored.

To check this, you can use the etcdctl command line program to retrieve the contents of your secret data.

This example shows how to check this for encrypting the Secret API.

Create a new Secret called secret1 in the default namespace:

kubectl create secret generic secret1 -n default --from-literal=mykey=mydata

Using the etcdctl command line tool, read that Secret out of etcd:

ETCDCTL_API=3 etcdctl get /registry/secrets/default/secret1 [...] | hexdump -C

where [...] must be the additional arguments for connecting to the etcd server.

For example:

ETCDCTL_API=3 etcdctl \
   --cacert=/etc/kubernetes/pki/etcd/ca.crt   \
   --cert=/etc/kubernetes/pki/etcd/server.crt \
   --key=/etc/kubernetes/pki/etcd/server.key  \
   get /registry/secrets/default/secret1 | hexdump -C

The output is similar to this (abbreviated):

00000000  2f 72 65 67 69 73 74 72  79 2f 73 65 63 72 65 74  |/registry/secret|
00000010  73 2f 64 65 66 61 75 6c  74 2f 73 65 63 72 65 74  |s/default/secret|
00000020  31 0a 6b 38 73 3a 65 6e  63 3a 61 65 73 63 62 63  |1.k8s:enc:aescbc|
00000030  3a 76 31 3a 6b 65 79 31  3a c7 6c e7 d3 09 bc 06  |:v1:key1:.l.....|
00000040  25 51 91 e4 e0 6c e5 b1  4d 7a 8b 3d b9 c2 7c 6e  |%Q...l..Mz.=..|n|
00000050  b4 79 df 05 28 ae 0d 8e  5f 35 13 2c c0 18 99 3e  |.y..(..._5.,...>|
[...]
00000110  23 3a 0d fc 28 ca 48 2d  6b 2d 46 cc 72 0b 70 4c  |#:..(.H-k-F.r.pL|
00000120  a5 fc 35 43 12 4e 60 ef  bf 6f fe cf df 0b ad 1f  |..5C.N`..o......|
00000130  82 c4 88 53 02 da 3e 66  ff 0a                    |...S..>f..|
0000013a

Verify the stored Secret is prefixed with k8s:enc:aescbc:v1: which indicates the aescbc provider has encrypted the resulting data. Confirm that the key name shown in etcd matches the key name specified in the EncryptionConfiguration mentioned above. In this example, you can see that the encryption key named key1 is used in etcd and in EncryptionConfiguration.
Verify the Secret is correctly decrypted when retrieved via the API:
```
kubectl get secret secret1 -n default -o yaml
```
The output should contain mykey: bXlkYXRh, with contents of mydata encoded using base64; read decoding a Secret to learn how to completely decode the Secret.

Ensure all relevant data are encrypted

It's often not enough to make sure that new objects get encrypted: you also want that encryption to apply to the objects that are already stored.

For this example, you have configured your cluster so that Secrets are encrypted on write. Performing a replace operation for each Secret will encrypt that content at rest, where the objects are unchanged.

You can make this change across all Secrets in your cluster:

# Run this as an administrator that can read and write all Secrets
kubectl get secrets --all-namespaces -o json | kubectl replace -f -

The command above reads all Secrets and then updates them with the same data, in order to apply server side encryption.

Note:

If an error occurs due to a conflicting write, retry the command. It is safe to run that command more than once.

For larger clusters, you may wish to subdivide the Secrets by namespace, or script an update.

Prevent plain text retrieval

If you want to make sure that the only access to a particular API kind is done using encryption, you can remove the API server's ability to read that API's backing data as plaintext.

Warning:

Making this change prevents the API server from retrieving resources that are marked as encrypted at rest, but are actually stored in the clear.

When you have configured encryption at rest for an API (for example: the API kind Secret, representing secrets resources in the core API group), you must ensure that all those resources in this cluster really are encrypted at rest. Check this before you carry on with the next steps.

Once all Secrets in your cluster are encrypted, you can remove the identity part of the encryption configuration. For example:

---
apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:
  - resources:
      - secrets
    providers:
      - aescbc:
          keys:
            - name: key1
              secret: <BASE 64 ENCODED SECRET>
      - identity: {} # REMOVE THIS LINE

…and then restart each API server in turn. This change prevents the API server from accessing a plain-text Secret, even by accident.

Rotate a decryption key

Changing an encryption key for Kubernetes without incurring downtime requires a multi-step operation, especially in the presence of a highly-available deployment where multiple kube-apiserver processes are running.

Generate a new key and add it as the second key entry for the current provider on all control plane nodes.
Restart all kube-apiserver processes, to ensure each server can decrypt any data that are encrypted with the new key.
Make a secure backup of the new encryption key. If you lose all copies of this key you would need to delete all the resources were encrypted under the lost key, and workloads may not operate as expected during the time that at-rest encryption is broken.
Make the new key the first entry in the keys array so that it is used for encryption-at-rest for new writes
Restart all kube-apiserver processes to ensure each control plane host now encrypts using the new key
As a privileged user, run kubectl get secrets --all-namespaces -o json | kubectl replace -f - to encrypt all existing Secrets with the new key
After you have updated all existing Secrets to use the new key and have made a secure backup of the new key, remove the old decryption key from the configuration.

Decrypt all data

This example shows how to stop encrypting the Secret API at rest. If you are encrypting other API kinds, adjust the steps to match.

To disable encryption at rest, place the identity provider as the first entry in your encryption configuration file:

---
apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:
  - resources:
      - secrets
      # list any other resources here that you previously were
      # encrypting at rest
    providers:
      - identity: {} # add this line
      - aescbc:
          keys:
            - name: key1
              secret: <BASE 64 ENCODED SECRET> # keep this in place
                                               # make sure it comes after "identity"

Then run the following command to force decryption of all Secrets:

kubectl get secrets --all-namespaces -o json | kubectl replace -f -

Once you have replaced all existing encrypted resources with backing data that don't use encryption, you can remove the encryption settings from the kube-apiserver.

Configure automatic reloading

You can configure automatic reloading of encryption provider configuration. That setting determines whether the API server should load the file you specify for --encryption-provider-config only once at startup, or automatically whenever you change that file. Enabling this option allows you to change the keys for encryption at rest without restarting the API server.

To allow automatic reloading, configure the API server to run with: --encryption-provider-config-automatic-reload=true. When enabled, file changes are polled every minute to observe the modifications. The apiserver_encryption_config_controller_automatic_reload_last_timestamp_seconds metric identifies when the new config becomes effective. This allows encryption keys to be rotated without restarting the API server.

What's next

Read about decrypting data that are already stored at rest
Learn more about the EncryptionConfiguration configuration API (v1).

2.24 - Decrypt Confidential Data that is Already Encrypted at Rest

This page shows how to switch from encryption of API data at rest, so that API data are stored unencrypted. You might want to do this to improve performance; usually, though, if it was a good idea to encrypt some data, it's also a good idea to leave them encrypted.

Note:

This task covers encryption for resource data stored using the Kubernetes API. For example, you can encrypt Secret objects, including the key-value data they contain.

If you wanted to manage encryption for data in filesystems that are mounted into containers, you instead need to either:

use a storage integration that provides encrypted volumes
encrypt the data within your own application

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes
This task assumes that you are running the Kubernetes API server as a static pod on each control plane node.
Your cluster's control plane must use etcd v3.x (major version 3, any minor version).
To encrypt a custom resource, your cluster must be running Kubernetes v1.26 or newer.
You should have some API data that are already encrypted.

To check the version, enter kubectl version.

Determine whether encryption at rest is already enabled

By default, the API server uses an identity provider that stores plain-text representations of resources. The default identity provider does not provide any confidentiality protection.

The format of that configuration file is YAML, representing a configuration API kind named EncryptionConfiguration. You can see an example configuration in Encryption at rest configuration.

If --encryption-provider-config is set, check which resources (such as secrets) are configured for encryption, and what provider is used. Make sure that the preferred provider for that resource type is not identity; you only set identity (no encryption) as default when you want to disable encryption at rest. Verify that the first-listed provider for a resource is something other than identity, which means that any new information written to resources of that type will be encrypted as configured. If you do see identity as the first-listed provider for any resource, this means that those resources are being written out to etcd without encryption.

Decrypt all data

This example shows how to stop encrypting the Secret API at rest. If you are encrypting other API kinds, adjust the steps to match.

Locate the encryption configuration file

First, find the API server configuration files. On each control plane node, static Pod manifest for the kube-apiserver specifies a command line argument, --encryption-provider-config. You are likely to find that this file is mounted into the static Pod using a hostPath volume mount. Once you locate the volume you can find the file on the node filesystem and inspect it.

Configure the API server to decrypt objects

To disable encryption at rest, place the identity provider as the first entry in your encryption configuration file.

For example, if your existing EncryptionConfiguration file reads:

---
apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:
  - resources:
      - secrets
    providers:
      - aescbc:
          keys:
            # Do not use this (invalid) example key for encryption
            - name: example
              secret: 2KfZgdiq2K0g2YrYpyDYs9mF2LPZhQ==

then change it to:

---
apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:
  - resources:
      - secrets
    providers:
      - identity: {} # add this line
      - aescbc:
          keys:
            - name: example
              secret: 2KfZgdiq2K0g2YrYpyDYs9mF2LPZhQ==

and restart the kube-apiserver Pod on this node.

Reconfigure other control plane hosts

If you have multiple API servers in your cluster, you should deploy the changes in turn to each API server.

Make sure that you use the same encryption configuration on each control plane host.

Force decryption

Then run the following command to force decryption of all Secrets:

# If you are decrypting a different kind of object, change "secrets" to match.
kubectl get secrets --all-namespaces -o json | kubectl replace -f -

Once you have replaced all existing encrypted resources with backing data that don't use encryption, you can remove the encryption settings from the kube-apiserver.

The command line options to remove are:

--encryption-provider-config
--encryption-provider-config-automatic-reload

Restart the kube-apiserver Pod again to apply the new configuration.

Reconfigure other control plane hosts

If you have multiple API servers in your cluster, you should again deploy the changes in turn to each API server.

Make sure that you use the same encryption configuration on each control plane host.

What's next

Learn more about the EncryptionConfiguration configuration API (v1).

2.25 - Guaranteed Scheduling For Critical Add-On Pods

Kubernetes core components such as the API server, scheduler, and controller-manager run on a control plane node. However, add-ons must run on a regular cluster node. Some of these add-ons are critical to a fully functional cluster, such as metrics-server, DNS, and UI. A cluster may stop working properly if a critical add-on is evicted (either manually or as a side effect of another operation like upgrade) and becomes pending (for example when the cluster is highly utilized and either there are other pending pods that schedule into the space vacated by the evicted critical add-on pod or the amount of resources available on the node changed for some other reason).

Note that marking a pod as critical is not meant to prevent evictions entirely; it only prevents the pod from becoming permanently unavailable. A static pod marked as critical can't be evicted. However, non-static pods marked as critical are always rescheduled.

Marking pod as critical

To mark a Pod as critical, set priorityClassName for that Pod to system-cluster-critical or system-node-critical. system-node-critical is the highest available priority, even higher than system-cluster-critical.

2.26 - IP Masquerade Agent User Guide

This page shows how to configure and enable the ip-masq-agent.

Before you begin

To check the version, enter kubectl version.

IP Masquerade Agent User Guide

The ip-masq-agent configures iptables rules to hide a pod's IP address behind the cluster node's IP address. This is typically done when sending traffic to destinations outside the cluster's pod CIDR range.

Key Terms

NAT (Network Address Translation): Is a method of remapping one IP address to another by modifying either the source and/or destination address information in the IP header. Typically performed by a device doing IP routing.
Masquerading: A form of NAT that is typically used to perform a many to one address translation, where multiple source IP addresses are masked behind a single address, which is typically the device doing the IP routing. In Kubernetes this is the Node's IP address.
CIDR (Classless Inter-Domain Routing): Based on the variable-length subnet masking, allows specifying arbitrary-length prefixes. CIDR introduced a new method of representation for IP addresses, now commonly known as CIDR notation, in which an address or routing prefix is written with a suffix indicating the number of bits of the prefix, such as 192.168.2.0/24.
Link Local: A link-local address is a network address that is valid only for communications within the network segment or the broadcast domain that the host is connected to. Link-local addresses for IPv4 are defined in the address block 169.254.0.0/16 in CIDR notation.

The ip-masq-agent configures iptables rules to handle masquerading node/pod IP addresses when sending traffic to destinations outside the cluster node's IP and the Cluster IP range. This essentially hides pod IP addresses behind the cluster node's IP address. In some environments, traffic to "external" addresses must come from a known machine address. For example, in Google Cloud, any traffic to the internet must come from a VM's IP. When containers are used, as in Google Kubernetes Engine, the Pod IP will be rejected for egress. To avoid this, we must hide the Pod IP behind the VM's own IP address - generally known as "masquerade". By default, the agent is configured to treat the three private IP ranges specified by RFC 1918 as non-masquerade CIDR. These ranges are 10.0.0.0/8, 172.16.0.0/12, and 192.168.0.0/16. The agent will also treat link-local (169.254.0.0/16) as a non-masquerade CIDR by default. The agent is configured to reload its configuration from the location /etc/config/ip-masq-agent every 60 seconds, which is also configurable.

masq/non-masq example

The agent configuration file must be written in YAML or JSON syntax, and may contain three optional keys:

nonMasqueradeCIDRs: A list of strings in CIDR notation that specify the non-masquerade ranges.
masqLinkLocal: A Boolean (true/false) which indicates whether to masquerade traffic to the link local prefix 169.254.0.0/16. False by default.
resyncInterval: A time interval at which the agent attempts to reload config from disk. For example: '30s', where 's' means seconds, 'ms' means milliseconds.

Traffic to 10.0.0.0/8, 172.16.0.0/12 and 192.168.0.0/16 ranges will NOT be masqueraded. Any other traffic (assumed to be internet) will be masqueraded. An example of a local destination from a pod could be its Node's IP address as well as another node's address or one of the IP addresses in Cluster's IP range. Any other traffic will be masqueraded by default. The below entries show the default set of rules that are applied by the ip-masq-agent:

iptables -t nat -L IP-MASQ-AGENT

target     prot opt source               destination
RETURN     all  --  anywhere             169.254.0.0/16       /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
RETURN     all  --  anywhere             10.0.0.0/8           /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
RETURN     all  --  anywhere             172.16.0.0/12        /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
RETURN     all  --  anywhere             192.168.0.0/16       /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
MASQUERADE  all  --  anywhere             anywhere             /* ip-masq-agent: outbound traffic should be subject to MASQUERADE (this match must come after cluster-local CIDR matches) */ ADDRTYPE match dst-type !LOCAL

By default, in GCE/Google Kubernetes Engine, if network policy is enabled or you are using a cluster CIDR not in the 10.0.0.0/8 range, the ip-masq-agent will run in your cluster. If you are running in another environment, you can add the ip-masq-agent DaemonSet to your cluster.

Create an ip-masq-agent

To create an ip-masq-agent, run the following kubectl command:

kubectl apply -f https://raw.githubusercontent.com/kubernetes-sigs/ip-masq-agent/master/ip-masq-agent.yaml

You must also apply the appropriate node label to any nodes in your cluster that you want the agent to run on.

kubectl label nodes my-node node.kubernetes.io/masq-agent-ds-ready=true

More information can be found in the ip-masq-agent documentation here.

In most cases, the default set of rules should be sufficient; however, if this is not the case for your cluster, you can create and apply a ConfigMap to customize the IP ranges that are affected. For example, to allow only 10.0.0.0/8 to be considered by the ip-masq-agent, you can create the following ConfigMap in a file called "config".

Note:

It is important that the file is called config since, by default, that will be used as the key for lookup by the ip-masq-agent:

nonMasqueradeCIDRs:
  - 10.0.0.0/8
resyncInterval: 60s

Run the following command to add the configmap to your cluster:

kubectl create configmap ip-masq-agent --from-file=config --namespace=kube-system

This will update a file located at /etc/config/ip-masq-agent which is periodically checked every resyncInterval and applied to the cluster node. After the resync interval has expired, you should see the iptables rules reflect your changes:

iptables -t nat -L IP-MASQ-AGENT

Chain IP-MASQ-AGENT (1 references)
target     prot opt source               destination
RETURN     all  --  anywhere             169.254.0.0/16       /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
RETURN     all  --  anywhere             10.0.0.0/8           /* ip-masq-agent: cluster-local
MASQUERADE  all  --  anywhere             anywhere             /* ip-masq-agent: outbound traffic should be subject to MASQUERADE (this match must come after cluster-local CIDR matches) */ ADDRTYPE match dst-type !LOCAL

By default, the link local range (169.254.0.0/16) is also handled by the ip-masq agent, which sets up the appropriate iptables rules. To have the ip-masq-agent ignore link local, you can set masqLinkLocal to true in the ConfigMap.

nonMasqueradeCIDRs:
  - 10.0.0.0/8
resyncInterval: 60s
masqLinkLocal: true

2.27 - Limit Storage Consumption

This example demonstrates how to limit the amount of storage consumed in a namespace.

The following resources are used in the demonstration: ResourceQuota, LimitRange, and PersistentVolumeClaim.

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes
To check the version, enter kubectl version.

Scenario: Limiting Storage Consumption

The cluster-admin is operating a cluster on behalf of a user population and the admin wants to control how much storage a single namespace can consume in order to control cost.

The admin would like to limit:

The number of persistent volume claims in a namespace
The amount of storage each claim can request
The amount of cumulative storage the namespace can have

LimitRange to limit requests for storage

Adding a LimitRange to a namespace enforces storage request sizes to a minimum and maximum. Storage is requested via PersistentVolumeClaim. The admission controller that enforces limit ranges will reject any PVC that is above or below the values set by the admin.

In this example, a PVC requesting 10Gi of storage would be rejected because it exceeds the 2Gi max.

apiVersion: v1
kind: LimitRange
metadata:
  name: storagelimits
spec:
  limits:
  - type: PersistentVolumeClaim
    max:
      storage: 2Gi
    min:
      storage: 1Gi

Minimum storage requests are used when the underlying storage provider requires certain minimums. For example, AWS EBS volumes have a 1Gi minimum requirement.

ResourceQuota to limit PVC count and cumulative storage capacity

Admins can limit the number of PVCs in a namespace as well as the cumulative capacity of those PVCs. New PVCs that exceed either maximum value will be rejected.

In this example, a 6th PVC in the namespace would be rejected because it exceeds the maximum count of 5. Alternatively, a 5Gi maximum quota when combined with the 2Gi max limit above, cannot have 3 PVCs where each has 2Gi. That would be 6Gi requested for a namespace capped at 5Gi.

apiVersion: v1
kind: ResourceQuota
metadata:
  name: storagequota
spec:
  hard:
    persistentvolumeclaims: "5"
    requests.storage: "5Gi"

Summary

A limit range can put a ceiling on how much storage is requested while a resource quota can effectively cap the storage consumed by a namespace through claim counts and cumulative storage capacity. The allows a cluster-admin to plan their cluster's storage budget without risk of any one project going over their allotment.

2.28 - Migrate Replicated Control Plane To Use Cloud Controller Manager

Background

As part of the cloud provider extraction effort, all cloud specific controllers must be moved out of the kube-controller-manager. All existing clusters that run cloud controllers in the kube-controller-manager must migrate to instead run the controllers in a cloud provider specific cloud-controller-manager.

Leader Migration provides a mechanism in which HA clusters can safely migrate "cloud specific" controllers between the kube-controller-manager and the cloud-controller-manager via a shared resource lock between the two components while upgrading the replicated control plane. For a single-node control plane, or if unavailability of controller managers can be tolerated during the upgrade, Leader Migration is not needed and this guide can be ignored.

Leader Migration can be enabled by setting --enable-leader-migration on kube-controller-manager or cloud-controller-manager. Leader Migration only applies during the upgrade and can be safely disabled or left enabled after the upgrade is complete.

This guide walks you through the manual process of upgrading the control plane from kube-controller-manager with built-in cloud provider to running both kube-controller-manager and cloud-controller-manager. If you use a tool to deploy and manage the cluster, please refer to the documentation of the tool and the cloud provider for specific instructions of the migration.

Before you begin

It is assumed that the control plane is running Kubernetes version N and to be upgraded to version N + 1. Although it is possible to migrate within the same version, ideally the migration should be performed as part of an upgrade so that changes of configuration can be aligned to each release. The exact versions of N and N + 1 depend on each cloud provider. For example, if a cloud provider builds a cloud-controller-manager to work with Kubernetes 1.24, then N can be 1.23 and N + 1 can be 1.24.

The control plane nodes should run kube-controller-manager with Leader Election enabled, which is the default. As of version N, an in-tree cloud provider must be set with --cloud-provider flag and cloud-controller-manager should not yet be deployed.

The out-of-tree cloud provider must have built a cloud-controller-manager with Leader Migration implementation. If the cloud provider imports k8s.io/cloud-provider and k8s.io/controller-manager of version v0.21.0 or later, Leader Migration will be available. However, for version before v0.22.0, Leader Migration is alpha and requires feature gate ControllerManagerLeaderMigration to be enabled in cloud-controller-manager.

This guide assumes that kubelet of each control plane node starts kube-controller-manager and cloud-controller-manager as static pods defined by their manifests. If the components run in a different setting, please adjust the steps accordingly.

For authorization, this guide assumes that the cluster uses RBAC. If another authorization mode grants permissions to kube-controller-manager and cloud-controller-manager components, please grant the needed access in a way that matches the mode.

Grant access to Migration Lease

The default permissions of the controller manager allow only accesses to their main Lease. In order for the migration to work, accesses to another Lease are required.

You can grant kube-controller-manager full access to the leases API by modifying the system::leader-locking-kube-controller-manager role. This task guide assumes that the name of the migration lease is cloud-provider-extraction-migration.

kubectl patch -n kube-system role 'system::leader-locking-kube-controller-manager' -p '{"rules": [ {"apiGroups":[ "coordination.k8s.io"], "resources": ["leases"], "resourceNames": ["cloud-provider-extraction-migration"], "verbs": ["create", "list", "get", "update"] } ]}' --type=merge`

Do the same to the system::leader-locking-cloud-controller-manager role.

kubectl patch -n kube-system role 'system::leader-locking-cloud-controller-manager' -p '{"rules": [ {"apiGroups":[ "coordination.k8s.io"], "resources": ["leases"], "resourceNames": ["cloud-provider-extraction-migration"], "verbs": ["create", "list", "get", "update"] } ]}' --type=merge`

Initial Leader Migration configuration

Leader Migration optionally takes a configuration file representing the state of controller-to-manager assignment. At this moment, with in-tree cloud provider, kube-controller-manager runs route, service, and cloud-node-lifecycle. The following example configuration shows the assignment.

Leader Migration can be enabled without a configuration. Please see Default Configuration for details.

kind: LeaderMigrationConfiguration
apiVersion: controllermanager.config.k8s.io/v1
leaderName: cloud-provider-extraction-migration
controllerLeaders:
  - name: route
    component: kube-controller-manager
  - name: service
    component: kube-controller-manager
  - name: cloud-node-lifecycle
    component: kube-controller-manager

Alternatively, because the controllers can run under either controller managers, setting component to * for both sides makes the configuration file consistent between both parties of the migration.

# wildcard version
kind: LeaderMigrationConfiguration
apiVersion: controllermanager.config.k8s.io/v1
leaderName: cloud-provider-extraction-migration
controllerLeaders:
  - name: route
    component: *
  - name: service
    component: *
  - name: cloud-node-lifecycle
    component: *

On each control plane node, save the content to /etc/leadermigration.conf, and update the manifest of kube-controller-manager so that the file is mounted inside the container at the same location. Also, update the same manifest to add the following arguments:

--enable-leader-migration to enable Leader Migration on the controller manager
--leader-migration-config=/etc/leadermigration.conf to set configuration file

Restart kube-controller-manager on each node. At this moment, kube-controller-manager has leader migration enabled and is ready for the migration.

Deploy Cloud Controller Manager

In version N + 1, the desired state of controller-to-manager assignment can be represented by a new configuration file, shown as follows. Please note component field of each controllerLeaders changing from kube-controller-manager to cloud-controller-manager. Alternatively, use the wildcard version mentioned above, which has the same effect.

kind: LeaderMigrationConfiguration
apiVersion: controllermanager.config.k8s.io/v1
leaderName: cloud-provider-extraction-migration
controllerLeaders:
  - name: route
    component: cloud-controller-manager
  - name: service
    component: cloud-controller-manager
  - name: cloud-node-lifecycle
    component: cloud-controller-manager

When creating control plane nodes of version N + 1, the content should be deployed to /etc/leadermigration.conf. The manifest of cloud-controller-manager should be updated to mount the configuration file in the same manner as kube-controller-manager of version N. Similarly, add --enable-leader-migration and --leader-migration-config=/etc/leadermigration.conf to the arguments of cloud-controller-manager.

Create a new control plane node of version N + 1 with the updated cloud-controller-manager manifest, and with the --cloud-provider flag set to external for kube-controller-manager. kube-controller-manager of version N + 1 MUST NOT have Leader Migration enabled because, with an external cloud provider, it does not run the migrated controllers anymore, and thus it is not involved in the migration.

Please refer to Cloud Controller Manager Administration for more detail on how to deploy cloud-controller-manager.

Upgrade Control Plane

The control plane now contains nodes of both version N and N + 1. The nodes of version N run kube-controller-manager only, and these of version N + 1 run both kube-controller-manager and cloud-controller-manager. The migrated controllers, as specified in the configuration, are running under either kube-controller-manager of version N or cloud-controller-manager of version N + 1 depending on which controller manager holds the migration lease. No controller will ever be running under both controller managers at any time.

In a rolling manner, create a new control plane node of version N + 1 and bring down one of version N until the control plane contains only nodes of version N + 1. If a rollback from version N + 1 to N is required, add nodes of version N with Leader Migration enabled for kube-controller-manager back to the control plane, replacing one of version N + 1 each time until there are only nodes of version N.

(Optional) Disable Leader Migration

Now that the control plane has been upgraded to run both kube-controller-manager and cloud-controller-manager of version N + 1, Leader Migration has finished its job and can be safely disabled to save one Lease resource. It is safe to re-enable Leader Migration for the rollback in the future.

In a rolling manager, update manifest of cloud-controller-manager to unset both --enable-leader-migration and --leader-migration-config= flag, also remove the mount of /etc/leadermigration.conf, and finally remove /etc/leadermigration.conf. To re-enable Leader Migration, recreate the configuration file and add its mount and the flags that enable Leader Migration back to cloud-controller-manager.

Default Configuration

Starting Kubernetes 1.22, Leader Migration provides a default configuration suitable for the default controller-to-manager assignment. The default configuration can be enabled by setting --enable-leader-migration but without --leader-migration-config=.

For kube-controller-manager and cloud-controller-manager, if there are no flags that enable any in-tree cloud provider or change ownership of controllers, the default configuration can be used to avoid manual creation of the configuration file.

Special case: migrating the Node IPAM controller

If your cloud provider provides an implementation of Node IPAM controller, you should switch to the implementation in cloud-controller-manager. Disable Node IPAM controller in kube-controller-manager of version N + 1 by adding --controllers=*,-nodeipam to its flags. Then add nodeipam to the list of migrated controllers.

# wildcard version, with nodeipam
kind: LeaderMigrationConfiguration
apiVersion: controllermanager.config.k8s.io/v1
leaderName: cloud-provider-extraction-migration
controllerLeaders:
  - name: route
    component: *
  - name: service
    component: *
  - name: cloud-node-lifecycle
    component: *
  - name: nodeipam
-   component: *

What's next

Read the Controller Manager Leader Migration enhancement proposal.

2.29 - Namespaces Walkthrough

Kubernetes namespaces help different projects, teams, or customers to share a Kubernetes cluster.

It does this by providing the following:

A scope for Names.
A mechanism to attach authorization and policy to a subsection of the cluster.

Use of multiple namespaces is optional.

This example demonstrates how to use Kubernetes namespaces to subdivide your cluster.

Before you begin

To check the version, enter kubectl version.

Prerequisites

This example assumes the following:

You have an existing Kubernetes cluster.
You have a basic understanding of Kubernetes Pods, Services, and Deployments.

Understand the default namespace

By default, a Kubernetes cluster will instantiate a default namespace when provisioning the cluster to hold the default set of Pods, Services, and Deployments used by the cluster.

Assuming you have a fresh cluster, you can inspect the available namespaces by doing the following:

kubectl get namespaces

NAME      STATUS    AGE
default   Active    13m

Create new namespaces

For this exercise, we will create two additional Kubernetes namespaces to hold our content.

Let's imagine a scenario where an organization is using a shared Kubernetes cluster for development and production use cases.

The development team would like to maintain a space in the cluster where they can get a view on the list of Pods, Services, and Deployments they use to build and run their application. In this space, Kubernetes resources come and go, and the restrictions on who can or cannot modify resources are relaxed to enable agile development.

The operations team would like to maintain a space in the cluster where they can enforce strict procedures on who can or cannot manipulate the set of Pods, Services, and Deployments that run the production site.

One pattern this organization could follow is to partition the Kubernetes cluster into two namespaces: development and production.

Let's create two new namespaces to hold our work.

Use the file namespace-dev.yaml which describes a development namespace:

admin/namespace-dev.yaml

apiVersion: v1
kind: Namespace
metadata:
  name: development
  labels:
    name: development

Create the development namespace using kubectl.

kubectl create -f https://k8s.io/examples/admin/namespace-dev.yaml

Save the following contents into file namespace-prod.yaml which describes a production namespace:

admin/namespace-prod.yaml

apiVersion: v1
kind: Namespace
metadata:
  name: production
  labels:
    name: production

And then let's create the production namespace using kubectl.

kubectl create -f https://k8s.io/examples/admin/namespace-prod.yaml

To be sure things are right, let's list all of the namespaces in our cluster.

kubectl get namespaces --show-labels

NAME          STATUS    AGE       LABELS
default       Active    32m       <none>
development   Active    29s       name=development
production    Active    23s       name=production

Create pods in each namespace

A Kubernetes namespace provides the scope for Pods, Services, and Deployments in the cluster.

Users interacting with one namespace do not see the content in another namespace.

To demonstrate this, let's spin up a simple Deployment and Pods in the development namespace.

We first check what is the current context:

kubectl config view

apiVersion: v1
clusters:
- cluster:
    certificate-authority-data: REDACTED
    server: https://130.211.122.180
  name: lithe-cocoa-92103_kubernetes
contexts:
- context:
    cluster: lithe-cocoa-92103_kubernetes
    user: lithe-cocoa-92103_kubernetes
  name: lithe-cocoa-92103_kubernetes
current-context: lithe-cocoa-92103_kubernetes
kind: Config
preferences: {}
users:
- name: lithe-cocoa-92103_kubernetes
  user:
    client-certificate-data: REDACTED
    client-key-data: REDACTED
    token: 65rZW78y8HbwXXtSXuUw9DbP4FLjHi4b
- name: lithe-cocoa-92103_kubernetes-basic-auth
  user:
    password: h5M0FtUUIflBSdI7
    username: admin

kubectl config current-context

lithe-cocoa-92103_kubernetes

The next step is to define a context for the kubectl client to work in each namespace. The value of "cluster" and "user" fields are copied from the current context.

kubectl config set-context dev --namespace=development \
  --cluster=lithe-cocoa-92103_kubernetes \
  --user=lithe-cocoa-92103_kubernetes

kubectl config set-context prod --namespace=production \
  --cluster=lithe-cocoa-92103_kubernetes \
  --user=lithe-cocoa-92103_kubernetes

By default, the above commands add two contexts that are saved into file .kube/config. You can now view the contexts and alternate against the two new request contexts depending on which namespace you wish to work against.

To view the new contexts:

kubectl config view

apiVersion: v1
clusters:
- cluster:
    certificate-authority-data: REDACTED
    server: https://130.211.122.180
  name: lithe-cocoa-92103_kubernetes
contexts:
- context:
    cluster: lithe-cocoa-92103_kubernetes
    user: lithe-cocoa-92103_kubernetes
  name: lithe-cocoa-92103_kubernetes
- context:
    cluster: lithe-cocoa-92103_kubernetes
    namespace: development
    user: lithe-cocoa-92103_kubernetes
  name: dev
- context:
    cluster: lithe-cocoa-92103_kubernetes
    namespace: production
    user: lithe-cocoa-92103_kubernetes
  name: prod
current-context: lithe-cocoa-92103_kubernetes
kind: Config
preferences: {}
users:
- name: lithe-cocoa-92103_kubernetes
  user:
    client-certificate-data: REDACTED
    client-key-data: REDACTED
    token: 65rZW78y8HbwXXtSXuUw9DbP4FLjHi4b
- name: lithe-cocoa-92103_kubernetes-basic-auth
  user:
    password: h5M0FtUUIflBSdI7
    username: admin

Let's switch to operate in the development namespace.

kubectl config use-context dev

You can verify your current context by doing the following:

kubectl config current-context

dev

At this point, all requests we make to the Kubernetes cluster from the command line are scoped to the development namespace.

Let's create some contents.

admin/snowflake-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: snowflake
  name: snowflake
spec:
  replicas: 2
  selector:
    matchLabels:
      app: snowflake
  template:
    metadata:
      labels:
        app: snowflake
    spec:
      containers:
      - image: registry.k8s.io/serve_hostname
        imagePullPolicy: Always
        name: snowflake

Apply the manifest to create a Deployment

kubectl apply -f https://k8s.io/examples/admin/snowflake-deployment.yaml

We have created a deployment whose replica size is 2 that is running the pod called snowflake with a basic container that serves the hostname.

kubectl get deployment

NAME         READY   UP-TO-DATE   AVAILABLE   AGE
snowflake    2/2     2            2           2m

kubectl get pods -l app=snowflake

NAME                         READY     STATUS    RESTARTS   AGE
snowflake-3968820950-9dgr8   1/1       Running   0          2m
snowflake-3968820950-vgc4n   1/1       Running   0          2m

And this is great, developers are able to do what they want, and they do not have to worry about affecting content in the production namespace.

Let's switch to the production namespace and show how resources in one namespace are hidden from the other.

kubectl config use-context prod

The production namespace should be empty, and the following commands should return nothing.

kubectl get deployment
kubectl get pods

Production likes to run cattle, so let's create some cattle pods.

kubectl create deployment cattle --image=registry.k8s.io/serve_hostname --replicas=5

kubectl get deployment

NAME         READY   UP-TO-DATE   AVAILABLE   AGE
cattle       5/5     5            5           10s

kubectl get pods -l app=cattle

NAME                      READY     STATUS    RESTARTS   AGE
cattle-2263376956-41xy6   1/1       Running   0          34s
cattle-2263376956-kw466   1/1       Running   0          34s
cattle-2263376956-n4v97   1/1       Running   0          34s
cattle-2263376956-p5p3i   1/1       Running   0          34s
cattle-2263376956-sxpth   1/1       Running   0          34s

At this point, it should be clear that the resources users create in one namespace are hidden from the other namespace.

As the policy support in Kubernetes evolves, we will extend this scenario to show how you can provide different authorization rules for each namespace.

2.30 - Operating etcd clusters for Kubernetes

etcd is a consistent and highly-available key value store used as Kubernetes' backing store for all cluster data.

If your Kubernetes cluster uses etcd as its backing store, make sure you have a back up plan for the data.

You can find in-depth information about etcd in the official documentation.

Before you begin

Before you follow steps in this page to deploy, manage, back up or restore etcd, you need to understand the typical expectations for operating an etcd cluster. Refer to the etcd documentation for more context.

Key details include:

The minimum recommended etcd versions to run in production are 3.4.22+ and 3.5.6+.
etcd is a leader-based distributed system. Ensure that the leader periodically send heartbeats on time to all followers to keep the cluster stable.
You should run etcd as a cluster with an odd number of members.
Aim to ensure that no resource starvation occurs.

Performance and stability of the cluster is sensitive to network and disk I/O. Any resource starvation can lead to heartbeat timeout, causing instability of the cluster. An unstable etcd indicates that no leader is elected. Under such circumstances, a cluster cannot make any changes to its current state, which implies no new pods can be scheduled.

Resource requirements for etcd

Operating etcd with limited resources is suitable only for testing purposes. For deploying in production, advanced hardware configuration is required. Before deploying etcd in production, see resource requirement reference.

Keeping etcd clusters stable is critical to the stability of Kubernetes clusters. Therefore, run etcd clusters on dedicated machines or isolated environments for guaranteed resource requirements.

Tools

Depending on which specific outcome you're working on, you will need the etcdctl tool or the etcdutl tool (you may need both).

Understanding etcdctl and etcdutl

etcdctl and etcdutl are command-line tools used to interact with etcd clusters, but they serve different purposes:

etcdctl: This is the primary command-line client for interacting with etcd over a network. It is used for day-to-day operations such as managing keys and values, administering the cluster, checking health, and more.
etcdutl: This is an administration utility designed to operate directly on etcd data files, including migrating data between etcd versions, defragmenting the database, restoring snapshots, and validating data consistency. For network operations, etcdctl should be used.

For more information on etcdutl, you can refer to the etcd recovery documentation.

Starting etcd clusters

This section covers starting a single-node and multi-node etcd cluster.

This guide assumes that etcd is already installed.

Single-node etcd cluster

Use a single-node etcd cluster only for testing purposes.

Run the following:

etcd --listen-client-urls=http://$PRIVATE_IP:2379 \
   --advertise-client-urls=http://$PRIVATE_IP:2379

Start the Kubernetes API server with the flag --etcd-servers=$PRIVATE_IP:2379.

Make sure PRIVATE_IP is set to your etcd client IP.

Multi-node etcd cluster

For durability and high availability, run etcd as a multi-node cluster in production and back it up periodically. A five-member cluster is recommended in production. For more information, see FAQ documentation.

As you're using Kubernetes, you have the option to run etcd as a container inside one or more Pods. The kubeadm tool sets up etcd static pods by default, or you can deploy a separate cluster and instruct kubeadm to use that etcd cluster as the control plane's backing store.

You configure an etcd cluster either by static member information or by dynamic discovery. For more information on clustering, see etcd clustering documentation.

For an example, consider a five-member etcd cluster running with the following client URLs: http://$IP1:2379, http://$IP2:2379, http://$IP3:2379, http://$IP4:2379, and http://$IP5:2379. To start a Kubernetes API server:

Run the following:

etcd --listen-client-urls=http://$IP1:2379,http://$IP2:2379,http://$IP3:2379,http://$IP4:2379,http://$IP5:2379 --advertise-client-urls=http://$IP1:2379,http://$IP2:2379,http://$IP3:2379,http://$IP4:2379,http://$IP5:2379

Start the Kubernetes API servers with the flag --etcd-servers=$IP1:2379,$IP2:2379,$IP3:2379,$IP4:2379,$IP5:2379.

Make sure the IP<n> variables are set to your client IP addresses.

Multi-node etcd cluster with load balancer

To run a load balancing etcd cluster:

Set up an etcd cluster.
Configure a load balancer in front of the etcd cluster. For example, let the address of the load balancer be $LB.
Start Kubernetes API Servers with the flag --etcd-servers=$LB:2379.

Securing etcd clusters

Access to etcd is equivalent to root permission in the cluster so ideally only the API server should have access to it. Considering the sensitivity of the data, it is recommended to grant permission to only those nodes that require access to etcd clusters.

To secure etcd, either set up firewall rules or use the security features provided by etcd. etcd security features depend on x509 Public Key Infrastructure (PKI). To begin, establish secure communication channels by generating a key and certificate pair. For example, use key pairs peer.key and peer.cert for securing communication between etcd members, and client.key and client.cert for securing communication between etcd and its clients. See the example scripts provided by the etcd project to generate key pairs and CA files for client authentication.

Securing communication

To configure etcd with secure peer communication, specify flags --peer-key-file=peer.key and --peer-cert-file=peer.cert, and use HTTPS as the URL schema.

Similarly, to configure etcd with secure client communication, specify flags --key-file=k8sclient.key and --cert-file=k8sclient.cert, and use HTTPS as the URL schema. Here is an example on a client command that uses secure communication:

ETCDCTL_API=3 etcdctl --endpoints 10.2.0.9:2379 \
  --cert=/etc/kubernetes/pki/etcd/server.crt \
  --key=/etc/kubernetes/pki/etcd/server.key \
  --cacert=/etc/kubernetes/pki/etcd/ca.crt \
  member list

Limiting access of etcd clusters

After configuring secure communication, restrict the access of the etcd cluster to only the Kubernetes API servers using TLS authentication.

For example, consider key pairs k8sclient.key and k8sclient.cert that are trusted by the CA etcd.ca. When etcd is configured with --client-cert-auth along with TLS, it verifies the certificates from clients by using system CAs or the CA passed in by --trusted-ca-file flag. Specifying flags --client-cert-auth=true and --trusted-ca-file=etcd.ca will restrict the access to clients with the certificate k8sclient.cert.

Once etcd is configured correctly, only clients with valid certificates can access it. To give Kubernetes API servers the access, configure them with the flags --etcd-certfile=k8sclient.cert, --etcd-keyfile=k8sclient.key and --etcd-cafile=ca.cert.

Note:

etcd authentication is not planned for Kubernetes.

Replacing a failed etcd member

etcd cluster achieves high availability by tolerating minor member failures. However, to improve the overall health of the cluster, replace failed members immediately. When multiple members fail, replace them one by one. Replacing a failed member involves two steps: removing the failed member and adding a new member.

Though etcd keeps unique member IDs internally, it is recommended to use a unique name for each member to avoid human errors. For example, consider a three-member etcd cluster. Let the URLs be, member1=http://10.0.0.1, member2=http://10.0.0.2, and member3=http://10.0.0.3. When member1 fails, replace it with member4=http://10.0.0.4.

Get the member ID of the failed member1:

etcdctl --endpoints=http://10.0.0.2,http://10.0.0.3 member list

The following message is displayed:

8211f1d0f64f3269, started, member1, http://10.0.0.1:2380, http://10.0.0.1:2379
91bc3c398fb3c146, started, member2, http://10.0.0.2:2380, http://10.0.0.2:2379
fd422379fda50e48, started, member3, http://10.0.0.3:2380, http://10.0.0.3:2379

Do either of the following:
1. If each Kubernetes API server is configured to communicate with all etcd members, remove the failed member from the --etcd-servers flag, then restart each Kubernetes API server.
2. If each Kubernetes API server communicates with a single etcd member, then stop the Kubernetes API server that communicates with the failed etcd.
Stop the etcd server on the broken node. It is possible that other clients besides the Kubernetes API server are causing traffic to etcd and it is desirable to stop all traffic to prevent writes to the data directory.

Remove the failed member:

etcdctl member remove 8211f1d0f64f3269

The following message is displayed:

Removed member 8211f1d0f64f3269 from cluster

Add the new member:

etcdctl member add member4 --peer-urls=http://10.0.0.4:2380

The following message is displayed:

Member 2be1eb8f84b7f63e added to cluster ef37ad9dc622a7c4

Start the newly added member on a machine with the IP 10.0.0.4:

export ETCD_NAME="member4"
export ETCD_INITIAL_CLUSTER="member2=http://10.0.0.2:2380,member3=http://10.0.0.3:2380,member4=http://10.0.0.4:2380"
export ETCD_INITIAL_CLUSTER_STATE=existing
etcd [flags]

Do either of the following:
1. If each Kubernetes API server is configured to communicate with all etcd members, add the newly added member to the --etcd-servers flag, then restart each Kubernetes API server.
2. If each Kubernetes API server communicates with a single etcd member, start the Kubernetes API server that was stopped in step 2. Then configure Kubernetes API server clients to again route requests to the Kubernetes API server that was stopped. This can often be done by configuring a load balancer.

For more information on cluster reconfiguration, see etcd reconfiguration documentation.

Backing up an etcd cluster

All Kubernetes objects are stored in etcd. Periodically backing up the etcd cluster data is important to recover Kubernetes clusters under disaster scenarios, such as losing all control plane nodes. The snapshot file contains all the Kubernetes state and critical information. In order to keep the sensitive Kubernetes data safe, encrypt the snapshot files.

Backing up an etcd cluster can be accomplished in two ways: etcd built-in snapshot and volume snapshot.

Built-in snapshot

etcd supports built-in snapshot. A snapshot may either be created from a live member with the etcdctl snapshot save command or by copying the member/snap/db file from an etcd data directory that is not currently used by an etcd process. Creating the snapshot will not affect the performance of the member.

Below is an example for creating a snapshot of the keyspace served by $ENDPOINT to the file snapshot.db:

ETCDCTL_API=3 etcdctl --endpoints $ENDPOINT snapshot save snapshot.db

Verify the snapshot:

Use etcdutl
Use etcdctl (Deprecated)

The below example depicts the usage of the etcdutl tool for verifying a snapshot:

etcdutl --write-out=table snapshot status snapshot.db

This should generate an output resembling the example provided below:

+----------+----------+------------+------------+
|   HASH   | REVISION | TOTAL KEYS | TOTAL SIZE |
+----------+----------+------------+------------+
| fe01cf57 |       10 |          7 | 2.1 MB     |
+----------+----------+------------+------------+

Note:

The usage of etcdctl snapshot status has been deprecated since etcd v3.5.x and is slated for removal from etcd v3.6. It is recommended to utilize etcdutl instead.

The below example depicts the usage of the etcdctl tool for verifying a snapshot:

export ETCDCTL_API=3
etcdctl --write-out=table snapshot status snapshot.db

This should generate an output resembling the example provided below:

Deprecated: Use `etcdutl snapshot status` instead.

+----------+----------+------------+------------+
|   HASH   | REVISION | TOTAL KEYS | TOTAL SIZE |
+----------+----------+------------+------------+
| fe01cf57 |       10 |          7 | 2.1 MB     |
+----------+----------+------------+------------+

Volume snapshot

If etcd is running on a storage volume that supports backup, such as Amazon Elastic Block Store, back up etcd data by creating a snapshot of the storage volume.

Snapshot using etcdctl options

We can also create the snapshot using various options given by etcdctl. For example:

ETCDCTL_API=3 etcdctl -h

will list various options available from etcdctl. For example, you can create a snapshot by specifying the endpoint, certificates and key as shown below:

ETCDCTL_API=3 etcdctl --endpoints=https://127.0.0.1:2379 \
  --cacert=<trusted-ca-file> --cert=<cert-file> --key=<key-file> \
  snapshot save <backup-file-location>

where trusted-ca-file, cert-file and key-file can be obtained from the description of the etcd Pod.

Scaling out etcd clusters

Scaling out etcd clusters increases availability by trading off performance. Scaling does not increase cluster performance nor capability. A general rule is not to scale out or in etcd clusters. Do not configure any auto scaling groups for etcd clusters. It is strongly recommended to always run a static five-member etcd cluster for production Kubernetes clusters at any officially supported scale.

A reasonable scaling is to upgrade a three-member cluster to a five-member one, when more reliability is desired. See etcd reconfiguration documentation for information on how to add members into an existing cluster.

Restoring an etcd cluster

Caution:

If any API servers are running in your cluster, you should not attempt to restore instances of etcd. Instead, follow these steps to restore etcd:

stop all API server instances
restore state in all etcd instances
restart all API server instances

The Kubernetes project also recommends restarting Kubernetes components (kube-scheduler, kube-controller-manager, kubelet) to ensure that they don't rely on some stale data. In practice the restore takes a bit of time. During the restoration, critical components will lose leader lock and restart themselves.

etcd supports restoring from snapshots that are taken from an etcd process of the major.minor version. Restoring a version from a different patch version of etcd is also supported. A restore operation is employed to recover the data of a failed cluster.

Before starting the restore operation, a snapshot file must be present. It can either be a snapshot file from a previous backup operation, or from a remaining data directory.

Use etcdutl
Use etcdctl (Deprecated)

When restoring the cluster using etcdutl, use the --data-dir option to specify to which folder the cluster should be restored:

etcdutl --data-dir <data-dir-location> snapshot restore snapshot.db

where <data-dir-location> is a directory that will be created during the restore process.

Note:

The usage of etcdctl for restoring has been deprecated since etcd v3.5.x and is slated for removal from etcd v3.6. It is recommended to utilize etcdutl instead.

The below example depicts the usage of the etcdctl tool for the restore operation:

export ETCDCTL_API=3
etcdctl --data-dir <data-dir-location> snapshot restore snapshot.db

If <data-dir-location> is the same folder as before, delete it and stop the etcd process before restoring the cluster. Otherwise, change etcd configuration and restart the etcd process after restoration to have it use the new data directory: first change /etc/kubernetes/manifests/etcd.yaml's volumes.hostPath.path for name: etcd-data to <data-dir-location>, then execute kubectl -n kube-system delete pod <name-of-etcd-pod> or systemctl restart kubelet.service (or both).

For more information and examples on restoring a cluster from a snapshot file, see etcd disaster recovery documentation.

If the access URLs of the restored cluster are changed from the previous cluster, the Kubernetes API server must be reconfigured accordingly. In this case, restart Kubernetes API servers with the flag --etcd-servers=$NEW_ETCD_CLUSTER instead of the flag --etcd-servers=$OLD_ETCD_CLUSTER. Replace $NEW_ETCD_CLUSTER and $OLD_ETCD_CLUSTER with the respective IP addresses. If a load balancer is used in front of an etcd cluster, you might need to update the load balancer instead.

If the majority of etcd members have permanently failed, the etcd cluster is considered failed. In this scenario, Kubernetes cannot make any changes to its current state. Although the scheduled pods might continue to run, no new pods can be scheduled. In such cases, recover the etcd cluster and potentially reconfigure Kubernetes API servers to fix the issue.

Upgrading etcd clusters

Caution:

Before you start an upgrade, back up your etcd cluster first.

For details on etcd upgrade, refer to the etcd upgrades documentation.

Maintaining etcd clusters

For more details on etcd maintenance, please refer to the etcd maintenance documentation.

Cluster defragmentation

🛇 This item links to a third party project or product that is not part of Kubernetes itself. More information

Defragmentation is an expensive operation, so it should be executed as infrequently as possible. On the other hand, it's also necessary to make sure any etcd member will not exceed the storage quota. The Kubernetes project recommends that when you perform defragmentation, you use a tool such as etcd-defrag.

You can also run the defragmentation tool as a Kubernetes CronJob, to make sure that defragmentation happens regularly. See etcd-defrag-cronjob.yaml for details.

2.31 - Reserve Compute Resources for System Daemons

Kubernetes nodes can be scheduled to Capacity. Pods can consume all the available capacity on a node by default. This is an issue because nodes typically run quite a few system daemons that power the OS and Kubernetes itself. Unless resources are set aside for these system daemons, pods and system daemons compete for resources and lead to resource starvation issues on the node.

The kubelet exposes a feature named 'Node Allocatable' that helps to reserve compute resources for system daemons. Kubernetes recommends cluster administrators to configure 'Node Allocatable' based on their workload density on each node.

Before you begin

You can configure below kubelet configuration settings using the kubelet configuration file.

Node Allocatable

node capacity

'Allocatable' on a Kubernetes node is defined as the amount of compute resources that are available for pods. The scheduler does not over-subscribe 'Allocatable'. 'CPU', 'memory' and 'ephemeral-storage' are supported as of now.

Node Allocatable is exposed as part of v1.Node object in the API and as part of kubectl describe node in the CLI.

Resources can be reserved for two categories of system daemons in the kubelet.

Enabling QoS and Pod level cgroups

To properly enforce node allocatable constraints on the node, you must enable the new cgroup hierarchy via the cgroupsPerQOS setting. This setting is enabled by default. When enabled, the kubelet will parent all end-user pods under a cgroup hierarchy managed by the kubelet.

Configuring a cgroup driver

The kubelet supports manipulation of the cgroup hierarchy on the host using a cgroup driver. The driver is configured via the cgroupDriver setting.

The supported values are the following:

cgroupfs is the default driver that performs direct manipulation of the cgroup filesystem on the host in order to manage cgroup sandboxes.
systemd is an alternative driver that manages cgroup sandboxes using transient slices for resources that are supported by that init system.

Depending on the configuration of the associated container runtime, operators may have to choose a particular cgroup driver to ensure proper system behavior. For example, if operators use the systemd cgroup driver provided by the containerd runtime, the kubelet must be configured to use the systemd cgroup driver.

Kube Reserved

KubeletConfiguration Setting: kubeReserved: {}. Example value {cpu: 100m, memory: 100Mi, ephemeral-storage: 1Gi, pid=1000}
KubeletConfiguration Setting: kubeReservedCgroup: ""

kubeReserved is meant to capture resource reservation for kubernetes system daemons like the kubelet, container runtime, etc. It is not meant to reserve resources for system daemons that are run as pods. kubeReserved is typically a function of pod density on the nodes.

In addition to cpu, memory, and ephemeral-storage, pid may be specified to reserve the specified number of process IDs for kubernetes system daemons.

To optionally enforce kubeReserved on kubernetes system daemons, specify the parent control group for kube daemons as the value for kubeReservedCgroup setting, and add kube-reserved to enforceNodeAllocatable.

It is recommended that the kubernetes system daemons are placed under a top level control group (runtime.slice on systemd machines for example). Each system daemon should ideally run within its own child control group. Refer to the design proposal for more details on recommended control group hierarchy.

Note that Kubelet does not create kubeReservedCgroup if it doesn't exist. The kubelet will fail to start if an invalid cgroup is specified. With systemd cgroup driver, you should follow a specific pattern for the name of the cgroup you define: the name should be the value you set for kubeReservedCgroup, with .slice appended.

System Reserved

KubeletConfiguration Setting: systemReserved: {}. Example value {cpu: 100m, memory: 100Mi, ephemeral-storage: 1Gi, pid=1000}
KubeletConfiguration Setting: systemReservedCgroup: ""

systemReserved is meant to capture resource reservation for OS system daemons like sshd, udev, etc. systemReserved should reserve memory for the kernel too since kernel memory is not accounted to pods in Kubernetes at this time. Reserving resources for user login sessions is also recommended (user.slice in systemd world).

In addition to cpu, memory, and ephemeral-storage, pid may be specified to reserve the specified number of process IDs for OS system daemons.

To optionally enforce systemReserved on system daemons, specify the parent control group for OS system daemons as the value for systemReservedCgroup setting, and add system-reserved to enforceNodeAllocatable.

It is recommended that the OS system daemons are placed under a top level control group (system.slice on systemd machines for example).

Note that kubelet does not create systemReservedCgroup if it doesn't exist. kubelet will fail if an invalid cgroup is specified. With systemd cgroup driver, you should follow a specific pattern for the name of the cgroup you define: the name should be the value you set for systemReservedCgroup, with .slice appended.

Explicitly Reserved CPU List

FEATURE STATE: Kubernetes v1.17 [stable]

KubeletConfiguration Setting: reservedSystemCPUs:. Example value 0-3

reservedSystemCPUs is meant to define an explicit CPU set for OS system daemons and kubernetes system daemons. reservedSystemCPUs is for systems that do not intend to define separate top level cgroups for OS system daemons and kubernetes system daemons with regard to cpuset resource. If the Kubelet does not have kubeReservedCgroup and systemReservedCgroup, the explicit cpuset provided by reservedSystemCPUs will take precedence over the CPUs defined by kubeReservedCgroup and systemReservedCgroup options.

This option is specifically designed for Telco/NFV use cases where uncontrolled interrupts/timers may impact the workload performance. you can use this option to define the explicit cpuset for the system/kubernetes daemons as well as the interrupts/timers, so the rest CPUs on the system can be used exclusively for workloads, with less impact from uncontrolled interrupts/timers. To move the system daemon, kubernetes daemons and interrupts/timers to the explicit cpuset defined by this option, other mechanism outside Kubernetes should be used. For example: in Centos, you can do this using the tuned toolset.

Eviction Thresholds

KubeletConfiguration Setting: evictionHard: {memory.available: "100Mi", nodefs.available: "10%", nodefs.inodesFree: "5%", imagefs.available: "15%"}. Example value: {memory.available: "<500Mi"}

Memory pressure at the node level leads to System OOMs which affects the entire node and all pods running on it. Nodes can go offline temporarily until memory has been reclaimed. To avoid (or reduce the probability of) system OOMs kubelet provides out of resource management. Evictions are supported for memory and ephemeral-storage only. By reserving some memory via evictionHard setting, the kubelet attempts to evict pods whenever memory availability on the node drops below the reserved value. Hypothetically, if system daemons did not exist on a node, pods cannot use more than capacity - eviction-hard. For this reason, resources reserved for evictions are not available for pods.

Enforcing Node Allocatable

KubeletConfiguration setting: enforceNodeAllocatable: [pods]. Example value: [pods,system-reserved,kube-reserved]

The scheduler treats 'Allocatable' as the available capacity for pods.

kubelet enforce 'Allocatable' across pods by default. Enforcement is performed by evicting pods whenever the overall usage across all pods exceeds 'Allocatable'. More details on eviction policy can be found on the node pressure eviction page. This enforcement is controlled by specifying pods value to the KubeletConfiguration setting enforceNodeAllocatable.

Optionally, kubelet can be made to enforce kubeReserved and systemReserved by specifying kube-reserved & system-reserved values in the same setting. Note that to enforce kubeReserved or systemReserved, kubeReservedCgroup or systemReservedCgroup needs to be specified respectively.

General Guidelines

System daemons are expected to be treated similar to Guaranteed pods. System daemons can burst within their bounding control groups and this behavior needs to be managed as part of kubernetes deployments. For example, kubelet should have its own control group and share kubeReserved resources with the container runtime. However, Kubelet cannot burst and use up all available Node resources if kubeReserved is enforced.

Be extra careful while enforcing systemReserved reservation since it can lead to critical system services being CPU starved, OOM killed, or unable to fork on the node. The recommendation is to enforce systemReserved only if a user has profiled their nodes exhaustively to come up with precise estimates and is confident in their ability to recover if any process in that group is oom-killed.

To begin with enforce 'Allocatable' on pods.
Once adequate monitoring and alerting is in place to track kube system daemons, attempt to enforce kubeReserved based on usage heuristics.
If absolutely necessary, enforce systemReserved over time.

The resource requirements of kube system daemons may grow over time as more and more features are added. Over time, kubernetes project will attempt to bring down utilization of node system daemons, but that is not a priority as of now. So expect a drop in Allocatable capacity in future releases.

Example Scenario

Here is an example to illustrate Node Allocatable computation:

Node has 32Gi of memory, 16 CPUs and 100Gi of Storage
kubeReserved is set to {cpu: 1000m, memory: 2Gi, ephemeral-storage: 1Gi}
systemReserved is set to {cpu: 500m, memory: 1Gi, ephemeral-storage: 1Gi}
evictionHard is set to {memory.available: "<500Mi", nodefs.available: "<10%"}

Under this scenario, 'Allocatable' will be 14.5 CPUs, 28.5Gi of memory and 88Gi of local storage. Scheduler ensures that the total memory requests across all pods on this node does not exceed 28.5Gi and storage doesn't exceed 88Gi. Kubelet evicts pods whenever the overall memory usage across pods exceeds 28.5Gi, or if overall disk usage exceeds 88Gi. If all processes on the node consume as much CPU as they can, pods together cannot consume more than 14.5 CPUs.

If kubeReserved and/or systemReserved is not enforced and system daemons exceed their reservation, kubelet evicts pods whenever the overall node memory usage is higher than 31.5Gi or storage is greater than 90Gi.

2.32 - Running Kubernetes Node Components as a Non-root User

FEATURE STATE: Kubernetes v1.22 [alpha]

This document describes how to run Kubernetes Node components such as kubelet, CRI, OCI, and CNI without root privileges, by using a user namespace.

This technique is also known as rootless mode.

Note:

This document describes how to run Kubernetes Node components (and hence pods) as a non-root user.

If you are just looking for how to run a pod as a non-root user, see SecurityContext.

Before you begin

Your Kubernetes server must be at or later than version 1.22. To check the version, enter kubectl version.

Enable Cgroup v2
Enable systemd with user session
Configure several sysctl values, depending on host Linux distribution
Ensure that your unprivileged user is listed in /etc/subuid and /etc/subgid
Enable the KubeletInUserNamespace feature gate

Running Kubernetes inside Rootless Docker/Podman

kind

kind supports running Kubernetes inside Rootless Docker or Rootless Podman.

See Running kind with Rootless Docker.

minikube

minikube also supports running Kubernetes inside Rootless Docker or Rootless Podman.

See the Minikube documentation:

Running Kubernetes inside Unprivileged Containers

sysbox

Sysbox is an open-source container runtime (similar to "runc") that supports running system-level workloads such as Docker and Kubernetes inside unprivileged containers isolated with the Linux user namespace.

See Sysbox Quick Start Guide: Kubernetes-in-Docker for more info.

Sysbox supports running Kubernetes inside unprivileged containers without requiring Cgroup v2 and without the KubeletInUserNamespace feature gate. It does this by exposing specially crafted /proc and /sys filesystems inside the container plus several other advanced OS virtualization techniques.

Running Rootless Kubernetes directly on a host

K3s

K3s experimentally supports rootless mode.

See Running K3s with Rootless mode for the usage.

Usernetes

Usernetes is a reference distribution of Kubernetes that can be installed under $HOME directory without the root privilege.

Usernetes supports both containerd and CRI-O as CRI runtimes. Usernetes supports multi-node clusters using Flannel (VXLAN).

See the Usernetes repo for the usage.

Manually deploy a node that runs the kubelet in a user namespace

This section provides hints for running Kubernetes in a user namespace manually.

Note:

This section is intended to be read by developers of Kubernetes distributions, not by end users.

Creating a user namespace

The first step is to create a user namespace.

If you are trying to run Kubernetes in a user-namespaced container such as Rootless Docker/Podman or LXC/LXD, you are all set, and you can go to the next subsection.

Otherwise you have to create a user namespace by yourself, by calling unshare(2) with CLONE_NEWUSER.

A user namespace can be also unshared by using command line tools such as:

After unsharing the user namespace, you will also have to unshare other namespaces such as mount namespace.

You do not need to call chroot() nor pivot_root() after unsharing the mount namespace, however, you have to mount writable filesystems on several directories in the namespace.

At least, the following directories need to be writable in the namespace (not outside the namespace):

/etc
/run
/var/logs
/var/lib/kubelet
/var/lib/cni
/var/lib/containerd (for containerd)
/var/lib/containers (for CRI-O)

Creating a delegated cgroup tree

In addition to the user namespace, you also need to have a writable cgroup tree with cgroup v2.

Note:

Kubernetes support for running Node components in user namespaces requires cgroup v2. Cgroup v1 is not supported.

If you are trying to run Kubernetes in Rootless Docker/Podman or LXC/LXD on a systemd-based host, you are all set.

Otherwise you have to create a systemd unit with Delegate=yes property to delegate a cgroup tree with writable permission.

On your node, systemd must already be configured to allow delegation; for more details, see cgroup v2 in the Rootless Containers documentation.

Configuring network

The network namespace of the Node components has to have a non-loopback interface, which can be for example configured with slirp4netns, VPNKit, or lxc-user-nic(1).

The network namespaces of the Pods can be configured with regular CNI plugins. For multi-node networking, Flannel (VXLAN, 8472/UDP) is known to work.

Ports such as the kubelet port (10250/TCP) and NodePort service ports have to be exposed from the Node network namespace to the host with an external port forwarder, such as RootlessKit, slirp4netns, or socat(1).

You can use the port forwarder from K3s. See Running K3s in Rootless Mode for more details. The implementation can be found in the pkg/rootlessports package of k3s.

Configuring CRI

The kubelet relies on a container runtime. You should deploy a container runtime such as containerd or CRI-O and ensure that it is running within the user namespace before the kubelet starts.

containerd
CRI-O

Running CRI plugin of containerd in a user namespace is supported since containerd 1.4.

Running containerd within a user namespace requires the following configurations.

version = 2

[plugins."io.containerd.grpc.v1.cri"]
# Disable AppArmor
  disable_apparmor = true
# Ignore an error during setting oom_score_adj
  restrict_oom_score_adj = true
# Disable hugetlb cgroup v2 controller (because systemd does not support delegating hugetlb controller)
  disable_hugetlb_controller = true

[plugins."io.containerd.grpc.v1.cri".containerd]
# Using non-fuse overlayfs is also possible for kernel >= 5.11, but requires SELinux to be disabled
  snapshotter = "fuse-overlayfs"

[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc.options]
# We use cgroupfs that is delegated by systemd, so we do not use SystemdCgroup driver
# (unless you run another systemd in the namespace)
  SystemdCgroup = false

The default path of the configuration file is /etc/containerd/config.toml. The path can be specified with containerd -c /path/to/containerd/config.toml.

Running CRI-O in a user namespace is supported since CRI-O 1.22.

CRI-O requires an environment variable _CRIO_ROOTLESS=1 to be set.

The following configurations are also recommended:

[crio]
  storage_driver = "overlay"
# Using non-fuse overlayfs is also possible for kernel >= 5.11, but requires SELinux to be disabled
  storage_option = ["overlay.mount_program=/usr/local/bin/fuse-overlayfs"]

[crio.runtime]
# We use cgroupfs that is delegated by systemd, so we do not use "systemd" driver
# (unless you run another systemd in the namespace)
  cgroup_manager = "cgroupfs"

The default path of the configuration file is /etc/crio/crio.conf. The path can be specified with crio --config /path/to/crio/crio.conf.

Configuring kubelet

Running kubelet in a user namespace requires the following configuration:

apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
featureGates:
  KubeletInUserNamespace: true
# We use cgroupfs that is delegated by systemd, so we do not use "systemd" driver
# (unless you run another systemd in the namespace)
cgroupDriver: "cgroupfs"

When the KubeletInUserNamespace feature gate is enabled, the kubelet ignores errors that may happen during setting the following sysctl values on the node.

vm.overcommit_memory
vm.panic_on_oom
kernel.panic
kernel.panic_on_oops
kernel.keys.root_maxkeys
kernel.keys.root_maxbytes.

Within a user namespace, the kubelet also ignores any error raised from trying to open /dev/kmsg. This feature gate also allows kube-proxy to ignore an error during setting RLIMIT_NOFILE.

The KubeletInUserNamespace feature gate was introduced in Kubernetes v1.22 with "alpha" status.

Running kubelet in a user namespace without using this feature gate is also possible by mounting a specially crafted proc filesystem (as done by Sysbox), but not officially supported.

Configuring kube-proxy

Running kube-proxy in a user namespace requires the following configuration:

apiVersion: kubeproxy.config.k8s.io/v1alpha1
kind: KubeProxyConfiguration
mode: "iptables" # or "userspace"
conntrack:
# Skip setting sysctl value "net.netfilter.nf_conntrack_max"
  maxPerCore: 0
# Skip setting "net.netfilter.nf_conntrack_tcp_timeout_established"
  tcpEstablishedTimeout: 0s
# Skip setting "net.netfilter.nf_conntrack_tcp_timeout_close"
  tcpCloseWaitTimeout: 0s

Caveats

Most of "non-local" volume drivers such as nfs and iscsi do not work. Local volumes like local, hostPath, emptyDir, configMap, secret, and downwardAPI are known to work.
Some CNI plugins may not work. Flannel (VXLAN) is known to work.

For more on this, see the Caveats and Future work page on the rootlesscontaine.rs website.

2.33 - Safely Drain a Node

This page shows how to safely drain a node, optionally respecting the PodDisruptionBudget you have defined.

Before you begin

This task assumes that you have met the following prerequisites:

You do not require your applications to be highly available during the node drain, or
You have read about the PodDisruptionBudget concept, and have configured PodDisruptionBudgets for applications that need them.

(Optional) Configure a disruption budget

To ensure that your workloads remain available during maintenance, you can configure a PodDisruptionBudget.

If availability is important for any applications that run or could run on the node(s) that you are draining, configure a PodDisruptionBudgets first and then continue following this guide.

It is recommended to set AlwaysAllow Unhealthy Pod Eviction Policy to your PodDisruptionBudgets to support eviction of misbehaving applications during a node drain. The default behavior is to wait for the application pods to become healthy before the drain can proceed.

Use `kubectl drain` to remove a node from service

You can use kubectl drain to safely evict all of your pods from a node before you perform maintenance on the node (e.g. kernel upgrade, hardware maintenance, etc.). Safe evictions allow the pod's containers to gracefully terminate and will respect the PodDisruptionBudgets you have specified.

Note:

By default kubectl drain ignores certain system pods on the node that cannot be killed; see the kubectl drain documentation for more details.

When kubectl drain returns successfully, that indicates that all of the pods (except the ones excluded as described in the previous paragraph) have been safely evicted (respecting the desired graceful termination period, and respecting the PodDisruptionBudget you have defined). It is then safe to bring down the node by powering down its physical machine or, if running on a cloud platform, deleting its virtual machine.

Note:

If any new Pods tolerate the node.kubernetes.io/unschedulable taint, then those Pods might be scheduled to the node you have drained. Avoid tolerating that taint other than for DaemonSets.

If you or another API user directly set the nodeName field for a Pod (bypassing the scheduler), then the Pod is bound to the specified node and will run there, even though you have drained that node and marked it unschedulable.

First, identify the name of the node you wish to drain. You can list all of the nodes in your cluster with

kubectl get nodes

Next, tell Kubernetes to drain the node:

kubectl drain --ignore-daemonsets <node name>

If there are pods managed by a DaemonSet, you will need to specify --ignore-daemonsets with kubectl to successfully drain the node. The kubectl drain subcommand on its own does not actually drain a node of its DaemonSet pods: the DaemonSet controller (part of the control plane) immediately replaces missing Pods with new equivalent Pods. The DaemonSet controller also creates Pods that ignore unschedulable taints, which allows the new Pods to launch onto a node that you are draining.

Once it returns (without giving an error), you can power down the node (or equivalently, if on a cloud platform, delete the virtual machine backing the node). If you leave the node in the cluster during the maintenance operation, you need to run

kubectl uncordon <node name>

afterwards to tell Kubernetes that it can resume scheduling new pods onto the node.

Draining multiple nodes in parallel

The kubectl drain command should only be issued to a single node at a time. However, you can run multiple kubectl drain commands for different nodes in parallel, in different terminals or in the background. Multiple drain commands running concurrently will still respect the PodDisruptionBudget you specify.

For example, if you have a StatefulSet with three replicas and have set a PodDisruptionBudget for that set specifying minAvailable: 2, kubectl drain only evicts a pod from the StatefulSet if all three replicas pods are healthy; if then you issue multiple drain commands in parallel, Kubernetes respects the PodDisruptionBudget and ensures that only 1 (calculated as replicas - minAvailable) Pod is unavailable at any given time. Any drains that would cause the number of healthy replicas to fall below the specified budget are blocked.

The Eviction API

If you prefer not to use kubectl drain (such as to avoid calling to an external command, or to get finer control over the pod eviction process), you can also programmatically cause evictions using the eviction API.

For more information, see API-initiated eviction.

What's next

Follow steps to protect your application by configuring a Pod Disruption Budget.

2.34 - Securing a Cluster

This document covers topics related to protecting a cluster from accidental or malicious access and provides recommendations on overall security.

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes
To check the version, enter kubectl version.

Controlling access to the Kubernetes API

As Kubernetes is entirely API-driven, controlling and limiting who can access the cluster and what actions they are allowed to perform is the first line of defense.

Use Transport Layer Security (TLS) for all API traffic

Kubernetes expects that all API communication in the cluster is encrypted by default with TLS, and the majority of installation methods will allow the necessary certificates to be created and distributed to the cluster components. Note that some components and installation methods may enable local ports over HTTP and administrators should familiarize themselves with the settings of each component to identify potentially unsecured traffic.

API Authentication

Choose an authentication mechanism for the API servers to use that matches the common access patterns when you install a cluster. For instance, small, single-user clusters may wish to use a simple certificate or static Bearer token approach. Larger clusters may wish to integrate an existing OIDC or LDAP server that allow users to be subdivided into groups.

All API clients must be authenticated, even those that are part of the infrastructure like nodes, proxies, the scheduler, and volume plugins. These clients are typically service accounts or use x509 client certificates, and they are created automatically at cluster startup or are setup as part of the cluster installation.

Consult the authentication reference document for more information.

API Authorization

Once authenticated, every API call is also expected to pass an authorization check. Kubernetes ships an integrated Role-Based Access Control (RBAC) component that matches an incoming user or group to a set of permissions bundled into roles. These permissions combine verbs (get, create, delete) with resources (pods, services, nodes) and can be namespace-scoped or cluster-scoped. A set of out-of-the-box roles are provided that offer reasonable default separation of responsibility depending on what actions a client might want to perform. It is recommended that you use the Node and RBAC authorizers together, in combination with the NodeRestriction admission plugin.

As with authentication, simple and broad roles may be appropriate for smaller clusters, but as more users interact with the cluster, it may become necessary to separate teams into separate namespaces with more limited roles.

With authorization, it is important to understand how updates on one object may cause actions in other places. For instance, a user may not be able to create pods directly, but allowing them to create a deployment, which creates pods on their behalf, will let them create those pods indirectly. Likewise, deleting a node from the API will result in the pods scheduled to that node being terminated and recreated on other nodes. The out-of-the box roles represent a balance between flexibility and common use cases, but more limited roles should be carefully reviewed to prevent accidental escalation. You can make roles specific to your use case if the out-of-box ones don't meet your needs.

Consult the authorization reference section for more information.

Controlling access to the Kubelet

Kubelets expose HTTPS endpoints which grant powerful control over the node and containers. By default Kubelets allow unauthenticated access to this API.

Production clusters should enable Kubelet authentication and authorization.

Consult the Kubelet authentication/authorization reference for more information.

Controlling the capabilities of a workload or user at runtime

Authorization in Kubernetes is intentionally high level, focused on coarse actions on resources. More powerful controls exist as policies to limit by use case how those objects act on the cluster, themselves, and other resources.

Limiting resource usage on a cluster

Resource quota limits the number or capacity of resources granted to a namespace. This is most often used to limit the amount of CPU, memory, or persistent disk a namespace can allocate, but can also control how many pods, services, or volumes exist in each namespace.

Limit ranges restrict the maximum or minimum size of some of the resources above, to prevent users from requesting unreasonably high or low values for commonly reserved resources like memory, or to provide default limits when none are specified.

Controlling what privileges containers run with

A pod definition contains a security context that allows it to request access to run as a specific Linux user on a node (like root), access to run privileged or access the host network, and other controls that would otherwise allow it to run unfettered on a hosting node.

You can configure Pod security admission to enforce use of a particular Pod Security Standard in a namespace, or to detect breaches.

Generally, most application workloads need limited access to host resources so they can successfully run as a root process (uid 0) without access to host information. However, considering the privileges associated with the root user, you should write application containers to run as a non-root user. Similarly, administrators who wish to prevent client applications from escaping their containers should apply the Baseline or Restricted Pod Security Standard.

Preventing containers from loading unwanted kernel modules

The Linux kernel automatically loads kernel modules from disk if needed in certain circumstances, such as when a piece of hardware is attached or a filesystem is mounted. Of particular relevance to Kubernetes, even unprivileged processes can cause certain network-protocol-related kernel modules to be loaded, just by creating a socket of the appropriate type. This may allow an attacker to exploit a security hole in a kernel module that the administrator assumed was not in use.

To prevent specific modules from being automatically loaded, you can uninstall them from the node, or add rules to block them. On most Linux distributions, you can do that by creating a file such as /etc/modprobe.d/kubernetes-blacklist.conf with contents like:

# DCCP is unlikely to be needed, has had multiple serious
# vulnerabilities, and is not well-maintained.
blacklist dccp

# SCTP is not used in most Kubernetes clusters, and has also had
# vulnerabilities in the past.
blacklist sctp

To block module loading more generically, you can use a Linux Security Module (such as SELinux) to completely deny the module_request permission to containers, preventing the kernel from loading modules for containers under any circumstances. (Pods would still be able to use modules that had been loaded manually, or modules that were loaded by the kernel on behalf of some more-privileged process.)

Restricting network access

The network policies for a namespace allows application authors to restrict which pods in other namespaces may access pods and ports within their namespaces. Many of the supported Kubernetes networking providers now respect network policy.

Quota and limit ranges can also be used to control whether users may request node ports or load-balanced services, which on many clusters can control whether those users applications are visible outside of the cluster.

Additional protections may be available that control network rules on a per-plugin or per- environment basis, such as per-node firewalls, physically separating cluster nodes to prevent cross talk, or advanced networking policy.

Restricting cloud metadata API access

Cloud platforms (AWS, Azure, GCE, etc.) often expose metadata services locally to instances. By default these APIs are accessible by pods running on an instance and can contain cloud credentials for that node, or provisioning data such as kubelet credentials. These credentials can be used to escalate within the cluster or to other cloud services under the same account.

When running Kubernetes on a cloud platform, limit permissions given to instance credentials, use network policies to restrict pod access to the metadata API, and avoid using provisioning data to deliver secrets.

Controlling which nodes pods may access

By default, there are no restrictions on which nodes may run a pod. Kubernetes offers a rich set of policies for controlling placement of pods onto nodes and the taint-based pod placement and eviction that are available to end users. For many clusters use of these policies to separate workloads can be a convention that authors adopt or enforce via tooling.

As an administrator, a beta admission plugin PodNodeSelector can be used to force pods within a namespace to default or require a specific node selector, and if end users cannot alter namespaces, this can strongly limit the placement of all of the pods in a specific workload.

Protecting cluster components from compromise

This section describes some common patterns for protecting clusters from compromise.

Restrict access to etcd

Write access to the etcd backend for the API is equivalent to gaining root on the entire cluster, and read access can be used to escalate fairly quickly. Administrators should always use strong credentials from the API servers to their etcd server, such as mutual auth via TLS client certificates, and it is often recommended to isolate the etcd servers behind a firewall that only the API servers may access.

Caution:

Allowing other components within the cluster to access the master etcd instance with read or write access to the full keyspace is equivalent to granting cluster-admin access. Using separate etcd instances for non-master components or using etcd ACLs to restrict read and write access to a subset of the keyspace is strongly recommended.

Enable audit logging

The audit logger is a beta feature that records actions taken by the API for later analysis in the event of a compromise. It is recommended to enable audit logging and archive the audit file on a secure server.

Restrict access to alpha or beta features

Alpha and beta Kubernetes features are in active development and may have limitations or bugs that result in security vulnerabilities. Always assess the value an alpha or beta feature may provide against the possible risk to your security posture. When in doubt, disable features you do not use.

Rotate infrastructure credentials frequently

The shorter the lifetime of a secret or credential the harder it is for an attacker to make use of that credential. Set short lifetimes on certificates and automate their rotation. Use an authentication provider that can control how long issued tokens are available and use short lifetimes where possible. If you use service-account tokens in external integrations, plan to rotate those tokens frequently. For example, once the bootstrap phase is complete, a bootstrap token used for setting up nodes should be revoked or its authorization removed.

Review third party integrations before enabling them

Many third party integrations to Kubernetes may alter the security profile of your cluster. When enabling an integration, always review the permissions that an extension requests before granting it access. For example, many security integrations may request access to view all secrets on your cluster which is effectively making that component a cluster admin. When in doubt, restrict the integration to functioning in a single namespace if possible.

Components that create pods may also be unexpectedly powerful if they can do so inside namespaces like the kube-system namespace, because those pods can gain access to service account secrets or run with elevated permissions if those service accounts are granted access to permissive PodSecurityPolicies.

If you use Pod Security admission and allow any component to create Pods within a namespace that permits privileged Pods, those Pods may be able to escape their containers and use this widened access to elevate their privileges.

You should not allow untrusted components to create Pods in any system namespace (those with names that start with kube-) nor in any namespace where that access grant allows the possibility of privilege escalation.

Encrypt secrets at rest

In general, the etcd database will contain any information accessible via the Kubernetes API and may grant an attacker significant visibility into the state of your cluster. Always encrypt your backups using a well reviewed backup and encryption solution, and consider using full disk encryption where possible.

Kubernetes supports optional encryption at rest for information in the Kubernetes API. This lets you ensure that when Kubernetes stores data for objects (for example, Secret or ConfigMap objects), the API server writes an encrypted representation of the object. That encryption means that even someone who has access to etcd backup data is unable to view the content of those objects. In Kubernetes 1.30 you can also encrypt custom resources; encryption-at-rest for extension APIs defined in CustomResourceDefinitions was added to Kubernetes as part of the v1.26 release.

Receiving alerts for security updates and reporting vulnerabilities

Join the kubernetes-announce group for emails about security announcements. See the security reporting page for more on how to report vulnerabilities.

What's next

Security Checklist for additional information on Kubernetes security guidance.

2.35 - Set Kubelet Parameters Via A Configuration File

Before you begin

Some steps in this page use the jq tool. If you don't have jq, you can install it via your operating system's software sources, or fetch it from https://jqlang.github.io/jq/.

Some steps also involve installing curl, which can be installed via your operating system's software sources.

A subset of the kubelet's configuration parameters may be set via an on-disk config file, as a substitute for command-line flags.

Providing parameters via a config file is the recommended approach because it simplifies node deployment and configuration management.

Create the config file

The subset of the kubelet's configuration that can be configured via a file is defined by the KubeletConfiguration struct.

The configuration file must be a JSON or YAML representation of the parameters in this struct. Make sure the kubelet has read permissions on the file.

Here is an example of what this file might look like:

apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
address: "192.168.0.8"
port: 20250
serializeImagePulls: false
evictionHard:
    memory.available:  "100Mi"
    nodefs.available:  "10%"
    nodefs.inodesFree: "5%"
    imagefs.available: "15%"

In this example, the kubelet is configured with the following settings:

address: The kubelet will serve on IP address 192.168.0.8.
port: The kubelet will serve on port 20250.
serializeImagePulls: Image pulls will be done in parallel.
evictionHard: The kubelet will evict Pods under one of the following conditions:
- When the node's available memory drops below 100MiB.
- When the node's main filesystem's available space is less than 10%.
- When the image filesystem's available space is less than 15%.
- When more than 95% of the node's main filesystem's inodes are in use.

Note:

In the example, by changing the default value of only one parameter for evictionHard, the default values of other parameters will not be inherited and will be set to zero. In order to provide custom values, you should provide all the threshold values respectively.

The imagefs is an optional filesystem that container runtimes use to store container images and container writable layers.

Start a kubelet process configured via the config file

Note:

If you use kubeadm to initialize your cluster, use the kubelet-config while creating your cluster with kubeadm init. See configuring kubelet using kubeadm for details.

Start the kubelet with the --config flag set to the path of the kubelet's config file. The kubelet will then load its config from this file.

Note that command line flags which target the same value as a config file will override that value. This helps ensure backwards compatibility with the command-line API.

Note that relative file paths in the kubelet config file are resolved relative to the location of the kubelet config file, whereas relative paths in command line flags are resolved relative to the kubelet's current working directory.

Note that some default values differ between command-line flags and the kubelet config file. If --config is provided and the values are not specified via the command line, the defaults for the KubeletConfiguration version apply. In the above example, this version is kubelet.config.k8s.io/v1beta1.

Drop-in directory for kubelet configuration files

FEATURE STATE: Kubernetes v1.30 [beta]

You can specify a drop-in configuration directory for the kubelet. By default, the kubelet does not look for drop-in configuration files anywhere - you must specify a path. For example: --config-dir=/etc/kubernetes/kubelet.conf.d

For Kubernetes v1.28 to v1.29, you can only specify --config-dir if you also set the environment variable KUBELET_CONFIG_DROPIN_DIR_ALPHA for the kubelet process (the value of that variable does not matter).

Note:

The suffix of a valid kubelet drop-in configuration file must be .conf. For instance: 99-kubelet-address.conf

The kubelet processes files in its config drop-in directory by sorting the entire file name alphanumerically. For instance, 00-kubelet.conf is processed first, and then overridden with a file named 01-kubelet.conf.

These files may contain partial configurations but should not be invalid and must include type metadata, specifically apiVersion and kind. Validation is only performed on the final resulting configuration structure stored internally in the kubelet. This offers flexibility in managing and merging kubelet configurations from different sources while preventing undesirable configurations. However, it is important to note that behavior varies based on the data type of the configuration fields.

Different data types in the kubelet configuration structure merge differently. See the reference document for more information.

Kubelet configuration merging order

On startup, the kubelet merges configuration from:

Feature gates specified over the command line (lowest precedence).
The kubelet configuration.
Drop-in configuration files, according to sort order.
Command line arguments excluding feature gates (highest precedence).

Note:

The config drop-in dir mechanism for the kubelet is similar but different from how the kubeadm tool allows you to patch configuration. The kubeadm tool uses a specific patching strategy for its configuration, whereas the only patch strategy for kubelet configuration drop-in files is replace. The kubelet determines the order of merges based on sorting the suffixes alphanumerically, and replaces every field present in a higher priority file.

Viewing the kubelet configuration

Since the configuration could now be spread over multiple files with this feature, if someone wants to inspect the final actuated configuration, they can follow these steps to inspect the kubelet configuration:

Start a proxy server using kubectl proxy in your terminal.
```
kubectl proxy
```
Which gives output like:
```
Starting to serve on 127.0.0.1:8001
```

Open another terminal window and use curl to fetch the kubelet configuration. Replace <node-name> with the actual name of your node:

curl -X GET http://127.0.0.1:8001/api/v1/nodes/<node-name>/proxy/configz | jq .

{
  "kubeletconfig": {
    "enableServer": true,
    "staticPodPath": "/var/run/kubernetes/static-pods",
    "syncFrequency": "1m0s",
    "fileCheckFrequency": "20s",
    "httpCheckFrequency": "20s",
    "address": "192.168.1.16",
    "port": 10250,
    "readOnlyPort": 10255,
    "tlsCertFile": "/var/lib/kubelet/pki/kubelet.crt",
    "tlsPrivateKeyFile": "/var/lib/kubelet/pki/kubelet.key",
    "rotateCertificates": true,
    "authentication": {
      "x509": {
        "clientCAFile": "/var/run/kubernetes/client-ca.crt"
      },
      "webhook": {
        "enabled": true,
        "cacheTTL": "2m0s"
      },
      "anonymous": {
        "enabled": true
      }
    },
    "authorization": {
      "mode": "AlwaysAllow",
      "webhook": {
        "cacheAuthorizedTTL": "5m0s",
        "cacheUnauthorizedTTL": "30s"
      }
    },
    "registryPullQPS": 5,
    "registryBurst": 10,
    "eventRecordQPS": 50,
    "eventBurst": 100,
    "enableDebuggingHandlers": true,
    "healthzPort": 10248,
    "healthzBindAddress": "127.0.0.1",
    "oomScoreAdj": -999,
    "clusterDomain": "cluster.local",
    "clusterDNS": [
      "10.0.0.10"
    ],
    "streamingConnectionIdleTimeout": "4h0m0s",
    "nodeStatusUpdateFrequency": "10s",
    "nodeStatusReportFrequency": "5m0s",
    "nodeLeaseDurationSeconds": 40,
    "imageMinimumGCAge": "2m0s",
    "imageMaximumGCAge": "0s",
    "imageGCHighThresholdPercent": 85,
    "imageGCLowThresholdPercent": 80,
    "volumeStatsAggPeriod": "1m0s",
    "cgroupsPerQOS": true,
    "cgroupDriver": "systemd",
    "cpuManagerPolicy": "none",
    "cpuManagerReconcilePeriod": "10s",
    "memoryManagerPolicy": "None",
    "topologyManagerPolicy": "none",
    "topologyManagerScope": "container",
    "runtimeRequestTimeout": "2m0s",
    "hairpinMode": "promiscuous-bridge",
    "maxPods": 110,
    "podPidsLimit": -1,
    "resolvConf": "/run/systemd/resolve/resolv.conf",
    "cpuCFSQuota": true,
    "cpuCFSQuotaPeriod": "100ms",
    "nodeStatusMaxImages": 50,
    "maxOpenFiles": 1000000,
    "contentType": "application/vnd.kubernetes.protobuf",
    "kubeAPIQPS": 50,
    "kubeAPIBurst": 100,
    "serializeImagePulls": true,
    "evictionHard": {
      "imagefs.available": "15%",
      "memory.available": "100Mi",
      "nodefs.available": "10%",
      "nodefs.inodesFree": "5%"
    },
    "evictionPressureTransitionPeriod": "1m0s",
    "enableControllerAttachDetach": true,
    "makeIPTablesUtilChains": true,
    "iptablesMasqueradeBit": 14,
    "iptablesDropBit": 15,
    "featureGates": {
      "AllAlpha": false
    },
    "failSwapOn": false,
    "memorySwap": {},
    "containerLogMaxSize": "10Mi",
    "containerLogMaxFiles": 5,
    "configMapAndSecretChangeDetectionStrategy": "Watch",
    "enforceNodeAllocatable": [
      "pods"
    ],
    "volumePluginDir": "/usr/libexec/kubernetes/kubelet-plugins/volume/exec/",
    "logging": {
      "format": "text",
      "flushFrequency": "5s",
      "verbosity": 3,
      "options": {
        "json": {
          "infoBufferSize": "0"
        }
      }
    },
    "enableSystemLogHandler": true,
    "enableSystemLogQuery": false,
    "shutdownGracePeriod": "0s",
    "shutdownGracePeriodCriticalPods": "0s",
    "enableProfilingHandler": true,
    "enableDebugFlagsHandler": true,
    "seccompDefault": false,
    "memoryThrottlingFactor": 0.9,
    "registerNode": true,
    "localStorageCapacityIsolation": true,
    "containerRuntimeEndpoint": "unix:///var/run/crio/crio.sock"
  }
}

What's next

Learn more about kubelet configuration by checking the KubeletConfiguration reference.
Learn more about kubelet configuration merging in the reference document.

2.36 - Share a Cluster with Namespaces

This page shows how to view, work in, and delete namespaces. The page also shows how to use Kubernetes namespaces to subdivide your cluster.

Before you begin

Have an existing Kubernetes cluster.
You have a basic understanding of Kubernetes Pods, Services, and Deployments.

Viewing namespaces

List the current namespaces in a cluster using:

kubectl get namespaces

NAME              STATUS   AGE
default           Active   11d
kube-node-lease   Active   11d
kube-public       Active   11d
kube-system       Active   11d

Kubernetes starts with four initial namespaces:

default The default namespace for objects with no other namespace
kube-node-lease This namespace holds Lease objects associated with each node. Node leases allow the kubelet to send heartbeats so that the control plane can detect node failure.
kube-public This namespace is created automatically and is readable by all users (including those not authenticated). This namespace is mostly reserved for cluster usage, in case that some resources should be visible and readable publicly throughout the whole cluster. The public aspect of this namespace is only a convention, not a requirement.
kube-system The namespace for objects created by the Kubernetes system

You can also get the summary of a specific namespace using:

kubectl get namespaces <name>

Or you can get detailed information with:

kubectl describe namespaces <name>

Name:           default
Labels:         <none>
Annotations:    <none>
Status:         Active

No resource quota.

Resource Limits
 Type       Resource    Min Max Default
 ----               --------    --- --- ---
 Container          cpu         -   -   100m

Note that these details show both resource quota (if present) as well as resource limit ranges.

Resource quota tracks aggregate usage of resources in the Namespace and allows cluster operators to define Hard resource usage limits that a Namespace may consume.

A limit range defines min/max constraints on the amount of resources a single entity can consume in a Namespace.

See Admission control: Limit Range

A namespace can be in one of two phases:

Active the namespace is in use
Terminating the namespace is being deleted, and can not be used for new objects

For more details, see Namespace in the API reference.

Creating a new namespace

Note:

Avoid creating namespace with prefix kube-, since it is reserved for Kubernetes system namespaces.

Create a new YAML file called my-namespace.yaml with the contents:

apiVersion: v1
kind: Namespace
metadata:
  name: <insert-namespace-name-here>

Then run:

kubectl create -f ./my-namespace.yaml

Alternatively, you can create namespace using below command:

kubectl create namespace <insert-namespace-name-here>

The name of your namespace must be a valid DNS label.

There's an optional field finalizers, which allows observables to purge resources whenever the namespace is deleted. Keep in mind that if you specify a nonexistent finalizer, the namespace will be created but will get stuck in the Terminating state if the user tries to delete it.

More information on finalizers can be found in the namespace design doc.

Deleting a namespace

Delete a namespace with

kubectl delete namespaces <insert-some-namespace-name>

Warning:

This deletes everything under the namespace!

This delete is asynchronous, so for a time you will see the namespace in the Terminating state.

Subdividing your cluster using Kubernetes namespaces

By default, a Kubernetes cluster will instantiate a default namespace when provisioning the cluster to hold the default set of Pods, Services, and Deployments used by the cluster.

Assuming you have a fresh cluster, you can introspect the available namespaces by doing the following:

kubectl get namespaces

NAME      STATUS    AGE
default   Active    13m

Create new namespaces

For this exercise, we will create two additional Kubernetes namespaces to hold our content.

In a scenario where an organization is using a shared Kubernetes cluster for development and production use cases:

The development team would like to maintain a space in the cluster where they can get a view on the list of Pods, Services, and Deployments they use to build and run their application. In this space, Kubernetes resources come and go, and the restrictions on who can or cannot modify resources are relaxed to enable agile development.
The operations team would like to maintain a space in the cluster where they can enforce strict procedures on who can or cannot manipulate the set of Pods, Services, and Deployments that run the production site.

One pattern this organization could follow is to partition the Kubernetes cluster into two namespaces: development and production. Let's create two new namespaces to hold our work.

Create the development namespace using kubectl:

kubectl create -f https://k8s.io/examples/admin/namespace-dev.json

And then let's create the production namespace using kubectl:

kubectl create -f https://k8s.io/examples/admin/namespace-prod.json

To be sure things are right, list all of the namespaces in our cluster.

kubectl get namespaces --show-labels

NAME          STATUS    AGE       LABELS
default       Active    32m       <none>
development   Active    29s       name=development
production    Active    23s       name=production

Create pods in each namespace

A Kubernetes namespace provides the scope for Pods, Services, and Deployments in the cluster. Users interacting with one namespace do not see the content in another namespace. To demonstrate this, let's spin up a simple Deployment and Pods in the development namespace.

kubectl create deployment snowflake \
  --image=registry.k8s.io/serve_hostname \
  -n=development --replicas=2

We have created a deployment whose replica size is 2 that is running the pod called snowflake with a basic container that serves the hostname.

kubectl get deployment -n=development

NAME         READY   UP-TO-DATE   AVAILABLE   AGE
snowflake    2/2     2            2           2m

kubectl get pods -l app=snowflake -n=development

NAME                         READY     STATUS    RESTARTS   AGE
snowflake-3968820950-9dgr8   1/1       Running   0          2m
snowflake-3968820950-vgc4n   1/1       Running   0          2m

And this is great, developers are able to do what they want, and they do not have to worry about affecting content in the production namespace.

Let's switch to the production namespace and show how resources in one namespace are hidden from the other. The production namespace should be empty, and the following commands should return nothing.

kubectl get deployment -n=production
kubectl get pods -n=production

Production likes to run cattle, so let's create some cattle pods.

kubectl create deployment cattle --image=registry.k8s.io/serve_hostname -n=production
kubectl scale deployment cattle --replicas=5 -n=production

kubectl get deployment -n=production

NAME         READY   UP-TO-DATE   AVAILABLE   AGE
cattle       5/5     5            5           10s

kubectl get pods -l app=cattle -n=production

NAME                      READY     STATUS    RESTARTS   AGE
cattle-2263376956-41xy6   1/1       Running   0          34s
cattle-2263376956-kw466   1/1       Running   0          34s
cattle-2263376956-n4v97   1/1       Running   0          34s
cattle-2263376956-p5p3i   1/1       Running   0          34s
cattle-2263376956-sxpth   1/1       Running   0          34s

At this point, it should be clear that the resources users create in one namespace are hidden from the other namespace.

As the policy support in Kubernetes evolves, we will extend this scenario to show how you can provide different authorization rules for each namespace.

Understanding the motivation for using namespaces

A single cluster should be able to satisfy the needs of multiple users or groups of users (henceforth in this document a user community).

Kubernetes namespaces help different projects, teams, or customers to share a Kubernetes cluster.

It does this by providing the following:

A scope for names.
A mechanism to attach authorization and policy to a subsection of the cluster.

Use of multiple namespaces is optional.

Each user community wants to be able to work in isolation from other communities. Each user community has its own:

resources (pods, services, replication controllers, etc.)
policies (who can or cannot perform actions in their community)
constraints (this community is allowed this much quota, etc.)

A cluster operator may create a Namespace for each unique user community.

The Namespace provides a unique scope for:

named resources (to avoid basic naming collisions)
delegated management authority to trusted users
ability to limit community resource consumption

Use cases include:

As a cluster operator, I want to support multiple user communities on a single cluster.
As a cluster operator, I want to delegate authority to partitions of the cluster to trusted users in those communities.
As a cluster operator, I want to limit the amount of resources each community can consume in order to limit the impact to other communities using the cluster.
As a cluster user, I want to interact with resources that are pertinent to my user community in isolation of what other user communities are doing on the cluster.

Understanding namespaces and DNS

When you create a Service, it creates a corresponding DNS entry. This entry is of the form <service-name>.<namespace-name>.svc.cluster.local, which means that if a container uses <service-name> it will resolve to the service which is local to a namespace. This is useful for using the same configuration across multiple namespaces such as Development, Staging and Production. If you want to reach across namespaces, you need to use the fully qualified domain name (FQDN).

What's next

Learn more about setting the namespace preference.
Learn more about setting the namespace for a request
See namespaces design.

2.37 - Upgrade A Cluster

This page provides an overview of the steps you should follow to upgrade a Kubernetes cluster.

The way that you upgrade a cluster depends on how you initially deployed it and on any subsequent changes.

At a high level, the steps you perform are:

Upgrade the control plane
Upgrade the nodes in your cluster
Upgrade clients such as kubectl
Adjust manifests and other resources based on the API changes that accompany the new Kubernetes version

Before you begin

You must have an existing cluster. This page is about upgrading from Kubernetes 1.29 to Kubernetes 1.30. If your cluster is not currently running Kubernetes 1.29 then please check the documentation for the version of Kubernetes that you plan to upgrade to.

Upgrade approaches

kubeadm

If your cluster was deployed using the kubeadm tool, refer to Upgrading kubeadm clusters for detailed information on how to upgrade the cluster.

Once you have upgraded the cluster, remember to install the latest version of kubectl.

Manual deployments

Caution:

These steps do not account for third-party extensions such as network and storage plugins.

You should manually update the control plane following this sequence:

etcd (all instances)
kube-apiserver (all control plane hosts)
kube-controller-manager
kube-scheduler
cloud controller manager, if you use one

At this point you should install the latest version of kubectl.

For each node in your cluster, drain that node and then either replace it with a new node that uses the 1.30 kubelet, or upgrade the kubelet on that node and bring the node back into service.

Caution:

Draining nodes before upgrading kubelet ensures that pods are re-admitted and containers are re-created, which may be necessary to resolve some security issues or other important bugs.

Other deployments

Refer to the documentation for your cluster deployment tool to learn the recommended set up steps for maintenance.

Post-upgrade tasks

Switch your cluster's storage API version

The objects that are serialized into etcd for a cluster's internal representation of the Kubernetes resources active in the cluster are written using a particular version of the API.

When the supported API changes, these objects may need to be rewritten in the newer API. Failure to do this will eventually result in resources that are no longer decodable or usable by the Kubernetes API server.

For each affected object, fetch it using the latest supported API and then write it back also using the latest supported API.

Update manifests

Upgrading to a new Kubernetes version can provide new APIs.

You can use kubectl convert command to convert manifests between different API versions. For example:

kubectl convert -f pod.yaml --output-version v1

The kubectl tool replaces the contents of pod.yaml with a manifest that sets kind to Pod (unchanged), but with a revised apiVersion.

Device Plugins

If your cluster is running device plugins and the node needs to be upgraded to a Kubernetes release with a newer device plugin API version, device plugins must be upgraded to support both version before the node is upgraded in order to guarantee that device allocations continue to complete successfully during the upgrade.

Refer to API compatibility and Kubelet Device Manager API Versions for more details.

2.38 - Use Cascading Deletion in a Cluster

This page shows you how to specify the type of cascading deletion to use in your cluster during garbage collection.

Before you begin

You also need to create a sample Deployment to experiment with the different types of cascading deletion. You will need to recreate the Deployment for each type.

Check owner references on your pods

Check that the ownerReferences field is present on your pods:

kubectl get pods -l app=nginx --output=yaml

The output has an ownerReferences field similar to this:

apiVersion: v1
    ...
    ownerReferences:
    - apiVersion: apps/v1
      blockOwnerDeletion: true
      controller: true
      kind: ReplicaSet
      name: nginx-deployment-6b474476c4
      uid: 4fdcd81c-bd5d-41f7-97af-3a3b759af9a7
    ...

Use foreground cascading deletion

By default, Kubernetes uses background cascading deletion to delete dependents of an object. You can switch to foreground cascading deletion using either kubectl or the Kubernetes API, depending on the Kubernetes version your cluster runs. To check the version, enter kubectl version.

You can delete objects using foreground cascading deletion using kubectl or the Kubernetes API.

Using kubectl

Run the following command:

kubectl delete deployment nginx-deployment --cascade=foreground

Using the Kubernetes API

Start a local proxy session:
```
kubectl proxy --port=8080
```

Use curl to trigger deletion:

curl -X DELETE localhost:8080/apis/apps/v1/namespaces/default/deployments/nginx-deployment \
    -d '{"kind":"DeleteOptions","apiVersion":"v1","propagationPolicy":"Foreground"}' \
    -H "Content-Type: application/json"

The output contains a foregroundDeletion finalizer like this:

"kind": "Deployment",
"apiVersion": "apps/v1",
"metadata": {
    "name": "nginx-deployment",
    "namespace": "default",
    "uid": "d1ce1b02-cae8-4288-8a53-30e84d8fa505",
    "resourceVersion": "1363097",
    "creationTimestamp": "2021-07-08T20:24:37Z",
    "deletionTimestamp": "2021-07-08T20:27:39Z",
    "finalizers": [
      "foregroundDeletion"
    ]
    ...

Use background cascading deletion

Create a sample Deployment.
Use either kubectl or the Kubernetes API to delete the Deployment, depending on the Kubernetes version your cluster runs. To check the version, enter kubectl version.

You can delete objects using background cascading deletion using kubectl or the Kubernetes API.

Kubernetes uses background cascading deletion by default, and does so even if you run the following commands without the --cascade flag or the propagationPolicy argument.

Using kubectl

Run the following command:

kubectl delete deployment nginx-deployment --cascade=background

Using the Kubernetes API

Start a local proxy session:
```
kubectl proxy --port=8080
```

Use curl to trigger deletion:

curl -X DELETE localhost:8080/apis/apps/v1/namespaces/default/deployments/nginx-deployment \
    -d '{"kind":"DeleteOptions","apiVersion":"v1","propagationPolicy":"Background"}' \
    -H "Content-Type: application/json"

The output is similar to this:

"kind": "Status",
"apiVersion": "v1",
...
"status": "Success",
"details": {
    "name": "nginx-deployment",
    "group": "apps",
    "kind": "deployments",
    "uid": "cc9eefb9-2d49-4445-b1c1-d261c9396456"
}

Delete owner objects and orphan dependents

By default, when you tell Kubernetes to delete an object, the controller also deletes dependent objects. You can make Kubernetes orphan these dependents using kubectl or the Kubernetes API, depending on the Kubernetes version your cluster runs. To check the version, enter kubectl version.

Using kubectl

Run the following command:

kubectl delete deployment nginx-deployment --cascade=orphan

Using the Kubernetes API

Start a local proxy session:
```
kubectl proxy --port=8080
```

Use curl to trigger deletion:

curl -X DELETE localhost:8080/apis/apps/v1/namespaces/default/deployments/nginx-deployment \
    -d '{"kind":"DeleteOptions","apiVersion":"v1","propagationPolicy":"Orphan"}' \
    -H "Content-Type: application/json"

The output contains orphan in the finalizers field, similar to this:

"kind": "Deployment",
"apiVersion": "apps/v1",
"namespace": "default",
"uid": "6f577034-42a0-479d-be21-78018c466f1f",
"creationTimestamp": "2021-07-09T16:46:37Z",
"deletionTimestamp": "2021-07-09T16:47:08Z",
"deletionGracePeriodSeconds": 0,
"finalizers": [
  "orphan"
],
...

You can check that the Pods managed by the Deployment are still running:

kubectl get pods -l app=nginx

What's next

Learn about owners and dependents in Kubernetes.
Learn about Kubernetes finalizers.
Learn about garbage collection.

2.39 - Using a KMS provider for data encryption

This page shows how to configure a Key Management Service (KMS) provider and plugin to enable secret data encryption. In Kubernetes 1.30 there are two versions of KMS at-rest encryption. You should use KMS v2 if feasible because KMS v1 is deprecated (since Kubernetes v1.28) and disabled by default (since Kubernetes v1.29). KMS v2 offers significantly better performance characteristics than KMS v1.

Caution:

This documentation is for the generally available implementation of KMS v2 (and for the deprecated version 1 implementation). If you are using any control plane components older than Kubernetes v1.29, please check the equivalent page in the documentation for the version of Kubernetes that your cluster is running. Earlier releases of Kubernetes had different behavior that may be relevant for information security.

Before you begin

The version of Kubernetes that you need depends on which KMS API version you have selected. Kubernetes recommends using KMS v2.

If you selected KMS API v2, you should use Kubernetes v1.30 (if you are running a different version of Kubernetes that also supports the v2 KMS API, switch to the documentation for that version of Kubernetes).
If you selected KMS API v1 to support clusters prior to version v1.27 or if you have a legacy KMS plugin that only supports KMS v1, any supported Kubernetes version will work. This API is deprecated as of Kubernetes v1.28. Kubernetes does not recommend the use of this API.

To check the version, enter kubectl version.

KMS v1

FEATURE STATE: Kubernetes v1.28 [deprecated]

Kubernetes version 1.10.0 or later is required
For version 1.29 and later, the v1 implementation of KMS is disabled by default. To enable the feature, set --feature-gates=KMSv1=true to configure a KMS v1 provider.
Your cluster must use etcd v3 or later

KMS v2

FEATURE STATE: Kubernetes v1.29 [stable]

Your cluster must use etcd v3 or later

KMS encryption and per-object encryption keys

The KMS encryption provider uses an envelope encryption scheme to encrypt data in etcd. The data is encrypted using a data encryption key (DEK). The DEKs are encrypted with a key encryption key (KEK) that is stored and managed in a remote KMS.

If you use the (deprecated) v1 implementation of KMS, a new DEK is generated for each encryption.

With KMS v2, a new DEK is generated per encryption: the API server uses a key derivation function to generate single use data encryption keys from a secret seed combined with some random data. The seed is rotated whenever the KEK is rotated (see the Understanding key_id and Key Rotation section below for more details).

The KMS provider uses gRPC to communicate with a specific KMS plugin over a UNIX domain socket. The KMS plugin, which is implemented as a gRPC server and deployed on the same host(s) as the Kubernetes control plane, is responsible for all communication with the remote KMS.

Configuring the KMS provider

To configure a KMS provider on the API server, include a provider of type kms in the providers array in the encryption configuration file and set the following properties:

KMS v1

apiVersion: API Version for KMS provider. Leave this value empty or set it to v1.
name: Display name of the KMS plugin. Cannot be changed once set.
endpoint: Listen address of the gRPC server (KMS plugin). The endpoint is a UNIX domain socket.
cachesize: Number of data encryption keys (DEKs) to be cached in the clear. When cached, DEKs can be used without another call to the KMS; whereas DEKs that are not cached require a call to the KMS to unwrap.
timeout: How long should kube-apiserver wait for kms-plugin to respond before returning an error (default is 3 seconds).

KMS v2

apiVersion: API Version for KMS provider. Set this to v2.
name: Display name of the KMS plugin. Cannot be changed once set.
endpoint: Listen address of the gRPC server (KMS plugin). The endpoint is a UNIX domain socket.
timeout: How long should kube-apiserver wait for kms-plugin to respond before returning an error (default is 3 seconds).

KMS v2 does not support the cachesize property. All data encryption keys (DEKs) will be cached in the clear once the server has unwrapped them via a call to the KMS. Once cached, DEKs can be used to perform decryption indefinitely without making a call to the KMS.

See Understanding the encryption at rest configuration.

Implementing a KMS plugin

To implement a KMS plugin, you can develop a new plugin gRPC server or enable a KMS plugin already provided by your cloud provider. You then integrate the plugin with the remote KMS and deploy it on the Kubernetes control plane.

Enabling the KMS supported by your cloud provider

Refer to your cloud provider for instructions on enabling the cloud provider-specific KMS plugin.

Developing a KMS plugin gRPC server

You can develop a KMS plugin gRPC server using a stub file available for Go. For other languages, you use a proto file to create a stub file that you can use to develop the gRPC server code.

KMS v1

Using Go: Use the functions and data structures in the stub file: api.pb.go to develop the gRPC server code
Using languages other than Go: Use the protoc compiler with the proto file: api.proto to generate a stub file for the specific language

KMS v2

Using Go: A high level library is provided to make the process easier. Low level implementations can use the functions and data structures in the stub file: api.pb.go to develop the gRPC server code
Using languages other than Go: Use the protoc compiler with the proto file: api.proto to generate a stub file for the specific language

Then use the functions and data structures in the stub file to develop the server code.

Notes

KMS v1

kms plugin version: v1beta1

In response to procedure call Version, a compatible KMS plugin should return v1beta1 as VersionResponse.version.
message version: v1beta1

All messages from KMS provider have the version field set to v1beta1.
protocol: UNIX domain socket (unix)

The plugin is implemented as a gRPC server that listens at UNIX domain socket. The plugin deployment should create a file on the file system to run the gRPC unix domain socket connection. The API server (gRPC client) is configured with the KMS provider (gRPC server) unix domain socket endpoint in order to communicate with it. An abstract Linux socket may be used by starting the endpoint with /@, i.e. unix:///@foo. Care must be taken when using this type of socket as they do not have concept of ACL (unlike traditional file based sockets). However, they are subject to Linux networking namespace, so will only be accessible to containers within the same pod unless host networking is used.

KMS v2

KMS plugin version: v2

In response to the Status remote procedure call, a compatible KMS plugin should return its KMS compatibility version as StatusResponse.version. That status response should also include "ok" as StatusResponse.healthz and a key_id (remote KMS KEK ID) as StatusResponse.key_id. The Kubernetes project recommends you make your plugin compatible with the stable v2 KMS API. Kubernetes 1.30 also supports the v2beta1 API for KMS; future Kubernetes releases are likely to continue supporting that beta version.

The API server polls the Status procedure call approximately every minute when everything is healthy, and every 10 seconds when the plugin is not healthy. Plugins must take care to optimize this call as it will be under constant load.
Encryption

The EncryptRequest procedure call provides the plaintext and a UID for logging purposes. The response must include the ciphertext, the key_id for the KEK used, and, optionally, any metadata that the KMS plugin needs to aid in future DecryptRequest calls (via the annotations field). The plugin must guarantee that any distinct plaintext results in a distinct response (ciphertext, key_id, annotations).

If the plugin returns a non-empty annotations map, all map keys must be fully qualified domain names such as example.com. An example use case of annotation is {"kms.example.io/remote-kms-auditid":"<audit ID used by the remote KMS>"}

The API server does not perform the EncryptRequest procedure call at a high rate. Plugin implementations should still aim to keep each request's latency at under 100 milliseconds.
Decryption

The DecryptRequest procedure call provides the (ciphertext, key_id, annotations) from EncryptRequest and a UID for logging purposes. As expected, it is the inverse of the EncryptRequest call. Plugins must verify that the key_id is one that they understand - they must not attempt to decrypt data unless they are sure that it was encrypted by them at an earlier time.

The API server may perform thousands of DecryptRequest procedure calls on startup to fill its watch cache. Thus plugin implementations must perform these calls as quickly as possible, and should aim to keep each request's latency at under 10 milliseconds.
Understanding key_id and Key Rotation

The key_id is the public, non-secret name of the remote KMS KEK that is currently in use. It may be logged during regular operation of the API server, and thus must not contain any private data. Plugin implementations are encouraged to use a hash to avoid leaking any data. The KMS v2 metrics take care to hash this value before exposing it via the /metrics endpoint.

The API server considers the key_id returned from the Status procedure call to be authoritative. Thus, a change to this value signals to the API server that the remote KEK has changed, and data encrypted with the old KEK should be marked stale when a no-op write is performed (as described below). If an EncryptRequest procedure call returns a key_id that is different from Status, the response is thrown away and the plugin is considered unhealthy. Thus implementations must guarantee that the key_id returned from Status will be the same as the one returned by EncryptRequest. Furthermore, plugins must ensure that the key_id is stable and does not flip-flop between values (i.e. during a remote KEK rotation).

Plugins must not re-use key_ids, even in situations where a previously used remote KEK has been reinstated. For example, if a plugin was using key_id=A, switched to key_id=B, and then went back to key_id=A - instead of reporting key_id=A the plugin should report some derivative value such as key_id=A_001 or use a new value such as key_id=C.

Since the API server polls Status about every minute, key_id rotation is not immediate. Furthermore, the API server will coast on the last valid state for about three minutes. Thus if a user wants to take a passive approach to storage migration (i.e. by waiting), they must schedule a migration to occur at 3 + N + M minutes after the remote KEK has been rotated (N is how long it takes the plugin to observe the key_id change and M is the desired buffer to allow config changes to be processed - a minimum M of five minutes is recommend). Note that no API server restart is required to perform KEK rotation.

Caution:
Because you don't control the number of writes performed with the DEK, the Kubernetes project recommends rotating the KEK at least every 90 days.
protocol: UNIX domain socket (unix)

The plugin is implemented as a gRPC server that listens at UNIX domain socket. The plugin deployment should create a file on the file system to run the gRPC unix domain socket connection. The API server (gRPC client) is configured with the KMS provider (gRPC server) unix domain socket endpoint in order to communicate with it. An abstract Linux socket may be used by starting the endpoint with /@, i.e. unix:///@foo. Care must be taken when using this type of socket as they do not have concept of ACL (unlike traditional file based sockets). However, they are subject to Linux networking namespace, so will only be accessible to containers within the same pod unless host networking is used.

Integrating a KMS plugin with the remote KMS

The KMS plugin can communicate with the remote KMS using any protocol supported by the KMS. All configuration data, including authentication credentials the KMS plugin uses to communicate with the remote KMS, are stored and managed by the KMS plugin independently. The KMS plugin can encode the ciphertext with additional metadata that may be required before sending it to the KMS for decryption (KMS v2 makes this process easier by providing a dedicated annotations field).

Deploying the KMS plugin

Ensure that the KMS plugin runs on the same host(s) as the Kubernetes API server(s).

Encrypting your data with the KMS provider

To encrypt the data:

Create a new EncryptionConfiguration file using the appropriate properties for the kms provider to encrypt resources like Secrets and ConfigMaps. If you want to encrypt an extension API that is defined in a CustomResourceDefinition, your cluster must be running Kubernetes v1.26 or newer.
Set the --encryption-provider-config flag on the kube-apiserver to point to the location of the configuration file.
--encryption-provider-config-automatic-reload boolean argument determines if the file set by --encryption-provider-config should be automatically reloaded if the disk contents change.
Restart your API server.

KMS v1

apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:
  - resources:
      - secrets
      - configmaps
      - pandas.awesome.bears.example
    providers:
      - kms:
          name: myKmsPluginFoo
          endpoint: unix:///tmp/socketfile-foo.sock
          cachesize: 100
          timeout: 3s
      - kms:
          name: myKmsPluginBar
          endpoint: unix:///tmp/socketfile-bar.sock
          cachesize: 100
          timeout: 3s

KMS v2

apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:
  - resources:
      - secrets
      - configmaps
      - pandas.awesome.bears.example
    providers:
      - kms:
          apiVersion: v2
          name: myKmsPluginFoo
          endpoint: unix:///tmp/socketfile-foo.sock
          timeout: 3s
      - kms:
          apiVersion: v2
          name: myKmsPluginBar
          endpoint: unix:///tmp/socketfile-bar.sock
          timeout: 3s

Setting --encryption-provider-config-automatic-reload to true collapses all health checks to a single health check endpoint. Individual health checks are only available when KMS v1 providers are in use and the encryption config is not auto-reloaded.

The following table summarizes the health check endpoints for each KMS version:

KMS configurations	Without Automatic Reload	With Automatic Reload
KMS v1 only	Individual Healthchecks	Single Healthcheck
KMS v2 only	Single Healthcheck	Single Healthcheck
Both KMS v1 and v2	Individual Healthchecks	Single Healthcheck
No KMS	None	Single Healthcheck

Single Healthcheck means that the only health check endpoint is /healthz/kms-providers.

Individual Healthchecks means that each KMS plugin has an associated health check endpoint based on its location in the encryption config: /healthz/kms-provider-0, /healthz/kms-provider-1 etc.

These healthcheck endpoint paths are hard coded and generated/controlled by the server. The indices for individual healthchecks corresponds to the order in which the KMS encryption config is processed.

Until the steps defined in Ensuring all secrets are encrypted are performed, the providers list should end with the identity: {} provider to allow unencrypted data to be read. Once all resources are encrypted, the identity provider should be removed to prevent the API server from honoring unencrypted data.

For details about the EncryptionConfiguration format, please check the API server encryption API reference.

Verifying that the data is encrypted

When encryption at rest is correctly configured, resources are encrypted on write. After restarting your kube-apiserver, any newly created or updated Secret or other resource types configured in EncryptionConfiguration should be encrypted when stored. To verify, you can use the etcdctl command line program to retrieve the contents of your secret data.

Create a new secret called secret1 in the default namespace:

kubectl create secret generic secret1 -n default --from-literal=mykey=mydata

Using the etcdctl command line, read that secret out of etcd:
```
ETCDCTL_API=3 etcdctl get /kubernetes.io/secrets/default/secret1 [...] | hexdump -C
```
where [...] contains the additional arguments for connecting to the etcd server.
Verify the stored secret is prefixed with k8s:enc:kms:v1: for KMS v1 or prefixed with k8s:enc:kms:v2: for KMS v2, which indicates that the kms provider has encrypted the resulting data.
Verify that the secret is correctly decrypted when retrieved via the API:
```
kubectl describe secret secret1 -n default
```
The Secret should contain mykey: mydata

Ensuring all secrets are encrypted

When encryption at rest is correctly configured, resources are encrypted on write. Thus we can perform an in-place no-op update to ensure that data is encrypted.

The following command reads all secrets and then updates them to apply server side encryption. If an error occurs due to a conflicting write, retry the command. For larger clusters, you may wish to subdivide the secrets by namespace or script an update.

kubectl get secrets --all-namespaces -o json | kubectl replace -f -

Switching from a local encryption provider to the KMS provider

To switch from a local encryption provider to the kms provider and re-encrypt all of the secrets:

Add the kms provider as the first entry in the configuration file as shown in the following example.

apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:
  - resources:
      - secrets
    providers:
      - kms:
          apiVersion: v2
          name : myKmsPlugin
          endpoint: unix:///tmp/socketfile.sock
      - aescbc:
          keys:
            - name: key1
              secret: <BASE 64 ENCODED SECRET>

Restart all kube-apiserver processes.
Run the following command to force all secrets to be re-encrypted using the kms provider.
```
kubectl get secrets --all-namespaces -o json | kubectl replace -f -
```

What's next

If you no longer want to use encryption for data persisted in the Kubernetes API, read decrypt data that are already stored at rest.

2.40 - Using CoreDNS for Service Discovery

This page describes the CoreDNS upgrade process and how to install CoreDNS instead of kube-dns.

Before you begin

Your Kubernetes server must be at or later than version v1.9. To check the version, enter kubectl version.

About CoreDNS

CoreDNS is a flexible, extensible DNS server that can serve as the Kubernetes cluster DNS. Like Kubernetes, the CoreDNS project is hosted by the CNCF.

You can use CoreDNS instead of kube-dns in your cluster by replacing kube-dns in an existing deployment, or by using tools like kubeadm that will deploy and upgrade the cluster for you.

Installing CoreDNS

For manual deployment or replacement of kube-dns, see the documentation at the CoreDNS website.

Migrating to CoreDNS

Upgrading an existing cluster with kubeadm

In Kubernetes version 1.21, kubeadm removed its support for kube-dns as a DNS application. For kubeadm v1.30, the only supported cluster DNS application is CoreDNS.

You can move to CoreDNS when you use kubeadm to upgrade a cluster that is using kube-dns. In this case, kubeadm generates the CoreDNS configuration ("Corefile") based upon the kube-dns ConfigMap, preserving configurations for stub domains, and upstream name server.

Upgrading CoreDNS

You can check the version of CoreDNS that kubeadm installs for each version of Kubernetes in the page CoreDNS version in Kubernetes.

CoreDNS can be upgraded manually in case you want to only upgrade CoreDNS or use your own custom image. There is a helpful guideline and walkthrough available to ensure a smooth upgrade. Make sure the existing CoreDNS configuration ("Corefile") is retained when upgrading your cluster.

If you are upgrading your cluster using the kubeadm tool, kubeadm can take care of retaining the existing CoreDNS configuration automatically.

Tuning CoreDNS

When resource utilisation is a concern, it may be useful to tune the configuration of CoreDNS. For more details, check out the documentation on scaling CoreDNS.

What's next

You can configure CoreDNS to support many more use cases than kube-dns does by modifying the CoreDNS configuration ("Corefile"). For more information, see the documentation for the kubernetes CoreDNS plugin, or read the Custom DNS Entries for Kubernetes. in the CoreDNS blog.

2.41 - Using NodeLocal DNSCache in Kubernetes Clusters

FEATURE STATE: Kubernetes v1.18 [stable]

This page provides an overview of NodeLocal DNSCache feature in Kubernetes.

Before you begin

To check the version, enter kubectl version.

Introduction

NodeLocal DNSCache improves Cluster DNS performance by running a DNS caching agent on cluster nodes as a DaemonSet. In today's architecture, Pods in 'ClusterFirst' DNS mode reach out to a kube-dns serviceIP for DNS queries. This is translated to a kube-dns/CoreDNS endpoint via iptables rules added by kube-proxy. With this new architecture, Pods will reach out to the DNS caching agent running on the same node, thereby avoiding iptables DNAT rules and connection tracking. The local caching agent will query kube-dns service for cache misses of cluster hostnames ("cluster.local" suffix by default).

Motivation

With the current DNS architecture, it is possible that Pods with the highest DNS QPS have to reach out to a different node, if there is no local kube-dns/CoreDNS instance. Having a local cache will help improve the latency in such scenarios.
Skipping iptables DNAT and connection tracking will help reduce conntrack races and avoid UDP DNS entries filling up conntrack table.
Connections from the local caching agent to kube-dns service can be upgraded to TCP. TCP conntrack entries will be removed on connection close in contrast with UDP entries that have to timeout (default nf_conntrack_udp_timeout is 30 seconds)
Upgrading DNS queries from UDP to TCP would reduce tail latency attributed to dropped UDP packets and DNS timeouts usually up to 30s (3 retries + 10s timeout). Since the nodelocal cache listens for UDP DNS queries, applications don't need to be changed.
Metrics & visibility into DNS requests at a node level.
Negative caching can be re-enabled, thereby reducing the number of queries for the kube-dns service.

Architecture Diagram

This is the path followed by DNS Queries after NodeLocal DNSCache is enabled:

NodeLocal DNSCache flow — Nodelocal DNSCache flow
This image shows how NodeLocal DNSCache handles DNS queries.

Configuration

Note:

The local listen IP address for NodeLocal DNSCache can be any address that can be guaranteed to not collide with any existing IP in your cluster. It's recommended to use an address with a local scope, for example, from the 'link-local' range '169.254.0.0/16' for IPv4 or from the 'Unique Local Address' range in IPv6 'fd00::/8'.

This feature can be enabled using the following steps:

Prepare a manifest similar to the sample nodelocaldns.yaml and save it as nodelocaldns.yaml.
If using IPv6, the CoreDNS configuration file needs to enclose all the IPv6 addresses into square brackets if used in 'IP:Port' format. If you are using the sample manifest from the previous point, this will require you to modify the configuration line L70 like this: "health [__PILLAR__LOCAL__DNS__]:8080"
Substitute the variables in the manifest with the right values:
```
kubedns=`kubectl get svc kube-dns -n kube-system -o jsonpath={.spec.clusterIP}`
domain=<cluster-domain>
localdns=<node-local-address>
```
<cluster-domain> is "cluster.local" by default. <node-local-address> is the local listen IP address chosen for NodeLocal DNSCache.
- If kube-proxy is running in IPTABLES mode:
```
sed -i "s/__PILLAR__LOCAL__DNS__/$localdns/g; s/__PILLAR__DNS__DOMAIN__/$domain/g; s/__PILLAR__DNS__SERVER__/$kubedns/g" nodelocaldns.yaml
```
  __PILLAR__CLUSTER__DNS__ and __PILLAR__UPSTREAM__SERVERS__ will be populated by the node-local-dns pods. In this mode, the node-local-dns pods listen on both the kube-dns service IP as well as <node-local-address>, so pods can look up DNS records using either IP address.
- If kube-proxy is running in IPVS mode:
```
sed -i "s/__PILLAR__LOCAL__DNS__/$localdns/g; s/__PILLAR__DNS__DOMAIN__/$domain/g; s/,__PILLAR__DNS__SERVER__//g; s/__PILLAR__CLUSTER__DNS__/$kubedns/g" nodelocaldns.yaml
```
  In this mode, the node-local-dns pods listen only on <node-local-address>. The node-local-dns interface cannot bind the kube-dns cluster IP since the interface used for IPVS loadbalancing already uses this address. __PILLAR__UPSTREAM__SERVERS__ will be populated by the node-local-dns pods.
Run kubectl create -f nodelocaldns.yaml
If using kube-proxy in IPVS mode, --cluster-dns flag to kubelet needs to be modified to use <node-local-address> that NodeLocal DNSCache is listening on. Otherwise, there is no need to modify the value of the --cluster-dns flag, since NodeLocal DNSCache listens on both the kube-dns service IP as well as <node-local-address>.

Once enabled, the node-local-dns Pods will run in the kube-system namespace on each of the cluster nodes. This Pod runs CoreDNS in cache mode, so all CoreDNS metrics exposed by the different plugins will be available on a per-node basis.

You can disable this feature by removing the DaemonSet, using kubectl delete -f <manifest>. You should also revert any changes you made to the kubelet configuration.

StubDomains and Upstream server Configuration

StubDomains and upstream servers specified in the kube-dns ConfigMap in the kube-system namespace are automatically picked up by node-local-dns pods. The ConfigMap contents need to follow the format shown in the example. The node-local-dns ConfigMap can also be modified directly with the stubDomain configuration in the Corefile format. Some cloud providers might not allow modifying node-local-dns ConfigMap directly. In those cases, the kube-dns ConfigMap can be updated.

Setting memory limits

The node-local-dns Pods use memory for storing cache entries and processing queries. Since they do not watch Kubernetes objects, the cluster size or the number of Services / EndpointSlices do not directly affect memory usage. Memory usage is influenced by the DNS query pattern. From CoreDNS docs,

The default cache size is 10000 entries, which uses about 30 MB when completely filled.

This would be the memory usage for each server block (if the cache gets completely filled). Memory usage can be reduced by specifying smaller cache sizes.

The number of concurrent queries is linked to the memory demand, because each extra goroutine used for handling a query requires an amount of memory. You can set an upper limit using the max_concurrent option in the forward plugin.

If a node-local-dns Pod attempts to use more memory than is available (because of total system resources, or because of a configured resource limit), the operating system may shut down that pod's container. If this happens, the container that is terminated (“OOMKilled”) does not clean up the custom packet filtering rules that it previously added during startup. The node-local-dns container should get restarted (since managed as part of a DaemonSet), but this will lead to a brief DNS downtime each time that the container fails: the packet filtering rules direct DNS queries to a local Pod that is unhealthy.

You can determine a suitable memory limit by running node-local-dns pods without a limit and measuring the peak usage. You can also set up and use a VerticalPodAutoscaler in recommender mode, and then check its recommendations.

2.42 - Using sysctls in a Kubernetes Cluster

FEATURE STATE: Kubernetes v1.21 [stable]

This document describes how to configure and use kernel parameters within a Kubernetes cluster using the sysctl interface.

Note:

Starting from Kubernetes version 1.23, the kubelet supports the use of either / or . as separators for sysctl names. Starting from Kubernetes version 1.25, setting Sysctls for a Pod supports setting sysctls with slashes. For example, you can represent the same sysctl name as kernel.shm_rmid_forced using a period as the separator, or as kernel/shm_rmid_forced using a slash as a separator. For more sysctl parameter conversion method details, please refer to the page sysctl.d(5) from the Linux man-pages project.

Before you begin

Note:

sysctl is a Linux-specific command-line tool used to configure various kernel parameters and it is not available on non-Linux operating systems.

For some steps, you also need to be able to reconfigure the command line options for the kubelets running on your cluster.

Listing all Sysctl Parameters

In Linux, the sysctl interface allows an administrator to modify kernel parameters at runtime. Parameters are available via the /proc/sys/ virtual process file system. The parameters cover various subsystems such as:

kernel (common prefix: kernel.)
networking (common prefix: net.)
virtual memory (common prefix: vm.)
MDADM (common prefix: dev.)
More subsystems are described in Kernel docs.

To get a list of all parameters, you can run

sudo sysctl -a

Safe and Unsafe Sysctls

Kubernetes classes sysctls as either safe or unsafe. In addition to proper namespacing, a safe sysctl must be properly isolated between pods on the same node. This means that setting a safe sysctl for one pod

must not have any influence on any other pod on the node
must not allow to harm the node's health
must not allow to gain CPU or memory resources outside of the resource limits of a pod.

By far, most of the namespaced sysctls are not necessarily considered safe. The following sysctls are supported in the safe set:

kernel.shm_rmid_forced;
net.ipv4.ip_local_port_range;
net.ipv4.tcp_syncookies;
net.ipv4.ping_group_range (since Kubernetes 1.18);
net.ipv4.ip_unprivileged_port_start (since Kubernetes 1.22);
net.ipv4.ip_local_reserved_ports (since Kubernetes 1.27, needs kernel 3.16+);
net.ipv4.tcp_keepalive_time (since Kubernetes 1.29, needs kernel 4.5+);
net.ipv4.tcp_fin_timeout (since Kubernetes 1.29, needs kernel 4.6+);
net.ipv4.tcp_keepalive_intvl (since Kubernetes 1.29, needs kernel 4.5+);
net.ipv4.tcp_keepalive_probes (since Kubernetes 1.29, needs kernel 4.5+).

Note:

There are some exceptions to the set of safe sysctls:

The net.* sysctls are not allowed with host networking enabled.
The net.ipv4.tcp_syncookies sysctl is not namespaced on Linux kernel version 4.4 or lower.

This list will be extended in future Kubernetes versions when the kubelet supports better isolation mechanisms.

Enabling Unsafe Sysctls

All safe sysctls are enabled by default.

All unsafe sysctls are disabled by default and must be allowed manually by the cluster admin on a per-node basis. Pods with disabled unsafe sysctls will be scheduled, but will fail to launch.

With the warning above in mind, the cluster admin can allow certain unsafe sysctls for very special situations such as high-performance or real-time application tuning. Unsafe sysctls are enabled on a node-by-node basis with a flag of the kubelet; for example:

kubelet --allowed-unsafe-sysctls \
  'kernel.msg*,net.core.somaxconn' ...

For Minikube, this can be done via the extra-config flag:

minikube start --extra-config="kubelet.allowed-unsafe-sysctls=kernel.msg*,net.core.somaxconn"...

Only namespaced sysctls can be enabled this way.

Setting Sysctls for a Pod

A number of sysctls are namespaced in today's Linux kernels. This means that they can be set independently for each pod on a node. Only namespaced sysctls are configurable via the pod securityContext within Kubernetes.

The following sysctls are known to be namespaced. This list could change in future versions of the Linux kernel.

kernel.shm*,
kernel.msg*,
kernel.sem,
fs.mqueue.*,
Those net.* that can be set in container networking namespace. However, there are exceptions (e.g., net.netfilter.nf_conntrack_max and net.netfilter.nf_conntrack_expect_max can be set in container networking namespace but are unnamespaced before Linux 5.12.2).

Sysctls with no namespace are called node-level sysctls. If you need to set them, you must manually configure them on each node's operating system, or by using a DaemonSet with privileged containers.

Use the pod securityContext to configure namespaced sysctls. The securityContext applies to all containers in the same pod.

This example uses the pod securityContext to set a safe sysctl kernel.shm_rmid_forced and two unsafe sysctls net.core.somaxconn and kernel.msgmax. There is no distinction between safe and unsafe sysctls in the specification.

Warning:

Only modify sysctl parameters after you understand their effects, to avoid destabilizing your operating system.

apiVersion: v1
kind: Pod
metadata:
  name: sysctl-example
spec:
  securityContext:
    sysctls:
    - name: kernel.shm_rmid_forced
      value: "0"
    - name: net.core.somaxconn
      value: "1024"
    - name: kernel.msgmax
      value: "65536"
  ...

Warning:

Due to their nature of being unsafe, the use of unsafe sysctls is at-your-own-risk and can lead to severe problems like wrong behavior of containers, resource shortage or complete breakage of a node.

It is good practice to consider nodes with special sysctl settings as tainted within a cluster, and only schedule pods onto them which need those sysctl settings. It is suggested to use the Kubernetes taints and toleration feature to implement this.

A pod with the unsafe sysctls will fail to launch on any node which has not enabled those two unsafe sysctls explicitly. As with node-level sysctls it is recommended to use taints and toleration feature or taints on nodes to schedule those pods onto the right nodes.

2.43 - Utilizing the NUMA-aware Memory Manager

FEATURE STATE: Kubernetes v1.22 [beta]

The Kubernetes Memory Manager enables the feature of guaranteed memory (and hugepages) allocation for pods in the Guaranteed QoS class.

The Memory Manager employs hint generation protocol to yield the most suitable NUMA affinity for a pod. The Memory Manager feeds the central manager (Topology Manager) with these affinity hints. Based on both the hints and Topology Manager policy, the pod is rejected or admitted to the node.

Moreover, the Memory Manager ensures that the memory which a pod requests is allocated from a minimum number of NUMA nodes.

The Memory Manager is only pertinent to Linux based hosts.

Before you begin

Your Kubernetes server must be at or later than version v1.21. To check the version, enter kubectl version.

To align memory resources with other requested resources in a Pod spec:

the CPU Manager should be enabled and proper CPU Manager policy should be configured on a Node. See control CPU Management Policies;
the Topology Manager should be enabled and proper Topology Manager policy should be configured on a Node. See control Topology Management Policies.

Starting from v1.22, the Memory Manager is enabled by default through MemoryManager feature gate.

Preceding v1.22, the kubelet must be started with the following flag:

--feature-gates=MemoryManager=true

in order to enable the Memory Manager feature.

How Memory Manager Operates?

The Memory Manager currently offers the guaranteed memory (and hugepages) allocation for Pods in Guaranteed QoS class. To immediately put the Memory Manager into operation follow the guidelines in the section Memory Manager configuration, and subsequently, prepare and deploy a Guaranteed pod as illustrated in the section Placing a Pod in the Guaranteed QoS class.

The Memory Manager is a Hint Provider, and it provides topology hints for the Topology Manager which then aligns the requested resources according to these topology hints. It also enforces cgroups (i.e. cpuset.mems) for pods. The complete flow diagram concerning pod admission and deployment process is illustrated in Memory Manager KEP: Design Overview and below:

Memory Manager in the pod admission and deployment process

During this process, the Memory Manager updates its internal counters stored in Node Map and Memory Maps to manage guaranteed memory allocation.

The Memory Manager updates the Node Map during the startup and runtime as follows.

Startup

This occurs once a node administrator employs --reserved-memory (section Reserved memory flag). In this case, the Node Map becomes updated to reflect this reservation as illustrated in Memory Manager KEP: Memory Maps at start-up (with examples).

The administrator must provide --reserved-memory flag when Static policy is configured.

Runtime

Reference Memory Manager KEP: Memory Maps at runtime (with examples) illustrates how a successful pod deployment affects the Node Map, and it also relates to how potential Out-of-Memory (OOM) situations are handled further by Kubernetes or operating system.

Important topic in the context of Memory Manager operation is the management of NUMA groups. Each time pod's memory request is in excess of single NUMA node capacity, the Memory Manager attempts to create a group that comprises several NUMA nodes and features extend memory capacity. The problem has been solved as elaborated in Memory Manager KEP: How to enable the guaranteed memory allocation over many NUMA nodes?. Also, reference Memory Manager KEP: Simulation - how the Memory Manager works? (by examples) illustrates how the management of groups occurs.

Memory Manager configuration

Other Managers should be first pre-configured. Next, the Memory Manager feature should be enabled and be run with Static policy (section Static policy). Optionally, some amount of memory can be reserved for system or kubelet processes to increase node stability (section Reserved memory flag).

Policies

Memory Manager supports two policies. You can select a policy via a kubelet flag --memory-manager-policy:

None (default)
Static

None policy

This is the default policy and does not affect the memory allocation in any way. It acts the same as if the Memory Manager is not present at all.

The None policy returns default topology hint. This special hint denotes that Hint Provider (Memory Manager in this case) has no preference for NUMA affinity with any resource.

Static policy

In the case of the Guaranteed pod, the Static Memory Manager policy returns topology hints relating to the set of NUMA nodes where the memory can be guaranteed, and reserves the memory through updating the internal NodeMap object.

In the case of the BestEffort or Burstable pod, the Static Memory Manager policy sends back the default topology hint as there is no request for the guaranteed memory, and does not reserve the memory in the internal NodeMap object.

Reserved memory flag

The Node Allocatable mechanism is commonly used by node administrators to reserve K8S node system resources for the kubelet or operating system processes in order to enhance the node stability. A dedicated set of flags can be used for this purpose to set the total amount of reserved memory for a node. This pre-configured value is subsequently utilized to calculate the real amount of node's "allocatable" memory available to pods.

The Kubernetes scheduler incorporates "allocatable" to optimise pod scheduling process. The foregoing flags include --kube-reserved, --system-reserved and --eviction-threshold. The sum of their values will account for the total amount of reserved memory.

A new --reserved-memory flag was added to Memory Manager to allow for this total reserved memory to be split (by a node administrator) and accordingly reserved across many NUMA nodes.

The flag specifies a comma-separated list of memory reservations of different memory types per NUMA node. Memory reservations across multiple NUMA nodes can be specified using semicolon as separator. This parameter is only useful in the context of the Memory Manager feature. The Memory Manager will not use this reserved memory for the allocation of container workloads.

For example, if you have a NUMA node "NUMA0" with 10Gi of memory available, and the --reserved-memory was specified to reserve 1Gi of memory at "NUMA0", the Memory Manager assumes that only 9Gi is available for containers.

You can omit this parameter, however, you should be aware that the quantity of reserved memory from all NUMA nodes should be equal to the quantity of memory specified by the Node Allocatable feature. If at least one node allocatable parameter is non-zero, you will need to specify --reserved-memory for at least one NUMA node. In fact, eviction-hard threshold value is equal to 100Mi by default, so if Static policy is used, --reserved-memory is obligatory.

Also, avoid the following configurations:

duplicates, i.e. the same NUMA node or memory type, but with a different value;
setting zero limit for any of memory types;
NUMA node IDs that do not exist in the machine hardware;
memory type names different than memory or hugepages-<size> (hugepages of particular <size> should also exist).

Syntax:

--reserved-memory N:memory-type1=value1,memory-type2=value2,...

N (integer) - NUMA node index, e.g. 0
memory-type (string) - represents memory type:
- memory - conventional memory
- hugepages-2Mi or hugepages-1Gi - hugepages
value (string) - the quantity of reserved memory, e.g. 1Gi

Example usage:

--reserved-memory 0:memory=1Gi,hugepages-1Gi=2Gi

--reserved-memory 0:memory=1Gi --reserved-memory 1:memory=2Gi

--reserved-memory '0:memory=1Gi;1:memory=2Gi'

When you specify values for --reserved-memory flag, you must comply with the setting that you prior provided via Node Allocatable Feature flags. That is, the following rule must be obeyed for each memory type:

sum(reserved-memory(i)) = kube-reserved + system-reserved + eviction-threshold,

where i is an index of a NUMA node.

If you do not follow the formula above, the Memory Manager will show an error on startup.

In other words, the example above illustrates that for the conventional memory (type=memory), we reserve 3Gi in total, i.e.:

sum(reserved-memory(i)) = reserved-memory(0) + reserved-memory(1) = 1Gi + 2Gi = 3Gi

An example of kubelet command-line arguments relevant to the node Allocatable configuration:

--kube-reserved=cpu=500m,memory=50Mi
--system-reserved=cpu=123m,memory=333Mi
--eviction-hard=memory.available<500Mi

Note:

The default hard eviction threshold is 100MiB, and not zero. Remember to increase the quantity of memory that you reserve by setting --reserved-memory by that hard eviction threshold. Otherwise, the kubelet will not start Memory Manager and display an error.

Here is an example of a correct configuration:

--feature-gates=MemoryManager=true
--kube-reserved=cpu=4,memory=4Gi
--system-reserved=cpu=1,memory=1Gi
--memory-manager-policy=Static
--reserved-memory '0:memory=3Gi;1:memory=2148Mi'

Let us validate the configuration above:

kube-reserved + system-reserved + eviction-hard(default) = reserved-memory(0) + reserved-memory(1)
4GiB + 1GiB + 100MiB = 3GiB + 2148MiB
5120MiB + 100MiB = 3072MiB + 2148MiB
5220MiB = 5220MiB (which is correct)

Placing a Pod in the Guaranteed QoS class

If the selected policy is anything other than None, the Memory Manager identifies pods that are in the Guaranteed QoS class. The Memory Manager provides specific topology hints to the Topology Manager for each Guaranteed pod. For pods in a QoS class other than Guaranteed, the Memory Manager provides default topology hints to the Topology Manager.

The following excerpts from pod manifests assign a pod to the Guaranteed QoS class.

Pod with integer CPU(s) runs in the Guaranteed QoS class, when requests are equal to limits:

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "2"
        example.com/device: "1"
      requests:
        memory: "200Mi"
        cpu: "2"
        example.com/device: "1"

Also, a pod sharing CPU(s) runs in the Guaranteed QoS class, when requests are equal to limits.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "300m"
        example.com/device: "1"
      requests:
        memory: "200Mi"
        cpu: "300m"
        example.com/device: "1"

Notice that both CPU and memory requests must be specified for a Pod to lend it to Guaranteed QoS class.

Troubleshooting

The following means can be used to troubleshoot the reason why a pod could not be deployed or became rejected at a node:

pod status - indicates topology affinity errors
system logs - include valuable information for debugging, e.g., about generated hints
state file - the dump of internal state of the Memory Manager (includes Node Map and Memory Maps)
starting from v1.22, the device plugin resource API can be used to retrieve information about the memory reserved for containers

Pod status (TopologyAffinityError)

This error typically occurs in the following situations:

a node has not enough resources available to satisfy the pod's request
the pod's request is rejected due to particular Topology Manager policy constraints

The error appears in the status of a pod:

kubectl get pods

NAME         READY   STATUS                  RESTARTS   AGE
guaranteed   0/1     TopologyAffinityError   0          113s

Use kubectl describe pod <id> or kubectl get events to obtain detailed error message:

Warning  TopologyAffinityError  10m   kubelet, dell8  Resources cannot be allocated with Topology locality

System logs

Search system logs with respect to a particular pod.

The set of hints that Memory Manager generated for the pod can be found in the logs. Also, the set of hints generated by CPU Manager should be present in the logs.

Topology Manager merges these hints to calculate a single best hint. The best hint should be also present in the logs.

The best hint indicates where to allocate all the resources. Topology Manager tests this hint against its current policy, and based on the verdict, it either admits the pod to the node or rejects it.

Also, search the logs for occurrences associated with the Memory Manager, e.g. to find out information about cgroups and cpuset.mems updates.

Examine the memory manager state on a node

Let us first deploy a sample Guaranteed pod whose specification is as follows:

apiVersion: v1
kind: Pod
metadata:
  name: guaranteed
spec:
  containers:
  - name: guaranteed
    image: consumer
    imagePullPolicy: Never
    resources:
      limits:
        cpu: "2"
        memory: 150Gi
      requests:
        cpu: "2"
        memory: 150Gi
    command: ["sleep","infinity"]

Next, let us log into the node where it was deployed and examine the state file in /var/lib/kubelet/memory_manager_state:

{
   "policyName":"Static",
   "machineState":{
      "0":{
         "numberOfAssignments":1,
         "memoryMap":{
            "hugepages-1Gi":{
               "total":0,
               "systemReserved":0,
               "allocatable":0,
               "reserved":0,
               "free":0
            },
            "memory":{
               "total":134987354112,
               "systemReserved":3221225472,
               "allocatable":131766128640,
               "reserved":131766128640,
               "free":0
            }
         },
         "nodes":[
            0,
            1
         ]
      },
      "1":{
         "numberOfAssignments":1,
         "memoryMap":{
            "hugepages-1Gi":{
               "total":0,
               "systemReserved":0,
               "allocatable":0,
               "reserved":0,
               "free":0
            },
            "memory":{
               "total":135286722560,
               "systemReserved":2252341248,
               "allocatable":133034381312,
               "reserved":29295144960,
               "free":103739236352
            }
         },
         "nodes":[
            0,
            1
         ]
      }
   },
   "entries":{
      "fa9bdd38-6df9-4cf9-aa67-8c4814da37a8":{
         "guaranteed":[
            {
               "numaAffinity":[
                  0,
                  1
               ],
               "type":"memory",
               "size":161061273600
            }
         ]
      }
   },
   "checksum":4142013182
}

It can be deduced from the state file that the pod was pinned to both NUMA nodes, i.e.:

"numaAffinity":[
   0,
   1
],

Pinned term means that pod's memory consumption is constrained (through cgroups configuration) to these NUMA nodes.

This automatically implies that Memory Manager instantiated a new group that comprises these two NUMA nodes, i.e. 0 and 1 indexed NUMA nodes.

Notice that the management of groups is handled in a relatively complex manner, and further elaboration is provided in Memory Manager KEP in this and this sections.

In order to analyse memory resources available in a group,the corresponding entries from NUMA nodes belonging to the group must be added up.

For example, the total amount of free "conventional" memory in the group can be computed by adding up the free memory available at every NUMA node in the group, i.e., in the "memory" section of NUMA node 0 ("free":0) and NUMA node 1 ("free":103739236352). So, the total amount of free "conventional" memory in this group is equal to 0 + 103739236352 bytes.

The line "systemReserved":3221225472 indicates that the administrator of this node reserved 3221225472 bytes (i.e. 3Gi) to serve kubelet and system processes at NUMA node 0, by using --reserved-memory flag.

Device plugin resource API

The kubelet provides a PodResourceLister gRPC service to enable discovery of resources and associated metadata. By using its List gRPC endpoint, information about reserved memory for each container can be retrieved, which is contained in protobuf ContainerMemory message. This information can be retrieved solely for pods in Guaranteed QoS class.

What's next

2.44 - Verify Signed Kubernetes Artifacts

FEATURE STATE: Kubernetes v1.26 [beta]

Before you begin

You will need to have the following tools installed:

cosign (install guide)
curl (often provided by your operating system)
jq (download jq)

Verifying binary signatures

The Kubernetes release process signs all binary artifacts (tarballs, SPDX files, standalone binaries) by using cosign's keyless signing. To verify a particular binary, retrieve it together with its signature and certificate:

URL=https://dl.k8s.io/release/v1.30.0/bin/linux/amd64
BINARY=kubectl

FILES=(
    "$BINARY"
    "$BINARY.sig"
    "$BINARY.cert"
)

for FILE in "${FILES[@]}"; do
    curl -sSfL --retry 3 --retry-delay 3 "$URL/$FILE" -o "$FILE"
done

Then verify the blob by using cosign verify-blob:

cosign verify-blob "$BINARY" \
  --signature "$BINARY".sig \
  --certificate "$BINARY".cert \
  --certificate-identity krel-staging@k8s-releng-prod.iam.gserviceaccount.com \
  --certificate-oidc-issuer https://accounts.google.com

Note:

Cosign 2.0 requires the --certificate-identity and --certificate-oidc-issuer options.

To learn more about keyless signing, please refer to Keyless Signatures.

Previous versions of Cosign required that you set COSIGN_EXPERIMENTAL=1.

For additional information, please refer to the sigstore Blog

Verifying image signatures

For a complete list of images that are signed please refer to Releases.

Pick one image from this list and verify its signature using the cosign verify command:

cosign verify registry.k8s.io/kube-apiserver-amd64:v1.30.0 \
  --certificate-identity krel-trust@k8s-releng-prod.iam.gserviceaccount.com \
  --certificate-oidc-issuer https://accounts.google.com \
  | jq .

Verifying images for all control plane components

To verify all signed control plane images for the latest stable version (v1.30.0), please run the following commands:

curl -Ls "https://sbom.k8s.io/$(curl -Ls https://dl.k8s.io/release/stable.txt)/release" \
  | grep "SPDXID: SPDXRef-Package-registry.k8s.io" \
  | grep -v sha256 | cut -d- -f3- | sed 's/-/\//' | sed 's/-v1/:v1/' \
  | sort > images.txt
input=images.txt
while IFS= read -r image
do
  cosign verify "$image" \
    --certificate-identity krel-trust@k8s-releng-prod.iam.gserviceaccount.com \
    --certificate-oidc-issuer https://accounts.google.com \
    | jq .
done < "$input"

Once you have verified an image, you can specify the image by its digest in your Pod manifests as per this example:

registry-url/image-name@sha256:45b23dee08af5e43a7fea6c4cf9c25ccf269ee113168c19722f87876677c5cb2

For more information, please refer to the Image Pull Policy section.

Verifying Image Signatures with Admission Controller

For non-control plane images (for example conformance image), signatures can also be verified at deploy time using sigstore policy-controller admission controller.

Here are some helpful resources to get started with policy-controller:

Verify the Software Bill Of Materials

You can verify the Kubernetes Software Bill of Materials (SBOM) by using the sigstore certificate and signature, or the corresponding SHA files:

# Retrieve the latest available Kubernetes release version
VERSION=$(curl -Ls https://dl.k8s.io/release/stable.txt)

# Verify the SHA512 sum
curl -Ls "https://sbom.k8s.io/$VERSION/release" -o "$VERSION.spdx"
echo "$(curl -Ls "https://sbom.k8s.io/$VERSION/release.sha512") $VERSION.spdx" | sha512sum --check

# Verify the SHA256 sum
echo "$(curl -Ls "https://sbom.k8s.io/$VERSION/release.sha256") $VERSION.spdx" | sha256sum --check

# Retrieve sigstore signature and certificate
curl -Ls "https://sbom.k8s.io/$VERSION/release.sig" -o "$VERSION.spdx.sig"
curl -Ls "https://sbom.k8s.io/$VERSION/release.cert" -o "$VERSION.spdx.cert"

# Verify the sigstore signature
cosign verify-blob \
    --certificate "$VERSION.spdx.cert" \
    --signature "$VERSION.spdx.sig" \
    --certificate-identity krel-staging@k8s-releng-prod.iam.gserviceaccount.com \
    --certificate-oidc-issuer https://accounts.google.com \
    "$VERSION.spdx"

3 - Configure Pods and Containers

Perform common configuration tasks for Pods and containers.

3.1 - Assign Memory Resources to Containers and Pods

This page shows how to assign a memory request and a memory limit to a Container. A Container is guaranteed to have as much memory as it requests, but is not allowed to use more memory than its limit.

Before you begin

To check the version, enter kubectl version.

Each node in your cluster must have at least 300 MiB of memory.

A few of the steps on this page require you to run the metrics-server service in your cluster. If you have the metrics-server running, you can skip those steps.

If you are running Minikube, run the following command to enable the metrics-server:

minikube addons enable metrics-server

To see whether the metrics-server is running, or another provider of the resource metrics API (metrics.k8s.io), run the following command:

kubectl get apiservices

If the resource metrics API is available, the output includes a reference to metrics.k8s.io.

NAME
v1beta1.metrics.k8s.io

Create a namespace

Create a namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace mem-example

Specify a memory request and a memory limit

To specify a memory request for a Container, include the resources:requests field in the Container's resource manifest. To specify a memory limit, include resources:limits.

In this exercise, you create a Pod that has one Container. The Container has a memory request of 100 MiB and a memory limit of 200 MiB. Here's the configuration file for the Pod:

pods/resource/memory-request-limit.yaml

apiVersion: v1
kind: Pod
metadata:
  name: memory-demo
  namespace: mem-example
spec:
  containers:
  - name: memory-demo-ctr
    image: polinux/stress
    resources:
      requests:
        memory: "100Mi"
      limits:
        memory: "200Mi"
    command: ["stress"]
    args: ["--vm", "1", "--vm-bytes", "150M", "--vm-hang", "1"]

The args section in the configuration file provides arguments for the Container when it starts. The "--vm-bytes", "150M" arguments tell the Container to attempt to allocate 150 MiB of memory.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/resource/memory-request-limit.yaml --namespace=mem-example

Verify that the Pod Container is running:

kubectl get pod memory-demo --namespace=mem-example

View detailed information about the Pod:

kubectl get pod memory-demo --output=yaml --namespace=mem-example

The output shows that the one Container in the Pod has a memory request of 100 MiB and a memory limit of 200 MiB.

...
resources:
  requests:
    memory: 100Mi
  limits:
    memory: 200Mi
...

Run kubectl top to fetch the metrics for the pod:

kubectl top pod memory-demo --namespace=mem-example

The output shows that the Pod is using about 162,900,000 bytes of memory, which is about 150 MiB. This is greater than the Pod's 100 MiB request, but within the Pod's 200 MiB limit.

NAME                        CPU(cores)   MEMORY(bytes)
memory-demo                 <something>  162856960

Delete your Pod:

kubectl delete pod memory-demo --namespace=mem-example

Exceed a Container's memory limit

A Container can exceed its memory request if the Node has memory available. But a Container is not allowed to use more than its memory limit. If a Container allocates more memory than its limit, the Container becomes a candidate for termination. If the Container continues to consume memory beyond its limit, the Container is terminated. If a terminated Container can be restarted, the kubelet restarts it, as with any other type of runtime failure.

In this exercise, you create a Pod that attempts to allocate more memory than its limit. Here is the configuration file for a Pod that has one Container with a memory request of 50 MiB and a memory limit of 100 MiB:

pods/resource/memory-request-limit-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: memory-demo-2
  namespace: mem-example
spec:
  containers:
  - name: memory-demo-2-ctr
    image: polinux/stress
    resources:
      requests:
        memory: "50Mi"
      limits:
        memory: "100Mi"
    command: ["stress"]
    args: ["--vm", "1", "--vm-bytes", "250M", "--vm-hang", "1"]

In the args section of the configuration file, you can see that the Container will attempt to allocate 250 MiB of memory, which is well above the 100 MiB limit.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/resource/memory-request-limit-2.yaml --namespace=mem-example

View detailed information about the Pod:

kubectl get pod memory-demo-2 --namespace=mem-example

At this point, the Container might be running or killed. Repeat the preceding command until the Container is killed:

NAME            READY     STATUS      RESTARTS   AGE
memory-demo-2   0/1       OOMKilled   1          24s

Get a more detailed view of the Container status:

kubectl get pod memory-demo-2 --output=yaml --namespace=mem-example

The output shows that the Container was killed because it is out of memory (OOM):

lastState:
   terminated:
     containerID: 65183c1877aaec2e8427bc95609cc52677a454b56fcb24340dbd22917c23b10f
     exitCode: 137
     finishedAt: 2017-06-20T20:52:19Z
     reason: OOMKilled
     startedAt: null

The Container in this exercise can be restarted, so the kubelet restarts it. Repeat this command several times to see that the Container is repeatedly killed and restarted:

kubectl get pod memory-demo-2 --namespace=mem-example

The output shows that the Container is killed, restarted, killed again, restarted again, and so on:

kubectl get pod memory-demo-2 --namespace=mem-example
NAME            READY     STATUS      RESTARTS   AGE
memory-demo-2   0/1       OOMKilled   1          37s


kubectl get pod memory-demo-2 --namespace=mem-example
NAME            READY     STATUS    RESTARTS   AGE
memory-demo-2   1/1       Running   2          40s

View detailed information about the Pod history:

kubectl describe pod memory-demo-2 --namespace=mem-example

The output shows that the Container starts and fails repeatedly:

... Normal  Created   Created container with id 66a3a20aa7980e61be4922780bf9d24d1a1d8b7395c09861225b0eba1b1f8511
... Warning BackOff   Back-off restarting failed container

View detailed information about your cluster's Nodes:

kubectl describe nodes

The output includes a record of the Container being killed because of an out-of-memory condition:

Warning OOMKilling Memory cgroup out of memory: Kill process 4481 (stress) score 1994 or sacrifice child

Delete your Pod:

kubectl delete pod memory-demo-2 --namespace=mem-example

Specify a memory request that is too big for your Nodes

Memory requests and limits are associated with Containers, but it is useful to think of a Pod as having a memory request and limit. The memory request for the Pod is the sum of the memory requests for all the Containers in the Pod. Likewise, the memory limit for the Pod is the sum of the limits of all the Containers in the Pod.

Pod scheduling is based on requests. A Pod is scheduled to run on a Node only if the Node has enough available memory to satisfy the Pod's memory request.

In this exercise, you create a Pod that has a memory request so big that it exceeds the capacity of any Node in your cluster. Here is the configuration file for a Pod that has one Container with a request for 1000 GiB of memory, which likely exceeds the capacity of any Node in your cluster.

pods/resource/memory-request-limit-3.yaml

apiVersion: v1
kind: Pod
metadata:
  name: memory-demo-3
  namespace: mem-example
spec:
  containers:
  - name: memory-demo-3-ctr
    image: polinux/stress
    resources:
      requests:
        memory: "1000Gi"
      limits:
        memory: "1000Gi"
    command: ["stress"]
    args: ["--vm", "1", "--vm-bytes", "150M", "--vm-hang", "1"]

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/resource/memory-request-limit-3.yaml --namespace=mem-example

View the Pod status:

kubectl get pod memory-demo-3 --namespace=mem-example

The output shows that the Pod status is PENDING. That is, the Pod is not scheduled to run on any Node, and it will remain in the PENDING state indefinitely:

kubectl get pod memory-demo-3 --namespace=mem-example
NAME            READY     STATUS    RESTARTS   AGE
memory-demo-3   0/1       Pending   0          25s

View detailed information about the Pod, including events:

kubectl describe pod memory-demo-3 --namespace=mem-example

The output shows that the Container cannot be scheduled because of insufficient memory on the Nodes:

Events:
  ...  Reason            Message
       ------            -------
  ...  FailedScheduling  No nodes are available that match all of the following predicates:: Insufficient memory (3).

Memory units

The memory resource is measured in bytes. You can express memory as a plain integer or a fixed-point integer with one of these suffixes: E, P, T, G, M, K, Ei, Pi, Ti, Gi, Mi, Ki. For example, the following represent approximately the same value:

128974848, 129e6, 129M, 123Mi

Delete your Pod:

kubectl delete pod memory-demo-3 --namespace=mem-example

If you do not specify a memory limit

If you do not specify a memory limit for a Container, one of the following situations applies:

The Container has no upper bound on the amount of memory it uses. The Container could use all of the memory available on the Node where it is running which in turn could invoke the OOM Killer. Further, in case of an OOM Kill, a container with no resource limits will have a greater chance of being killed.
The Container is running in a namespace that has a default memory limit, and the Container is automatically assigned the default limit. Cluster administrators can use a LimitRange to specify a default value for the memory limit.

Motivation for memory requests and limits

By configuring memory requests and limits for the Containers that run in your cluster, you can make efficient use of the memory resources available on your cluster's Nodes. By keeping a Pod's memory request low, you give the Pod a good chance of being scheduled. By having a memory limit that is greater than the memory request, you accomplish two things:

The Pod can have bursts of activity where it makes use of memory that happens to be available.
The amount of memory a Pod can use during a burst is limited to some reasonable amount.

Clean up

Delete your namespace. This deletes all the Pods that you created for this task:

kubectl delete namespace mem-example

What's next

For app developers

For cluster administrators

3.2 - Assign CPU Resources to Containers and Pods

This page shows how to assign a CPU request and a CPU limit to a container. Containers cannot use more CPU than the configured limit. Provided the system has CPU time free, a container is guaranteed to be allocated as much CPU as it requests.

Before you begin

To check the version, enter kubectl version.

Your cluster must have at least 1 CPU available for use to run the task examples.

A few of the steps on this page require you to run the metrics-server service in your cluster. If you have the metrics-server running, you can skip those steps.

If you are running Minikube, run the following command to enable metrics-server:

minikube addons enable metrics-server

To see whether metrics-server (or another provider of the resource metrics API, metrics.k8s.io) is running, type the following command:

kubectl get apiservices

If the resource metrics API is available, the output will include a reference to metrics.k8s.io.

NAME
v1beta1.metrics.k8s.io

Create a namespace

Create a Namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace cpu-example

Specify a CPU request and a CPU limit

To specify a CPU request for a container, include the resources:requests field in the Container resource manifest. To specify a CPU limit, include resources:limits.

In this exercise, you create a Pod that has one container. The container has a request of 0.5 CPU and a limit of 1 CPU. Here is the configuration file for the Pod:

pods/resource/cpu-request-limit.yaml

apiVersion: v1
kind: Pod
metadata:
  name: cpu-demo
  namespace: cpu-example
spec:
  containers:
  - name: cpu-demo-ctr
    image: vish/stress
    resources:
      limits:
        cpu: "1"
      requests:
        cpu: "0.5"
    args:
    - -cpus
    - "2"

The args section of the configuration file provides arguments for the container when it starts. The -cpus "2" argument tells the Container to attempt to use 2 CPUs.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/resource/cpu-request-limit.yaml --namespace=cpu-example

Verify that the Pod is running:

kubectl get pod cpu-demo --namespace=cpu-example

View detailed information about the Pod:

kubectl get pod cpu-demo --output=yaml --namespace=cpu-example

The output shows that the one container in the Pod has a CPU request of 500 milliCPU and a CPU limit of 1 CPU.

resources:
  limits:
    cpu: "1"
  requests:
    cpu: 500m

Use kubectl top to fetch the metrics for the Pod:

kubectl top pod cpu-demo --namespace=cpu-example

This example output shows that the Pod is using 974 milliCPU, which is slightly less than the limit of 1 CPU specified in the Pod configuration.

NAME                        CPU(cores)   MEMORY(bytes)
cpu-demo                    974m         <something>

Recall that by setting -cpu "2", you configured the Container to attempt to use 2 CPUs, but the Container is only being allowed to use about 1 CPU. The container's CPU use is being throttled, because the container is attempting to use more CPU resources than its limit.

Note:

Another possible explanation for the CPU use being below 1.0 is that the Node might not have enough CPU resources available. Recall that the prerequisites for this exercise require your cluster to have at least 1 CPU available for use. If your Container runs on a Node that has only 1 CPU, the Container cannot use more than 1 CPU regardless of the CPU limit specified for the Container.

CPU units

The CPU resource is measured in CPU units. One CPU, in Kubernetes, is equivalent to:

1 AWS vCPU
1 GCP Core
1 Azure vCore
1 Hyperthread on a bare-metal Intel processor with Hyperthreading

Fractional values are allowed. A Container that requests 0.5 CPU is guaranteed half as much CPU as a Container that requests 1 CPU. You can use the suffix m to mean milli. For example 100m CPU, 100 milliCPU, and 0.1 CPU are all the same. Precision finer than 1m is not allowed.

CPU is always requested as an absolute quantity, never as a relative quantity; 0.1 is the same amount of CPU on a single-core, dual-core, or 48-core machine.

Delete your Pod:

kubectl delete pod cpu-demo --namespace=cpu-example

Specify a CPU request that is too big for your Nodes

CPU requests and limits are associated with Containers, but it is useful to think of a Pod as having a CPU request and limit. The CPU request for a Pod is the sum of the CPU requests for all the Containers in the Pod. Likewise, the CPU limit for a Pod is the sum of the CPU limits for all the Containers in the Pod.

Pod scheduling is based on requests. A Pod is scheduled to run on a Node only if the Node has enough CPU resources available to satisfy the Pod CPU request.

In this exercise, you create a Pod that has a CPU request so big that it exceeds the capacity of any Node in your cluster. Here is the configuration file for a Pod that has one Container. The Container requests 100 CPU, which is likely to exceed the capacity of any Node in your cluster.

pods/resource/cpu-request-limit-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: cpu-demo-2
  namespace: cpu-example
spec:
  containers:
  - name: cpu-demo-ctr-2
    image: vish/stress
    resources:
      limits:
        cpu: "100"
      requests:
        cpu: "100"
    args:
    - -cpus
    - "2"

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/resource/cpu-request-limit-2.yaml --namespace=cpu-example

View the Pod status:

kubectl get pod cpu-demo-2 --namespace=cpu-example

The output shows that the Pod status is Pending. That is, the Pod has not been scheduled to run on any Node, and it will remain in the Pending state indefinitely:

NAME         READY     STATUS    RESTARTS   AGE
cpu-demo-2   0/1       Pending   0          7m

View detailed information about the Pod, including events:

kubectl describe pod cpu-demo-2 --namespace=cpu-example

The output shows that the Container cannot be scheduled because of insufficient CPU resources on the Nodes:

Events:
  Reason                        Message
  ------                        -------
  FailedScheduling      No nodes are available that match all of the following predicates:: Insufficient cpu (3).

Delete your Pod:

kubectl delete pod cpu-demo-2 --namespace=cpu-example

If you do not specify a CPU limit

If you do not specify a CPU limit for a Container, then one of these situations applies:

The Container has no upper bound on the CPU resources it can use. The Container could use all of the CPU resources available on the Node where it is running.
The Container is running in a namespace that has a default CPU limit, and the Container is automatically assigned the default limit. Cluster administrators can use a LimitRange to specify a default value for the CPU limit.

If you specify a CPU limit but do not specify a CPU request

If you specify a CPU limit for a Container but do not specify a CPU request, Kubernetes automatically assigns a CPU request that matches the limit. Similarly, if a Container specifies its own memory limit, but does not specify a memory request, Kubernetes automatically assigns a memory request that matches the limit.

Motivation for CPU requests and limits

By configuring the CPU requests and limits of the Containers that run in your cluster, you can make efficient use of the CPU resources available on your cluster Nodes. By keeping a Pod CPU request low, you give the Pod a good chance of being scheduled. By having a CPU limit that is greater than the CPU request, you accomplish two things:

The Pod can have bursts of activity where it makes use of CPU resources that happen to be available.
The amount of CPU resources a Pod can use during a burst is limited to some reasonable amount.

Clean up

Delete your namespace:

kubectl delete namespace cpu-example

What's next

For app developers

For cluster administrators

3.3 - Configure GMSA for Windows Pods and containers

FEATURE STATE: Kubernetes v1.18 [stable]

This page shows how to configure Group Managed Service Accounts (GMSA) for Pods and containers that will run on Windows nodes. Group Managed Service Accounts are a specific type of Active Directory account that provides automatic password management, simplified service principal name (SPN) management, and the ability to delegate the management to other administrators across multiple servers.

In Kubernetes, GMSA credential specs are configured at a Kubernetes cluster-wide scope as Custom Resources. Windows Pods, as well as individual containers within a Pod, can be configured to use a GMSA for domain based functions (e.g. Kerberos authentication) when interacting with other Windows services.

Before you begin

You need to have a Kubernetes cluster and the kubectl command-line tool must be configured to communicate with your cluster. The cluster is expected to have Windows worker nodes. This section covers a set of initial steps required once for each cluster:

Install the GMSACredentialSpec CRD

A CustomResourceDefinition(CRD) for GMSA credential spec resources needs to be configured on the cluster to define the custom resource type GMSACredentialSpec. Download the GMSA CRD YAML and save it as gmsa-crd.yaml. Next, install the CRD with kubectl apply -f gmsa-crd.yaml

Install webhooks to validate GMSA users

Two webhooks need to be configured on the Kubernetes cluster to populate and validate GMSA credential spec references at the Pod or container level:

A mutating webhook that expands references to GMSAs (by name from a Pod specification) into the full credential spec in JSON form within the Pod spec.
A validating webhook ensures all references to GMSAs are authorized to be used by the Pod service account.

Installing the above webhooks and associated objects require the steps below:

Create a certificate key pair (that will be used to allow the webhook container to communicate to the cluster)
Install a secret with the certificate from above.
Create a deployment for the core webhook logic.
Create the validating and mutating webhook configurations referring to the deployment.

A script can be used to deploy and configure the GMSA webhooks and associated objects mentioned above. The script can be run with a --dry-run=server option to allow you to review the changes that would be made to your cluster.

The YAML template used by the script may also be used to deploy the webhooks and associated objects manually (with appropriate substitutions for the parameters)

Configure GMSAs and Windows nodes in Active Directory

Before Pods in Kubernetes can be configured to use GMSAs, the desired GMSAs need to be provisioned in Active Directory as described in the Windows GMSA documentation. Windows worker nodes (that are part of the Kubernetes cluster) need to be configured in Active Directory to access the secret credentials associated with the desired GMSA as described in the Windows GMSA documentation.

Create GMSA credential spec resources

With the GMSACredentialSpec CRD installed (as described earlier), custom resources containing GMSA credential specs can be configured. The GMSA credential spec does not contain secret or sensitive data. It is information that a container runtime can use to describe the desired GMSA of a container to Windows. GMSA credential specs can be generated in YAML format with a utility PowerShell script.

Following are the steps for generating a GMSA credential spec YAML manually in JSON format and then converting it:

Import the CredentialSpec module: ipmo CredentialSpec.psm1
Create a credential spec in JSON format using New-CredentialSpec. To create a GMSA credential spec named WebApp1, invoke New-CredentialSpec -Name WebApp1 -AccountName WebApp1 -Domain $(Get-ADDomain -Current LocalComputer)
Use Get-CredentialSpec to show the path of the JSON file.
Convert the credspec file from JSON to YAML format and apply the necessary header fields apiVersion, kind, metadata and credspec to make it a GMSACredentialSpec custom resource that can be configured in Kubernetes.

The following YAML configuration describes a GMSA credential spec named gmsa-WebApp1:

apiVersion: windows.k8s.io/v1
kind: GMSACredentialSpec
metadata:
  name: gmsa-WebApp1  # This is an arbitrary name but it will be used as a reference
credspec:
  ActiveDirectoryConfig:
    GroupManagedServiceAccounts:
    - Name: WebApp1   # Username of the GMSA account
      Scope: CONTOSO  # NETBIOS Domain Name
    - Name: WebApp1   # Username of the GMSA account
      Scope: contoso.com # DNS Domain Name
  CmsPlugins:
  - ActiveDirectory
  DomainJoinConfig:
    DnsName: contoso.com  # DNS Domain Name
    DnsTreeName: contoso.com # DNS Domain Name Root
    Guid: 244818ae-87ac-4fcd-92ec-e79e5252348a  # GUID of the Domain
    MachineAccountName: WebApp1 # Username of the GMSA account
    NetBiosName: CONTOSO  # NETBIOS Domain Name
    Sid: S-1-5-21-2126449477-2524075714-3094792973 # SID of the Domain

The above credential spec resource may be saved as gmsa-Webapp1-credspec.yaml and applied to the cluster using: kubectl apply -f gmsa-Webapp1-credspec.yml

Configure cluster role to enable RBAC on specific GMSA credential specs

A cluster role needs to be defined for each GMSA credential spec resource. This authorizes the use verb on a specific GMSA resource by a subject which is typically a service account. The following example shows a cluster role that authorizes usage of the gmsa-WebApp1 credential spec from above. Save the file as gmsa-webapp1-role.yaml and apply using kubectl apply -f gmsa-webapp1-role.yaml

# Create the Role to read the credspec
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: webapp1-role
rules:
- apiGroups: ["windows.k8s.io"]
  resources: ["gmsacredentialspecs"]
  verbs: ["use"]
  resourceNames: ["gmsa-WebApp1"]

Assign role to service accounts to use specific GMSA credspecs

A service account (that Pods will be configured with) needs to be bound to the cluster role create above. This authorizes the service account to use the desired GMSA credential spec resource. The following shows the default service account being bound to a cluster role webapp1-role to use gmsa-WebApp1 credential spec resource created above.

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: allow-default-svc-account-read-on-gmsa-WebApp1
  namespace: default
subjects:
- kind: ServiceAccount
  name: default
  namespace: default
roleRef:
  kind: ClusterRole
  name: webapp1-role
  apiGroup: rbac.authorization.k8s.io

Configure GMSA credential spec reference in Pod spec

The Pod spec field securityContext.windowsOptions.gmsaCredentialSpecName is used to specify references to desired GMSA credential spec custom resources in Pod specs. This configures all containers in the Pod spec to use the specified GMSA. A sample Pod spec with the annotation populated to refer to gmsa-WebApp1:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    run: with-creds
  name: with-creds
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      run: with-creds
  template:
    metadata:
      labels:
        run: with-creds
    spec:
      securityContext:
        windowsOptions:
          gmsaCredentialSpecName: gmsa-webapp1
      containers:
      - image: mcr.microsoft.com/windows/servercore/iis:windowsservercore-ltsc2019
        imagePullPolicy: Always
        name: iis
      nodeSelector:
        kubernetes.io/os: windows

Individual containers in a Pod spec can also specify the desired GMSA credspec using a per-container securityContext.windowsOptions.gmsaCredentialSpecName field. For example:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    run: with-creds
  name: with-creds
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      run: with-creds
  template:
    metadata:
      labels:
        run: with-creds
    spec:
      containers:
      - image: mcr.microsoft.com/windows/servercore/iis:windowsservercore-ltsc2019
        imagePullPolicy: Always
        name: iis
        securityContext:
          windowsOptions:
            gmsaCredentialSpecName: gmsa-Webapp1
      nodeSelector:
        kubernetes.io/os: windows

As Pod specs with GMSA fields populated (as described above) are applied in a cluster, the following sequence of events take place:

The mutating webhook resolves and expands all references to GMSA credential spec resources to the contents of the GMSA credential spec.
The validating webhook ensures the service account associated with the Pod is authorized for the use verb on the specified GMSA credential spec.
The container runtime configures each Windows container with the specified GMSA credential spec so that the container can assume the identity of the GMSA in Active Directory and access services in the domain using that identity.

Authenticating to network shares using hostname or FQDN

If you are experiencing issues connecting to SMB shares from Pods using hostname or FQDN, but are able to access the shares via their IPv4 address then make sure the following registry key is set on the Windows nodes.

reg add "HKLM\SYSTEM\CurrentControlSet\Services\hns\State" /v EnableCompartmentNamespace /t REG_DWORD /d 1

Running Pods will then need to be recreated to pick up the behavior changes. More information on how this registry key is used can be found here

Troubleshooting

If you are having difficulties getting GMSA to work in your environment, there are a few troubleshooting steps you can take.

First, make sure the credspec has been passed to the Pod. To do this you will need to exec into one of your Pods and check the output of the nltest.exe /parentdomain command.

In the example below the Pod did not get the credspec correctly:

kubectl exec -it iis-auth-7776966999-n5nzr powershell.exe

nltest.exe /parentdomain results in the following error:

Getting parent domain failed: Status = 1722 0x6ba RPC_S_SERVER_UNAVAILABLE

If your Pod did get the credspec correctly, then next check communication with the domain. First, from inside of your Pod, quickly do an nslookup to find the root of your domain.

This will tell us 3 things:

The Pod can reach the DC
The DC can reach the Pod
DNS is working correctly.

If the DNS and communication test passes, next you will need to check if the Pod has established secure channel communication with the domain. To do this, again, exec into your Pod and run the nltest.exe /query command.

nltest.exe /query

Results in the following output:

I_NetLogonControl failed: Status = 1722 0x6ba RPC_S_SERVER_UNAVAILABLE

This tells us that for some reason, the Pod was unable to logon to the domain using the account specified in the credspec. You can try to repair the secure channel by running the following:

nltest /sc_reset:domain.example

If the command is successful you will see and output similar to this:

Flags: 30 HAS_IP  HAS_TIMESERV
Trusted DC Name \\dc10.domain.example
Trusted DC Connection Status Status = 0 0x0 NERR_Success
The command completed successfully

If the above corrects the error, you can automate the step by adding the following lifecycle hook to your Pod spec. If it did not correct the error, you will need to examine your credspec again and confirm that it is correct and complete.

        image: registry.domain.example/iis-auth:1809v1
        lifecycle:
          postStart:
            exec:
              command: ["powershell.exe","-command","do { Restart-Service -Name netlogon } while ( $($Result = (nltest.exe /query); if ($Result -like '*0x0 NERR_Success*') {return $true} else {return $false}) -eq $false)"]
        imagePullPolicy: IfNotPresent

If you add the lifecycle section show above to your Pod spec, the Pod will execute the commands listed to restart the netlogon service until the nltest.exe /query command exits without error.

3.4 - Resize CPU and Memory Resources assigned to Containers

FEATURE STATE: Kubernetes v1.27 [alpha]

This page assumes that you are familiar with Quality of Service for Kubernetes Pods.

This page shows how to resize CPU and memory resources assigned to containers of a running pod without restarting the pod or its containers. A Kubernetes node allocates resources for a pod based on its requests, and restricts the pod's resource usage based on the limits specified in the pod's containers.

Changing the resource allocation for a running Pod requires the InPlacePodVerticalScaling feature gate to be enabled. The alternative is to delete the Pod and let the workload controller make a replacement Pod that has a different resource requirement.

For in-place resize of pod resources:

Container's resource requests and limits are mutable for CPU and memory resources.
allocatedResources field in containerStatuses of the Pod's status reflects the resources allocated to the pod's containers.
resources field in containerStatuses of the Pod's status reflects the actual resource requests and limits that are configured on the running containers as reported by the container runtime.
resize field in the Pod's status shows the status of the last requested pending resize. It can have the following values:
- Proposed: This value indicates an acknowledgement of the requested resize and that the request was validated and recorded.
- InProgress: This value indicates that the node has accepted the resize request and is in the process of applying it to the pod's containers.
- Deferred: This value means that the requested resize cannot be granted at this time, and the node will keep retrying. The resize may be granted when other pods leave and free up node resources.
- Infeasible: is a signal that the node cannot accommodate the requested resize. This can happen if the requested resize exceeds the maximum resources the node can ever allocate for a pod.

Before you begin

Your Kubernetes server must be at or later than version 1.27. To check the version, enter kubectl version.

The InPlacePodVerticalScaling feature gate must be enabled for your control plane and for all nodes in your cluster.

Container Resize Policies

Resize policies allow for a more fine-grained control over how pod's containers are resized for CPU and memory resources. For example, the container's application may be able to handle CPU resources resized without being restarted, but resizing memory may require that the application hence the containers be restarted.

To enable this, the Container specification allows users to specify a resizePolicy. The following restart policies can be specified for resizing CPU and memory:

NotRequired: Resize the container's resources while it is running.
RestartContainer: Restart the container and apply new resources upon restart.

If resizePolicy[*].restartPolicy is not specified, it defaults to NotRequired.

Note:

If the Pod's restartPolicy is Never, container's resize restart policy must be set to NotRequired for all Containers in the Pod.

Below example shows a Pod whose Container's CPU can be resized without restart, but resizing memory requires the container to be restarted.

apiVersion: v1
kind: Pod
metadata:
  name: qos-demo-5
  namespace: qos-example
spec:
  containers:
  - name: qos-demo-ctr-5
    image: nginx
    resizePolicy:
    - resourceName: cpu
      restartPolicy: NotRequired
    - resourceName: memory
      restartPolicy: RestartContainer
    resources:
      limits:
        memory: "200Mi"
        cpu: "700m"
      requests:
        memory: "200Mi"
        cpu: "700m"

Note:

In the above example, if desired requests or limits for both CPU and memory have changed, the container will be restarted in order to resize its memory.

Create a pod with resource requests and limits

You can create a Guaranteed or Burstable Quality of Service class pod by specifying requests and/or limits for a pod's containers.

Consider the following manifest for a Pod that has one Container.

pods/qos/qos-pod-5.yaml

apiVersion: v1
kind: Pod
metadata:
  name: qos-demo-5
  namespace: qos-example
spec:
  containers:
  - name: qos-demo-ctr-5
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "700m"
      requests:
        memory: "200Mi"
        cpu: "700m"

Create the pod in the qos-example namespace:

kubectl create namespace qos-example
kubectl create -f https://k8s.io/examples/pods/qos/qos-pod-5.yaml

This pod is classified as a Guaranteed QoS class requesting 700m CPU and 200Mi memory.

View detailed information about the pod:

kubectl get pod qos-demo-5 --output=yaml --namespace=qos-example

Also notice that the values of resizePolicy[*].restartPolicy defaulted to NotRequired, indicating that CPU and memory can be resized while container is running.

spec:
  containers:
    ...
    resizePolicy:
    - resourceName: cpu
      restartPolicy: NotRequired
    - resourceName: memory
      restartPolicy: NotRequired
    resources:
      limits:
        cpu: 700m
        memory: 200Mi
      requests:
        cpu: 700m
        memory: 200Mi
...
  containerStatuses:
...
    name: qos-demo-ctr-5
    ready: true
...
    allocatedResources:
      cpu: 700m
      memory: 200Mi
    resources:
      limits:
        cpu: 700m
        memory: 200Mi
      requests:
        cpu: 700m
        memory: 200Mi
    restartCount: 0
    started: true
...
  qosClass: Guaranteed

Updating the pod's resources

Let's say the CPU requirements have increased, and 0.8 CPU is now desired. This may be specified manually, or determined and programmatically applied by an entity such as VerticalPodAutoscaler (VPA).

Note:

While you can change a Pod's requests and limits to express new desired resources, you cannot change the QoS class in which the Pod was created.

Now, patch the Pod's Container with CPU requests & limits both set to 800m:

kubectl -n qos-example patch pod qos-demo-5 --patch '{"spec":{"containers":[{"name":"qos-demo-ctr-5", "resources":{"requests":{"cpu":"800m"}, "limits":{"cpu":"800m"}}}]}}'

Query the Pod's detailed information after the Pod has been patched.

kubectl get pod qos-demo-5 --output=yaml --namespace=qos-example

The Pod's spec below reflects the updated CPU requests and limits.

spec:
  containers:
    ...
    resources:
      limits:
        cpu: 800m
        memory: 200Mi
      requests:
        cpu: 800m
        memory: 200Mi
...
  containerStatuses:
...
    allocatedResources:
      cpu: 800m
      memory: 200Mi
    resources:
      limits:
        cpu: 800m
        memory: 200Mi
      requests:
        cpu: 800m
        memory: 200Mi
    restartCount: 0
    started: true

Observe that the allocatedResources values have been updated to reflect the new desired CPU requests. This indicates that node was able to accommodate the increased CPU resource needs.

In the Container's status, updated CPU resource values shows that new CPU resources have been applied. The Container's restartCount remains unchanged, indicating that container's CPU resources were resized without restarting the container.

Clean up

Delete your namespace:

kubectl delete namespace qos-example

What's next

For application developers

For cluster administrators

3.5 - Configure RunAsUserName for Windows pods and containers

FEATURE STATE: Kubernetes v1.18 [stable]

This page shows how to use the runAsUserName setting for Pods and containers that will run on Windows nodes. This is roughly equivalent of the Linux-specific runAsUser setting, allowing you to run applications in a container as a different username than the default.

Before you begin

You need to have a Kubernetes cluster and the kubectl command-line tool must be configured to communicate with your cluster. The cluster is expected to have Windows worker nodes where pods with containers running Windows workloads will get scheduled.

Set the Username for a Pod

To specify the username with which to execute the Pod's container processes, include the securityContext field (PodSecurityContext) in the Pod specification, and within it, the windowsOptions (WindowsSecurityContextOptions) field containing the runAsUserName field.

The Windows security context options that you specify for a Pod apply to all Containers and init Containers in the Pod.

Here is a configuration file for a Windows Pod that has the runAsUserName field set:

windows/run-as-username-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: run-as-username-pod-demo
spec:
  securityContext:
    windowsOptions:
      runAsUserName: "ContainerUser"
  containers:
  - name: run-as-username-demo
    image: mcr.microsoft.com/windows/servercore:ltsc2019
    command: ["ping", "-t", "localhost"]
  nodeSelector:
    kubernetes.io/os: windows

Create the Pod:

kubectl apply -f https://k8s.io/examples/windows/run-as-username-pod.yaml

Verify that the Pod's Container is running:

kubectl get pod run-as-username-pod-demo

Get a shell to the running Container:

kubectl exec -it run-as-username-pod-demo -- powershell

Check that the shell is running user the correct username:

echo $env:USERNAME

The output should be:

ContainerUser

Set the Username for a Container

To specify the username with which to execute a Container's processes, include the securityContext field (SecurityContext) in the Container manifest, and within it, the windowsOptions (WindowsSecurityContextOptions) field containing the runAsUserName field.

The Windows security context options that you specify for a Container apply only to that individual Container, and they override the settings made at the Pod level.

Here is the configuration file for a Pod that has one Container, and the runAsUserName field is set at the Pod level and the Container level:

windows/run-as-username-container.yaml

apiVersion: v1
kind: Pod
metadata:
  name: run-as-username-container-demo
spec:
  securityContext:
    windowsOptions:
      runAsUserName: "ContainerUser"
  containers:
  - name: run-as-username-demo
    image: mcr.microsoft.com/windows/servercore:ltsc2019
    command: ["ping", "-t", "localhost"]
    securityContext:
        windowsOptions:
            runAsUserName: "ContainerAdministrator"
  nodeSelector:
    kubernetes.io/os: windows

Create the Pod:

kubectl apply -f https://k8s.io/examples/windows/run-as-username-container.yaml

Verify that the Pod's Container is running:

kubectl get pod run-as-username-container-demo

Get a shell to the running Container:

kubectl exec -it run-as-username-container-demo -- powershell

Check that the shell is running user the correct username (the one set at the Container level):

echo $env:USERNAME

The output should be:

ContainerAdministrator

Windows Username limitations

In order to use this feature, the value set in the runAsUserName field must be a valid username. It must have the following format: DOMAIN\USER, where DOMAIN\ is optional. Windows user names are case insensitive. Additionally, there are some restrictions regarding the DOMAIN and USER:

The runAsUserName field cannot be empty, and it cannot contain control characters (ASCII values: 0x00-0x1F, 0x7F)
The DOMAIN must be either a NetBios name, or a DNS name, each with their own restrictions:
- NetBios names: maximum 15 characters, cannot start with . (dot), and cannot contain the following characters: \ / : * ? " < > |
- DNS names: maximum 255 characters, contains only alphanumeric characters, dots, and dashes, and it cannot start or end with a . (dot) or - (dash).
The USER must have at most 20 characters, it cannot contain only dots or spaces, and it cannot contain the following characters: " / \ [ ] : ; | = , + * ? < > @.

Examples of acceptable values for the runAsUserName field: ContainerAdministrator, ContainerUser, NT AUTHORITY\NETWORK SERVICE, NT AUTHORITY\LOCAL SERVICE.

For more information about these limtations, check here and here.

What's next

3.6 - Create a Windows HostProcess Pod

FEATURE STATE: Kubernetes v1.26 [stable]

Windows HostProcess containers enable you to run containerized workloads on a Windows host. These containers operate as normal processes but have access to the host network namespace, storage, and devices when given the appropriate user privileges. HostProcess containers can be used to deploy network plugins, storage configurations, device plugins, kube-proxy, and other components to Windows nodes without the need for dedicated proxies or the direct installation of host services.

Administrative tasks such as installation of security patches, event log collection, and more can be performed without requiring cluster operators to log onto each Windows node. HostProcess containers can run as any user that is available on the host or is in the domain of the host machine, allowing administrators to restrict resource access through user permissions. While neither filesystem or process isolation are supported, a new volume is created on the host upon starting the container to give it a clean and consolidated workspace. HostProcess containers can also be built on top of existing Windows base images and do not inherit the same compatibility requirements as Windows server containers, meaning that the version of the base images does not need to match that of the host. It is, however, recommended that you use the same base image version as your Windows Server container workloads to ensure you do not have any unused images taking up space on the node. HostProcess containers also support volume mounts within the container volume.

When should I use a Windows HostProcess container?

When you need to perform tasks which require the networking namespace of the host. HostProcess containers have access to the host's network interfaces and IP addresses.
You need access to resources on the host such as the filesystem, event logs, etc.
Installation of specific device drivers or Windows services.
Consolidation of administrative tasks and security policies. This reduces the degree of privileges needed by Windows nodes.

Before you begin

This task guide is specific to Kubernetes v1.30. If you are not running Kubernetes v1.30, check the documentation for that version of Kubernetes.

In Kubernetes 1.30, the HostProcess container feature is enabled by default. The kubelet will communicate with containerd directly by passing the hostprocess flag via CRI. You can use the latest version of containerd (v1.6+) to run HostProcess containers. How to install containerd.

Limitations

These limitations are relevant for Kubernetes v1.30:

HostProcess containers require containerd 1.6 or higher container runtime and containerd 1.7 is recommended.
HostProcess pods can only contain HostProcess containers. This is a current limitation of the Windows OS; non-privileged Windows containers cannot share a vNIC with the host IP namespace.
HostProcess containers run as a process on the host and do not have any degree of isolation other than resource constraints imposed on the HostProcess user account. Neither filesystem or Hyper-V isolation are supported for HostProcess containers.
Volume mounts are supported and are mounted under the container volume. See Volume Mounts
A limited set of host user accounts are available for HostProcess containers by default. See Choosing a User Account.
Resource limits (disk, memory, cpu count) are supported in the same fashion as processes on the host.
Both Named pipe mounts and Unix domain sockets are not supported and should instead be accessed via their path on the host (e.g. \\.\pipe\*)

HostProcess Pod configuration requirements

Enabling a Windows HostProcess pod requires setting the right configurations in the pod security configuration. Of the policies defined in the Pod Security Standards HostProcess pods are disallowed by the baseline and restricted policies. It is therefore recommended that HostProcess pods run in alignment with the privileged profile.

When running under the privileged policy, here are the configurations which need to be set to enable the creation of a HostProcess pod:

Privileged policy specification
Control	Policy
`securityContext.windowsOptions.hostProcess`	Windows pods offer the ability to run HostProcess containers which enables privileged access to the Windows node. Allowed Values `true`
`hostNetwork`	Pods container HostProcess containers must use the host's network namespace. Allowed Values `true`
`securityContext.windowsOptions.runAsUserName`	Specification of which user the HostProcess container should run as is required for the pod spec. Allowed Values `NT AUTHORITY\SYSTEM` `NT AUTHORITY\Local service` `NT AUTHORITY\NetworkService` Local usergroup names (see below)
`runAsNonRoot`	Because HostProcess containers have privileged access to the host, the `runAsNonRoot` field cannot be set to true. Allowed Values Undefined/Nil `false`

Example manifest (excerpt)

spec:
  securityContext:
    windowsOptions:
      hostProcess: true
      runAsUserName: "NT AUTHORITY\\Local service"
  hostNetwork: true
  containers:
  - name: test
    image: image1:latest
    command:
      - ping
      - -t
      - 127.0.0.1
  nodeSelector:
    "kubernetes.io/os": windows

Volume mounts

HostProcess containers support the ability to mount volumes within the container volume space. Volume mount behavior differs depending on the version of containerd runtime used by on the node.

Containerd v1.6

Applications running inside the container can access volume mounts directly via relative or absolute paths. An environment variable $CONTAINER_SANDBOX_MOUNT_POINT is set upon container creation and provides the absolute host path to the container volume. Relative paths are based upon the .spec.containers.volumeMounts.mountPath configuration.

To access service account tokens (for example) the following path structures are supported within the container:

.\var\run\secrets\kubernetes.io\serviceaccount\
$CONTAINER_SANDBOX_MOUNT_POINT\var\run\secrets\kubernetes.io\serviceaccount\

Containerd v1.7 (and greater)

Applications running inside the container can access volume mounts directly via the volumeMount's specified mountPath (just like Linux and non-HostProcess Windows containers).

For backwards compatibility volumes can also be accessed via using the same relative paths configured by containerd v1.6.

As an example, to access service account tokens within the container you would use one of the following paths:

c:\var\run\secrets\kubernetes.io\serviceaccount
/var/run/secrets/kubernetes.io/serviceaccount/
$CONTAINER_SANDBOX_MOUNT_POINT\var\run\secrets\kubernetes.io\serviceaccount\

Resource limits

Resource limits (disk, memory, cpu count) are applied to the job and are job wide. For example, with a limit of 10MB set, the memory allocated for any HostProcess job object will be capped at 10MB. This is the same behavior as other Windows container types. These limits would be specified the same way they are currently for whatever orchestrator or runtime is being used. The only difference is in the disk resource usage calculation used for resource tracking due to the difference in how HostProcess containers are bootstrapped.

Choosing a user account

System accounts

By default, HostProcess containers support the ability to run as one of three supported Windows service accounts:

You should select an appropriate Windows service account for each HostProcess container, aiming to limit the degree of privileges so as to avoid accidental (or even malicious) damage to the host. The LocalSystem service account has the highest level of privilege of the three and should be used only if absolutely necessary. Where possible, use the LocalService service account as it is the least privileged of the three options.

Local accounts

If configured, HostProcess containers can also run as local user accounts which allows for node operators to give fine-grained access to workloads.

To run HostProcess containers as a local user; A local usergroup must first be created on the node and the name of that local usergroup must be specified in the runAsUserName field in the deployment. Prior to initializing the HostProcess container, a new ephemeral local user account to be created and joined to the specified usergroup, from which the container is run. This provides a number a benefits including eliminating the need to manage passwords for local user accounts. An initial HostProcess container running as a service account can be used to prepare the user groups for later HostProcess containers.

Note:

Running HostProcess containers as local user accounts requires containerd v1.7+

Example:

Create a local user group on the node (this can be done in another HostProcess container).
```
net localgroup hpc-localgroup /add
```
Grant access to desired resources on the node to the local usergroup. This can be done with tools like icacls.

Set runAsUserName to the name of the local usergroup for the pod or individual containers.

securityContext:
  windowsOptions:
    hostProcess: true
    runAsUserName: hpc-localgroup

Schedule the pod!

Base Image for HostProcess Containers

HostProcess containers can be built from any of the existing Windows Container base images.

Additionally a new base mage has been created just for HostProcess containers! For more information please check out the windows-host-process-containers-base-image github project.

Troubleshooting HostProcess containers

HostProcess containers fail to start with failed to create user process token: failed to logon user: Access is denied.: unknown

Ensure containerd is running as LocalSystem or LocalService service accounts. User accounts (even Administrator accounts) do not have permissions to create logon tokens for any of the supported user accounts.

3.7 - Configure Quality of Service for Pods

This page shows how to configure Pods so that they will be assigned particular Quality of Service (QoS) classes. Kubernetes uses QoS classes to make decisions about evicting Pods when Node resources are exceeded.

When Kubernetes creates a Pod it assigns one of these QoS classes to the Pod:

Before you begin

You also need to be able to create and delete namespaces.

Create a namespace

Create a namespace so that the resources you create in this exercise are isolated from the rest of your cluster.

kubectl create namespace qos-example

Create a Pod that gets assigned a QoS class of Guaranteed

For a Pod to be given a QoS class of Guaranteed:

Every Container in the Pod must have a memory limit and a memory request.
For every Container in the Pod, the memory limit must equal the memory request.
Every Container in the Pod must have a CPU limit and a CPU request.
For every Container in the Pod, the CPU limit must equal the CPU request.

These restrictions apply to init containers and app containers equally. Ephemeral containers cannot define resources so these restrictions do not apply.

Here is a manifest for a Pod that has one Container. The Container has a memory limit and a memory request, both equal to 200 MiB. The Container has a CPU limit and a CPU request, both equal to 700 milliCPU:

pods/qos/qos-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: qos-demo
  namespace: qos-example
spec:
  containers:
  - name: qos-demo-ctr
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "700m"
      requests:
        memory: "200Mi"
        cpu: "700m"

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/qos/qos-pod.yaml --namespace=qos-example

View detailed information about the Pod:

kubectl get pod qos-demo --namespace=qos-example --output=yaml

The output shows that Kubernetes gave the Pod a QoS class of Guaranteed. The output also verifies that the Pod Container has a memory request that matches its memory limit, and it has a CPU request that matches its CPU limit.

spec:
  containers:
    ...
    resources:
      limits:
        cpu: 700m
        memory: 200Mi
      requests:
        cpu: 700m
        memory: 200Mi
    ...
status:
  qosClass: Guaranteed

Note:

If a Container specifies its own memory limit, but does not specify a memory request, Kubernetes automatically assigns a memory request that matches the limit. Similarly, if a Container specifies its own CPU limit, but does not specify a CPU request, Kubernetes automatically assigns a CPU request that matches the limit.

Clean up

Delete your Pod:

kubectl delete pod qos-demo --namespace=qos-example

Create a Pod that gets assigned a QoS class of Burstable

A Pod is given a QoS class of Burstable if:

The Pod does not meet the criteria for QoS class Guaranteed.
At least one Container in the Pod has a memory or CPU request or limit.

Here is a manifest for a Pod that has one Container. The Container has a memory limit of 200 MiB and a memory request of 100 MiB.

pods/qos/qos-pod-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: qos-demo-2
  namespace: qos-example
spec:
  containers:
  - name: qos-demo-2-ctr
    image: nginx
    resources:
      limits:
        memory: "200Mi"
      requests:
        memory: "100Mi"

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/qos/qos-pod-2.yaml --namespace=qos-example

View detailed information about the Pod:

kubectl get pod qos-demo-2 --namespace=qos-example --output=yaml

The output shows that Kubernetes gave the Pod a QoS class of Burstable:

spec:
  containers:
  - image: nginx
    imagePullPolicy: Always
    name: qos-demo-2-ctr
    resources:
      limits:
        memory: 200Mi
      requests:
        memory: 100Mi
  ...
status:
  qosClass: Burstable

Clean up

Delete your Pod:

kubectl delete pod qos-demo-2 --namespace=qos-example

Create a Pod that gets assigned a QoS class of BestEffort

For a Pod to be given a QoS class of BestEffort, the Containers in the Pod must not have any memory or CPU limits or requests.

Here is a manifest for a Pod that has one Container. The Container has no memory or CPU limits or requests:

pods/qos/qos-pod-3.yaml

apiVersion: v1
kind: Pod
metadata:
  name: qos-demo-3
  namespace: qos-example
spec:
  containers:
  - name: qos-demo-3-ctr
    image: nginx

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/qos/qos-pod-3.yaml --namespace=qos-example

View detailed information about the Pod:

kubectl get pod qos-demo-3 --namespace=qos-example --output=yaml

The output shows that Kubernetes gave the Pod a QoS class of BestEffort:

spec:
  containers:
    ...
    resources: {}
  ...
status:
  qosClass: BestEffort

Clean up

Delete your Pod:

kubectl delete pod qos-demo-3 --namespace=qos-example

Create a Pod that has two Containers

Here is a manifest for a Pod that has two Containers. One container specifies a memory request of 200 MiB. The other Container does not specify any requests or limits.

pods/qos/qos-pod-4.yaml

apiVersion: v1
kind: Pod
metadata:
  name: qos-demo-4
  namespace: qos-example
spec:
  containers:

  - name: qos-demo-4-ctr-1
    image: nginx
    resources:
      requests:
        memory: "200Mi"

  - name: qos-demo-4-ctr-2
    image: redis

Notice that this Pod meets the criteria for QoS class Burstable. That is, it does not meet the criteria for QoS class Guaranteed, and one of its Containers has a memory request.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/qos/qos-pod-4.yaml --namespace=qos-example

View detailed information about the Pod:

kubectl get pod qos-demo-4 --namespace=qos-example --output=yaml

The output shows that Kubernetes gave the Pod a QoS class of Burstable:

spec:
  containers:
    ...
    name: qos-demo-4-ctr-1
    resources:
      requests:
        memory: 200Mi
    ...
    name: qos-demo-4-ctr-2
    resources: {}
    ...
status:
  qosClass: Burstable

Retrieve the QoS class for a Pod

Rather than see all the fields, you can view just the field you need:

kubectl --namespace=qos-example get pod qos-demo-4 -o jsonpath='{ .status.qosClass}{"\n"}'

Burstable

Clean up

Delete your namespace:

kubectl delete namespace qos-example

What's next

For app developers

For cluster administrators

3.8 - Assign Extended Resources to a Container

FEATURE STATE: Kubernetes v1.30 [stable]

This page shows how to assign extended resources to a Container.

Before you begin

To check the version, enter kubectl version.

Before you do this exercise, do the exercise in Advertise Extended Resources for a Node. That will configure one of your Nodes to advertise a dongle resource.

Assign an extended resource to a Pod

To request an extended resource, include the resources:requests field in your Container manifest. Extended resources are fully qualified with any domain outside of *.kubernetes.io/. Valid extended resource names have the form example.com/foo where example.com is replaced with your organization's domain and foo is a descriptive resource name.

Here is the configuration file for a Pod that has one Container:

pods/resource/extended-resource-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: extended-resource-demo
spec:
  containers:
  - name: extended-resource-demo-ctr
    image: nginx
    resources:
      requests:
        example.com/dongle: 3
      limits:
        example.com/dongle: 3

In the configuration file, you can see that the Container requests 3 dongles.

Create a Pod:

kubectl apply -f https://k8s.io/examples/pods/resource/extended-resource-pod.yaml

Verify that the Pod is running:

kubectl get pod extended-resource-demo

Describe the Pod:

kubectl describe pod extended-resource-demo

The output shows dongle requests:

Limits:
  example.com/dongle: 3
Requests:
  example.com/dongle: 3

Attempt to create a second Pod

Here is the configuration file for a Pod that has one Container. The Container requests two dongles.

pods/resource/extended-resource-pod-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: extended-resource-demo-2
spec:
  containers:
  - name: extended-resource-demo-2-ctr
    image: nginx
    resources:
      requests:
        example.com/dongle: 2
      limits:
        example.com/dongle: 2

Kubernetes will not be able to satisfy the request for two dongles, because the first Pod used three of the four available dongles.

Attempt to create a Pod:

kubectl apply -f https://k8s.io/examples/pods/resource/extended-resource-pod-2.yaml

Describe the Pod

kubectl describe pod extended-resource-demo-2

The output shows that the Pod cannot be scheduled, because there is no Node that has 2 dongles available:

Conditions:
  Type    Status
  PodScheduled  False
...
Events:
  ...
  ... Warning   FailedScheduling  pod (extended-resource-demo-2) failed to fit in any node
fit failure summary on nodes : Insufficient example.com/dongle (1)

View the Pod status:

kubectl get pod extended-resource-demo-2

The output shows that the Pod was created, but not scheduled to run on a Node. It has a status of Pending:

NAME                       READY     STATUS    RESTARTS   AGE
extended-resource-demo-2   0/1       Pending   0          6m

Clean up

Delete the Pods that you created for this exercise:

kubectl delete pod extended-resource-demo
kubectl delete pod extended-resource-demo-2

What's next

For application developers

For cluster administrators

Advertise Extended Resources for a Node

3.9 - Configure a Pod to Use a Volume for Storage

This page shows how to configure a Pod to use a Volume for storage.

A Container's file system lives only as long as the Container does. So when a Container terminates and restarts, filesystem changes are lost. For more consistent storage that is independent of the Container, you can use a Volume. This is especially important for stateful applications, such as key-value stores (such as Redis) and databases.

Before you begin

To check the version, enter kubectl version.

Configure a volume for a Pod

In this exercise, you create a Pod that runs one Container. This Pod has a Volume of type emptyDir that lasts for the life of the Pod, even if the Container terminates and restarts. Here is the configuration file for the Pod:

pods/storage/redis.yaml

apiVersion: v1
kind: Pod
metadata:
  name: redis
spec:
  containers:
  - name: redis
    image: redis
    volumeMounts:
    - name: redis-storage
      mountPath: /data/redis
  volumes:
  - name: redis-storage
    emptyDir: {}

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/storage/redis.yaml

Verify that the Pod's Container is running, and then watch for changes to the Pod:

kubectl get pod redis --watch

The output looks like this:

NAME      READY     STATUS    RESTARTS   AGE
redis     1/1       Running   0          13s

In another terminal, get a shell to the running Container:
```
kubectl exec -it redis -- /bin/bash
```

In your shell, go to /data/redis, and then create a file:

root@redis:/data# cd /data/redis/
root@redis:/data/redis# echo Hello > test-file

In your shell, list the running processes:

root@redis:/data/redis# apt-get update
root@redis:/data/redis# apt-get install procps
root@redis:/data/redis# ps aux

The output is similar to this:

USER       PID %CPU %MEM    VSZ   RSS TTY      STAT START   TIME COMMAND
redis        1  0.1  0.1  33308  3828 ?        Ssl  00:46   0:00 redis-server *:6379
root        12  0.0  0.0  20228  3020 ?        Ss   00:47   0:00 /bin/bash
root        15  0.0  0.0  17500  2072 ?        R+   00:48   0:00 ps aux

In your shell, kill the Redis process:
```
root@redis:/data/redis# kill <pid>
```
where <pid> is the Redis process ID (PID).

In your original terminal, watch for changes to the Redis Pod. Eventually, you will see something like this:

NAME      READY     STATUS     RESTARTS   AGE
redis     1/1       Running    0          13s
redis     0/1       Completed  0         6m
redis     1/1       Running    1         6m

At this point, the Container has terminated and restarted. This is because the Redis Pod has a restartPolicy of Always.

Get a shell into the restarted Container:
```
kubectl exec -it redis -- /bin/bash
```

In your shell, go to /data/redis, and verify that test-file is still there.

root@redis:/data/redis# cd /data/redis/
root@redis:/data/redis# ls
test-file

Delete the Pod that you created for this exercise:
```
kubectl delete pod redis
```

What's next

See Volume.
See Pod.
In addition to the local disk storage provided by emptyDir, Kubernetes supports many different network-attached storage solutions, including PD on GCE and EBS on EC2, which are preferred for critical data and will handle details such as mounting and unmounting the devices on the nodes. See Volumes for more details.

3.10 - Configure a Pod to Use a PersistentVolume for Storage

This page shows you how to configure a Pod to use a PersistentVolumeClaim for storage. Here is a summary of the process:

You, as cluster administrator, create a PersistentVolume backed by physical storage. You do not associate the volume with any Pod.
You, now taking the role of a developer / cluster user, create a PersistentVolumeClaim that is automatically bound to a suitable PersistentVolume.
You create a Pod that uses the above PersistentVolumeClaim for storage.

Before you begin

You need to have a Kubernetes cluster that has only one Node, and the kubectl command-line tool must be configured to communicate with your cluster. If you do not already have a single-node cluster, you can create one by using Minikube.
Familiarize yourself with the material in Persistent Volumes.

Create an index.html file on your Node

Open a shell to the single Node in your cluster. How you open a shell depends on how you set up your cluster. For example, if you are using Minikube, you can open a shell to your Node by entering minikube ssh.

In your shell on that Node, create a /mnt/data directory:

# This assumes that your Node uses "sudo" to run commands
# as the superuser
sudo mkdir /mnt/data

In the /mnt/data directory, create an index.html file:

# This again assumes that your Node uses "sudo" to run commands
# as the superuser
sudo sh -c "echo 'Hello from Kubernetes storage' > /mnt/data/index.html"

Note:

If your Node uses a tool for superuser access other than sudo, you can usually make this work if you replace sudo with the name of the other tool.

Test that the index.html file exists:

cat /mnt/data/index.html

The output should be:

Hello from Kubernetes storage

You can now close the shell to your Node.

Create a PersistentVolume

In this exercise, you create a hostPath PersistentVolume. Kubernetes supports hostPath for development and testing on a single-node cluster. A hostPath PersistentVolume uses a file or directory on the Node to emulate network-attached storage.

In a production cluster, you would not use hostPath. Instead a cluster administrator would provision a network resource like a Google Compute Engine persistent disk, an NFS share, or an Amazon Elastic Block Store volume. Cluster administrators can also use StorageClasses to set up dynamic provisioning.

Here is the configuration file for the hostPath PersistentVolume:

pods/storage/pv-volume.yaml

apiVersion: v1
kind: PersistentVolume
metadata:
  name: task-pv-volume
  labels:
    type: local
spec:
  storageClassName: manual
  capacity:
    storage: 10Gi
  accessModes:
    - ReadWriteOnce
  hostPath:
    path: "/mnt/data"

The configuration file specifies that the volume is at /mnt/data on the cluster's Node. The configuration also specifies a size of 10 gibibytes and an access mode of ReadWriteOnce, which means the volume can be mounted as read-write by a single Node. It defines the StorageClass name manual for the PersistentVolume, which will be used to bind PersistentVolumeClaim requests to this PersistentVolume.

Note:

This example uses the ReadWriteOnce access mode, for simplicity. For production use, the Kubernetes project recommends using the ReadWriteOncePod access mode instead.

Create the PersistentVolume:

kubectl apply -f https://k8s.io/examples/pods/storage/pv-volume.yaml

View information about the PersistentVolume:

kubectl get pv task-pv-volume

The output shows that the PersistentVolume has a STATUS of Available. This means it has not yet been bound to a PersistentVolumeClaim.

NAME             CAPACITY   ACCESSMODES   RECLAIMPOLICY   STATUS      CLAIM     STORAGECLASS   REASON    AGE
task-pv-volume   10Gi       RWO           Retain          Available             manual                   4s

Create a PersistentVolumeClaim

The next step is to create a PersistentVolumeClaim. Pods use PersistentVolumeClaims to request physical storage. In this exercise, you create a PersistentVolumeClaim that requests a volume of at least three gibibytes that can provide read-write access for at most one Node at a time.

Here is the configuration file for the PersistentVolumeClaim:

pods/storage/pv-claim.yaml

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: task-pv-claim
spec:
  storageClassName: manual
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 3Gi

Create the PersistentVolumeClaim:

kubectl apply -f https://k8s.io/examples/pods/storage/pv-claim.yaml

After you create the PersistentVolumeClaim, the Kubernetes control plane looks for a PersistentVolume that satisfies the claim's requirements. If the control plane finds a suitable PersistentVolume with the same StorageClass, it binds the claim to the volume.

Look again at the PersistentVolume:

kubectl get pv task-pv-volume

Now the output shows a STATUS of Bound.

NAME             CAPACITY   ACCESSMODES   RECLAIMPOLICY   STATUS    CLAIM                   STORAGECLASS   REASON    AGE
task-pv-volume   10Gi       RWO           Retain          Bound     default/task-pv-claim   manual                   2m

Look at the PersistentVolumeClaim:

kubectl get pvc task-pv-claim

The output shows that the PersistentVolumeClaim is bound to your PersistentVolume, task-pv-volume.

NAME            STATUS    VOLUME           CAPACITY   ACCESSMODES   STORAGECLASS   AGE
task-pv-claim   Bound     task-pv-volume   10Gi       RWO           manual         30s

Create a Pod

The next step is to create a Pod that uses your PersistentVolumeClaim as a volume.

Here is the configuration file for the Pod:

pods/storage/pv-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: task-pv-pod
spec:
  volumes:
    - name: task-pv-storage
      persistentVolumeClaim:
        claimName: task-pv-claim
  containers:
    - name: task-pv-container
      image: nginx
      ports:
        - containerPort: 80
          name: "http-server"
      volumeMounts:
        - mountPath: "/usr/share/nginx/html"
          name: task-pv-storage

Notice that the Pod's configuration file specifies a PersistentVolumeClaim, but it does not specify a PersistentVolume. From the Pod's point of view, the claim is a volume.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/storage/pv-pod.yaml

Verify that the container in the Pod is running;

kubectl get pod task-pv-pod

Get a shell to the container running in your Pod:

kubectl exec -it task-pv-pod -- /bin/bash

In your shell, verify that nginx is serving the index.html file from the hostPath volume:

# Be sure to run these 3 commands inside the root shell that comes from
# running "kubectl exec" in the previous step
apt update
apt install curl
curl http://localhost/

The output shows the text that you wrote to the index.html file on the hostPath volume:

Hello from Kubernetes storage

If you see that message, you have successfully configured a Pod to use storage from a PersistentVolumeClaim.

Clean up

Delete the Pod, the PersistentVolumeClaim and the PersistentVolume:

kubectl delete pod task-pv-pod
kubectl delete pvc task-pv-claim
kubectl delete pv task-pv-volume

If you don't already have a shell open to the Node in your cluster, open a new shell the same way that you did earlier.

In the shell on your Node, remove the file and directory that you created:

# This assumes that your Node uses "sudo" to run commands
# as the superuser
sudo rm /mnt/data/index.html
sudo rmdir /mnt/data

You can now close the shell to your Node.

Mounting the same persistentVolume in two places

pods/storage/pv-duplicate.yaml


apiVersion: v1
kind: Pod
metadata:
  name: test
spec:
  containers:
    - name: test
      image: nginx
      volumeMounts:
        # a mount for site-data
        - name: config
          mountPath: /usr/share/nginx/html
          subPath: html
        # another mount for nginx config
        - name: config
          mountPath: /etc/nginx/nginx.conf
          subPath: nginx.conf
  volumes:
    - name: config
      persistentVolumeClaim:
        claimName: test-nfs-claim

You can perform 2 volume mounts on your nginx container:

/usr/share/nginx/html for the static website
/etc/nginx/nginx.conf for the default config

Access control

Storage configured with a group ID (GID) allows writing only by Pods using the same GID. Mismatched or missing GIDs cause permission denied errors. To reduce the need for coordination with users, an administrator can annotate a PersistentVolume with a GID. Then the GID is automatically added to any Pod that uses the PersistentVolume.

Use the pv.beta.kubernetes.io/gid annotation as follows:

apiVersion: v1
kind: PersistentVolume
metadata:
  name: pv1
  annotations:
    pv.beta.kubernetes.io/gid: "1234"

When a Pod consumes a PersistentVolume that has a GID annotation, the annotated GID is applied to all containers in the Pod in the same way that GIDs specified in the Pod's security context are. Every GID, whether it originates from a PersistentVolume annotation or the Pod's specification, is applied to the first process run in each container.

Note:

When a Pod consumes a PersistentVolume, the GIDs associated with the PersistentVolume are not present on the Pod resource itself.

What's next

Learn more about PersistentVolumes.
Read the Persistent Storage design document.

Reference

3.11 - Configure a Pod to Use a Projected Volume for Storage

This page shows how to use a projected Volume to mount several existing volume sources into the same directory. Currently, secret, configMap, downwardAPI, and serviceAccountToken volumes can be projected.

Note:

serviceAccountToken is not a volume type.

Before you begin

To check the version, enter kubectl version.

Configure a projected volume for a pod

In this exercise, you create username and password Secrets from local files. You then create a Pod that runs one container, using a projected Volume to mount the Secrets into the same shared directory.

Here is the configuration file for the Pod:

pods/storage/projected.yaml

apiVersion: v1
kind: Pod
metadata:
  name: test-projected-volume
spec:
  containers:
  - name: test-projected-volume
    image: busybox:1.28
    args:
    - sleep
    - "86400"
    volumeMounts:
    - name: all-in-one
      mountPath: "/projected-volume"
      readOnly: true
  volumes:
  - name: all-in-one
    projected:
      sources:
      - secret:
          name: user
      - secret:
          name: pass

Create the Secrets:

# Create files containing the username and password:
echo -n "admin" > ./username.txt
echo -n "1f2d1e2e67df" > ./password.txt

# Package these files into secrets:
kubectl create secret generic user --from-file=./username.txt
kubectl create secret generic pass --from-file=./password.txt

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/storage/projected.yaml

Verify that the Pod's container is running, and then watch for changes to the Pod:

kubectl get --watch pod test-projected-volume

The output looks like this:

NAME                    READY     STATUS    RESTARTS   AGE
test-projected-volume   1/1       Running   0          14s

In another terminal, get a shell to the running container:
```
kubectl exec -it test-projected-volume -- /bin/sh
```
In your shell, verify that the projected-volume directory contains your projected sources:
```
ls /projected-volume/
```

Clean up

Delete the Pod and the Secrets:

kubectl delete pod test-projected-volume
kubectl delete secret user pass

What's next

Learn more about projected volumes.
Read the all-in-one volume design document.

3.12 - Configure a Security Context for a Pod or Container

A security context defines privilege and access control settings for a Pod or Container. Security context settings include, but are not limited to:

Discretionary Access Control: Permission to access an object, like a file, is based on user ID (UID) and group ID (GID).
Security Enhanced Linux (SELinux): Objects are assigned security labels.
Running as privileged or unprivileged.
Linux Capabilities: Give a process some privileges, but not all the privileges of the root user.
AppArmor: Use program profiles to restrict the capabilities of individual programs.
Seccomp: Filter a process's system calls.
allowPrivilegeEscalation: Controls whether a process can gain more privileges than its parent process. This bool directly controls whether the no_new_privs flag gets set on the container process. allowPrivilegeEscalation is always true when the container:
- is run as privileged, or
- has CAP_SYS_ADMIN
readOnlyRootFilesystem: Mounts the container's root filesystem as read-only.

The above bullets are not a complete set of security context settings -- please see SecurityContext for a comprehensive list.

Before you begin

To check the version, enter kubectl version.

Set the security context for a Pod

To specify security settings for a Pod, include the securityContext field in the Pod specification. The securityContext field is a PodSecurityContext object. The security settings that you specify for a Pod apply to all Containers in the Pod. Here is a configuration file for a Pod that has a securityContext and an emptyDir volume:

pods/security/security-context.yaml

apiVersion: v1
kind: Pod
metadata:
  name: security-context-demo
spec:
  securityContext:
    runAsUser: 1000
    runAsGroup: 3000
    fsGroup: 2000
  volumes:
  - name: sec-ctx-vol
    emptyDir: {}
  containers:
  - name: sec-ctx-demo
    image: busybox:1.28
    command: [ "sh", "-c", "sleep 1h" ]
    volumeMounts:
    - name: sec-ctx-vol
      mountPath: /data/demo
    securityContext:
      allowPrivilegeEscalation: false

In the configuration file, the runAsUser field specifies that for any Containers in the Pod, all processes run with user ID 1000. The runAsGroup field specifies the primary group ID of 3000 for all processes within any containers of the Pod. If this field is omitted, the primary group ID of the containers will be root(0). Any files created will also be owned by user 1000 and group 3000 when runAsGroup is specified. Since fsGroup field is specified, all processes of the container are also part of the supplementary group ID 2000. The owner for volume /data/demo and any files created in that volume will be Group ID 2000.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/security/security-context.yaml

Verify that the Pod's Container is running:

kubectl get pod security-context-demo

Get a shell to the running Container:

kubectl exec -it security-context-demo -- sh

In your shell, list the running processes:

ps

The output shows that the processes are running as user 1000, which is the value of runAsUser:

PID   USER     TIME  COMMAND
    1 1000      0:00 sleep 1h
    6 1000      0:00 sh
...

In your shell, navigate to /data, and list the one directory:

cd /data
ls -l

The output shows that the /data/demo directory has group ID 2000, which is the value of fsGroup.

drwxrwsrwx 2 root 2000 4096 Jun  6 20:08 demo

In your shell, navigate to /data/demo, and create a file:

cd demo
echo hello > testfile

List the file in the /data/demo directory:

ls -l

The output shows that testfile has group ID 2000, which is the value of fsGroup.

-rw-r--r-- 1 1000 2000 6 Jun  6 20:08 testfile

Run the following command:

id

The output is similar to this:

uid=1000 gid=3000 groups=2000

From the output, you can see that gid is 3000 which is same as the runAsGroup field. If the runAsGroup was omitted, the gid would remain as 0 (root) and the process will be able to interact with files that are owned by the root(0) group and groups that have the required group permissions for the root (0) group.

Exit your shell:

exit

Configure volume permission and ownership change policy for Pods

FEATURE STATE: Kubernetes v1.23 [stable]

By default, Kubernetes recursively changes ownership and permissions for the contents of each volume to match the fsGroup specified in a Pod's securityContext when that volume is mounted. For large volumes, checking and changing ownership and permissions can take a lot of time, slowing Pod startup. You can use the fsGroupChangePolicy field inside a securityContext to control the way that Kubernetes checks and manages ownership and permissions for a volume.

fsGroupChangePolicy - fsGroupChangePolicy defines behavior for changing ownership and permission of the volume before being exposed inside a Pod. This field only applies to volume types that support fsGroup controlled ownership and permissions. This field has two possible values:

OnRootMismatch: Only change permissions and ownership if the permission and the ownership of root directory does not match with expected permissions of the volume. This could help shorten the time it takes to change ownership and permission of a volume.
Always: Always change permission and ownership of the volume when volume is mounted.

For example:

securityContext:
  runAsUser: 1000
  runAsGroup: 3000
  fsGroup: 2000
  fsGroupChangePolicy: "OnRootMismatch"

Note:

This field has no effect on ephemeral volume types such as secret, configMap, and emptydir.

Delegating volume permission and ownership change to CSI driver

FEATURE STATE: Kubernetes v1.26 [stable]

If you deploy a Container Storage Interface (CSI) driver which supports the VOLUME_MOUNT_GROUP NodeServiceCapability, the process of setting file ownership and permissions based on the fsGroup specified in the securityContext will be performed by the CSI driver instead of Kubernetes. In this case, since Kubernetes doesn't perform any ownership and permission change, fsGroupChangePolicy does not take effect, and as specified by CSI, the driver is expected to mount the volume with the provided fsGroup, resulting in a volume that is readable/writable by the fsGroup.

Set the security context for a Container

To specify security settings for a Container, include the securityContext field in the Container manifest. The securityContext field is a SecurityContext object. Security settings that you specify for a Container apply only to the individual Container, and they override settings made at the Pod level when there is overlap. Container settings do not affect the Pod's Volumes.

Here is the configuration file for a Pod that has one Container. Both the Pod and the Container have a securityContext field:

pods/security/security-context-2.yaml

apiVersion: v1
kind: Pod
metadata:
  name: security-context-demo-2
spec:
  securityContext:
    runAsUser: 1000
  containers:
  - name: sec-ctx-demo-2
    image: gcr.io/google-samples/hello-app:2.0
    securityContext:
      runAsUser: 2000
      allowPrivilegeEscalation: false

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/security/security-context-2.yaml

Verify that the Pod's Container is running:

kubectl get pod security-context-demo-2

Get a shell into the running Container:

kubectl exec -it security-context-demo-2 -- sh

In your shell, list the running processes:

ps aux

The output shows that the processes are running as user 2000. This is the value of runAsUser specified for the Container. It overrides the value 1000 that is specified for the Pod.

USER       PID %CPU %MEM    VSZ   RSS TTY      STAT START   TIME COMMAND
2000         1  0.0  0.0   4336   764 ?        Ss   20:36   0:00 /bin/sh -c node server.js
2000         8  0.1  0.5 772124 22604 ?        Sl   20:36   0:00 node server.js
...

Exit your shell:

exit

Set capabilities for a Container

With Linux capabilities, you can grant certain privileges to a process without granting all the privileges of the root user. To add or remove Linux capabilities for a Container, include the capabilities field in the securityContext section of the Container manifest.

First, see what happens when you don't include a capabilities field. Here is configuration file that does not add or remove any Container capabilities:

pods/security/security-context-3.yaml

apiVersion: v1
kind: Pod
metadata:
  name: security-context-demo-3
spec:
  containers:
  - name: sec-ctx-3
    image: gcr.io/google-samples/hello-app:2.0

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/security/security-context-3.yaml

Verify that the Pod's Container is running:

kubectl get pod security-context-demo-3

Get a shell into the running Container:

kubectl exec -it security-context-demo-3 -- sh

In your shell, list the running processes:

ps aux

The output shows the process IDs (PIDs) for the Container:

USER  PID %CPU %MEM    VSZ   RSS TTY   STAT START   TIME COMMAND
root    1  0.0  0.0   4336   796 ?     Ss   18:17   0:00 /bin/sh -c node server.js
root    5  0.1  0.5 772124 22700 ?     Sl   18:17   0:00 node server.js

In your shell, view the status for process 1:

cd /proc/1
cat status

The output shows the capabilities bitmap for the process:

...
CapPrm:	00000000a80425fb
CapEff:	00000000a80425fb
...

Make a note of the capabilities bitmap, and then exit your shell:

exit

Next, run a Container that is the same as the preceding container, except that it has additional capabilities set.

Here is the configuration file for a Pod that runs one Container. The configuration adds the CAP_NET_ADMIN and CAP_SYS_TIME capabilities:

pods/security/security-context-4.yaml

apiVersion: v1
kind: Pod
metadata:
  name: security-context-demo-4
spec:
  containers:
  - name: sec-ctx-4
    image: gcr.io/google-samples/hello-app:2.0
    securityContext:
      capabilities:
        add: ["NET_ADMIN", "SYS_TIME"]

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/security/security-context-4.yaml

Get a shell into the running Container:

kubectl exec -it security-context-demo-4 -- sh

In your shell, view the capabilities for process 1:

cd /proc/1
cat status

The output shows capabilities bitmap for the process:

...
CapPrm:	00000000aa0435fb
CapEff:	00000000aa0435fb
...

Compare the capabilities of the two Containers:

00000000a80425fb
00000000aa0435fb

In the capability bitmap of the first container, bits 12 and 25 are clear. In the second container, bits 12 and 25 are set. Bit 12 is CAP_NET_ADMIN, and bit 25 is CAP_SYS_TIME. See capability.h for definitions of the capability constants.

Note:

Linux capability constants have the form CAP_XXX. But when you list capabilities in your container manifest, you must omit the CAP_ portion of the constant. For example, to add CAP_SYS_TIME, include SYS_TIME in your list of capabilities.

Set the Seccomp Profile for a Container

To set the Seccomp profile for a Container, include the seccompProfile field in the securityContext section of your Pod or Container manifest. The seccompProfile field is a SeccompProfile object consisting of type and localhostProfile. Valid options for type include RuntimeDefault, Unconfined, and Localhost. localhostProfile must only be set if type: Localhost. It indicates the path of the pre-configured profile on the node, relative to the kubelet's configured Seccomp profile location (configured with the --root-dir flag).

Here is an example that sets the Seccomp profile to the node's container runtime default profile:

...
securityContext:
  seccompProfile:
    type: RuntimeDefault

Here is an example that sets the Seccomp profile to a pre-configured file at <kubelet-root-dir>/seccomp/my-profiles/profile-allow.json:

...
securityContext:
  seccompProfile:
    type: Localhost
    localhostProfile: my-profiles/profile-allow.json

Assign SELinux labels to a Container

To assign SELinux labels to a Container, include the seLinuxOptions field in the securityContext section of your Pod or Container manifest. The seLinuxOptions field is an SELinuxOptions object. Here's an example that applies an SELinux level:

...
securityContext:
  seLinuxOptions:
    level: "s0:c123,c456"

Note:

To assign SELinux labels, the SELinux security module must be loaded on the host operating system.

Efficient SELinux volume relabeling

FEATURE STATE: Kubernetes v1.28 [beta]

Note:

Kubernetes v1.27 introduced an early limited form of this behavior that was only applicable to volumes (and PersistentVolumeClaims) using the ReadWriteOncePod access mode.

As an alpha feature, you can enable the SELinuxMount feature gate to widen that performance improvement to other kinds of PersistentVolumeClaims, as explained in detail below.

By default, the container runtime recursively assigns SELinux label to all files on all Pod volumes. To speed up this process, Kubernetes can change the SELinux label of a volume instantly by using a mount option -o context=<label>.

To benefit from this speedup, all these conditions must be met:

The feature gates ReadWriteOncePod and SELinuxMountReadWriteOncePod must be enabled.
Pod must use PersistentVolumeClaim with applicable accessModes and feature gates:
- Either the volume has accessModes: ["ReadWriteOncePod"], and feature gate SELinuxMountReadWriteOncePod is enabled.
- Or the volume can use any other access modes and both feature gates SELinuxMountReadWriteOncePod and SELinuxMount must be enabled.
Pod (or all its Containers that use the PersistentVolumeClaim) must have seLinuxOptions set.
The corresponding PersistentVolume must be either:
- A volume that uses the legacy in-tree iscsi, rbd or fc volume type.
- Or a volume that uses a CSI driver. The CSI driver must announce that it supports mounting with -o context by setting spec.seLinuxMount: true in its CSIDriver instance.

For any other volume types, SELinux relabelling happens another way: the container runtime recursively changes the SELinux label for all inodes (files and directories) in the volume. The more files and directories in the volume, the longer that relabelling takes.

Managing access to the `/proc` filesystem

FEATURE STATE: Kubernetes v1.12 [alpha]

For runtimes that follow the OCI runtime specification, containers default to running in a mode where there are multiple paths that are both masked and read-only. The result of this is the container has these paths present inside the container's mount namespace, and they can function similarly to if the container was an isolated host, but the container process cannot write to them. The list of masked and read-only paths are as follows:

Masked Paths:
- /proc/asound
- /proc/acpi
- /proc/kcore
- /proc/keys
- /proc/latency_stats
- /proc/timer_list
- /proc/timer_stats
- /proc/sched_debug
- /proc/scsi
- /sys/firmware
- /sys/devices/virtual/powercap
Read-Only Paths:
- /proc/bus
- /proc/fs
- /proc/irq
- /proc/sys
- /proc/sysrq-trigger

For some Pods, you might want to bypass that default masking of paths. The most common context for wanting this is if you are trying to run containers within a Kubernetes container (within a pod).

The securityContext field procMount allows a user to request a container's /proc be Unmasked, or be mounted as read-write by the container process. This also applies to /sys/firmware which is not in /proc.

...
securityContext:
  procMount: Unmasked

Note:

Setting procMount to Unmasked requires the spec.hostUsers value in the pod spec to be false. In other words: a container that wishes to have an Unmasked /proc or unmasked /sys must also be in a user namespace. Kubernetes v1.12 to v1.29 did not enforce that requirement.

Discussion

The security context for a Pod applies to the Pod's Containers and also to the Pod's Volumes when applicable. Specifically fsGroup and seLinuxOptions are applied to Volumes as follows:

fsGroup: Volumes that support ownership management are modified to be owned and writable by the GID specified in fsGroup. See the Ownership Management design document for more details.
seLinuxOptions: Volumes that support SELinux labeling are relabeled to be accessible by the label specified under seLinuxOptions. Usually you only need to set the level section. This sets the Multi-Category Security (MCS) label given to all Containers in the Pod as well as the Volumes.

Warning:

After you specify an MCS label for a Pod, all Pods with the same label can access the Volume. If you need inter-Pod protection, you must assign a unique MCS label to each Pod.

Clean up

Delete the Pod:

kubectl delete pod security-context-demo
kubectl delete pod security-context-demo-2
kubectl delete pod security-context-demo-3
kubectl delete pod security-context-demo-4

What's next

PodSecurityContext
SecurityContext
CRI Plugin Config Guide
Security Contexts design document
Ownership Management design document
PodSecurity Admission
AllowPrivilegeEscalation design document
For more information about security mechanisms in Linux, see Overview of Linux Kernel Security Features (Note: Some information is out of date)
Read about User Namespaces for Linux pods.
Masked Paths in the OCI Runtime Specification

3.13 - Configure Service Accounts for Pods

Kubernetes offers two distinct ways for clients that run within your cluster, or that otherwise have a relationship to your cluster's control plane to authenticate to the API server.

A service account provides an identity for processes that run in a Pod, and maps to a ServiceAccount object. When you authenticate to the API server, you identify yourself as a particular user. Kubernetes recognises the concept of a user, however, Kubernetes itself does not have a User API.

This task guide is about ServiceAccounts, which do exist in the Kubernetes API. The guide shows you some ways to configure ServiceAccounts for Pods.

Before you begin

Use the default service account to access the API server

When Pods contact the API server, Pods authenticate as a particular ServiceAccount (for example, default). There is always at least one ServiceAccount in each namespace.

Every Kubernetes namespace contains at least one ServiceAccount: the default ServiceAccount for that namespace, named default. If you do not specify a ServiceAccount when you create a Pod, Kubernetes automatically assigns the ServiceAccount named default in that namespace.

You can fetch the details for a Pod you have created. For example:

kubectl get pods/<podname> -o yaml

In the output, you see a field spec.serviceAccountName. Kubernetes automatically sets that value if you don't specify it when you create a Pod.

An application running inside a Pod can access the Kubernetes API using automatically mounted service account credentials. See accessing the Cluster to learn more.

When a Pod authenticates as a ServiceAccount, its level of access depends on the authorization plugin and policy in use.

Opt out of API credential automounting

If you don't want the kubelet to automatically mount a ServiceAccount's API credentials, you can opt out of the default behavior. You can opt out of automounting API credentials on /var/run/secrets/kubernetes.io/serviceaccount/token for a service account by setting automountServiceAccountToken: false on the ServiceAccount:

For example:

apiVersion: v1
kind: ServiceAccount
metadata:
  name: build-robot
automountServiceAccountToken: false
...

You can also opt out of automounting API credentials for a particular Pod:

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  serviceAccountName: build-robot
  automountServiceAccountToken: false
  ...

If both the ServiceAccount and the Pod's .spec specify a value for automountServiceAccountToken, the Pod spec takes precedence.

Use more than one ServiceAccount

Every namespace has at least one ServiceAccount: the default ServiceAccount resource, called default. You can list all ServiceAccount resources in your current namespace with:

kubectl get serviceaccounts

The output is similar to this:

NAME      SECRETS    AGE
default   1          1d

You can create additional ServiceAccount objects like this:

kubectl apply -f - <<EOF
apiVersion: v1
kind: ServiceAccount
metadata:
  name: build-robot
EOF

The name of a ServiceAccount object must be a valid DNS subdomain name.

If you get a complete dump of the service account object, like this:

kubectl get serviceaccounts/build-robot -o yaml

The output is similar to this:

apiVersion: v1
kind: ServiceAccount
metadata:
  creationTimestamp: 2019-06-16T00:12:34Z
  name: build-robot
  namespace: default
  resourceVersion: "272500"
  uid: 721ab723-13bc-11e5-aec2-42010af0021e

You can use authorization plugins to set permissions on service accounts.

To use a non-default service account, set the spec.serviceAccountName field of a Pod to the name of the ServiceAccount you wish to use.

You can only set the serviceAccountName field when creating a Pod, or in a template for a new Pod. You cannot update the .spec.serviceAccountName field of a Pod that already exists.

Note:

The .spec.serviceAccount field is a deprecated alias for .spec.serviceAccountName. If you want to remove the fields from a workload resource, set both fields to empty explicitly on the pod template.

Cleanup

If you tried creating build-robot ServiceAccount from the example above, you can clean it up by running:

kubectl delete serviceaccount/build-robot

Manually create an API token for a ServiceAccount

Suppose you have an existing service account named "build-robot" as mentioned earlier.

You can get a time-limited API token for that ServiceAccount using kubectl:

kubectl create token build-robot

The output from that command is a token that you can use to authenticate as that ServiceAccount. You can request a specific token duration using the --duration command line argument to kubectl create token (the actual duration of the issued token might be shorter, or could even be longer).

When the ServiceAccountTokenNodeBinding and ServiceAccountTokenNodeBindingValidation features are enabled and the KUBECTL_NODE_BOUND_TOKENS environment variable is set to true, it is possible to create a service account token that is directly bound to a Node:

KUBECTL_NODE_BOUND_TOKENS=true kubectl create token build-robot --bound-object-kind Node --bound-object-name node-001 --bound-object-uid 123...456

The token will be valid until it expires or either the associated Node or service account are deleted.

Note:

Versions of Kubernetes before v1.22 automatically created long term credentials for accessing the Kubernetes API. This older mechanism was based on creating token Secrets that could then be mounted into running Pods. In more recent versions, including Kubernetes v1.30, API credentials are obtained directly by using the TokenRequest API, and are mounted into Pods using a projected volume. The tokens obtained using this method have bounded lifetimes, and are automatically invalidated when the Pod they are mounted into is deleted.

You can still manually create a service account token Secret; for example, if you need a token that never expires. However, using the TokenRequest subresource to obtain a token to access the API is recommended instead.

Manually create a long-lived API token for a ServiceAccount

If you want to obtain an API token for a ServiceAccount, you create a new Secret with a special annotation, kubernetes.io/service-account.name.

kubectl apply -f - <<EOF
apiVersion: v1
kind: Secret
metadata:
  name: build-robot-secret
  annotations:
    kubernetes.io/service-account.name: build-robot
type: kubernetes.io/service-account-token
EOF

If you view the Secret using:

kubectl get secret/build-robot-secret -o yaml

you can see that the Secret now contains an API token for the "build-robot" ServiceAccount.

Because of the annotation you set, the control plane automatically generates a token for that ServiceAccounts, and stores them into the associated Secret. The control plane also cleans up tokens for deleted ServiceAccounts.

kubectl describe secrets/build-robot-secret

The output is similar to this:

Name:           build-robot-secret
Namespace:      default
Labels:         <none>
Annotations:    kubernetes.io/service-account.name: build-robot
                kubernetes.io/service-account.uid: da68f9c6-9d26-11e7-b84e-002dc52800da

Type:   kubernetes.io/service-account-token

Data
====
ca.crt:         1338 bytes
namespace:      7 bytes
token:          ...

Note:

The content of token is omitted here.

Take care not to display the contents of a kubernetes.io/service-account-token Secret somewhere that your terminal / computer screen could be seen by an onlooker.

When you delete a ServiceAccount that has an associated Secret, the Kubernetes control plane automatically cleans up the long-lived token from that Secret.

Note:

If you view the ServiceAccount using:

kubectl get serviceaccount build-robot -o yaml

You can't see the build-robot-secret Secret in the ServiceAccount API objects .secrets field because that field is only populated with auto-generated Secrets.

Add ImagePullSecrets to a service account

First, create an imagePullSecret. Next, verify it has been created. For example:

Create an imagePullSecret, as described in Specifying ImagePullSecrets on a Pod.

kubectl create secret docker-registry myregistrykey --docker-server=<registry name> \
        --docker-username=DUMMY_USERNAME --docker-password=DUMMY_DOCKER_PASSWORD \
        --docker-email=DUMMY_DOCKER_EMAIL

Verify it has been created.

kubectl get secrets myregistrykey

The output is similar to this:

NAME             TYPE                              DATA    AGE
myregistrykey    kubernetes.io/.dockerconfigjson   1       1d

Add image pull secret to service account

Next, modify the default service account for the namespace to use this Secret as an imagePullSecret.

kubectl patch serviceaccount default -p '{"imagePullSecrets": [{"name": "myregistrykey"}]}'

You can achieve the same outcome by editing the object manually:

kubectl edit serviceaccount/default

The output of the sa.yaml file is similar to this:

Your selected text editor will open with a configuration looking something like this:

apiVersion: v1
kind: ServiceAccount
metadata:
  creationTimestamp: 2021-07-07T22:02:39Z
  name: default
  namespace: default
  resourceVersion: "243024"
  uid: 052fb0f4-3d50-11e5-b066-42010af0d7b6

Using your editor, delete the line with key resourceVersion, add lines for imagePullSecrets: and save it. Leave the uid value set the same as you found it.

After you made those changes, the edited ServiceAccount looks something like this:

apiVersion: v1
kind: ServiceAccount
metadata:
  creationTimestamp: 2021-07-07T22:02:39Z
  name: default
  namespace: default
  uid: 052fb0f4-3d50-11e5-b066-42010af0d7b6
imagePullSecrets:
  - name: myregistrykey

Verify that imagePullSecrets are set for new Pods

Now, when a new Pod is created in the current namespace and using the default ServiceAccount, the new Pod has its spec.imagePullSecrets field set automatically:

kubectl run nginx --image=<registry name>/nginx --restart=Never
kubectl get pod nginx -o=jsonpath='{.spec.imagePullSecrets[0].name}{"\n"}'

The output is:

myregistrykey

ServiceAccount token volume projection

FEATURE STATE: Kubernetes v1.20 [stable]

Note:

To enable and use token request projection, you must specify each of the following command line arguments to kube-apiserver:

--service-account-issuer: defines the Identifier of the service account token issuer. You can specify the --service-account-issuer argument multiple times, this can be useful to enable a non-disruptive change of the issuer. When this flag is specified multiple times, the first is used to generate tokens and all are used to determine which issuers are accepted. You must be running Kubernetes v1.22 or later to be able to specify --service-account-issuer multiple times.
--service-account-key-file: specifies the path to a file containing PEM-encoded X.509 private or public keys (RSA or ECDSA), used to verify ServiceAccount tokens. The specified file can contain multiple keys, and the flag can be specified multiple times with different files. If specified multiple times, tokens signed by any of the specified keys are considered valid by the Kubernetes API server.
--service-account-signing-key-file: specifies the path to a file that contains the current private key of the service account token issuer. The issuer signs issued ID tokens with this private key.
--api-audiences (can be omitted): defines audiences for ServiceAccount tokens. The service account token authenticator validates that tokens used against the API are bound to at least one of these audiences. If api-audiences is specified multiple times, tokens for any of the specified audiences are considered valid by the Kubernetes API server. If you specify the --service-account-issuer command line argument but you don't set --api-audiences, the control plane defaults to a single element audience list that contains only the issuer URL.

The kubelet can also project a ServiceAccount token into a Pod. You can specify desired properties of the token, such as the audience and the validity duration. These properties are not configurable on the default ServiceAccount token. The token will also become invalid against the API when either the Pod or the ServiceAccount is deleted.

You can configure this behavior for the spec of a Pod using a projected volume type called ServiceAccountToken.

The token from this projected volume is a JSON Web Token (JWT). The JSON payload of this token follows a well defined schema - an example payload for a pod bound token:

{
  "aud": [  # matches the requested audiences, or the API server's default audiences when none are explicitly requested
    "https://kubernetes.default.svc"
  ],
  "exp": 1731613413,
  "iat": 1700077413,
  "iss": "https://kubernetes.default.svc",  # matches the first value passed to the --service-account-issuer flag
  "jti": "ea28ed49-2e11-4280-9ec5-bc3d1d84661a",  # ServiceAccountTokenJTI feature must be enabled for the claim to be present
  "kubernetes.io": {
    "namespace": "kube-system",
    "node": {  # ServiceAccountTokenPodNodeInfo feature must be enabled for the API server to add this node reference claim
      "name": "127.0.0.1",
      "uid": "58456cb0-dd00-45ed-b797-5578fdceaced"
    },
    "pod": {
      "name": "coredns-69cbfb9798-jv9gn",
      "uid": "778a530c-b3f4-47c0-9cd5-ab018fb64f33"
    },
    "serviceaccount": {
      "name": "coredns",
      "uid": "a087d5a0-e1dd-43ec-93ac-f13d89cd13af"
    },
    "warnafter": 1700081020
  },
  "nbf": 1700077413,
  "sub": "system:serviceaccount:kube-system:coredns"
}

Launch a Pod using service account token projection

To provide a Pod with a token with an audience of vault and a validity duration of two hours, you could define a Pod manifest that is similar to:

pods/pod-projected-svc-token.yaml

apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  containers:
  - image: nginx
    name: nginx
    volumeMounts:
    - mountPath: /var/run/secrets/tokens
      name: vault-token
  serviceAccountName: build-robot
  volumes:
  - name: vault-token
    projected:
      sources:
      - serviceAccountToken:
          path: vault-token
          expirationSeconds: 7200
          audience: vault

Create the Pod:

kubectl create -f https://k8s.io/examples/pods/pod-projected-svc-token.yaml

The kubelet will: request and store the token on behalf of the Pod; make the token available to the Pod at a configurable file path; and refresh the token as it approaches expiration. The kubelet proactively requests rotation for the token if it is older than 80% of its total time-to-live (TTL), or if the token is older than 24 hours.

The application is responsible for reloading the token when it rotates. It's often good enough for the application to load the token on a schedule (for example: once every 5 minutes), without tracking the actual expiry time.

Service account issuer discovery

FEATURE STATE: Kubernetes v1.21 [stable]

If you have enabled token projection for ServiceAccounts in your cluster, then you can also make use of the discovery feature. Kubernetes provides a way for clients to federate as an identity provider, so that one or more external systems can act as a relying party.

Note:

The issuer URL must comply with the OIDC Discovery Spec. In practice, this means it must use the https scheme, and should serve an OpenID provider configuration at {service-account-issuer}/.well-known/openid-configuration.

If the URL does not comply, ServiceAccount issuer discovery endpoints are not registered or accessible.

When enabled, the Kubernetes API server publishes an OpenID Provider Configuration document via HTTP. The configuration document is published at /.well-known/openid-configuration. The OpenID Provider Configuration is sometimes referred to as the discovery document. The Kubernetes API server publishes the related JSON Web Key Set (JWKS), also via HTTP, at /openid/v1/jwks.

Note:

The responses served at /.well-known/openid-configuration and /openid/v1/jwks are designed to be OIDC compatible, but not strictly OIDC compliant. Those documents contain only the parameters necessary to perform validation of Kubernetes service account tokens.

Clusters that use RBAC include a default ClusterRole called system:service-account-issuer-discovery. A default ClusterRoleBinding assigns this role to the system:serviceaccounts group, which all ServiceAccounts implicitly belong to. This allows pods running on the cluster to access the service account discovery document via their mounted service account token. Administrators may, additionally, choose to bind the role to system:authenticated or system:unauthenticated depending on their security requirements and which external systems they intend to federate with.

The JWKS response contains public keys that a relying party can use to validate the Kubernetes service account tokens. Relying parties first query for the OpenID Provider Configuration, and use the jwks_uri field in the response to find the JWKS.

In many cases, Kubernetes API servers are not available on the public internet, but public endpoints that serve cached responses from the API server can be made available by users or by service providers. In these cases, it is possible to override the jwks_uri in the OpenID Provider Configuration so that it points to the public endpoint, rather than the API server's address, by passing the --service-account-jwks-uri flag to the API server. Like the issuer URL, the JWKS URI is required to use the https scheme.

What's next

3.14 - Pull an Image from a Private Registry

This page shows how to create a Pod that uses a Secret to pull an image from a private container image registry or repository. There are many private registries in use. This task uses Docker Hub as an example registry.

🛇 This item links to a third party project or product that is not part of Kubernetes itself. More information

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes
To do this exercise, you need the docker command line tool, and a Docker ID for which you know the password.
If you are using a different private container registry, you need the command line tool for that registry and any login information for the registry.

Log in to Docker Hub

On your laptop, you must authenticate with a registry in order to pull a private image.

Use the docker tool to log in to Docker Hub. See the log in section of Docker ID accounts for more information.

docker login

When prompted, enter your Docker ID, and then the credential you want to use (access token, or the password for your Docker ID).

The login process creates or updates a config.json file that holds an authorization token. Review how Kubernetes interprets this file.

View the config.json file:

cat ~/.docker/config.json

The output contains a section similar to this:

{
    "auths": {
        "https://index.docker.io/v1/": {
            "auth": "c3R...zE2"
        }
    }
}

Note:

If you use a Docker credentials store, you won't see that auth entry but a credsStore entry with the name of the store as value. In that case, you can create a secret directly. See Create a Secret by providing credentials on the command line.

Create a Secret based on existing credentials

A Kubernetes cluster uses the Secret of kubernetes.io/dockerconfigjson type to authenticate with a container registry to pull a private image.

If you already ran docker login, you can copy that credential into Kubernetes:

kubectl create secret generic regcred \
    --from-file=.dockerconfigjson=<path/to/.docker/config.json> \
    --type=kubernetes.io/dockerconfigjson

If you need more control (for example, to set a namespace or a label on the new secret) then you can customise the Secret before storing it. Be sure to:

set the name of the data item to .dockerconfigjson
base64 encode the Docker configuration file and then paste that string, unbroken as the value for field data[".dockerconfigjson"]
set type to kubernetes.io/dockerconfigjson

Example:

apiVersion: v1
kind: Secret
metadata:
  name: myregistrykey
  namespace: awesomeapps
data:
  .dockerconfigjson: UmVhbGx5IHJlYWxseSByZWVlZWVlZWVlZWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGx5eXl5eXl5eXl5eXl5eXl5eXl5eSBsbGxsbGxsbGxsbGxsbG9vb29vb29vb29vb29vb29vb29vb29vb29vb25ubm5ubm5ubm5ubm5ubm5ubm5ubm5ubmdnZ2dnZ2dnZ2dnZ2dnZ2dnZ2cgYXV0aCBrZXlzCg==
type: kubernetes.io/dockerconfigjson

If you get the error message error: no objects passed to create, it may mean the base64 encoded string is invalid. If you get an error message like Secret "myregistrykey" is invalid: data[.dockerconfigjson]: invalid value ..., it means the base64 encoded string in the data was successfully decoded, but could not be parsed as a .docker/config.json file.

Create a Secret by providing credentials on the command line

Create this Secret, naming it regcred:

kubectl create secret docker-registry regcred --docker-server=<your-registry-server> --docker-username=<your-name> --docker-password=<your-pword> --docker-email=<your-email>

where:

<your-registry-server> is your Private Docker Registry FQDN. Use https://index.docker.io/v1/ for DockerHub.
<your-name> is your Docker username.
<your-pword> is your Docker password.
<your-email> is your Docker email.

You have successfully set your Docker credentials in the cluster as a Secret called regcred.

Note:

Typing secrets on the command line may store them in your shell history unprotected, and those secrets might also be visible to other users on your PC during the time that kubectl is running.

Inspecting the Secret `regcred`

To understand the contents of the regcred Secret you created, start by viewing the Secret in YAML format:

kubectl get secret regcred --output=yaml

The output is similar to this:

apiVersion: v1
kind: Secret
metadata:
  ...
  name: regcred
  ...
data:
  .dockerconfigjson: eyJodHRwczovL2luZGV4L ... J0QUl6RTIifX0=
type: kubernetes.io/dockerconfigjson

The value of the .dockerconfigjson field is a base64 representation of your Docker credentials.

To understand what is in the .dockerconfigjson field, convert the secret data to a readable format:

kubectl get secret regcred --output="jsonpath={.data.\.dockerconfigjson}" | base64 --decode

The output is similar to this:

{"auths":{"your.private.registry.example.com":{"username":"janedoe","password":"xxxxxxxxxxx","email":"jdoe@example.com","auth":"c3R...zE2"}}}

To understand what is in the auth field, convert the base64-encoded data to a readable format:

echo "c3R...zE2" | base64 --decode

The output, username and password concatenated with a :, is similar to this:

janedoe:xxxxxxxxxxx

Notice that the Secret data contains the authorization token similar to your local ~/.docker/config.json file.

You have successfully set your Docker credentials as a Secret called regcred in the cluster.

Create a Pod that uses your Secret

Here is a manifest for an example Pod that needs access to your Docker credentials in regcred:

pods/private-reg-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: private-reg
spec:
  containers:
  - name: private-reg-container
    image: <your-private-image>
  imagePullSecrets:
  - name: regcred

Download the above file onto your computer:

curl -L -o my-private-reg-pod.yaml https://k8s.io/examples/pods/private-reg-pod.yaml

In file my-private-reg-pod.yaml, replace <your-private-image> with the path to an image in a private registry such as:

your.private.registry.example.com/janedoe/jdoe-private:v1

To pull the image from the private registry, Kubernetes needs credentials. The imagePullSecrets field in the configuration file specifies that Kubernetes should get the credentials from a Secret named regcred.

Create a Pod that uses your Secret, and verify that the Pod is running:

kubectl apply -f my-private-reg-pod.yaml
kubectl get pod private-reg

Note:

To use image pull secrets for a Pod (or a Deployment, or other object that has a pod template that you are using), you need to make sure that the appropriate Secret does exist in the right namespace. The namespace to use is the same namespace where you defined the Pod.

Also, in case the Pod fails to start with the status ImagePullBackOff, view the Pod events:

kubectl describe pod private-reg

If you then see an event with the reason set to FailedToRetrieveImagePullSecret, Kubernetes can't find a Secret with name (regcred, in this example). If you specify that a Pod needs image pull credentials, the kubelet checks that it can access that Secret before attempting to pull the image.

Make sure that the Secret you have specified exists, and that its name is spelled properly.

Events:
  ...  Reason                           ...  Message
       ------                                -------
  ...  FailedToRetrieveImagePullSecret  ...  Unable to retrieve some image pull secrets (<regcred>); attempting to pull the image may not succeed.

What's next

Learn more about Secrets
- or read the API reference for Secret
Learn more about using a private registry.
Learn more about adding image pull secrets to a service account.
See kubectl create secret docker-registry.
See the imagePullSecrets field within the container definitions of a Pod

3.15 - Configure Liveness, Readiness and Startup Probes

This page shows how to configure liveness, readiness and startup probes for containers.

For more information about probes, see Liveness, Readiness and Startup Probes

The kubelet uses liveness probes to know when to restart a container. For example, liveness probes could catch a deadlock, where an application is running, but unable to make progress. Restarting a container in such a state can help to make the application more available despite bugs.

A common pattern for liveness probes is to use the same low-cost HTTP endpoint as for readiness probes, but with a higher failureThreshold. This ensures that the pod is observed as not-ready for some period of time before it is hard killed.

The kubelet uses readiness probes to know when a container is ready to start accepting traffic. A Pod is considered ready when all of its containers are ready. One use of this signal is to control which Pods are used as backends for Services. When a Pod is not ready, it is removed from Service load balancers.

The kubelet uses startup probes to know when a container application has started. If such a probe is configured, liveness and readiness probes do not start until it succeeds, making sure those probes don't interfere with the application startup. This can be used to adopt liveness checks on slow starting containers, avoiding them getting killed by the kubelet before they are up and running.

Caution:

Liveness probes can be a powerful way to recover from application failures, but they should be used with caution. Liveness probes must be configured carefully to ensure that they truly indicate unrecoverable application failure, for example a deadlock.

Note:

Incorrect implementation of liveness probes can lead to cascading failures. This results in restarting of container under high load; failed client requests as your application became less scalable; and increased workload on remaining pods due to some failed pods. Understand the difference between readiness and liveness probes and when to apply them for your app.

Before you begin

Define a liveness command

Many applications running for long periods of time eventually transition to broken states, and cannot recover except by being restarted. Kubernetes provides liveness probes to detect and remedy such situations.

In this exercise, you create a Pod that runs a container based on the registry.k8s.io/busybox image. Here is the configuration file for the Pod:

pods/probe/exec-liveness.yaml

apiVersion: v1
kind: Pod
metadata:
  labels:
    test: liveness
  name: liveness-exec
spec:
  containers:
  - name: liveness
    image: registry.k8s.io/busybox
    args:
    - /bin/sh
    - -c
    - touch /tmp/healthy; sleep 30; rm -f /tmp/healthy; sleep 600
    livenessProbe:
      exec:
        command:
        - cat
        - /tmp/healthy
      initialDelaySeconds: 5
      periodSeconds: 5

In the configuration file, you can see that the Pod has a single Container. The periodSeconds field specifies that the kubelet should perform a liveness probe every 5 seconds. The initialDelaySeconds field tells the kubelet that it should wait 5 seconds before performing the first probe. To perform a probe, the kubelet executes the command cat /tmp/healthy in the target container. If the command succeeds, it returns 0, and the kubelet considers the container to be alive and healthy. If the command returns a non-zero value, the kubelet kills the container and restarts it.

When the container starts, it executes this command:

/bin/sh -c "touch /tmp/healthy; sleep 30; rm -f /tmp/healthy; sleep 600"

For the first 30 seconds of the container's life, there is a /tmp/healthy file. So during the first 30 seconds, the command cat /tmp/healthy returns a success code. After 30 seconds, cat /tmp/healthy returns a failure code.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/probe/exec-liveness.yaml

Within 30 seconds, view the Pod events:

kubectl describe pod liveness-exec

The output indicates that no liveness probes have failed yet:

Type    Reason     Age   From               Message
----    ------     ----  ----               -------
Normal  Scheduled  11s   default-scheduler  Successfully assigned default/liveness-exec to node01
Normal  Pulling    9s    kubelet, node01    Pulling image "registry.k8s.io/busybox"
Normal  Pulled     7s    kubelet, node01    Successfully pulled image "registry.k8s.io/busybox"
Normal  Created    7s    kubelet, node01    Created container liveness
Normal  Started    7s    kubelet, node01    Started container liveness

After 35 seconds, view the Pod events again:

kubectl describe pod liveness-exec

At the bottom of the output, there are messages indicating that the liveness probes have failed, and the failed containers have been killed and recreated.

Type     Reason     Age                From               Message
----     ------     ----               ----               -------
Normal   Scheduled  57s                default-scheduler  Successfully assigned default/liveness-exec to node01
Normal   Pulling    55s                kubelet, node01    Pulling image "registry.k8s.io/busybox"
Normal   Pulled     53s                kubelet, node01    Successfully pulled image "registry.k8s.io/busybox"
Normal   Created    53s                kubelet, node01    Created container liveness
Normal   Started    53s                kubelet, node01    Started container liveness
Warning  Unhealthy  10s (x3 over 20s)  kubelet, node01    Liveness probe failed: cat: can't open '/tmp/healthy': No such file or directory
Normal   Killing    10s                kubelet, node01    Container liveness failed liveness probe, will be restarted

Wait another 30 seconds, and verify that the container has been restarted:

kubectl get pod liveness-exec

The output shows that RESTARTS has been incremented. Note that the RESTARTS counter increments as soon as a failed container comes back to the running state:

NAME            READY     STATUS    RESTARTS   AGE
liveness-exec   1/1       Running   1          1m

Define a liveness HTTP request

Another kind of liveness probe uses an HTTP GET request. Here is the configuration file for a Pod that runs a container based on the registry.k8s.io/e2e-test-images/agnhost image.

pods/probe/http-liveness.yaml

apiVersion: v1
kind: Pod
metadata:
  labels:
    test: liveness
  name: liveness-http
spec:
  containers:
  - name: liveness
    image: registry.k8s.io/e2e-test-images/agnhost:2.40
    args:
    - liveness
    livenessProbe:
      httpGet:
        path: /healthz
        port: 8080
        httpHeaders:
        - name: Custom-Header
          value: Awesome
      initialDelaySeconds: 3
      periodSeconds: 3

In the configuration file, you can see that the Pod has a single container. The periodSeconds field specifies that the kubelet should perform a liveness probe every 3 seconds. The initialDelaySeconds field tells the kubelet that it should wait 3 seconds before performing the first probe. To perform a probe, the kubelet sends an HTTP GET request to the server that is running in the container and listening on port 8080. If the handler for the server's /healthz path returns a success code, the kubelet considers the container to be alive and healthy. If the handler returns a failure code, the kubelet kills the container and restarts it.

Any code greater than or equal to 200 and less than 400 indicates success. Any other code indicates failure.

You can see the source code for the server in server.go.

For the first 10 seconds that the container is alive, the /healthz handler returns a status of 200. After that, the handler returns a status of 500.

http.HandleFunc("/healthz", func(w http.ResponseWriter, r *http.Request) {
    duration := time.Now().Sub(started)
    if duration.Seconds() > 10 {
        w.WriteHeader(500)
        w.Write([]byte(fmt.Sprintf("error: %v", duration.Seconds())))
    } else {
        w.WriteHeader(200)
        w.Write([]byte("ok"))
    }
})

The kubelet starts performing health checks 3 seconds after the container starts. So the first couple of health checks will succeed. But after 10 seconds, the health checks will fail, and the kubelet will kill and restart the container.

To try the HTTP liveness check, create a Pod:

kubectl apply -f https://k8s.io/examples/pods/probe/http-liveness.yaml

After 10 seconds, view Pod events to verify that liveness probes have failed and the container has been restarted:

kubectl describe pod liveness-http

In releases after v1.13, local HTTP proxy environment variable settings do not affect the HTTP liveness probe.

Define a TCP liveness probe

A third type of liveness probe uses a TCP socket. With this configuration, the kubelet will attempt to open a socket to your container on the specified port. If it can establish a connection, the container is considered healthy, if it can't it is considered a failure.

pods/probe/tcp-liveness-readiness.yaml

apiVersion: v1
kind: Pod
metadata:
  name: goproxy
  labels:
    app: goproxy
spec:
  containers:
  - name: goproxy
    image: registry.k8s.io/goproxy:0.1
    ports:
    - containerPort: 8080
    readinessProbe:
      tcpSocket:
        port: 8080
      initialDelaySeconds: 15
      periodSeconds: 10
    livenessProbe:
      tcpSocket:
        port: 8080
      initialDelaySeconds: 15
      periodSeconds: 10

As you can see, configuration for a TCP check is quite similar to an HTTP check. This example uses both readiness and liveness probes. The kubelet will run the first liveness probe 15 seconds after the container starts. This will attempt to connect to the goproxy container on port 8080. If the liveness probe fails, the container will be restarted. The kubelet will continue to run this check every 10 seconds.

In addition to the liveness probe, this configuration includes a readiness probe. The kubelet will run the first readiness probe 15 seconds after the container starts. Similar to the liveness probe, this will attempt to connect to the goproxy container on port 8080. If the probe succeeds, the Pod will be marked as ready and will receive traffic from services. If the readiness probe fails, the pod will be marked unready and will not receive traffic from any services.

To try the TCP liveness check, create a Pod:

kubectl apply -f https://k8s.io/examples/pods/probe/tcp-liveness-readiness.yaml

After 15 seconds, view Pod events to verify that liveness probes:

kubectl describe pod goproxy

Define a gRPC liveness probe

FEATURE STATE: Kubernetes v1.27 [stable]

If your application implements the gRPC Health Checking Protocol, this example shows how to configure Kubernetes to use it for application liveness checks. Similarly you can configure readiness and startup probes.

Here is an example manifest:

pods/probe/grpc-liveness.yaml

apiVersion: v1
kind: Pod
metadata:
  name: etcd-with-grpc
spec:
  containers:
  - name: etcd
    image: registry.k8s.io/etcd:3.5.1-0
    command: [ "/usr/local/bin/etcd", "--data-dir",  "/var/lib/etcd", "--listen-client-urls", "http://0.0.0.0:2379", "--advertise-client-urls", "http://127.0.0.1:2379", "--log-level", "debug"]
    ports:
    - containerPort: 2379
    livenessProbe:
      grpc:
        port: 2379
      initialDelaySeconds: 10

To use a gRPC probe, port must be configured. If you want to distinguish probes of different types and probes for different features you can use the service field. You can set service to the value liveness and make your gRPC Health Checking endpoint respond to this request differently than when you set service set to readiness. This lets you use the same endpoint for different kinds of container health check rather than listening on two different ports. If you want to specify your own custom service name and also specify a probe type, the Kubernetes project recommends that you use a name that concatenates those. For example: myservice-liveness (using - as a separator).

Note:

Unlike HTTP or TCP probes, you cannot specify the health check port by name, and you cannot configure a custom hostname.

Configuration problems (for example: incorrect port or service, unimplemented health checking protocol) are considered a probe failure, similar to HTTP and TCP probes.

To try the gRPC liveness check, create a Pod using the command below. In the example below, the etcd pod is configured to use gRPC liveness probe.

kubectl apply -f https://k8s.io/examples/pods/probe/grpc-liveness.yaml

After 15 seconds, view Pod events to verify that the liveness check has not failed:

kubectl describe pod etcd-with-grpc

When using a gRPC probe, there are some technical details to be aware of:

The probes run against the pod IP address or its hostname. Be sure to configure your gRPC endpoint to listen on the Pod's IP address.
The probes do not support any authentication parameters (like -tls).
There are no error codes for built-in probes. All errors are considered as probe failures.
If ExecProbeTimeout feature gate is set to false, grpc-health-probe does not respect the timeoutSeconds setting (which defaults to 1s), while built-in probe would fail on timeout.

Use a named port

You can use a named port for HTTP and TCP probes. gRPC probes do not support named ports.

For example:

ports:
- name: liveness-port
  containerPort: 8080

livenessProbe:
  httpGet:
    path: /healthz
    port: liveness-port

Protect slow starting containers with startup probes

Sometimes, you have to deal with applications that require additional startup time on their first initialization. In such cases, it can be tricky to set up liveness probe parameters without compromising the fast response to deadlocks that motivated such a probe. The solution is to set up a startup probe with the same command, HTTP or TCP check, with a failureThreshold * periodSeconds long enough to cover the worst case startup time.

So, the previous example would become:

ports:
- name: liveness-port
  containerPort: 8080

livenessProbe:
  httpGet:
    path: /healthz
    port: liveness-port
  failureThreshold: 1
  periodSeconds: 10

startupProbe:
  httpGet:
    path: /healthz
    port: liveness-port
  failureThreshold: 30
  periodSeconds: 10

Thanks to the startup probe, the application will have a maximum of 5 minutes (30 * 10 = 300s) to finish its startup. Once the startup probe has succeeded once, the liveness probe takes over to provide a fast response to container deadlocks. If the startup probe never succeeds, the container is killed after 300s and subject to the pod's restartPolicy.

Define readiness probes

Sometimes, applications are temporarily unable to serve traffic. For example, an application might need to load large data or configuration files during startup, or depend on external services after startup. In such cases, you don't want to kill the application, but you don't want to send it requests either. Kubernetes provides readiness probes to detect and mitigate these situations. A pod with containers reporting that they are not ready does not receive traffic through Kubernetes Services.

Note:

Readiness probes runs on the container during its whole lifecycle.

Caution:

The readiness and liveness probes do not depend on each other to succeed. If you want to wait before executing a readiness probe, you should use initialDelaySeconds or a startupProbe.

Readiness probes are configured similarly to liveness probes. The only difference is that you use the readinessProbe field instead of the livenessProbe field.

readinessProbe:
  exec:
    command:
    - cat
    - /tmp/healthy
  initialDelaySeconds: 5
  periodSeconds: 5

Configuration for HTTP and TCP readiness probes also remains identical to liveness probes.

Readiness and liveness probes can be used in parallel for the same container. Using both can ensure that traffic does not reach a container that is not ready for it, and that containers are restarted when they fail.

Configure Probes

Probes have a number of fields that you can use to more precisely control the behavior of startup, liveness and readiness checks:

initialDelaySeconds: Number of seconds after the container has started before startup, liveness or readiness probes are initiated. If a startup probe is defined, liveness and readiness probe delays do not begin until the startup probe has succeeded. If the value of periodSeconds is greater than initialDelaySeconds then the initialDelaySeconds will be ignored. Defaults to 0 seconds. Minimum value is 0.
periodSeconds: How often (in seconds) to perform the probe. Default to 10 seconds. The minimum value is 1.
timeoutSeconds: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1.
successThreshold: Minimum consecutive successes for the probe to be considered successful after having failed. Defaults to 1. Must be 1 for liveness and startup Probes. Minimum value is 1.
failureThreshold: After a probe fails failureThreshold times in a row, Kubernetes considers that the overall check has failed: the container is not ready/healthy/live. Defaults to 3. Minimum value is 1. For the case of a startup or liveness probe, if at least failureThreshold probes have failed, Kubernetes treats the container as unhealthy and triggers a restart for that specific container. The kubelet honors the setting of terminationGracePeriodSeconds for that container. For a failed readiness probe, the kubelet continues running the container that failed checks, and also continues to run more probes; because the check failed, the kubelet sets the Ready condition on the Pod to false.
terminationGracePeriodSeconds: configure a grace period for the kubelet to wait between triggering a shut down of the failed container, and then forcing the container runtime to stop that container. The default is to inherit the Pod-level value for terminationGracePeriodSeconds (30 seconds if not specified), and the minimum value is 1. See probe-level terminationGracePeriodSeconds for more detail.

Caution:

Incorrect implementation of readiness probes may result in an ever growing number of processes in the container, and resource starvation if this is left unchecked.

HTTP probes

HTTP probes have additional fields that can be set on httpGet:

host: Host name to connect to, defaults to the pod IP. You probably want to set "Host" in httpHeaders instead.
scheme: Scheme to use for connecting to the host (HTTP or HTTPS). Defaults to "HTTP".
path: Path to access on the HTTP server. Defaults to "/".
httpHeaders: Custom headers to set in the request. HTTP allows repeated headers.
port: Name or number of the port to access on the container. Number must be in the range 1 to 65535.

For an HTTP probe, the kubelet sends an HTTP request to the specified port and path to perform the check. The kubelet sends the probe to the Pod's IP address, unless the address is overridden by the optional host field in httpGet. If scheme field is set to HTTPS, the kubelet sends an HTTPS request skipping the certificate verification. In most scenarios, you do not want to set the host field. Here's one scenario where you would set it. Suppose the container listens on 127.0.0.1 and the Pod's hostNetwork field is true. Then host, under httpGet, should be set to 127.0.0.1. If your pod relies on virtual hosts, which is probably the more common case, you should not use host, but rather set the Host header in httpHeaders.

For an HTTP probe, the kubelet sends two request headers in addition to the mandatory Host header:

User-Agent: The default value is kube-probe/1.30, where 1.30 is the version of the kubelet.
Accept: The default value is */*.

You can override the default headers by defining httpHeaders for the probe. For example:

livenessProbe:
  httpGet:
    httpHeaders:
      - name: Accept
        value: application/json

startupProbe:
  httpGet:
    httpHeaders:
      - name: User-Agent
        value: MyUserAgent

You can also remove these two headers by defining them with an empty value.

livenessProbe:
  httpGet:
    httpHeaders:
      - name: Accept
        value: ""

startupProbe:
  httpGet:
    httpHeaders:
      - name: User-Agent
        value: ""

Note:

When the kubelet probes a Pod using HTTP, it only follows redirects if the redirect
is to the same host. If the kubelet receives 11 or more redirects during probing, the probe is considered successful and a related Event is created:

Events:
  Type     Reason        Age                     From               Message
  ----     ------        ----                    ----               -------
  Normal   Scheduled     29m                     default-scheduler  Successfully assigned default/httpbin-7b8bc9cb85-bjzwn to daocloud
  Normal   Pulling       29m                     kubelet            Pulling image "docker.io/kennethreitz/httpbin"
  Normal   Pulled        24m                     kubelet            Successfully pulled image "docker.io/kennethreitz/httpbin" in 5m12.402735213s
  Normal   Created       24m                     kubelet            Created container httpbin
  Normal   Started       24m                     kubelet            Started container httpbin
 Warning  ProbeWarning  4m11s (x1197 over 24m)  kubelet            Readiness probe warning: Probe terminated redirects

If the kubelet receives a redirect where the hostname is different from the request, the outcome of the probe is treated as successful and kubelet creates an event to report the redirect failure.

TCP probes

For a TCP probe, the kubelet makes the probe connection at the node, not in the Pod, which means that you can not use a service name in the host parameter since the kubelet is unable to resolve it.

Probe-level `terminationGracePeriodSeconds`

FEATURE STATE: Kubernetes v1.28 [stable]

In 1.25 and above, users can specify a probe-level terminationGracePeriodSeconds as part of the probe specification. When both a pod- and probe-level terminationGracePeriodSeconds are set, the kubelet will use the probe-level value.

When setting the terminationGracePeriodSeconds, please note the following:

The kubelet always honors the probe-level terminationGracePeriodSeconds field if it is present on a Pod.
If you have existing Pods where the terminationGracePeriodSeconds field is set and you no longer wish to use per-probe termination grace periods, you must delete those existing Pods.

For example:

spec:
  terminationGracePeriodSeconds: 3600  # pod-level
  containers:
  - name: test
    image: ...

    ports:
    - name: liveness-port
      containerPort: 8080

    livenessProbe:
      httpGet:
        path: /healthz
        port: liveness-port
      failureThreshold: 1
      periodSeconds: 60
      # Override pod-level terminationGracePeriodSeconds #
      terminationGracePeriodSeconds: 60

Probe-level terminationGracePeriodSeconds cannot be set for readiness probes. It will be rejected by the API server.

What's next

Learn more about Container Probes.

You can also read the API references for:

Pod, and specifically:
- container(s)
- probe(s)

3.16 - Assign Pods to Nodes

This page shows how to assign a Kubernetes Pod to a particular node in a Kubernetes cluster.

Before you begin

To check the version, enter kubectl version.

Add a label to a node

List the nodes in your cluster, along with their labels:

kubectl get nodes --show-labels

The output is similar to this:

NAME      STATUS    ROLES    AGE     VERSION        LABELS
worker0   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker0
worker1   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker1
worker2   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker2

Choose one of your nodes, and add a label to it:
```
kubectl label nodes <your-node-name> disktype=ssd
```
where <your-node-name> is the name of your chosen node.

Verify that your chosen node has a disktype=ssd label:

kubectl get nodes --show-labels

The output is similar to this:

NAME      STATUS    ROLES    AGE     VERSION        LABELS
worker0   Ready     <none>   1d      v1.13.0        ...,disktype=ssd,kubernetes.io/hostname=worker0
worker1   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker1
worker2   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker2

In the preceding output, you can see that the worker0 node has a disktype=ssd label.

Create a pod that gets scheduled to your chosen node

This pod configuration file describes a pod that has a node selector, disktype: ssd. This means that the pod will get scheduled on a node that has a disktype=ssd label.

pods/pod-nginx.yaml

apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    env: test
spec:
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent
  nodeSelector:
    disktype: ssd

Use the configuration file to create a pod that will get scheduled on your chosen node:
```
kubectl apply -f https://k8s.io/examples/pods/pod-nginx.yaml
```

Verify that the pod is running on your chosen node:

kubectl get pods --output=wide

The output is similar to this:

NAME     READY     STATUS    RESTARTS   AGE    IP           NODE
nginx    1/1       Running   0          13s    10.200.0.4   worker0

Create a pod that gets scheduled to specific node

You can also schedule a pod to one specific node via setting nodeName.

pods/pod-nginx-specific-node.yaml

apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  nodeName: foo-node # schedule pod to specific node
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent

Use the configuration file to create a pod that will get scheduled on foo-node only.

What's next

Learn more about labels and selectors.
Learn more about nodes.

3.17 - Assign Pods to Nodes using Node Affinity

This page shows how to assign a Kubernetes Pod to a particular node using Node Affinity in a Kubernetes cluster.

Before you begin

Your Kubernetes server must be at or later than version v1.10. To check the version, enter kubectl version.

Add a label to a node

List the nodes in your cluster, along with their labels:

kubectl get nodes --show-labels

The output is similar to this:

NAME      STATUS    ROLES    AGE     VERSION        LABELS
worker0   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker0
worker1   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker1
worker2   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker2

Choose one of your nodes, and add a label to it:
```
kubectl label nodes <your-node-name> disktype=ssd
```
where <your-node-name> is the name of your chosen node.

Verify that your chosen node has a disktype=ssd label:

kubectl get nodes --show-labels

The output is similar to this:

NAME      STATUS    ROLES    AGE     VERSION        LABELS
worker0   Ready     <none>   1d      v1.13.0        ...,disktype=ssd,kubernetes.io/hostname=worker0
worker1   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker1
worker2   Ready     <none>   1d      v1.13.0        ...,kubernetes.io/hostname=worker2

In the preceding output, you can see that the worker0 node has a disktype=ssd label.

Schedule a Pod using required node affinity

This manifest describes a Pod that has a requiredDuringSchedulingIgnoredDuringExecution node affinity,disktype: ssd. This means that the pod will get scheduled only on a node that has a disktype=ssd label.

pods/pod-nginx-required-affinity.yaml

apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: disktype
            operator: In
            values:
            - ssd            
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent

Apply the manifest to create a Pod that is scheduled onto your chosen node:

kubectl apply -f https://k8s.io/examples/pods/pod-nginx-required-affinity.yaml

Verify that the pod is running on your chosen node:

kubectl get pods --output=wide

The output is similar to this:

NAME     READY     STATUS    RESTARTS   AGE    IP           NODE
nginx    1/1       Running   0          13s    10.200.0.4   worker0

Schedule a Pod using preferred node affinity

This manifest describes a Pod that has a preferredDuringSchedulingIgnoredDuringExecution node affinity,disktype: ssd. This means that the pod will prefer a node that has a disktype=ssd label.

pods/pod-nginx-preferred-affinity.yaml

apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  affinity:
    nodeAffinity:
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
          - key: disktype
            operator: In
            values:
            - ssd          
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent

Apply the manifest to create a Pod that is scheduled onto your chosen node:

kubectl apply -f https://k8s.io/examples/pods/pod-nginx-preferred-affinity.yaml

Verify that the pod is running on your chosen node:

kubectl get pods --output=wide

The output is similar to this:

NAME     READY     STATUS    RESTARTS   AGE    IP           NODE
nginx    1/1       Running   0          13s    10.200.0.4   worker0

What's next

Learn more about Node Affinity.

3.18 - Configure Pod Initialization

This page shows how to use an Init Container to initialize a Pod before an application Container runs.

Before you begin

To check the version, enter kubectl version.

Create a Pod that has an Init Container

In this exercise you create a Pod that has one application Container and one Init Container. The init container runs to completion before the application container starts.

Here is the configuration file for the Pod:

pods/init-containers.yaml

apiVersion: v1
kind: Pod
metadata:
  name: init-demo
spec:
  containers:
  - name: nginx
    image: nginx
    ports:
    - containerPort: 80
    volumeMounts:
    - name: workdir
      mountPath: /usr/share/nginx/html
  # These containers are run during pod initialization
  initContainers:
  - name: install
    image: busybox:1.28
    command:
    - wget
    - "-O"
    - "/work-dir/index.html"
    - http://info.cern.ch
    volumeMounts:
    - name: workdir
      mountPath: "/work-dir"
  dnsPolicy: Default
  volumes:
  - name: workdir
    emptyDir: {}

In the configuration file, you can see that the Pod has a Volume that the init container and the application container share.

The init container mounts the shared Volume at /work-dir, and the application container mounts the shared Volume at /usr/share/nginx/html. The init container runs the following command and then terminates:

wget -O /work-dir/index.html http://info.cern.ch

Notice that the init container writes the index.html file in the root directory of the nginx server.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/init-containers.yaml

Verify that the nginx container is running:

kubectl get pod init-demo

The output shows that the nginx container is running:

NAME        READY     STATUS    RESTARTS   AGE
init-demo   1/1       Running   0          1m

Get a shell into the nginx container running in the init-demo Pod:

kubectl exec -it init-demo -- /bin/bash

In your shell, send a GET request to the nginx server:

root@nginx:~# apt-get update
root@nginx:~# apt-get install curl
root@nginx:~# curl localhost

The output shows that nginx is serving the web page that was written by the init container:

<html><head></head><body><header>
<title>http://info.cern.ch</title>
</header>

<h1>http://info.cern.ch - home of the first website</h1>
  ...
  <li><a href="http://info.cern.ch/hypertext/WWW/TheProject.html">Browse the first website</a></li>
  ...

What's next

Learn more about communicating between Containers running in the same Pod.
Learn more about Init Containers.
Learn more about Volumes.
Learn more about Debugging Init Containers

3.19 - Attach Handlers to Container Lifecycle Events

This page shows how to attach handlers to Container lifecycle events. Kubernetes supports the postStart and preStop events. Kubernetes sends the postStart event immediately after a Container is started, and it sends the preStop event immediately before the Container is terminated. A Container may specify one handler per event.

Before you begin

To check the version, enter kubectl version.

Define postStart and preStop handlers

In this exercise, you create a Pod that has one Container. The Container has handlers for the postStart and preStop events.

Here is the configuration file for the Pod:

pods/lifecycle-events.yaml

apiVersion: v1
kind: Pod
metadata:
  name: lifecycle-demo
spec:
  containers:
  - name: lifecycle-demo-container
    image: nginx
    lifecycle:
      postStart:
        exec:
          command: ["/bin/sh", "-c", "echo Hello from the postStart handler > /usr/share/message"]
      preStop:
        exec:
          command: ["/bin/sh","-c","nginx -s quit; while killall -0 nginx; do sleep 1; done"]

In the configuration file, you can see that the postStart command writes a message file to the Container's /usr/share directory. The preStop command shuts down nginx gracefully. This is helpful if the Container is being terminated because of a failure.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/lifecycle-events.yaml

Verify that the Container in the Pod is running:

kubectl get pod lifecycle-demo

Get a shell into the Container running in your Pod:

kubectl exec -it lifecycle-demo -- /bin/bash

In your shell, verify that the postStart handler created the message file:

root@lifecycle-demo:/# cat /usr/share/message

The output shows the text written by the postStart handler:

Hello from the postStart handler

Discussion

Kubernetes sends the postStart event immediately after the Container is created. There is no guarantee, however, that the postStart handler is called before the Container's entrypoint is called. The postStart handler runs asynchronously relative to the Container's code, but Kubernetes' management of the container blocks until the postStart handler completes. The Container's status is not set to RUNNING until the postStart handler completes.

Kubernetes sends the preStop event immediately before the Container is terminated. Kubernetes' management of the Container blocks until the preStop handler completes, unless the Pod's grace period expires. For more details, see Pod Lifecycle.

Note:

Kubernetes only sends the preStop event when a Pod or a container in the Pod is terminated. This means that the preStop hook is not invoked when the Pod is completed. About this limitation, please see Container hooks for the detail.

What's next

Learn more about Container lifecycle hooks.
Learn more about the lifecycle of a Pod.

Reference

Lifecycle
Container
See terminationGracePeriodSeconds in PodSpec

3.20 - Configure a Pod to Use a ConfigMap

Many applications rely on configuration which is used during either application initialization or runtime. Most times, there is a requirement to adjust values assigned to configuration parameters. ConfigMaps are a Kubernetes mechanism that let you inject configuration data into application pods.

The ConfigMap concept allow you to decouple configuration artifacts from image content to keep containerized applications portable. For example, you can download and run the same container image to spin up containers for the purposes of local development, system test, or running a live end-user workload.

This page provides a series of usage examples demonstrating how to create ConfigMaps and configure Pods using data stored in ConfigMaps.

Before you begin

You need to have the wget tool installed. If you have a different tool such as curl, and you do not have wget, you will need to adapt the step that downloads example data.

Create a ConfigMap

You can use either kubectl create configmap or a ConfigMap generator in kustomization.yaml to create a ConfigMap.

Create a ConfigMap using `kubectl create configmap`

Use the kubectl create configmap command to create ConfigMaps from directories, files, or literal values:

kubectl create configmap <map-name> <data-source>

where <map-name> is the name you want to assign to the ConfigMap and <data-source> is the directory, file, or literal value to draw the data from. The name of a ConfigMap object must be a valid DNS subdomain name.

When you are creating a ConfigMap based on a file, the key in the <data-source> defaults to the basename of the file, and the value defaults to the file content.

You can use kubectl describe or kubectl get to retrieve information about a ConfigMap.

Create a ConfigMap from a directory

You can use kubectl create configmap to create a ConfigMap from multiple files in the same directory. When you are creating a ConfigMap based on a directory, kubectl identifies files whose filename is a valid key in the directory and packages each of those files into the new ConfigMap. Any directory entries except regular files are ignored (for example: subdirectories, symlinks, devices, pipes, and more).

Note:

Each filename being used for ConfigMap creation must consist of only acceptable characters, which are: letters (A to Z and a to z), digits (0 to 9), '-', '_', or '.'. If you use kubectl create configmap with a directory where any of the file names contains an unacceptable character, the kubectl command may fail.

The kubectl command does not print an error when it encounters an invalid filename.

Create the local directory:

mkdir -p configure-pod-container/configmap/

Now, download the sample configuration and create the ConfigMap:

# Download the sample files into `configure-pod-container/configmap/` directory
wget https://kubernetes.io/examples/configmap/game.properties -O configure-pod-container/configmap/game.properties
wget https://kubernetes.io/examples/configmap/ui.properties -O configure-pod-container/configmap/ui.properties

# Create the ConfigMap
kubectl create configmap game-config --from-file=configure-pod-container/configmap/

The above command packages each file, in this case, game.properties and ui.properties in the configure-pod-container/configmap/ directory into the game-config ConfigMap. You can display details of the ConfigMap using the following command:

kubectl describe configmaps game-config

The output is similar to this:

Name:         game-config
Namespace:    default
Labels:       <none>
Annotations:  <none>

Data
====
game.properties:
----
enemies=aliens
lives=3
enemies.cheat=true
enemies.cheat.level=noGoodRotten
secret.code.passphrase=UUDDLRLRBABAS
secret.code.allowed=true
secret.code.lives=30
ui.properties:
----
color.good=purple
color.bad=yellow
allow.textmode=true
how.nice.to.look=fairlyNice

The game.properties and ui.properties files in the configure-pod-container/configmap/ directory are represented in the data section of the ConfigMap.

kubectl get configmaps game-config -o yaml

The output is similar to this:

apiVersion: v1
kind: ConfigMap
metadata:
  creationTimestamp: 2022-02-18T18:52:05Z
  name: game-config
  namespace: default
  resourceVersion: "516"
  uid: b4952dc3-d670-11e5-8cd0-68f728db1985
data:
  game.properties: |
    enemies=aliens
    lives=3
    enemies.cheat=true
    enemies.cheat.level=noGoodRotten
    secret.code.passphrase=UUDDLRLRBABAS
    secret.code.allowed=true
    secret.code.lives=30    
  ui.properties: |
    color.good=purple
    color.bad=yellow
    allow.textmode=true
    how.nice.to.look=fairlyNice

Create ConfigMaps from files

You can use kubectl create configmap to create a ConfigMap from an individual file, or from multiple files.

For example,

kubectl create configmap game-config-2 --from-file=configure-pod-container/configmap/game.properties

would produce the following ConfigMap:

kubectl describe configmaps game-config-2

where the output is similar to this:

Name:         game-config-2
Namespace:    default
Labels:       <none>
Annotations:  <none>

Data
====
game.properties:
----
enemies=aliens
lives=3
enemies.cheat=true
enemies.cheat.level=noGoodRotten
secret.code.passphrase=UUDDLRLRBABAS
secret.code.allowed=true
secret.code.lives=30

You can pass in the --from-file argument multiple times to create a ConfigMap from multiple data sources.

kubectl create configmap game-config-2 --from-file=configure-pod-container/configmap/game.properties --from-file=configure-pod-container/configmap/ui.properties

You can display details of the game-config-2 ConfigMap using the following command:

kubectl describe configmaps game-config-2

The output is similar to this:

Name:         game-config-2
Namespace:    default
Labels:       <none>
Annotations:  <none>

Data
====
game.properties:
----
enemies=aliens
lives=3
enemies.cheat=true
enemies.cheat.level=noGoodRotten
secret.code.passphrase=UUDDLRLRBABAS
secret.code.allowed=true
secret.code.lives=30
ui.properties:
----
color.good=purple
color.bad=yellow
allow.textmode=true
how.nice.to.look=fairlyNice

Use the option --from-env-file to create a ConfigMap from an env-file, for example:

# Env-files contain a list of environment variables.
# These syntax rules apply:
#   Each line in an env file has to be in VAR=VAL format.
#   Lines beginning with # (i.e. comments) are ignored.
#   Blank lines are ignored.
#   There is no special handling of quotation marks (i.e. they will be part of the ConfigMap value)).

# Download the sample files into `configure-pod-container/configmap/` directory
wget https://kubernetes.io/examples/configmap/game-env-file.properties -O configure-pod-container/configmap/game-env-file.properties
wget https://kubernetes.io/examples/configmap/ui-env-file.properties -O configure-pod-container/configmap/ui-env-file.properties

# The env-file `game-env-file.properties` looks like below
cat configure-pod-container/configmap/game-env-file.properties
enemies=aliens
lives=3
allowed="true"

# This comment and the empty line above it are ignored

kubectl create configmap game-config-env-file \
       --from-env-file=configure-pod-container/configmap/game-env-file.properties

would produce a ConfigMap. View the ConfigMap:

kubectl get configmap game-config-env-file -o yaml

the output is similar to:

apiVersion: v1
kind: ConfigMap
metadata:
  creationTimestamp: 2019-12-27T18:36:28Z
  name: game-config-env-file
  namespace: default
  resourceVersion: "809965"
  uid: d9d1ca5b-eb34-11e7-887b-42010a8002b8
data:
  allowed: '"true"'
  enemies: aliens
  lives: "3"

Starting with Kubernetes v1.23, kubectl supports the --from-env-file argument to be specified multiple times to create a ConfigMap from multiple data sources.

kubectl create configmap config-multi-env-files \
        --from-env-file=configure-pod-container/configmap/game-env-file.properties \
        --from-env-file=configure-pod-container/configmap/ui-env-file.properties

would produce the following ConfigMap:

kubectl get configmap config-multi-env-files -o yaml

where the output is similar to this:

apiVersion: v1
kind: ConfigMap
metadata:
  creationTimestamp: 2019-12-27T18:38:34Z
  name: config-multi-env-files
  namespace: default
  resourceVersion: "810136"
  uid: 252c4572-eb35-11e7-887b-42010a8002b8
data:
  allowed: '"true"'
  color: purple
  enemies: aliens
  how: fairlyNice
  lives: "3"
  textmode: "true"

Define the key to use when creating a ConfigMap from a file

You can define a key other than the file name to use in the data section of your ConfigMap when using the --from-file argument:

kubectl create configmap game-config-3 --from-file=<my-key-name>=<path-to-file>

where <my-key-name> is the key you want to use in the ConfigMap and <path-to-file> is the location of the data source file you want the key to represent.

For example:

kubectl create configmap game-config-3 --from-file=game-special-key=configure-pod-container/configmap/game.properties

would produce the following ConfigMap:

kubectl get configmaps game-config-3 -o yaml

where the output is similar to this:

apiVersion: v1
kind: ConfigMap
metadata:
  creationTimestamp: 2022-02-18T18:54:22Z
  name: game-config-3
  namespace: default
  resourceVersion: "530"
  uid: 05f8da22-d671-11e5-8cd0-68f728db1985
data:
  game-special-key: |
    enemies=aliens
    lives=3
    enemies.cheat=true
    enemies.cheat.level=noGoodRotten
    secret.code.passphrase=UUDDLRLRBABAS
    secret.code.allowed=true
    secret.code.lives=30

Create ConfigMaps from literal values

You can use kubectl create configmap with the --from-literal argument to define a literal value from the command line:

kubectl create configmap special-config --from-literal=special.how=very --from-literal=special.type=charm

You can pass in multiple key-value pairs. Each pair provided on the command line is represented as a separate entry in the data section of the ConfigMap.

kubectl get configmaps special-config -o yaml

The output is similar to this:

apiVersion: v1
kind: ConfigMap
metadata:
  creationTimestamp: 2022-02-18T19:14:38Z
  name: special-config
  namespace: default
  resourceVersion: "651"
  uid: dadce046-d673-11e5-8cd0-68f728db1985
data:
  special.how: very
  special.type: charm

Create a ConfigMap from generator

You can also create a ConfigMap from generators and then apply it to create the object in the cluster's API server. You should specify the generators in a kustomization.yaml file within a directory.

Generate ConfigMaps from files

For example, to generate a ConfigMap from files configure-pod-container/configmap/game.properties

# Create a kustomization.yaml file with ConfigMapGenerator
cat <<EOF >./kustomization.yaml
configMapGenerator:
- name: game-config-4
  options:
    labels:
      game-config: config-4
  files:
  - configure-pod-container/configmap/game.properties
EOF

Apply the kustomization directory to create the ConfigMap object:

kubectl apply -k .

configmap/game-config-4-m9dm2f92bt created

You can check that the ConfigMap was created like this:

kubectl get configmap

NAME                       DATA   AGE
game-config-4-m9dm2f92bt   1      37s

and also:

kubectl describe configmaps/game-config-4-m9dm2f92bt

Name:         game-config-4-m9dm2f92bt
Namespace:    default
Labels:       game-config=config-4
Annotations:  kubectl.kubernetes.io/last-applied-configuration:
                {"apiVersion":"v1","data":{"game.properties":"enemies=aliens\nlives=3\nenemies.cheat=true\nenemies.cheat.level=noGoodRotten\nsecret.code.p...

Data
====
game.properties:
----
enemies=aliens
lives=3
enemies.cheat=true
enemies.cheat.level=noGoodRotten
secret.code.passphrase=UUDDLRLRBABAS
secret.code.allowed=true
secret.code.lives=30
Events:  <none>

Notice that the generated ConfigMap name has a suffix appended by hashing the contents. This ensures that a new ConfigMap is generated each time the content is modified.

Define the key to use when generating a ConfigMap from a file

You can define a key other than the file name to use in the ConfigMap generator. For example, to generate a ConfigMap from files configure-pod-container/configmap/game.properties with the key game-special-key

# Create a kustomization.yaml file with ConfigMapGenerator
cat <<EOF >./kustomization.yaml
configMapGenerator:
- name: game-config-5
  options:
    labels:
      game-config: config-5
  files:
  - game-special-key=configure-pod-container/configmap/game.properties
EOF

Apply the kustomization directory to create the ConfigMap object.

kubectl apply -k .

configmap/game-config-5-m67dt67794 created

Generate ConfigMaps from literals

This example shows you how to create a ConfigMap from two literal key/value pairs: special.type=charm and special.how=very, using Kustomize and kubectl. To achieve this, you can specify the ConfigMap generator. Create (or replace) kustomization.yaml so that it has the following contents:

---
# kustomization.yaml contents for creating a ConfigMap from literals
configMapGenerator:
- name: special-config-2
  literals:
  - special.how=very
  - special.type=charm

Apply the kustomization directory to create the ConfigMap object:

kubectl apply -k .

configmap/special-config-2-c92b5mmcf2 created

Interim cleanup

Before proceeding, clean up some of the ConfigMaps you made:

kubectl delete configmap special-config
kubectl delete configmap env-config
kubectl delete configmap -l 'game-config in (config-4,config-5)'

Now that you have learned to define ConfigMaps, you can move on to the next section, and learn how to use these objects with Pods.

Define container environment variables using ConfigMap data

Define a container environment variable with data from a single ConfigMap

Define an environment variable as a key-value pair in a ConfigMap:

kubectl create configmap special-config --from-literal=special.how=very

Assign the special.how value defined in the ConfigMap to the SPECIAL_LEVEL_KEY environment variable in the Pod specification.

pods/pod-single-configmap-env-variable.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dapi-test-pod
spec:
  containers:
    - name: test-container
      image: registry.k8s.io/busybox
      command: [ "/bin/sh", "-c", "env" ]
      env:
        # Define the environment variable
        - name: SPECIAL_LEVEL_KEY
          valueFrom:
            configMapKeyRef:
              # The ConfigMap containing the value you want to assign to SPECIAL_LEVEL_KEY
              name: special-config
              # Specify the key associated with the value
              key: special.how
  restartPolicy: Never

Create the Pod:

kubectl create -f https://kubernetes.io/examples/pods/pod-single-configmap-env-variable.yaml

Now, the Pod's output includes environment variable SPECIAL_LEVEL_KEY=very.

Define container environment variables with data from multiple ConfigMaps

As with the previous example, create the ConfigMaps first. Here is the manifest you will use:

configmap/configmaps.yaml

apiVersion: v1
kind: ConfigMap
metadata:
  name: special-config
  namespace: default
data:
  special.how: very
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: env-config
  namespace: default
data:
  log_level: INFO

Create the ConfigMap:

kubectl create -f https://kubernetes.io/examples/configmap/configmaps.yaml

Define the environment variables in the Pod specification.

pods/pod-multiple-configmap-env-variable.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dapi-test-pod
spec:
  containers:
    - name: test-container
      image: registry.k8s.io/busybox
      command: [ "/bin/sh", "-c", "env" ]
      env:
        - name: SPECIAL_LEVEL_KEY
          valueFrom:
            configMapKeyRef:
              name: special-config
              key: special.how
        - name: LOG_LEVEL
          valueFrom:
            configMapKeyRef:
              name: env-config
              key: log_level
  restartPolicy: Never

Create the Pod:

kubectl create -f https://kubernetes.io/examples/pods/pod-multiple-configmap-env-variable.yaml

Now, the Pod's output includes environment variables SPECIAL_LEVEL_KEY=very and LOG_LEVEL=INFO.

Once you're happy to move on, delete that Pod:

kubectl delete pod dapi-test-pod --now

Configure all key-value pairs in a ConfigMap as container environment variables

Create a ConfigMap containing multiple key-value pairs.

configmap/configmap-multikeys.yaml

apiVersion: v1
kind: ConfigMap
metadata:
  name: special-config
  namespace: default
data:
  SPECIAL_LEVEL: very
  SPECIAL_TYPE: charm

Create the ConfigMap:

kubectl create -f https://kubernetes.io/examples/configmap/configmap-multikeys.yaml

Use envFrom to define all of the ConfigMap's data as container environment variables. The key from the ConfigMap becomes the environment variable name in the Pod.

pods/pod-configmap-envFrom.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dapi-test-pod
spec:
  containers:
    - name: test-container
      image: registry.k8s.io/busybox
      command: [ "/bin/sh", "-c", "env" ]
      envFrom:
      - configMapRef:
          name: special-config
  restartPolicy: Never

Create the Pod:

kubectl create -f https://kubernetes.io/examples/pods/pod-configmap-envFrom.yaml

Now, the Pod's output includes environment variables SPECIAL_LEVEL=very and SPECIAL_TYPE=charm.

Once you're happy to move on, delete that Pod:

kubectl delete pod dapi-test-pod --now

Use ConfigMap-defined environment variables in Pod commands

You can use ConfigMap-defined environment variables in the command and args of a container using the $(VAR_NAME) Kubernetes substitution syntax.

For example, the following Pod manifest:

pods/pod-configmap-env-var-valueFrom.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dapi-test-pod
spec:
  containers:
    - name: test-container
      image: registry.k8s.io/busybox
      command: [ "/bin/echo", "$(SPECIAL_LEVEL_KEY) $(SPECIAL_TYPE_KEY)" ]
      env:
        - name: SPECIAL_LEVEL_KEY
          valueFrom:
            configMapKeyRef:
              name: special-config
              key: SPECIAL_LEVEL
        - name: SPECIAL_TYPE_KEY
          valueFrom:
            configMapKeyRef:
              name: special-config
              key: SPECIAL_TYPE
  restartPolicy: Never

Create that Pod, by running:

kubectl create -f https://kubernetes.io/examples/pods/pod-configmap-env-var-valueFrom.yaml

That pod produces the following output from the test-container container:

kubectl logs dapi-test-pod

very charm

Once you're happy to move on, delete that Pod:

kubectl delete pod dapi-test-pod --now

Add ConfigMap data to a Volume

As explained in Create ConfigMaps from files, when you create a ConfigMap using --from-file, the filename becomes a key stored in the data section of the ConfigMap. The file contents become the key's value.

The examples in this section refer to a ConfigMap named special-config:

configmap/configmap-multikeys.yaml

apiVersion: v1
kind: ConfigMap
metadata:
  name: special-config
  namespace: default
data:
  SPECIAL_LEVEL: very
  SPECIAL_TYPE: charm

Create the ConfigMap:

kubectl create -f https://kubernetes.io/examples/configmap/configmap-multikeys.yaml

Populate a Volume with data stored in a ConfigMap

Add the ConfigMap name under the volumes section of the Pod specification. This adds the ConfigMap data to the directory specified as volumeMounts.mountPath (in this case, /etc/config). The command section lists directory files with names that match the keys in ConfigMap.

pods/pod-configmap-volume.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dapi-test-pod
spec:
  containers:
    - name: test-container
      image: registry.k8s.io/busybox
      command: [ "/bin/sh", "-c", "ls /etc/config/" ]
      volumeMounts:
      - name: config-volume
        mountPath: /etc/config
  volumes:
    - name: config-volume
      configMap:
        # Provide the name of the ConfigMap containing the files you want
        # to add to the container
        name: special-config
  restartPolicy: Never

Create the Pod:

kubectl create -f https://kubernetes.io/examples/pods/pod-configmap-volume.yaml

When the pod runs, the command ls /etc/config/ produces the output below:

SPECIAL_LEVEL
SPECIAL_TYPE

Text data is exposed as files using the UTF-8 character encoding. To use some other character encoding, use binaryData (see ConfigMap object for more details).

Note:

If there are any files in the /etc/config directory of that container image, the volume mount will make those files from the image inaccessible.

Once you're happy to move on, delete that Pod:

kubectl delete pod dapi-test-pod --now

Add ConfigMap data to a specific path in the Volume

Use the path field to specify the desired file path for specific ConfigMap items. In this case, the SPECIAL_LEVEL item will be mounted in the config-volume volume at /etc/config/keys.

pods/pod-configmap-volume-specific-key.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dapi-test-pod
spec:
  containers:
    - name: test-container
      image: registry.k8s.io/busybox
      command: [ "/bin/sh","-c","cat /etc/config/keys" ]
      volumeMounts:
      - name: config-volume
        mountPath: /etc/config
  volumes:
    - name: config-volume
      configMap:
        name: special-config
        items:
        - key: SPECIAL_LEVEL
          path: keys
  restartPolicy: Never

Create the Pod:

kubectl create -f https://kubernetes.io/examples/pods/pod-configmap-volume-specific-key.yaml

When the pod runs, the command cat /etc/config/keys produces the output below:

very

Caution:

Like before, all previous files in the /etc/config/ directory will be deleted.

Delete that Pod:

kubectl delete pod dapi-test-pod --now

Project keys to specific paths and file permissions

You can project keys to specific paths. Refer to the corresponding section in the Secrets guide for the syntax.
You can set POSIX permissions for keys. Refer to the corresponding section in the Secrets guide for the syntax.

Optional references

A ConfigMap reference may be marked optional. If the ConfigMap is non-existent, the mounted volume will be empty. If the ConfigMap exists, but the referenced key is non-existent, the path will be absent beneath the mount point. See Optional ConfigMaps for more details.

Mounted ConfigMaps are updated automatically

When a mounted ConfigMap is updated, the projected content is eventually updated too. This applies in the case where an optionally referenced ConfigMap comes into existence after a pod has started.

Kubelet checks whether the mounted ConfigMap is fresh on every periodic sync. However, it uses its local TTL-based cache for getting the current value of the ConfigMap. As a result, the total delay from the moment when the ConfigMap is updated to the moment when new keys are projected to the pod can be as long as kubelet sync period (1 minute by default) + TTL of ConfigMaps cache (1 minute by default) in kubelet. You can trigger an immediate refresh by updating one of the pod's annotations.

Note:

A container using a ConfigMap as a subPath volume will not receive ConfigMap updates.

Understanding ConfigMaps and Pods

The ConfigMap API resource stores configuration data as key-value pairs. The data can be consumed in pods or provide the configurations for system components such as controllers. ConfigMap is similar to Secrets, but provides a means of working with strings that don't contain sensitive information. Users and system components alike can store configuration data in ConfigMap.

Note:

ConfigMaps should reference properties files, not replace them. Think of the ConfigMap as representing something similar to the Linux /etc directory and its contents. For example, if you create a Kubernetes Volume from a ConfigMap, each data item in the ConfigMap is represented by an individual file in the volume.

The ConfigMap's data field contains the configuration data. As shown in the example below, this can be simple (like individual properties defined using --from-literal) or complex (like configuration files or JSON blobs defined using --from-file).

apiVersion: v1
kind: ConfigMap
metadata:
  creationTimestamp: 2016-02-18T19:14:38Z
  name: example-config
  namespace: default
data:
  # example of a simple property defined using --from-literal
  example.property.1: hello
  example.property.2: world
  # example of a complex property defined using --from-file
  example.property.file: |-
    property.1=value-1
    property.2=value-2
    property.3=value-3

When kubectl creates a ConfigMap from inputs that are not ASCII or UTF-8, the tool puts these into the binaryData field of the ConfigMap, and not in data. Both text and binary data sources can be combined in one ConfigMap.

If you want to view the binaryData keys (and their values) in a ConfigMap, you can run kubectl get configmap -o jsonpath='{.binaryData}' <name>.

Pods can load data from a ConfigMap that uses either data or binaryData.

Optional ConfigMaps

You can mark a reference to a ConfigMap as optional in a Pod specification. If the ConfigMap doesn't exist, the configuration for which it provides data in the Pod (for example: environment variable, mounted volume) will be empty. If the ConfigMap exists, but the referenced key is non-existent the data is also empty.

For example, the following Pod specification marks an environment variable from a ConfigMap as optional:

apiVersion: v1
kind: Pod
metadata:
  name: dapi-test-pod
spec:
  containers:
    - name: test-container
      image: gcr.io/google_containers/busybox
      command: ["/bin/sh", "-c", "env"]
      env:
        - name: SPECIAL_LEVEL_KEY
          valueFrom:
            configMapKeyRef:
              name: a-config
              key: akey
              optional: true # mark the variable as optional
  restartPolicy: Never

If you run this pod, and there is no ConfigMap named a-config, the output is empty. If you run this pod, and there is a ConfigMap named a-config but that ConfigMap doesn't have a key named akey, the output is also empty. If you do set a value for akey in the a-config ConfigMap, this pod prints that value and then terminates.

You can also mark the volumes and files provided by a ConfigMap as optional. Kubernetes always creates the mount paths for the volume, even if the referenced ConfigMap or key doesn't exist. For example, the following Pod specification marks a volume that references a ConfigMap as optional:

apiVersion: v1
kind: Pod
metadata:
  name: dapi-test-pod
spec:
  containers:
    - name: test-container
      image: gcr.io/google_containers/busybox
      command: ["/bin/sh", "-c", "ls /etc/config"]
      volumeMounts:
      - name: config-volume
        mountPath: /etc/config
  volumes:
    - name: config-volume
      configMap:
        name: no-config
        optional: true # mark the source ConfigMap as optional
  restartPolicy: Never

Restrictions

You must create the ConfigMap object before you reference it in a Pod specification. Alternatively, mark the ConfigMap reference as optional in the Pod spec (see Optional ConfigMaps). If you reference a ConfigMap that doesn't exist and you don't mark the reference as optional, the Pod won't start. Similarly, references to keys that don't exist in the ConfigMap will also prevent the Pod from starting, unless you mark the key references as optional.

If you use envFrom to define environment variables from ConfigMaps, keys that are considered invalid will be skipped. The pod will be allowed to start, but the invalid names will be recorded in the event log (InvalidVariableNames). The log message lists each skipped key. For example:

kubectl get events

The output is similar to this:

LASTSEEN FIRSTSEEN COUNT NAME          KIND  SUBOBJECT  TYPE      REASON                            SOURCE                MESSAGE
0s       0s        1     dapi-test-pod Pod              Warning   InvalidEnvironmentVariableNames   {kubelet, 127.0.0.1}  Keys [1badkey, 2alsobad] from the EnvFrom configMap default/myconfig were skipped since they are considered invalid environment variable names.

ConfigMaps reside in a specific Namespace. Pods can only refer to ConfigMaps that are in the same namespace as the Pod.
You can't use ConfigMaps for static pods, because the kubelet does not support this.

Cleaning up

Delete the ConfigMaps and Pods that you made:

kubectl delete configmaps/game-config configmaps/game-config-2 configmaps/game-config-3 \
               configmaps/game-config-env-file
kubectl delete pod dapi-test-pod --now

# You might already have removed the next set
kubectl delete configmaps/special-config configmaps/env-config
kubectl delete configmap -l 'game-config in (config-4,config-5)'

If you created a directory configure-pod-container and no longer need it, you should remove that too, or move it into the trash can / deleted files location.

What's next

Follow a real world example of Configuring Redis using a ConfigMap.
Follow an example of Updating configuration via a ConfigMap.

3.21 - Share Process Namespace between Containers in a Pod

This page shows how to configure process namespace sharing for a pod. When process namespace sharing is enabled, processes in a container are visible to all other containers in the same pod.

You can use this feature to configure cooperating containers, such as a log handler sidecar container, or to troubleshoot container images that don't include debugging utilities like a shell.

Before you begin

Configure a Pod

Process namespace sharing is enabled using the shareProcessNamespace field of .spec for a Pod. For example:

pods/share-process-namespace.yaml

apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  shareProcessNamespace: true
  containers:
  - name: nginx
    image: nginx
  - name: shell
    image: busybox:1.28
    command: ["sleep", "3600"]
    securityContext:
      capabilities:
        add:
        - SYS_PTRACE
    stdin: true
    tty: true

Create the pod nginx on your cluster:

kubectl apply -f https://k8s.io/examples/pods/share-process-namespace.yaml

Attach to the shell container and run ps:

kubectl attach -it nginx -c shell

If you don't see a command prompt, try pressing enter. In the container shell:

# run this inside the "shell" container
ps ax

The output is similar to this:

PID   USER     TIME  COMMAND
    1 root      0:00 /pause
    8 root      0:00 nginx: master process nginx -g daemon off;
   14 101       0:00 nginx: worker process
   15 root      0:00 sh
   21 root      0:00 ps ax

You can signal processes in other containers. For example, send SIGHUP to nginx to restart the worker process. This requires the SYS_PTRACE capability.

# run this inside the "shell" container
kill -HUP 8   # change "8" to match the PID of the nginx leader process, if necessary
ps ax

The output is similar to this:

PID   USER     TIME  COMMAND
    1 root      0:00 /pause
    8 root      0:00 nginx: master process nginx -g daemon off;
   15 root      0:00 sh
   22 101       0:00 nginx: worker process
   23 root      0:00 ps ax

It's even possible to access the file system of another container using the /proc/$pid/root link.

# run this inside the "shell" container
# change "8" to the PID of the Nginx process, if necessary
head /proc/8/root/etc/nginx/nginx.conf

The output is similar to this:

user  nginx;
worker_processes  1;

error_log  /var/log/nginx/error.log warn;
pid        /var/run/nginx.pid;


events {
    worker_connections  1024;

Pods share many resources so it makes sense they would also share a process namespace. Some containers may expect to be isolated from others, though, so it's important to understand the differences:

The container process no longer has PID 1. Some containers refuse to start without PID 1 (for example, containers using systemd) or run commands like kill -HUP 1 to signal the container process. In pods with a shared process namespace, kill -HUP 1 will signal the pod sandbox (/pause in the above example).
Processes are visible to other containers in the pod. This includes all information visible in /proc, such as passwords that were passed as arguments or environment variables. These are protected only by regular Unix permissions.
Container filesystems are visible to other containers in the pod through the /proc/$pid/root link. This makes debugging easier, but it also means that filesystem secrets are protected only by filesystem permissions.

3.22 - Use a User Namespace With a Pod

FEATURE STATE: Kubernetes v1.30 [beta]

This page shows how to configure a user namespace for pods. This allows you to isolate the user running inside the container from the one in the host.

A process running as root in a container can run as a different (non-root) user in the host; in other words, the process has full privileges for operations inside the user namespace, but is unprivileged for operations outside the namespace.

You can use this feature to reduce the damage a compromised container can do to the host or other pods in the same node. There are several security vulnerabilities rated either HIGH or CRITICAL that were not exploitable when user namespaces is active. It is expected user namespace will mitigate some future vulnerabilities too.

Without using a user namespace a container running as root, in the case of a container breakout, has root privileges on the node. And if some capability were granted to the container, the capabilities are valid on the host too. None of this is true when user namespaces are used.

Before you begin

Your Kubernetes server must be at or later than version v1.25. To check the version, enter kubectl version.

🛇 This item links to a third party project or product that is not part of Kubernetes itself. More information

The node OS needs to be Linux
You need to exec commands in the host
You need to be able to exec into pods
You need to enable the UserNamespacesSupport feature gate

Note:

The feature gate to enable user namespaces was previously named UserNamespacesStatelessPodsSupport, when only stateless pods were supported. Only Kubernetes v1.25 through to v1.27 recognise UserNamespacesStatelessPodsSupport.

The cluster that you're using must include at least one node that meets the requirements for using user namespaces with Pods.

If you have a mixture of nodes and only some of the nodes provide user namespace support for Pods, you also need to ensure that the user namespace Pods are scheduled to suitable nodes.

Run a Pod that uses a user namespace

A user namespace for a pod is enabled setting the hostUsers field of .spec to false. For example:

pods/user-namespaces-stateless.yaml

apiVersion: v1
kind: Pod
metadata:
  name: userns
spec:
  hostUsers: false
  containers:
  - name: shell
    command: ["sleep", "infinity"]
    image: debian

Create the pod on your cluster:

kubectl apply -f https://k8s.io/examples/pods/user-namespaces-stateless.yaml

Attach to the container and run readlink /proc/self/ns/user:
```
kubectl attach -it userns bash
```

Run this command:

readlink /proc/self/ns/user

The output is similar to:

user:[4026531837]

Also run:

cat /proc/self/uid_map

The output is similar to:

0  833617920      65536

Then, open a shell in the host and run the same commands.

The readlink command shows the user namespace the process is running in. It should be different when it is run on the host and inside the container.

The last number of the uid_map file inside the container must be 65536, on the host it must be a bigger number.

If you are running the kubelet inside a user namespace, you need to compare the output from running the command in the pod to the output of running in the host:

readlink /proc/$pid/ns/user

replacing $pid with the kubelet PID.

3.23 - Create static Pods

Static Pods are managed directly by the kubelet daemon on a specific node, without the API server observing them. Unlike Pods that are managed by the control plane (for example, a Deployment); instead, the kubelet watches each static Pod (and restarts it if it fails).

Static Pods are always bound to one Kubelet on a specific node.

The kubelet automatically tries to create a mirror Pod on the Kubernetes API server for each static Pod. This means that the Pods running on a node are visible on the API server, but cannot be controlled from there. The Pod names will be suffixed with the node hostname with a leading hyphen.

Note:

If you are running clustered Kubernetes and are using static Pods to run a Pod on every node, you should probably be using a instead.

Note:

The spec of a static Pod cannot refer to other API objects (e.g., , , , etc).

Note:

Static pods do not support ephemeral containers.

Before you begin

To check the version, enter kubectl version.

This page assumes you're using CRI-O to run Pods, and that your nodes are running the Fedora operating system. Instructions for other distributions or Kubernetes installations may vary.

Create a static pod

You can configure a static Pod with either a file system hosted configuration file or a web hosted configuration file.

Filesystem-hosted static Pod manifest

Manifests are standard Pod definitions in JSON or YAML format in a specific directory. Use the staticPodPath: <the directory> field in the kubelet configuration file, which periodically scans the directory and creates/deletes static Pods as YAML/JSON files appear/disappear there. Note that the kubelet will ignore files starting with dots when scanning the specified directory.

For example, this is how to start a simple web server as a static Pod:

Choose a node where you want to run the static Pod. In this example, it's my-node1.
```
ssh my-node1
```

Choose a directory, say /etc/kubernetes/manifests and place a web server Pod definition there, for example /etc/kubernetes/manifests/static-web.yaml:

# Run this command on the node where kubelet is running
mkdir -p /etc/kubernetes/manifests/
cat <<EOF >/etc/kubernetes/manifests/static-web.yaml
apiVersion: v1
kind: Pod
metadata:
  name: static-web
  labels:
    role: myrole
spec:
  containers:
    - name: web
      image: nginx
      ports:
        - name: web
          containerPort: 80
          protocol: TCP
EOF

Configure the kubelet on that node to set a staticPodPath value in the kubelet configuration file.
See Set Kubelet Parameters Via A Configuration File for more information.

An alternative and deprecated method is to configure the kubelet on that node to look for static Pod manifests locally, using a command line argument. To use the deprecated approach, start the kubelet with the
--pod-manifest-path=/etc/kubernetes/manifests/ argument.

Restart the kubelet. On Fedora, you would run:

# Run this command on the node where the kubelet is running
systemctl restart kubelet

Web-hosted static pod manifest

Kubelet periodically downloads a file specified by --manifest-url=<URL> argument and interprets it as a JSON/YAML file that contains Pod definitions. Similar to how filesystem-hosted manifests work, the kubelet refetches the manifest on a schedule. If there are changes to the list of static Pods, the kubelet applies them.

To use this approach:

Create a YAML file and store it on a web server so that you can pass the URL of that file to the kubelet.

apiVersion: v1
kind: Pod
metadata:
  name: static-web
  labels:
    role: myrole
spec:
  containers:
    - name: web
      image: nginx
      ports:
        - name: web
          containerPort: 80
          protocol: TCP

Configure the kubelet on your selected node to use this web manifest by running it with --manifest-url=<manifest-url>. On Fedora, edit /etc/kubernetes/kubelet to include this line:
```
KUBELET_ARGS="--cluster-dns=10.254.0.10 --cluster-domain=kube.local --manifest-url=<manifest-url>"
```

Restart the kubelet. On Fedora, you would run:

# Run this command on the node where the kubelet is running
systemctl restart kubelet

Observe static pod behavior

When the kubelet starts, it automatically starts all defined static Pods. As you have defined a static Pod and restarted the kubelet, the new static Pod should already be running.

You can view running containers (including static Pods) by running (on the node):

# Run this command on the node where the kubelet is running
crictl ps

The output might be something like:

CONTAINER       IMAGE                                 CREATED           STATE      NAME    ATTEMPT    POD ID
129fd7d382018   docker.io/library/nginx@sha256:...    11 minutes ago    Running    web     0          34533c6729106

Note:

crictl outputs the image URI and SHA-256 checksum. NAME will look more like: docker.io/library/nginx@sha256:0d17b565c37bcbd895e9d92315a05c1c3c9a29f762b011a10c54a66cd53c9b31.

You can see the mirror Pod on the API server:

kubectl get pods

NAME                  READY   STATUS    RESTARTS        AGE
static-web-my-node1   1/1     Running   0               2m

Note:

Make sure the kubelet has permission to create the mirror Pod in the API server. If not, the creation request is rejected by the API server.

Labels from the static Pod are propagated into the mirror Pod. You can use those labels as normal via selectors, etc.

If you try to use kubectl to delete the mirror Pod from the API server, the kubelet doesn't remove the static Pod:

kubectl delete pod static-web-my-node1

pod "static-web-my-node1" deleted

You can see that the Pod is still running:

kubectl get pods

NAME                  READY   STATUS    RESTARTS   AGE
static-web-my-node1   1/1     Running   0          4s

Back on your node where the kubelet is running, you can try to stop the container manually. You'll see that, after a time, the kubelet will notice and will restart the Pod automatically:

# Run these commands on the node where the kubelet is running
crictl stop 129fd7d382018 # replace with the ID of your container
sleep 20
crictl ps

CONTAINER       IMAGE                                 CREATED           STATE      NAME    ATTEMPT    POD ID
89db4553e1eeb   docker.io/library/nginx@sha256:...    19 seconds ago    Running    web     1          34533c6729106

Once you identify the right container, you can get the logs for that container with crictl:

# Run these commands on the node where the container is running
crictl logs <container_id>

10.240.0.48 - - [16/Nov/2022:12:45:49 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-"
10.240.0.48 - - [16/Nov/2022:12:45:50 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-"
10.240.0.48 - - [16/Nove/2022:12:45:51 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-"

To find more about how to debug using crictl, please visit Debugging Kubernetes nodes with crictl.

Dynamic addition and removal of static pods

The running kubelet periodically scans the configured directory (/etc/kubernetes/manifests in our example) for changes and adds/removes Pods as files appear/disappear in this directory.

# This assumes you are using filesystem-hosted static Pod configuration
# Run these commands on the node where the container is running
#
mv /etc/kubernetes/manifests/static-web.yaml /tmp
sleep 20
crictl ps
# You see that no nginx container is running
mv /tmp/static-web.yaml  /etc/kubernetes/manifests/
sleep 20
crictl ps

CONTAINER       IMAGE                                 CREATED           STATE      NAME    ATTEMPT    POD ID
f427638871c35   docker.io/library/nginx@sha256:...    19 seconds ago    Running    web     1          34533c6729106

What's next

3.24 - Translate a Docker Compose File to Kubernetes Resources

What's Kompose? It's a conversion tool for all things compose (namely Docker Compose) to container orchestrators (Kubernetes or OpenShift).

More information can be found on the Kompose website at http://kompose.io.

Before you begin

To check the version, enter kubectl version.

Install Kompose

We have multiple ways to install Kompose. Our preferred method is downloading the binary from the latest GitHub release.

Kompose is released via GitHub on a three-week cycle, you can see all current releases on the GitHub release page.

# Linux
curl -L https://github.com/kubernetes/kompose/releases/download/v1.34.0/kompose-linux-amd64 -o kompose

# macOS
curl -L https://github.com/kubernetes/kompose/releases/download/v1.34.0/kompose-darwin-amd64 -o kompose

# Windows
curl -L https://github.com/kubernetes/kompose/releases/download/v1.34.0/kompose-windows-amd64.exe -o kompose.exe

chmod +x kompose
sudo mv ./kompose /usr/local/bin/kompose

Alternatively, you can download the tarball.

Installing using go get pulls from the master branch with the latest development changes.

go get -u github.com/kubernetes/kompose

Kompose is in EPEL CentOS repository. If you don't have EPEL repository already installed and enabled you can do it by running sudo yum install epel-release.

If you have EPEL enabled in your system, you can install Kompose like any other package.

sudo yum -y install kompose

Kompose is in Fedora 24, 25 and 26 repositories. You can install it like any other package.

sudo dnf -y install kompose

On macOS you can install the latest release via Homebrew:

brew install kompose

Use Kompose

In a few steps, we'll take you from Docker Compose to Kubernetes. All you need is an existing docker-compose.yml file.

Go to the directory containing your docker-compose.yml file. If you don't have one, test using this one.


services:

  redis-leader:
    container_name: redis-leader
    image: redis
    ports:
      - "6379"

  redis-replica:
    container_name: redis-replica
    image: redis
    ports:
      - "6379"
    command: redis-server --replicaof redis-leader 6379 --dir /tmp

  web:
    container_name: web
    image: quay.io/kompose/web
    ports:
      - "8080:8080"
    environment:
      - GET_HOSTS_FROM=dns
    labels:
      kompose.service.type: LoadBalancer

To convert the docker-compose.yml file to files that you can use with kubectl, run kompose convert and then kubectl apply -f <output file>.

kompose convert

The output is similar to:

INFO Kubernetes file "redis-leader-service.yaml" created
INFO Kubernetes file "redis-replica-service.yaml" created
INFO Kubernetes file "web-tcp-service.yaml" created
INFO Kubernetes file "redis-leader-deployment.yaml" created
INFO Kubernetes file "redis-replica-deployment.yaml" created
INFO Kubernetes file "web-deployment.yaml" created

 kubectl apply -f web-tcp-service.yaml,redis-leader-service.yaml,redis-replica-service.yaml,web-deployment.yaml,redis-leader-deployment.yaml,redis-replica-deployment.yaml

The output is similar to:

deployment.apps/redis-leader created
deployment.apps/redis-replica created
deployment.apps/web created
service/redis-leader created
service/redis-replica created
service/web-tcp created

Your deployments are running in Kubernetes.

Access your application.

If you're already using minikube for your development process:

minikube service web-tcp

Otherwise, let's look up what IP your service is using!

kubectl describe svc web-tcp

 Name:                     web-tcp
 Namespace:                default
 Labels:                   io.kompose.service=web-tcp
 Annotations:              kompose.cmd: kompose convert
                           kompose.service.type: LoadBalancer
                           kompose.version: 1.33.0 (3ce457399)
 Selector:                 io.kompose.service=web
 Type:                     LoadBalancer
 IP Family Policy:         SingleStack
 IP Families:              IPv4
 IP:                       10.102.30.3
 IPs:                      10.102.30.3
 Port:                     8080  8080/TCP
 TargetPort:               8080/TCP
 NodePort:                 8080  31624/TCP
 Endpoints:                10.244.0.5:8080
 Session Affinity:         None
 External Traffic Policy:  Cluster
 Events:                   <none>

If you're using a cloud provider, your IP will be listed next to LoadBalancer Ingress.

curl http://192.0.2.89

Clean-up.

After you are finished testing out the example application deployment, simply run the following command in your shell to delete the resources used.

kubectl delete -f web-tcp-service.yaml,redis-leader-service.yaml,redis-replica-service.yaml,web-deployment.yaml,redis-leader-deployment.yaml,redis-replica-deployment.yaml

User Guide

CLI
- kompose convert
Documentation

Kompose has support for two providers: OpenShift and Kubernetes. You can choose a targeted provider using global option --provider. If no provider is specified, Kubernetes is set by default.

`kompose convert`

Kompose supports conversion of V1, V2, and V3 Docker Compose files into Kubernetes and OpenShift objects.

Kubernetes `kompose convert` example

kompose --file docker-voting.yml convert

WARN Unsupported key networks - ignoring
WARN Unsupported key build - ignoring
INFO Kubernetes file "worker-svc.yaml" created
INFO Kubernetes file "db-svc.yaml" created
INFO Kubernetes file "redis-svc.yaml" created
INFO Kubernetes file "result-svc.yaml" created
INFO Kubernetes file "vote-svc.yaml" created
INFO Kubernetes file "redis-deployment.yaml" created
INFO Kubernetes file "result-deployment.yaml" created
INFO Kubernetes file "vote-deployment.yaml" created
INFO Kubernetes file "worker-deployment.yaml" created
INFO Kubernetes file "db-deployment.yaml" created

ls

db-deployment.yaml  docker-compose.yml         docker-gitlab.yml  redis-deployment.yaml  result-deployment.yaml  vote-deployment.yaml  worker-deployment.yaml
db-svc.yaml         docker-voting.yml          redis-svc.yaml     result-svc.yaml        vote-svc.yaml           worker-svc.yaml

You can also provide multiple docker-compose files at the same time:

kompose -f docker-compose.yml -f docker-guestbook.yml convert

INFO Kubernetes file "frontend-service.yaml" created         
INFO Kubernetes file "mlbparks-service.yaml" created         
INFO Kubernetes file "mongodb-service.yaml" created          
INFO Kubernetes file "redis-master-service.yaml" created     
INFO Kubernetes file "redis-slave-service.yaml" created      
INFO Kubernetes file "frontend-deployment.yaml" created      
INFO Kubernetes file "mlbparks-deployment.yaml" created      
INFO Kubernetes file "mongodb-deployment.yaml" created       
INFO Kubernetes file "mongodb-claim0-persistentvolumeclaim.yaml" created
INFO Kubernetes file "redis-master-deployment.yaml" created  
INFO Kubernetes file "redis-slave-deployment.yaml" created

ls

mlbparks-deployment.yaml  mongodb-service.yaml                       redis-slave-service.jsonmlbparks-service.yaml  
frontend-deployment.yaml  mongodb-claim0-persistentvolumeclaim.yaml  redis-master-service.yaml
frontend-service.yaml     mongodb-deployment.yaml                    redis-slave-deployment.yaml
redis-master-deployment.yaml

When multiple docker-compose files are provided the configuration is merged. Any configuration that is common will be overridden by subsequent file.

OpenShift `kompose convert` example

kompose --provider openshift --file docker-voting.yml convert

WARN [worker] Service cannot be created because of missing port.
INFO OpenShift file "vote-service.yaml" created             
INFO OpenShift file "db-service.yaml" created               
INFO OpenShift file "redis-service.yaml" created            
INFO OpenShift file "result-service.yaml" created           
INFO OpenShift file "vote-deploymentconfig.yaml" created    
INFO OpenShift file "vote-imagestream.yaml" created         
INFO OpenShift file "worker-deploymentconfig.yaml" created  
INFO OpenShift file "worker-imagestream.yaml" created       
INFO OpenShift file "db-deploymentconfig.yaml" created      
INFO OpenShift file "db-imagestream.yaml" created           
INFO OpenShift file "redis-deploymentconfig.yaml" created   
INFO OpenShift file "redis-imagestream.yaml" created        
INFO OpenShift file "result-deploymentconfig.yaml" created  
INFO OpenShift file "result-imagestream.yaml" created

It also supports creating buildconfig for build directive in a service. By default, it uses the remote repo for the current git branch as the source repo, and the current branch as the source branch for the build. You can specify a different source repo and branch using --build-repo and --build-branch options respectively.

kompose --provider openshift --file buildconfig/docker-compose.yml convert

WARN [foo] Service cannot be created because of missing port.
INFO OpenShift Buildconfig using git@github.com:rtnpro/kompose.git::master as source.
INFO OpenShift file "foo-deploymentconfig.yaml" created     
INFO OpenShift file "foo-imagestream.yaml" created          
INFO OpenShift file "foo-buildconfig.yaml" created

Note:

If you are manually pushing the OpenShift artifacts using oc create -f, you need to ensure that you push the imagestream artifact before the buildconfig artifact, to workaround this OpenShift issue: https://github.com/openshift/origin/issues/4518 .

Alternative Conversions

The default kompose transformation will generate Kubernetes Deployments and Services, in yaml format. You have alternative option to generate json with -j. Also, you can alternatively generate Replication Controllers objects, Daemon Sets, or Helm charts.

kompose convert -j
INFO Kubernetes file "redis-svc.json" created
INFO Kubernetes file "web-svc.json" created
INFO Kubernetes file "redis-deployment.json" created
INFO Kubernetes file "web-deployment.json" created

The *-deployment.json files contain the Deployment objects.

kompose convert --replication-controller
INFO Kubernetes file "redis-svc.yaml" created
INFO Kubernetes file "web-svc.yaml" created
INFO Kubernetes file "redis-replicationcontroller.yaml" created
INFO Kubernetes file "web-replicationcontroller.yaml" created

The *-replicationcontroller.yaml files contain the Replication Controller objects. If you want to specify replicas (default is 1), use --replicas flag: kompose convert --replication-controller --replicas 3.

kompose convert --daemon-set
INFO Kubernetes file "redis-svc.yaml" created
INFO Kubernetes file "web-svc.yaml" created
INFO Kubernetes file "redis-daemonset.yaml" created
INFO Kubernetes file "web-daemonset.yaml" created

The *-daemonset.yaml files contain the DaemonSet objects.

If you want to generate a Chart to be used with Helm run:

kompose convert -c

INFO Kubernetes file "web-svc.yaml" created
INFO Kubernetes file "redis-svc.yaml" created
INFO Kubernetes file "web-deployment.yaml" created
INFO Kubernetes file "redis-deployment.yaml" created
chart created in "./docker-compose/"

tree docker-compose/

docker-compose
├── Chart.yaml
├── README.md
└── templates
    ├── redis-deployment.yaml
    ├── redis-svc.yaml
    ├── web-deployment.yaml
    └── web-svc.yaml

The chart structure is aimed at providing a skeleton for building your Helm charts.

Labels

kompose supports Kompose-specific labels within the docker-compose.yml file in order to explicitly define a service's behavior upon conversion.

kompose.service.type defines the type of service to be created.

For example:

version: "2"
services:
  nginx:
    image: nginx
    dockerfile: foobar
    build: ./foobar
    cap_add:
      - ALL
    container_name: foobar
    labels:
      kompose.service.type: nodeport

kompose.service.expose defines if the service needs to be made accessible from outside the cluster or not. If the value is set to "true", the provider sets the endpoint automatically, and for any other value, the value is set as the hostname. If multiple ports are defined in a service, the first one is chosen to be the exposed.
- For the Kubernetes provider, an ingress resource is created and it is assumed that an ingress controller has already been configured.
- For the OpenShift provider, a route is created.
For example:
```
version: "2"
services:
  web:
    image: tuna/docker-counter23
    ports:
    - "5000:5000"
    links:
    - redis
    labels:
      kompose.service.expose: "counter.example.com"
  redis:
    image: redis:3.0
    ports:
    - "6379"
```

The currently supported options are:

Key	Value
kompose.service.type	nodeport / clusterip / loadbalancer
kompose.service.expose	true / hostname

Note:

The kompose.service.type label should be defined with ports only, otherwise kompose will fail.

Restart

If you want to create normal pods without controllers you can use restart construct of docker-compose to define that. Follow table below to see what happens on the restart value.

`docker-compose` `restart`	object created	Pod `restartPolicy`
`""`	controller object	`Always`
`always`	controller object	`Always`
`on-failure`	Pod	`OnFailure`
`no`	Pod	`Never`

Note:

The controller object could be deployment or replicationcontroller.

For example, the pival service will become pod down here. This container calculated value of pi.

version: '2'

services:
  pival:
    image: perl
    command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
    restart: "on-failure"

Warning about Deployment Configurations

If the Docker Compose file has a volume specified for a service, the Deployment (Kubernetes) or DeploymentConfig (OpenShift) strategy is changed to "Recreate" instead of "RollingUpdate" (default). This is done to avoid multiple instances of a service from accessing a volume at the same time.

If the Docker Compose file has service name with _ in it (for example, web_service), then it will be replaced by - and the service name will be renamed accordingly (for example, web-service). Kompose does this because "Kubernetes" doesn't allow _ in object name.

Please note that changing service name might break some docker-compose files.

Docker Compose Versions

Kompose supports Docker Compose versions: 1, 2 and 3. We have limited support on versions 2.1 and 3.2 due to their experimental nature.

A full list on compatibility between all three versions is listed in our conversion document including a list of all incompatible Docker Compose keys.

3.25 - Enforce Pod Security Standards by Configuring the Built-in Admission Controller

Kubernetes provides a built-in admission controller to enforce the Pod Security Standards. You can configure this admission controller to set cluster-wide defaults and exemptions.

Before you begin

Following an alpha release in Kubernetes v1.22, Pod Security Admission became available by default in Kubernetes v1.23, as a beta. From version 1.25 onwards, Pod Security Admission is generally available.

To check the version, enter kubectl version.

If you are not running Kubernetes 1.30, you can switch to viewing this page in the documentation for the Kubernetes version that you are running.

Configure the Admission Controller

Note:

pod-security.admission.config.k8s.io/v1 configuration requires v1.25+. For v1.23 and v1.24, use v1beta1. For v1.22, use v1alpha1.

apiVersion: apiserver.config.k8s.io/v1
kind: AdmissionConfiguration
plugins:
- name: PodSecurity
  configuration:
    apiVersion: pod-security.admission.config.k8s.io/v1 # see compatibility note
    kind: PodSecurityConfiguration
    # Defaults applied when a mode label is not set.
    #
    # Level label values must be one of:
    # - "privileged" (default)
    # - "baseline"
    # - "restricted"
    #
    # Version label values must be one of:
    # - "latest" (default) 
    # - specific version like "v1.30"
    defaults:
      enforce: "privileged"
      enforce-version: "latest"
      audit: "privileged"
      audit-version: "latest"
      warn: "privileged"
      warn-version: "latest"
    exemptions:
      # Array of authenticated usernames to exempt.
      usernames: []
      # Array of runtime class names to exempt.
      runtimeClasses: []
      # Array of namespaces to exempt.
      namespaces: []

Note:

The above manifest needs to be specified via the --admission-control-config-file to kube-apiserver.

3.26 - Enforce Pod Security Standards with Namespace Labels

Namespaces can be labeled to enforce the Pod Security Standards. The three policies privileged, baseline and restricted broadly cover the security spectrum and are implemented by the Pod Security admission controller.

Before you begin

Pod Security Admission was available by default in Kubernetes v1.23, as a beta. From version 1.25 onwards, Pod Security Admission is generally available.

To check the version, enter kubectl version.

Requiring the `baseline` Pod Security Standard with namespace labels

This manifest defines a Namespace my-baseline-namespace that:

Blocks any pods that don't satisfy the baseline policy requirements.
Generates a user-facing warning and adds an audit annotation to any created pod that does not meet the restricted policy requirements.
Pins the versions of the baseline and restricted policies to v1.30.

apiVersion: v1
kind: Namespace
metadata:
  name: my-baseline-namespace
  labels:
    pod-security.kubernetes.io/enforce: baseline
    pod-security.kubernetes.io/enforce-version: v1.30

    # We are setting these to our _desired_ `enforce` level.
    pod-security.kubernetes.io/audit: restricted
    pod-security.kubernetes.io/audit-version: v1.30
    pod-security.kubernetes.io/warn: restricted
    pod-security.kubernetes.io/warn-version: v1.30

Add labels to existing namespaces with `kubectl label`

Note:

When an enforce policy (or version) label is added or changed, the admission plugin will test each pod in the namespace against the new policy. Violations are returned to the user as warnings.

It is helpful to apply the --dry-run flag when initially evaluating security profile changes for namespaces. The Pod Security Standard checks will still be run in dry run mode, giving you information about how the new policy would treat existing pods, without actually updating a policy.

kubectl label --dry-run=server --overwrite ns --all \
    pod-security.kubernetes.io/enforce=baseline

Applying to all namespaces

If you're just getting started with the Pod Security Standards, a suitable first step would be to configure all namespaces with audit annotations for a stricter level such as baseline:

kubectl label --overwrite ns --all \
  pod-security.kubernetes.io/audit=baseline \
  pod-security.kubernetes.io/warn=baseline

Note that this is not setting an enforce level, so that namespaces that haven't been explicitly evaluated can be distinguished. You can list namespaces without an explicitly set enforce level using this command:

kubectl get namespaces --selector='!pod-security.kubernetes.io/enforce'

Applying to a single namespace

You can update a specific namespace as well. This command adds the enforce=restricted policy to my-existing-namespace, pinning the restricted policy version to v1.30.

kubectl label --overwrite ns my-existing-namespace \
  pod-security.kubernetes.io/enforce=restricted \
  pod-security.kubernetes.io/enforce-version=v1.30

3.27 - Migrate from PodSecurityPolicy to the Built-In PodSecurity Admission Controller

This page describes the process of migrating from PodSecurityPolicies to the built-in PodSecurity admission controller. This can be done effectively using a combination of dry-run and audit and warn modes, although this becomes harder if mutating PSPs are used.

Before you begin

Your Kubernetes server must be at or later than version v1.22. To check the version, enter kubectl version.

If you are currently running a version of Kubernetes other than 1.30, you may want to switch to viewing this page in the documentation for the version of Kubernetes that you are actually running.

This page assumes you are already familiar with the basic Pod Security Admission concepts.

Overall approach

There are multiple strategies you can take for migrating from PodSecurityPolicy to Pod Security Admission. The following steps are one possible migration path, with a goal of minimizing both the risks of a production outage and of a security gap.

Decide whether Pod Security Admission is the right fit for your use case.
Review namespace permissions
Simplify & standardize PodSecurityPolicies
Update namespaces
1. Identify an appropriate Pod Security level
2. Verify the Pod Security level
3. Enforce the Pod Security level
4. Bypass PodSecurityPolicy
Review namespace creation processes
Disable PodSecurityPolicy

0. Decide whether Pod Security Admission is right for you

Pod Security Admission was designed to meet the most common security needs out of the box, and to provide a standard set of security levels across clusters. However, it is less flexible than PodSecurityPolicy. Notably, the following features are supported by PodSecurityPolicy but not Pod Security Admission:

Setting default security constraints - Pod Security Admission is a non-mutating admission controller, meaning it won't modify pods before validating them. If you were relying on this aspect of PSP, you will need to either modify your workloads to meet the Pod Security constraints, or use a Mutating Admission Webhook to make those changes. See Simplify & Standardize PodSecurityPolicies below for more detail.
Fine-grained control over policy definition - Pod Security Admission only supports 3 standard levels. If you require more control over specific constraints, then you will need to use a Validating Admission Webhook to enforce those policies.
Sub-namespace policy granularity - PodSecurityPolicy lets you bind different policies to different Service Accounts or users, even within a single namespace. This approach has many pitfalls and is not recommended, but if you require this feature anyway you will need to use a 3rd party webhook instead. The exception to this is if you only need to completely exempt specific users or RuntimeClasses, in which case Pod Security Admission does expose some static configuration for exemptions.

Even if Pod Security Admission does not meet all of your needs it was designed to be complementary to other policy enforcement mechanisms, and can provide a useful fallback running alongside other admission webhooks.

1. Review namespace permissions

Pod Security Admission is controlled by labels on namespaces. This means that anyone who can update (or patch or create) a namespace can also modify the Pod Security level for that namespace, which could be used to bypass a more restrictive policy. Before proceeding, ensure that only trusted, privileged users have these namespace permissions. It is not recommended to grant these powerful permissions to users that shouldn't have elevated permissions, but if you must you will need to use an admission webhook to place additional restrictions on setting Pod Security labels on Namespace objects.

2. Simplify & standardize PodSecurityPolicies

In this section, you will reduce mutating PodSecurityPolicies and remove options that are outside the scope of the Pod Security Standards. You should make the changes recommended here to an offline copy of the original PodSecurityPolicy being modified. The cloned PSP should have a different name that is alphabetically before the original (for example, prepend a 0 to it). Do not create the new policies in Kubernetes yet - that will be covered in the Rollout the updated policies section below.

2.a. Eliminate purely mutating fields

If a PodSecurityPolicy is mutating pods, then you could end up with pods that don't meet the Pod Security level requirements when you finally turn PodSecurityPolicy off. In order to avoid this, you should eliminate all PSP mutation prior to switching over. Unfortunately PSP does not cleanly separate mutating & validating fields, so this is not a straightforward migration.

You can start by eliminating the fields that are purely mutating, and don't have any bearing on the validating policy. These fields (also listed in the Mapping PodSecurityPolicies to Pod Security Standards reference) are:

.spec.defaultAllowPrivilegeEscalation
.spec.runtimeClass.defaultRuntimeClassName
.metadata.annotations['seccomp.security.alpha.kubernetes.io/defaultProfileName']
.metadata.annotations['apparmor.security.beta.kubernetes.io/defaultProfileName']
.spec.defaultAddCapabilities - Although technically a mutating & validating field, these should be merged into .spec.allowedCapabilities which performs the same validation without mutation.

Caution:

Removing these could result in workloads missing required configuration, and cause problems. See Rollout the updated policies below for advice on how to roll these changes out safely.

2.b. Eliminate options not covered by the Pod Security Standards

There are several fields in PodSecurityPolicy that are not covered by the Pod Security Standards. If you must enforce these options, you will need to supplement Pod Security Admission with an admission webhook, which is outside the scope of this guide.

First, you can remove the purely validating fields that the Pod Security Standards do not cover. These fields (also listed in the Mapping PodSecurityPolicies to Pod Security Standards reference with "no opinion") are:

.spec.allowedHostPaths
.spec.allowedFlexVolumes
.spec.allowedCSIDrivers
.spec.forbiddenSysctls
.spec.runtimeClass

You can also remove the following fields, that are related to POSIX / UNIX group controls.

Caution:

If any of these use the MustRunAs strategy they may be mutating! Removing these could result in workloads not setting the required groups, and cause problems. See Rollout the updated policies below for advice on how to roll these changes out safely.

.spec.runAsGroup
.spec.supplementalGroups
.spec.fsGroup

The remaining mutating fields are required to properly support the Pod Security Standards, and will need to be handled on a case-by-case basis later:

.spec.requiredDropCapabilities - Required to drop ALL for the Restricted profile.
.spec.seLinux - (Only mutating with the MustRunAs rule) required to enforce the SELinux requirements of the Baseline & Restricted profiles.
.spec.runAsUser - (Non-mutating with the RunAsAny rule) required to enforce RunAsNonRoot for the Restricted profile.
.spec.allowPrivilegeEscalation - (Only mutating if set to false) required for the Restricted profile.

2.c. Rollout the updated PSPs

Next, you can rollout the updated policies to your cluster. You should proceed with caution, as removing the mutating options may result in workloads missing required configuration.

For each updated PodSecurityPolicy:

Identify pods running under the original PSP. This can be done using the kubernetes.io/psp annotation. For example, using kubectl:

PSP_NAME="original" # Set the name of the PSP you're checking for
kubectl get pods --all-namespaces -o jsonpath="{range .items[?(@.metadata.annotations.kubernetes\.io\/psp=='$PSP_NAME')]}{.metadata.namespace} {.metadata.name}{'\n'}{end}"

Compare these running pods against the original pod spec to determine whether PodSecurityPolicy has modified the pod. For pods created by a workload resource you can compare the pod with the PodTemplate in the controller resource. If any changes are identified, the original Pod or PodTemplate should be updated with the desired configuration. The fields to review are:
- .metadata.annotations['container.apparmor.security.beta.kubernetes.io/*'] (replace * with each container name)
- .spec.runtimeClassName
- .spec.securityContext.fsGroup
- .spec.securityContext.seccompProfile
- .spec.securityContext.seLinuxOptions
- .spec.securityContext.supplementalGroups
- On containers, under .spec.containers[*] and .spec.initContainers[*]:
  - .securityContext.allowPrivilegeEscalation
  - .securityContext.capabilities.add
  - .securityContext.capabilities.drop
  - .securityContext.readOnlyRootFilesystem
  - .securityContext.runAsGroup
  - .securityContext.runAsNonRoot
  - .securityContext.runAsUser
  - .securityContext.seccompProfile
  - .securityContext.seLinuxOptions
Create the new PodSecurityPolicies. If any Roles or ClusterRoles are granting use on all PSPs this could cause the new PSPs to be used instead of their mutating counter-parts.
Update your authorization to grant access to the new PSPs. In RBAC this means updating any Roles or ClusterRoles that grant the use permission on the original PSP to also grant it to the updated PSP.
Verify: after some soak time, rerun the command from step 1 to see if any pods are still using the original PSPs. Note that pods need to be recreated after the new policies have been rolled out before they can be fully verified.
(optional) Once you have verified that the original PSPs are no longer in use, you can delete them.

3. Update Namespaces

The following steps will need to be performed on every namespace in the cluster. Commands referenced in these steps use the $NAMESPACE variable to refer to the namespace being updated.

3.a. Identify an appropriate Pod Security level

Start reviewing the Pod Security Standards and familiarizing yourself with the 3 different levels.

There are several ways to choose a Pod Security level for your namespace:

By security requirements for the namespace - If you are familiar with the expected access level for the namespace, you can choose an appropriate level based on those requirements, similar to how one might approach this on a new cluster.
By existing PodSecurityPolicies - Using the Mapping PodSecurityPolicies to Pod Security Standards reference you can map each PSP to a Pod Security Standard level. If your PSPs aren't based on the Pod Security Standards, you may need to decide between choosing a level that is at least as permissive as the PSP, and a level that is at least as restrictive. You can see which PSPs are in use for pods in a given namespace with this command:
```
kubectl get pods -n $NAMESPACE -o jsonpath="{.items[*].metadata.annotations.kubernetes\.io\/psp}" | tr " " "\n" | sort -u
```
By existing pods - Using the strategies under Verify the Pod Security level, you can test out both the Baseline and Restricted levels to see whether they are sufficiently permissive for existing workloads, and chose the least-privileged valid level.

Caution:

Options 2 & 3 above are based on existing pods, and may miss workloads that aren't currently running, such as CronJobs, scale-to-zero workloads, or other workloads that haven't rolled out.

3.b. Verify the Pod Security level

Once you have selected a Pod Security level for the namespace (or if you're trying several), it's a good idea to test it out first (you can skip this step if using the Privileged level). Pod Security includes several tools to help test and safely roll out profiles.

First, you can dry-run the policy, which will evaluate pods currently running in the namespace against the applied policy, without making the new policy take effect:

# $LEVEL is the level to dry-run, either "baseline" or "restricted".
kubectl label --dry-run=server --overwrite ns $NAMESPACE pod-security.kubernetes.io/enforce=$LEVEL

This command will return a warning for any existing pods that are not valid under the proposed level.

The second option is better for catching workloads that are not currently running: audit mode. When running under audit-mode (as opposed to enforcing), pods that violate the policy level are recorded in the audit logs, which can be reviewed later after some soak time, but are not forbidden. Warning mode works similarly, but returns the warning to the user immediately. You can set the audit level on a namespace with this command:

kubectl label --overwrite ns $NAMESPACE pod-security.kubernetes.io/audit=$LEVEL

If either of these approaches yield unexpected violations, you will need to either update the violating workloads to meet the policy requirements, or relax the namespace Pod Security level.

3.c. Enforce the Pod Security level

When you are satisfied that the chosen level can safely be enforced on the namespace, you can update the namespace to enforce the desired level:

kubectl label --overwrite ns $NAMESPACE pod-security.kubernetes.io/enforce=$LEVEL

3.d. Bypass PodSecurityPolicy

Finally, you can effectively bypass PodSecurityPolicy at the namespace level by binding the fully privileged PSP to all service accounts in the namespace.

# The following cluster-scoped commands are only needed once.
kubectl apply -f privileged-psp.yaml
kubectl create clusterrole privileged-psp --verb use --resource podsecuritypolicies.policy --resource-name privileged

# Per-namespace disable
kubectl create -n $NAMESPACE rolebinding disable-psp --clusterrole privileged-psp --group system:serviceaccounts:$NAMESPACE

Since the privileged PSP is non-mutating, and the PSP admission controller always prefers non-mutating PSPs, this will ensure that pods in this namespace are no longer being modified or restricted by PodSecurityPolicy.

The advantage to disabling PodSecurityPolicy on a per-namespace basis like this is if a problem arises you can easily roll the change back by deleting the RoleBinding. Just make sure the pre-existing PodSecurityPolicies are still in place!

# Undo PodSecurityPolicy disablement.
kubectl delete -n $NAMESPACE rolebinding disable-psp

4. Review namespace creation processes

Now that existing namespaces have been updated to enforce Pod Security Admission, you should ensure that your processes and/or policies for creating new namespaces are updated to ensure that an appropriate Pod Security profile is applied to new namespaces.

You can also statically configure the Pod Security admission controller to set a default enforce, audit, and/or warn level for unlabeled namespaces. See Configure the Admission Controller for more information.

5. Disable PodSecurityPolicy

Finally, you're ready to disable PodSecurityPolicy. To do so, you will need to modify the admission configuration of the API server: How do I turn off an admission controller?.

To verify that the PodSecurityPolicy admission controller is no longer enabled, you can manually run a test by impersonating a user without access to any PodSecurityPolicies (see the PodSecurityPolicy example), or by verifying in the API server logs. At startup, the API server outputs log lines listing the loaded admission controller plugins:

I0218 00:59:44.903329      13 plugins.go:158] Loaded 16 mutating admission controller(s) successfully in the following order: NamespaceLifecycle,LimitRanger,ServiceAccount,NodeRestriction,TaintNodesByCondition,Priority,DefaultTolerationSeconds,ExtendedResourceToleration,PersistentVolumeLabel,DefaultStorageClass,StorageObjectInUseProtection,RuntimeClass,DefaultIngressClass,MutatingAdmissionWebhook.
I0218 00:59:44.903350      13 plugins.go:161] Loaded 14 validating admission controller(s) successfully in the following order: LimitRanger,ServiceAccount,PodSecurity,Priority,PersistentVolumeClaimResize,RuntimeClass,CertificateApproval,CertificateSigning,CertificateSubjectRestriction,DenyServiceExternalIPs,ValidatingAdmissionWebhook,ResourceQuota.

You should see PodSecurity (in the validating admission controllers), and neither list should contain PodSecurityPolicy.

Once you are certain the PSP admission controller is disabled (and after sufficient soak time to be confident you won't need to roll back), you are free to delete your PodSecurityPolicies and any associated Roles, ClusterRoles, RoleBindings and ClusterRoleBindings (just make sure they don't grant any other unrelated permissions).

4 - Monitoring, Logging, and Debugging

Set up monitoring and logging to troubleshoot a cluster, or debug a containerized application.

Sometimes things go wrong. This guide is aimed at making them right. It has two sections:

Debugging your application - Useful for users who are deploying code into Kubernetes and wondering why it is not working.
Debugging your cluster - Useful for cluster administrators and people whose Kubernetes cluster is unhappy.

You should also check the known issues for the release you're using.

Getting help

If your problem isn't answered by any of the guides above, there are variety of ways for you to get help from the Kubernetes community.

Questions

The documentation on this site has been structured to provide answers to a wide range of questions. Concepts explain the Kubernetes architecture and how each component works, while Setup provides practical instructions for getting started. Tasks show how to accomplish commonly used tasks, and Tutorials are more comprehensive walkthroughs of real-world, industry-specific, or end-to-end development scenarios. The Reference section provides detailed documentation on the Kubernetes API and command-line interfaces (CLIs), such as kubectl.

Help! My question isn't covered! I need help now!

Stack Exchange, Stack Overflow, or Server Fault

If you have questions related to software development for your containerized app, you can ask those on Stack Overflow.

If you have Kubernetes questions related to cluster management or configuration, you can ask those on Server Fault.

There are also several more specific Stack Exchange network sites which might be the right place to ask Kubernetes questions in areas such as DevOps, Software Engineering, or InfoSec.

Someone else from the community may have already asked a similar question or may be able to help with your problem.

The Kubernetes team will also monitor posts tagged Kubernetes. If there aren't any existing questions that help, please ensure that your question is on-topic on Stack Overflow, Server Fault, or the Stack Exchange Network site you're asking on, and read through the guidance on how to ask a new question, before asking a new one!

Slack

Many people from the Kubernetes community hang out on Kubernetes Slack in the #kubernetes-users channel. Slack requires registration; you can request an invitation, and registration is open to everyone). Feel free to come and ask any and all questions. Once registered, access the Kubernetes organisation in Slack via your web browser or via Slack's own dedicated app.

Once you are registered, browse the growing list of channels for various subjects of interest. For example, people new to Kubernetes may also want to join the #kubernetes-novice channel. As another example, developers should join the #kubernetes-contributors channel.

There are also many country specific / local language channels. Feel free to join these channels for localized support and info:

Country / language specific Slack channels
Country	Channels
China	`#cn-users`, `#cn-events`
Finland	`#fi-users`
France	`#fr-users`, `#fr-events`
Germany	`#de-users`, `#de-events`
India	`#in-users`, `#in-events`
Italy	`#it-users`, `#it-events`
Japan	`#jp-users`, `#jp-events`
Korea	`#kr-users`
Netherlands	`#nl-users`
Norway	`#norw-users`
Poland	`#pl-users`
Russia	`#ru-users`
Spain	`#es-users`
Sweden	`#se-users`
Turkey	`#tr-users`, `#tr-events`

Forum

You're welcome to join the official Kubernetes Forum: discuss.kubernetes.io.

Bugs and feature requests

If you have what looks like a bug, or you would like to make a feature request, please use the GitHub issue tracking system.

Before you file an issue, please search existing issues to see if your issue is already covered.

If filing a bug, please include detailed information about how to reproduce the problem, such as:

Kubernetes version: kubectl version
Cloud provider, OS distro, network configuration, and container runtime version
Steps to reproduce the problem

4.1 - Troubleshooting Applications

Debugging common containerized application issues.

This doc contains a set of resources for fixing issues with containerized applications. It covers things like common issues with Kubernetes resources (like Pods, Services, or StatefulSets), advice on making sense of container termination messages, and ways to debug running containers.

4.1.1 - Debug Pods

This guide is to help users debug applications that are deployed into Kubernetes and not behaving correctly. This is not a guide for people who want to debug their cluster. For that you should check out this guide.

Diagnosing the problem

The first step in troubleshooting is triage. What is the problem? Is it your Pods, your Replication Controller or your Service?

Debugging Pods
Debugging Replication Controllers
Debugging Services

Debugging Pods

The first step in debugging a Pod is taking a look at it. Check the current state of the Pod and recent events with the following command:

kubectl describe pods ${POD_NAME}

Look at the state of the containers in the pod. Are they all Running? Have there been recent restarts?

Continue debugging depending on the state of the pods.

My pod stays pending

If a Pod is stuck in Pending it means that it can not be scheduled onto a node. Generally this is because there are insufficient resources of one type or another that prevent scheduling. Look at the output of the kubectl describe ... command above. There should be messages from the scheduler about why it can not schedule your pod. Reasons include:

You don't have enough resources: You may have exhausted the supply of CPU or Memory in your cluster, in this case you need to delete Pods, adjust resource requests, or add new nodes to your cluster. See Compute Resources document for more information.
You are using hostPort: When you bind a Pod to a hostPort there are a limited number of places that pod can be scheduled. In most cases, hostPort is unnecessary, try using a Service object to expose your Pod. If you do require hostPort then you can only schedule as many Pods as there are nodes in your Kubernetes cluster.

My pod stays waiting

If a Pod is stuck in the Waiting state, then it has been scheduled to a worker node, but it can't run on that machine. Again, the information from kubectl describe ... should be informative. The most common cause of Waiting pods is a failure to pull the image. There are three things to check:

Make sure that you have the name of the image correct.
Have you pushed the image to the registry?
Try to manually pull the image to see if the image can be pulled. For example, if you use Docker on your PC, run docker pull <image>.

My pod stays terminating

If a Pod is stuck in the Terminating state, it means that a deletion has been issued for the Pod, but the control plane is unable to delete the Pod object.

This typically happens if the Pod has a finalizer and there is an admission webhook installed in the cluster that prevents the control plane from removing the finalizer.

To identify this scenario, check if your cluster has any ValidatingWebhookConfiguration or MutatingWebhookConfiguration that target UPDATE operations for pods resources.

If the webhook is provided by a third-party:

Make sure you are using the latest version.
Disable the webhook for UPDATE operations.
Report an issue with the corresponding provider.

If you are the author of the webhook:

For a mutating webhook, make sure it never changes immutable fields on UPDATE operations. For example, changes to containers are usually not allowed.
For a validating webhook, make sure that your validation policies only apply to new changes. In other words, you should allow Pods with existing violations to pass validation. This allows Pods that were created before the validating webhook was installed to continue running.

My pod is crashing or otherwise unhealthy

Once your pod has been scheduled, the methods described in Debug Running Pods are available for debugging.

My pod is running but not doing what I told it to do

If your pod is not behaving as you expected, it may be that there was an error in your pod description (e.g. mypod.yaml file on your local machine), and that the error was silently ignored when you created the pod. Often a section of the pod description is nested incorrectly, or a key name is typed incorrectly, and so the key is ignored. For example, if you misspelled command as commnd then the pod will be created but will not use the command line you intended it to use.

The first thing to do is to delete your pod and try creating it again with the --validate option. For example, run kubectl apply --validate -f mypod.yaml. If you misspelled command as commnd then will give an error like this:

I0805 10:43:25.129850   46757 schema.go:126] unknown field: commnd
I0805 10:43:25.129973   46757 schema.go:129] this may be a false alarm, see https://github.com/kubernetes/kubernetes/issues/6842
pods/mypod

The next thing to check is whether the pod on the apiserver matches the pod you meant to create (e.g. in a yaml file on your local machine). For example, run kubectl get pods/mypod -o yaml > mypod-on-apiserver.yaml and then manually compare the original pod description, mypod.yaml with the one you got back from apiserver, mypod-on-apiserver.yaml. There will typically be some lines on the "apiserver" version that are not on the original version. This is expected. However, if there are lines on the original that are not on the apiserver version, then this may indicate a problem with your pod spec.

Debugging Replication Controllers

Replication controllers are fairly straightforward. They can either create Pods or they can't. If they can't create pods, then please refer to the instructions above to debug your pods.

You can also use kubectl describe rc ${CONTROLLER_NAME} to introspect events related to the replication controller.

Debugging Services

Services provide load balancing across a set of pods. There are several common problems that can make Services not work properly. The following instructions should help debug Service problems.

First, verify that there are endpoints for the service. For every Service object, the apiserver makes an endpoints resource available.

You can view this resource with:

kubectl get endpoints ${SERVICE_NAME}

Make sure that the endpoints match up with the number of pods that you expect to be members of your service. For example, if your Service is for an nginx container with 3 replicas, you would expect to see three different IP addresses in the Service's endpoints.

My service is missing endpoints

If you are missing endpoints, try listing pods using the labels that Service uses. Imagine that you have a Service where the labels are:

...
spec:
  - selector:
     name: nginx
     type: frontend

You can use:

kubectl get pods --selector=name=nginx,type=frontend

to list pods that match this selector. Verify that the list matches the Pods that you expect to provide your Service. Verify that the pod's containerPort matches up with the Service's targetPort

Network traffic is not forwarded

Please see debugging service for more information.

What's next

If none of the above solves your problem, follow the instructions in Debugging Service document to make sure that your Service is running, has Endpoints, and your Pods are actually serving; you have DNS working, iptables rules installed, and kube-proxy does not seem to be misbehaving.

You may also visit troubleshooting document for more information.

4.1.2 - Debug Services

An issue that comes up rather frequently for new installations of Kubernetes is that a Service is not working properly. You've run your Pods through a Deployment (or other workload controller) and created a Service, but you get no response when you try to access it. This document will hopefully help you to figure out what's going wrong.

Running commands in a Pod

For many steps here you will want to see what a Pod running in the cluster sees. The simplest way to do this is to run an interactive busybox Pod:

kubectl run -it --rm --restart=Never busybox --image=gcr.io/google-containers/busybox sh

Note:

If you don't see a command prompt, try pressing enter.

If you already have a running Pod that you prefer to use, you can run a command in it using:

kubectl exec <POD-NAME> -c <CONTAINER-NAME> -- <COMMAND>

Setup

For the purposes of this walk-through, let's run some Pods. Since you're probably debugging your own Service you can substitute your own details, or you can follow along and get a second data point.

kubectl create deployment hostnames --image=registry.k8s.io/serve_hostname

deployment.apps/hostnames created

kubectl commands will print the type and name of the resource created or mutated, which can then be used in subsequent commands.

Let's scale the deployment to 3 replicas.

kubectl scale deployment hostnames --replicas=3

deployment.apps/hostnames scaled

Note that this is the same as if you had started the Deployment with the following YAML:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: hostnames
  name: hostnames
spec:
  selector:
    matchLabels:
      app: hostnames
  replicas: 3
  template:
    metadata:
      labels:
        app: hostnames
    spec:
      containers:
      - name: hostnames
        image: registry.k8s.io/serve_hostname

The label "app" is automatically set by kubectl create deployment to the name of the Deployment.

You can confirm your Pods are running:

kubectl get pods -l app=hostnames

NAME                        READY     STATUS    RESTARTS   AGE
hostnames-632524106-bbpiw   1/1       Running   0          2m
hostnames-632524106-ly40y   1/1       Running   0          2m
hostnames-632524106-tlaok   1/1       Running   0          2m

You can also confirm that your Pods are serving. You can get the list of Pod IP addresses and test them directly.

kubectl get pods -l app=hostnames \
    -o go-template='{{range .items}}{{.status.podIP}}{{"\n"}}{{end}}'

10.244.0.5
10.244.0.6
10.244.0.7

The example container used for this walk-through serves its own hostname via HTTP on port 9376, but if you are debugging your own app, you'll want to use whatever port number your Pods are listening on.

From within a pod:

for ep in 10.244.0.5:9376 10.244.0.6:9376 10.244.0.7:9376; do
    wget -qO- $ep
done

This should produce something like:

hostnames-632524106-bbpiw
hostnames-632524106-ly40y
hostnames-632524106-tlaok

If you are not getting the responses you expect at this point, your Pods might not be healthy or might not be listening on the port you think they are. You might find kubectl logs to be useful for seeing what is happening, or perhaps you need to kubectl exec directly into your Pods and debug from there.

Assuming everything has gone to plan so far, you can start to investigate why your Service doesn't work.

Does the Service exist?

The astute reader will have noticed that you did not actually create a Service yet - that is intentional. This is a step that sometimes gets forgotten, and is the first thing to check.

What would happen if you tried to access a non-existent Service? If you have another Pod that consumes this Service by name you would get something like:

wget -O- hostnames

Resolving hostnames (hostnames)... failed: Name or service not known.
wget: unable to resolve host address 'hostnames'

The first thing to check is whether that Service actually exists:

kubectl get svc hostnames

No resources found.
Error from server (NotFound): services "hostnames" not found

Let's create the Service. As before, this is for the walk-through - you can use your own Service's details here.

kubectl expose deployment hostnames --port=80 --target-port=9376

service/hostnames exposed

And read it back:

kubectl get svc hostnames

NAME        TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)   AGE
hostnames   ClusterIP   10.0.1.175   <none>        80/TCP    5s

Now you know that the Service exists.

As before, this is the same as if you had started the Service with YAML:

apiVersion: v1
kind: Service
metadata:
  labels:
    app: hostnames
  name: hostnames
spec:
  selector:
    app: hostnames
  ports:
  - name: default
    protocol: TCP
    port: 80
    targetPort: 9376

In order to highlight the full range of configuration, the Service you created here uses a different port number than the Pods. For many real-world Services, these values might be the same.

Any Network Policy Ingress rules affecting the target Pods?

If you have deployed any Network Policy Ingress rules which may affect incoming traffic to hostnames-* Pods, these need to be reviewed.

Please refer to Network Policies for more details.

Does the Service work by DNS name?

One of the most common ways that clients consume a Service is through a DNS name.

From a Pod in the same Namespace:

nslookup hostnames

Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local

Name:      hostnames
Address 1: 10.0.1.175 hostnames.default.svc.cluster.local

If this fails, perhaps your Pod and Service are in different Namespaces, try a namespace-qualified name (again, from within a Pod):

nslookup hostnames.default

Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local

Name:      hostnames.default
Address 1: 10.0.1.175 hostnames.default.svc.cluster.local

If this works, you'll need to adjust your app to use a cross-namespace name, or run your app and Service in the same Namespace. If this still fails, try a fully-qualified name:

nslookup hostnames.default.svc.cluster.local

Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local

Name:      hostnames.default.svc.cluster.local
Address 1: 10.0.1.175 hostnames.default.svc.cluster.local

Note the suffix here: "default.svc.cluster.local". The "default" is the Namespace you're operating in. The "svc" denotes that this is a Service. The "cluster.local" is your cluster domain, which COULD be different in your own cluster.

You can also try this from a Node in the cluster:

Note:

10.0.0.10 is the cluster's DNS Service IP, yours might be different.

nslookup hostnames.default.svc.cluster.local 10.0.0.10

Server:         10.0.0.10
Address:        10.0.0.10#53

Name:   hostnames.default.svc.cluster.local
Address: 10.0.1.175

If you are able to do a fully-qualified name lookup but not a relative one, you need to check that your /etc/resolv.conf file in your Pod is correct. From within a Pod:

cat /etc/resolv.conf

You should see something like:

nameserver 10.0.0.10
search default.svc.cluster.local svc.cluster.local cluster.local example.com
options ndots:5

The nameserver line must indicate your cluster's DNS Service. This is passed into kubelet with the --cluster-dns flag.

The search line must include an appropriate suffix for you to find the Service name. In this case it is looking for Services in the local Namespace ("default.svc.cluster.local"), Services in all Namespaces ("svc.cluster.local"), and lastly for names in the cluster ("cluster.local"). Depending on your own install you might have additional records after that (up to 6 total). The cluster suffix is passed into kubelet with the --cluster-domain flag. Throughout this document, the cluster suffix is assumed to be "cluster.local". Your own clusters might be configured differently, in which case you should change that in all of the previous commands.

The options line must set ndots high enough that your DNS client library considers search paths at all. Kubernetes sets this to 5 by default, which is high enough to cover all of the DNS names it generates.

Does any Service work by DNS name?

If the above still fails, DNS lookups are not working for your Service. You can take a step back and see what else is not working. The Kubernetes master Service should always work. From within a Pod:

nslookup kubernetes.default

Server:    10.0.0.10
Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local

Name:      kubernetes.default
Address 1: 10.0.0.1 kubernetes.default.svc.cluster.local

If this fails, please see the kube-proxy section of this document, or even go back to the top of this document and start over, but instead of debugging your own Service, debug the DNS Service.

Does the Service work by IP?

Assuming you have confirmed that DNS works, the next thing to test is whether your Service works by its IP address. From a Pod in your cluster, access the Service's IP (from kubectl get above).

for i in $(seq 1 3); do 
    wget -qO- 10.0.1.175:80
done

This should produce something like:

hostnames-632524106-bbpiw
hostnames-632524106-ly40y
hostnames-632524106-tlaok

If your Service is working, you should get correct responses. If not, there are a number of things that could be going wrong. Read on.

Is the Service defined correctly?

It might sound silly, but you should really double and triple check that your Service is correct and matches your Pod's port. Read back your Service and verify it:

kubectl get service hostnames -o json

{
    "kind": "Service",
    "apiVersion": "v1",
    "metadata": {
        "name": "hostnames",
        "namespace": "default",
        "uid": "428c8b6c-24bc-11e5-936d-42010af0a9bc",
        "resourceVersion": "347189",
        "creationTimestamp": "2015-07-07T15:24:29Z",
        "labels": {
            "app": "hostnames"
        }
    },
    "spec": {
        "ports": [
            {
                "name": "default",
                "protocol": "TCP",
                "port": 80,
                "targetPort": 9376,
                "nodePort": 0
            }
        ],
        "selector": {
            "app": "hostnames"
        },
        "clusterIP": "10.0.1.175",
        "type": "ClusterIP",
        "sessionAffinity": "None"
    },
    "status": {
        "loadBalancer": {}
    }
}

Is the Service port you are trying to access listed in spec.ports[]?
Is the targetPort correct for your Pods (some Pods use a different port than the Service)?
If you meant to use a numeric port, is it a number (9376) or a string "9376"?
If you meant to use a named port, do your Pods expose a port with the same name?
Is the port's protocol correct for your Pods?

Does the Service have any Endpoints?

If you got this far, you have confirmed that your Service is correctly defined and is resolved by DNS. Now let's check that the Pods you ran are actually being selected by the Service.

Earlier you saw that the Pods were running. You can re-check that:

kubectl get pods -l app=hostnames

NAME                        READY     STATUS    RESTARTS   AGE
hostnames-632524106-bbpiw   1/1       Running   0          1h
hostnames-632524106-ly40y   1/1       Running   0          1h
hostnames-632524106-tlaok   1/1       Running   0          1h

The -l app=hostnames argument is a label selector configured on the Service.

The "AGE" column says that these Pods are about an hour old, which implies that they are running fine and not crashing.

The "RESTARTS" column says that these pods are not crashing frequently or being restarted. Frequent restarts could lead to intermittent connectivity issues. If the restart count is high, read more about how to debug pods.

Inside the Kubernetes system is a control loop which evaluates the selector of every Service and saves the results into a corresponding Endpoints object.

kubectl get endpoints hostnames

NAME        ENDPOINTS
hostnames   10.244.0.5:9376,10.244.0.6:9376,10.244.0.7:9376

This confirms that the endpoints controller has found the correct Pods for your Service. If the ENDPOINTS column is <none>, you should check that the spec.selector field of your Service actually selects for metadata.labels values on your Pods. A common mistake is to have a typo or other error, such as the Service selecting for app=hostnames, but the Deployment specifying run=hostnames, as in versions previous to 1.18, where the kubectl run command could have been also used to create a Deployment.

Are the Pods working?

At this point, you know that your Service exists and has selected your Pods. At the beginning of this walk-through, you verified the Pods themselves. Let's check again that the Pods are actually working - you can bypass the Service mechanism and go straight to the Pods, as listed by the Endpoints above.

Note:

These commands use the Pod port (9376), rather than the Service port (80).

From within a Pod:

for ep in 10.244.0.5:9376 10.244.0.6:9376 10.244.0.7:9376; do
    wget -qO- $ep
done

This should produce something like:

hostnames-632524106-bbpiw
hostnames-632524106-ly40y
hostnames-632524106-tlaok

You expect each Pod in the Endpoints list to return its own hostname. If this is not what happens (or whatever the correct behavior is for your own Pods), you should investigate what's happening there.

Is the kube-proxy working?

If you get here, your Service is running, has Endpoints, and your Pods are actually serving. At this point, the whole Service proxy mechanism is suspect. Let's confirm it, piece by piece.

The default implementation of Services, and the one used on most clusters, is kube-proxy. This is a program that runs on every node and configures one of a small set of mechanisms for providing the Service abstraction. If your cluster does not use kube-proxy, the following sections will not apply, and you will have to investigate whatever implementation of Services you are using.

Is kube-proxy running?

Confirm that kube-proxy is running on your Nodes. Running directly on a Node, you should get something like the below:

ps auxw | grep kube-proxy

root  4194  0.4  0.1 101864 17696 ?    Sl Jul04  25:43 /usr/local/bin/kube-proxy --master=https://kubernetes-master --kubeconfig=/var/lib/kube-proxy/kubeconfig --v=2

Next, confirm that it is not failing something obvious, like contacting the master. To do this, you'll have to look at the logs. Accessing the logs depends on your Node OS. On some OSes it is a file, such as /var/log/kube-proxy.log, while other OSes use journalctl to access logs. You should see something like:

I1027 22:14:53.995134    5063 server.go:200] Running in resource-only container "/kube-proxy"
I1027 22:14:53.998163    5063 server.go:247] Using iptables Proxier.
I1027 22:14:54.038140    5063 proxier.go:352] Setting endpoints for "kube-system/kube-dns:dns-tcp" to [10.244.1.3:53]
I1027 22:14:54.038164    5063 proxier.go:352] Setting endpoints for "kube-system/kube-dns:dns" to [10.244.1.3:53]
I1027 22:14:54.038209    5063 proxier.go:352] Setting endpoints for "default/kubernetes:https" to [10.240.0.2:443]
I1027 22:14:54.038238    5063 proxier.go:429] Not syncing iptables until Services and Endpoints have been received from master
I1027 22:14:54.040048    5063 proxier.go:294] Adding new service "default/kubernetes:https" at 10.0.0.1:443/TCP
I1027 22:14:54.040154    5063 proxier.go:294] Adding new service "kube-system/kube-dns:dns" at 10.0.0.10:53/UDP
I1027 22:14:54.040223    5063 proxier.go:294] Adding new service "kube-system/kube-dns:dns-tcp" at 10.0.0.10:53/TCP

If you see error messages about not being able to contact the master, you should double-check your Node configuration and installation steps.

One of the possible reasons that kube-proxy cannot run correctly is that the required conntrack binary cannot be found. This may happen on some Linux systems, depending on how you are installing the cluster, for example, you are installing Kubernetes from scratch. If this is the case, you need to manually install the conntrack package (e.g. sudo apt install conntrack on Ubuntu) and then retry.

Kube-proxy can run in one of a few modes. In the log listed above, the line Using iptables Proxier indicates that kube-proxy is running in "iptables" mode. The most common other mode is "ipvs".

Iptables mode

In "iptables" mode, you should see something like the following on a Node:

iptables-save | grep hostnames

-A KUBE-SEP-57KPRZ3JQVENLNBR -s 10.244.3.6/32 -m comment --comment "default/hostnames:" -j MARK --set-xmark 0x00004000/0x00004000
-A KUBE-SEP-57KPRZ3JQVENLNBR -p tcp -m comment --comment "default/hostnames:" -m tcp -j DNAT --to-destination 10.244.3.6:9376
-A KUBE-SEP-WNBA2IHDGP2BOBGZ -s 10.244.1.7/32 -m comment --comment "default/hostnames:" -j MARK --set-xmark 0x00004000/0x00004000
-A KUBE-SEP-WNBA2IHDGP2BOBGZ -p tcp -m comment --comment "default/hostnames:" -m tcp -j DNAT --to-destination 10.244.1.7:9376
-A KUBE-SEP-X3P2623AGDH6CDF3 -s 10.244.2.3/32 -m comment --comment "default/hostnames:" -j MARK --set-xmark 0x00004000/0x00004000
-A KUBE-SEP-X3P2623AGDH6CDF3 -p tcp -m comment --comment "default/hostnames:" -m tcp -j DNAT --to-destination 10.244.2.3:9376
-A KUBE-SERVICES -d 10.0.1.175/32 -p tcp -m comment --comment "default/hostnames: cluster IP" -m tcp --dport 80 -j KUBE-SVC-NWV5X2332I4OT4T3
-A KUBE-SVC-NWV5X2332I4OT4T3 -m comment --comment "default/hostnames:" -m statistic --mode random --probability 0.33332999982 -j KUBE-SEP-WNBA2IHDGP2BOBGZ
-A KUBE-SVC-NWV5X2332I4OT4T3 -m comment --comment "default/hostnames:" -m statistic --mode random --probability 0.50000000000 -j KUBE-SEP-X3P2623AGDH6CDF3
-A KUBE-SVC-NWV5X2332I4OT4T3 -m comment --comment "default/hostnames:" -j KUBE-SEP-57KPRZ3JQVENLNBR

For each port of each Service, there should be 1 rule in KUBE-SERVICES and one KUBE-SVC-<hash> chain. For each Pod endpoint, there should be a small number of rules in that KUBE-SVC-<hash> and one KUBE-SEP-<hash> chain with a small number of rules in it. The exact rules will vary based on your exact config (including node-ports and load-balancers).

IPVS mode

In "ipvs" mode, you should see something like the following on a Node:

ipvsadm -ln

Prot LocalAddress:Port Scheduler Flags
  -> RemoteAddress:Port           Forward Weight ActiveConn InActConn
...
TCP  10.0.1.175:80 rr
  -> 10.244.0.5:9376               Masq    1      0          0
  -> 10.244.0.6:9376               Masq    1      0          0
  -> 10.244.0.7:9376               Masq    1      0          0
...

For each port of each Service, plus any NodePorts, external IPs, and load-balancer IPs, kube-proxy will create a virtual server. For each Pod endpoint, it will create corresponding real servers. In this example, service hostnames(10.0.1.175:80) has 3 endpoints(10.244.0.5:9376, 10.244.0.6:9376, 10.244.0.7:9376).

Is kube-proxy proxying?

Assuming you do see one the above cases, try again to access your Service by IP from one of your Nodes:

curl 10.0.1.175:80

hostnames-632524106-bbpiw

If this still fails, look at the kube-proxy logs for specific lines like:

Setting endpoints for default/hostnames:default to [10.244.0.5:9376 10.244.0.6:9376 10.244.0.7:9376]

If you don't see those, try restarting kube-proxy with the -v flag set to 4, and then look at the logs again.

Edge case: A Pod fails to reach itself via the Service IP

This might sound unlikely, but it does happen and it is supposed to work.

This can happen when the network is not properly configured for "hairpin" traffic, usually when kube-proxy is running in iptables mode and Pods are connected with bridge network. The Kubelet exposes a hairpin-mode flag that allows endpoints of a Service to loadbalance back to themselves if they try to access their own Service VIP. The hairpin-mode flag must either be set to hairpin-veth or promiscuous-bridge.

The common steps to trouble shoot this are as follows:

Confirm hairpin-mode is set to hairpin-veth or promiscuous-bridge. You should see something like the below. hairpin-mode is set to promiscuous-bridge in the following example.

ps auxw | grep kubelet

root      3392  1.1  0.8 186804 65208 ?        Sl   00:51  11:11 /usr/local/bin/kubelet --enable-debugging-handlers=true --config=/etc/kubernetes/manifests --allow-privileged=True --v=4 --cluster-dns=10.0.0.10 --cluster-domain=cluster.local --configure-cbr0=true --cgroup-root=/ --system-cgroups=/system --hairpin-mode=promiscuous-bridge --runtime-cgroups=/docker-daemon --kubelet-cgroups=/kubelet --babysit-daemons=true --max-pods=110 --serialize-image-pulls=false --outofdisk-transition-frequency=0

Confirm the effective hairpin-mode. To do this, you'll have to look at kubelet log. Accessing the logs depends on your Node OS. On some OSes it is a file, such as /var/log/kubelet.log, while other OSes use journalctl to access logs. Please be noted that the effective hairpin mode may not match --hairpin-mode flag due to compatibility. Check if there is any log lines with key word hairpin in kubelet.log. There should be log lines indicating the effective hairpin mode, like something below.

I0629 00:51:43.648698    3252 kubelet.go:380] Hairpin mode set to "promiscuous-bridge"

If the effective hairpin mode is hairpin-veth, ensure the Kubelet has the permission to operate in /sys on node. If everything works properly, you should see something like:

for intf in /sys/devices/virtual/net/cbr0/brif/*; do cat $intf/hairpin_mode; done

If the effective hairpin mode is promiscuous-bridge, ensure Kubelet has the permission to manipulate linux bridge on node. If cbr0 bridge is used and configured properly, you should see:

ifconfig cbr0 |grep PROMISC

UP BROADCAST RUNNING PROMISC MULTICAST  MTU:1460  Metric:1

Seek help if none of above works out.

Seek help

If you get this far, something very strange is happening. Your Service is running, has Endpoints, and your Pods are actually serving. You have DNS working, and kube-proxy does not seem to be misbehaving. And yet your Service is not working. Please let us know what is going on, so we can help investigate!

What's next

Visit the troubleshooting overview document for more information.

4.1.3 - Debug a StatefulSet

This task shows you how to debug a StatefulSet.

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster.
You should have a StatefulSet running that you want to investigate.

Debugging a StatefulSet

In order to list all the pods which belong to a StatefulSet, which have a label app.kubernetes.io/name=MyApp set on them, you can use the following:

kubectl get pods -l app.kubernetes.io/name=MyApp

If you find that any Pods listed are in Unknown or Terminating state for an extended period of time, refer to the Deleting StatefulSet Pods task for instructions on how to deal with them. You can debug individual Pods in a StatefulSet using the Debugging Pods guide.

What's next

Learn more about debugging an init-container.

4.1.4 - Determine the Reason for Pod Failure

This page shows how to write and read a Container termination message.

Termination messages provide a way for containers to write information about fatal events to a location where it can be easily retrieved and surfaced by tools like dashboards and monitoring software. In most cases, information that you put in a termination message should also be written to the general Kubernetes logs.

Before you begin

Writing and reading a termination message

In this exercise, you create a Pod that runs one container. The manifest for that Pod specifies a command that runs when the container starts:

debug/termination.yaml

apiVersion: v1
kind: Pod
metadata:
  name: termination-demo
spec:
  containers:
  - name: termination-demo-container
    image: debian
    command: ["/bin/sh"]
    args: ["-c", "sleep 10 && echo Sleep expired > /dev/termination-log"]

Create a Pod based on the YAML configuration file:
```
kubectl apply -f https://k8s.io/examples/debug/termination.yaml
```
In the YAML file, in the command and args fields, you can see that the container sleeps for 10 seconds and then writes "Sleep expired" to the /dev/termination-log file. After the container writes the "Sleep expired" message, it terminates.
Display information about the Pod:
```
kubectl get pod termination-demo
```
Repeat the preceding command until the Pod is no longer running.

Display detailed information about the Pod:

kubectl get pod termination-demo --output=yaml

The output includes the "Sleep expired" message:

apiVersion: v1
kind: Pod
...
    lastState:
      terminated:
        containerID: ...
        exitCode: 0
        finishedAt: ...
        message: |
          Sleep expired          
        ...

Use a Go template to filter the output so that it includes only the termination message:

kubectl get pod termination-demo -o go-template="{{range .status.containerStatuses}}{{.lastState.terminated.message}}{{end}}"

If you are running a multi-container Pod, you can use a Go template to include the container's name. By doing so, you can discover which of the containers is failing:

kubectl get pod multi-container-pod -o go-template='{{range .status.containerStatuses}}{{printf "%s:\n%s\n\n" .name .lastState.terminated.message}}{{end}}'

Customizing the termination message

Kubernetes retrieves termination messages from the termination message file specified in the terminationMessagePath field of a Container, which has a default value of /dev/termination-log. By customizing this field, you can tell Kubernetes to use a different file. Kubernetes use the contents from the specified file to populate the Container's status message on both success and failure.

The termination message is intended to be brief final status, such as an assertion failure message. The kubelet truncates messages that are longer than 4096 bytes.

The total message length across all containers is limited to 12KiB, divided equally among each container. For example, if there are 12 containers (initContainers or containers), each has 1024 bytes of available termination message space.

The default termination message path is /dev/termination-log. You cannot set the termination message path after a Pod is launched.

In the following example, the container writes termination messages to /tmp/my-log for Kubernetes to retrieve:

apiVersion: v1
kind: Pod
metadata:
  name: msg-path-demo
spec:
  containers:
  - name: msg-path-demo-container
    image: debian
    terminationMessagePath: "/tmp/my-log"

Moreover, users can set the terminationMessagePolicy field of a Container for further customization. This field defaults to "File" which means the termination messages are retrieved only from the termination message file. By setting the terminationMessagePolicy to "FallbackToLogsOnError", you can tell Kubernetes to use the last chunk of container log output if the termination message file is empty and the container exited with an error. The log output is limited to 2048 bytes or 80 lines, whichever is smaller.

What's next

See the terminationMessagePath field in Container.
See ImagePullBackOff in Images.
Learn about retrieving logs.
Learn about Go templates.
Learn about Pod status and Pod phase.
Learn about container states.

4.1.5 - Debug Init Containers

This page shows how to investigate problems related to the execution of Init Containers. The example command lines below refer to the Pod as <pod-name> and the Init Containers as <init-container-1> and <init-container-2>.

Before you begin

To check the version, enter kubectl version.

You should be familiar with the basics of Init Containers.
You should have Configured an Init Container.

Checking the status of Init Containers

Display the status of your pod:

kubectl get pod <pod-name>

For example, a status of Init:1/2 indicates that one of two Init Containers has completed successfully:

NAME         READY     STATUS     RESTARTS   AGE
<pod-name>   0/1       Init:1/2   0          7s

See Understanding Pod status for more examples of status values and their meanings.

Getting details about Init Containers

View more detailed information about Init Container execution:

kubectl describe pod <pod-name>

For example, a Pod with two Init Containers might show the following:

Init Containers:
  <init-container-1>:
    Container ID:    ...
    ...
    State:           Terminated
      Reason:        Completed
      Exit Code:     0
      Started:       ...
      Finished:      ...
    Ready:           True
    Restart Count:   0
    ...
  <init-container-2>:
    Container ID:    ...
    ...
    State:           Waiting
      Reason:        CrashLoopBackOff
    Last State:      Terminated
      Reason:        Error
      Exit Code:     1
      Started:       ...
      Finished:      ...
    Ready:           False
    Restart Count:   3
    ...

You can also access the Init Container statuses programmatically by reading the status.initContainerStatuses field on the Pod Spec:

kubectl get pod nginx --template '{{.status.initContainerStatuses}}'

This command will return the same information as above in raw JSON.

Accessing logs from Init Containers

Pass the Init Container name along with the Pod name to access its logs.

kubectl logs <pod-name> -c <init-container-2>

Init Containers that run a shell script print commands as they're executed. For example, you can do this in Bash by running set -x at the beginning of the script.

Understanding Pod status

A Pod status beginning with Init: summarizes the status of Init Container execution. The table below describes some example status values that you might see while debugging Init Containers.

Status	Meaning
`Init:N/M`	The Pod has `M` Init Containers, and `N` have completed so far.
`Init:Error`	An Init Container has failed to execute.
`Init:CrashLoopBackOff`	An Init Container has failed repeatedly.
`Pending`	The Pod has not yet begun executing Init Containers.
`PodInitializing` or `Running`	The Pod has already finished executing Init Containers.

4.1.6 - Debug Running Pods

This page explains how to debug Pods running (or crashing) on a Node.

Before you begin

Your Pod should already be scheduled and running. If your Pod is not yet running, start with Debugging Pods.
For some of the advanced debugging steps you need to know on which Node the Pod is running and have shell access to run commands on that Node. You don't need that access to run the standard debug steps that use kubectl.

Using `kubectl describe pod` to fetch details about pods

For this example we'll use a Deployment to create two pods, similar to the earlier example.

application/nginx-with-request.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  replicas: 2
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx
        resources:
          limits:
            memory: "128Mi"
            cpu: "500m"
        ports:
        - containerPort: 80

Create deployment by running following command:

kubectl apply -f https://k8s.io/examples/application/nginx-with-request.yaml

deployment.apps/nginx-deployment created

Check pod status by following command:

kubectl get pods

NAME                                READY   STATUS    RESTARTS   AGE
nginx-deployment-67d4bdd6f5-cx2nz   1/1     Running   0          13s
nginx-deployment-67d4bdd6f5-w6kd7   1/1     Running   0          13s

We can retrieve a lot more information about each of these pods using kubectl describe pod. For example:

kubectl describe pod nginx-deployment-67d4bdd6f5-w6kd7

Name:         nginx-deployment-67d4bdd6f5-w6kd7
Namespace:    default
Priority:     0
Node:         kube-worker-1/192.168.0.113
Start Time:   Thu, 17 Feb 2022 16:51:01 -0500
Labels:       app=nginx
              pod-template-hash=67d4bdd6f5
Annotations:  <none>
Status:       Running
IP:           10.88.0.3
IPs:
  IP:           10.88.0.3
  IP:           2001:db8::1
Controlled By:  ReplicaSet/nginx-deployment-67d4bdd6f5
Containers:
  nginx:
    Container ID:   containerd://5403af59a2b46ee5a23fb0ae4b1e077f7ca5c5fb7af16e1ab21c00e0e616462a
    Image:          nginx
    Image ID:       docker.io/library/nginx@sha256:2834dc507516af02784808c5f48b7cbe38b8ed5d0f4837f16e78d00deb7e7767
    Port:           80/TCP
    Host Port:      0/TCP
    State:          Running
      Started:      Thu, 17 Feb 2022 16:51:05 -0500
    Ready:          True
    Restart Count:  0
    Limits:
      cpu:     500m
      memory:  128Mi
    Requests:
      cpu:        500m
      memory:     128Mi
    Environment:  <none>
    Mounts:
      /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-bgsgp (ro)
Conditions:
  Type              Status
  Initialized       True 
  Ready             True 
  ContainersReady   True 
  PodScheduled      True 
Volumes:
  kube-api-access-bgsgp:
    Type:                    Projected (a volume that contains injected data from multiple sources)
    TokenExpirationSeconds:  3607
    ConfigMapName:           kube-root-ca.crt
    ConfigMapOptional:       <nil>
    DownwardAPI:             true
QoS Class:                   Guaranteed
Node-Selectors:              <none>
Tolerations:                 node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
                             node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
  Type    Reason     Age   From               Message
  ----    ------     ----  ----               -------
  Normal  Scheduled  34s   default-scheduler  Successfully assigned default/nginx-deployment-67d4bdd6f5-w6kd7 to kube-worker-1
  Normal  Pulling    31s   kubelet            Pulling image "nginx"
  Normal  Pulled     30s   kubelet            Successfully pulled image "nginx" in 1.146417389s
  Normal  Created    30s   kubelet            Created container nginx
  Normal  Started    30s   kubelet            Started container nginx

Here you can see configuration information about the container(s) and Pod (labels, resource requirements, etc.), as well as status information about the container(s) and Pod (state, readiness, restart count, events, etc.).

The container state is one of Waiting, Running, or Terminated. Depending on the state, additional information will be provided -- here you can see that for a container in Running state, the system tells you when the container started.

Ready tells you whether the container passed its last readiness probe. (In this case, the container does not have a readiness probe configured; the container is assumed to be ready if no readiness probe is configured.)

Restart Count tells you how many times the container has been restarted; this information can be useful for detecting crash loops in containers that are configured with a restart policy of 'always.'

Currently the only Condition associated with a Pod is the binary Ready condition, which indicates that the pod is able to service requests and should be added to the load balancing pools of all matching services.

Lastly, you see a log of recent events related to your Pod. "From" indicates the component that is logging the event. "Reason" and "Message" tell you what happened.

Example: debugging Pending Pods

A common scenario that you can detect using events is when you've created a Pod that won't fit on any node. For example, the Pod might request more resources than are free on any node, or it might specify a label selector that doesn't match any nodes. Let's say we created the previous Deployment with 5 replicas (instead of 2) and requesting 600 millicores instead of 500, on a four-node cluster where each (virtual) machine has 1 CPU. In that case one of the Pods will not be able to schedule. (Note that because of the cluster addon pods such as fluentd, skydns, etc., that run on each node, if we requested 1000 millicores then none of the Pods would be able to schedule.)

kubectl get pods

NAME                                READY     STATUS    RESTARTS   AGE
nginx-deployment-1006230814-6winp   1/1       Running   0          7m
nginx-deployment-1006230814-fmgu3   1/1       Running   0          7m
nginx-deployment-1370807587-6ekbw   1/1       Running   0          1m
nginx-deployment-1370807587-fg172   0/1       Pending   0          1m
nginx-deployment-1370807587-fz9sd   0/1       Pending   0          1m

To find out why the nginx-deployment-1370807587-fz9sd pod is not running, we can use kubectl describe pod on the pending Pod and look at its events:

kubectl describe pod nginx-deployment-1370807587-fz9sd

  Name:		nginx-deployment-1370807587-fz9sd
  Namespace:	default
  Node:		/
  Labels:		app=nginx,pod-template-hash=1370807587
  Status:		Pending
  IP:
  Controllers:	ReplicaSet/nginx-deployment-1370807587
  Containers:
    nginx:
      Image:	nginx
      Port:	80/TCP
      QoS Tier:
        memory:	Guaranteed
        cpu:	Guaranteed
      Limits:
        cpu:	1
        memory:	128Mi
      Requests:
        cpu:	1
        memory:	128Mi
      Environment Variables:
  Volumes:
    default-token-4bcbi:
      Type:	Secret (a volume populated by a Secret)
      SecretName:	default-token-4bcbi
  Events:
    FirstSeen	LastSeen	Count	From			        SubobjectPath	Type		Reason			    Message
    ---------	--------	-----	----			        -------------	--------	------			    -------
    1m		    48s		    7	    {default-scheduler }			        Warning		FailedScheduling	pod (nginx-deployment-1370807587-fz9sd) failed to fit in any node
  fit failure on node (kubernetes-node-6ta5): Node didn't have enough resource: CPU, requested: 1000, used: 1420, capacity: 2000
  fit failure on node (kubernetes-node-wul5): Node didn't have enough resource: CPU, requested: 1000, used: 1100, capacity: 2000

Here you can see the event generated by the scheduler saying that the Pod failed to schedule for reason FailedScheduling (and possibly others). The message tells us that there were not enough resources for the Pod on any of the nodes.

To correct this situation, you can use kubectl scale to update your Deployment to specify four or fewer replicas. (Or you could leave the one Pod pending, which is harmless.)

Events such as the ones you saw at the end of kubectl describe pod are persisted in etcd and provide high-level information on what is happening in the cluster. To list all events you can use

kubectl get events

but you have to remember that events are namespaced. This means that if you're interested in events for some namespaced object (e.g. what happened with Pods in namespace my-namespace) you need to explicitly provide a namespace to the command:

kubectl get events --namespace=my-namespace

To see events from all namespaces, you can use the --all-namespaces argument.

In addition to kubectl describe pod, another way to get extra information about a pod (beyond what is provided by kubectl get pod) is to pass the -o yaml output format flag to kubectl get pod. This will give you, in YAML format, even more information than kubectl describe pod--essentially all of the information the system has about the Pod. Here you will see things like annotations (which are key-value metadata without the label restrictions, that is used internally by Kubernetes system components), restart policy, ports, and volumes.

kubectl get pod nginx-deployment-1006230814-6winp -o yaml

apiVersion: v1
kind: Pod
metadata:
  creationTimestamp: "2022-02-17T21:51:01Z"
  generateName: nginx-deployment-67d4bdd6f5-
  labels:
    app: nginx
    pod-template-hash: 67d4bdd6f5
  name: nginx-deployment-67d4bdd6f5-w6kd7
  namespace: default
  ownerReferences:
  - apiVersion: apps/v1
    blockOwnerDeletion: true
    controller: true
    kind: ReplicaSet
    name: nginx-deployment-67d4bdd6f5
    uid: 7d41dfd4-84c0-4be4-88ab-cedbe626ad82
  resourceVersion: "1364"
  uid: a6501da1-0447-4262-98eb-c03d4002222e
spec:
  containers:
  - image: nginx
    imagePullPolicy: Always
    name: nginx
    ports:
    - containerPort: 80
      protocol: TCP
    resources:
      limits:
        cpu: 500m
        memory: 128Mi
      requests:
        cpu: 500m
        memory: 128Mi
    terminationMessagePath: /dev/termination-log
    terminationMessagePolicy: File
    volumeMounts:
    - mountPath: /var/run/secrets/kubernetes.io/serviceaccount
      name: kube-api-access-bgsgp
      readOnly: true
  dnsPolicy: ClusterFirst
  enableServiceLinks: true
  nodeName: kube-worker-1
  preemptionPolicy: PreemptLowerPriority
  priority: 0
  restartPolicy: Always
  schedulerName: default-scheduler
  securityContext: {}
  serviceAccount: default
  serviceAccountName: default
  terminationGracePeriodSeconds: 30
  tolerations:
  - effect: NoExecute
    key: node.kubernetes.io/not-ready
    operator: Exists
    tolerationSeconds: 300
  - effect: NoExecute
    key: node.kubernetes.io/unreachable
    operator: Exists
    tolerationSeconds: 300
  volumes:
  - name: kube-api-access-bgsgp
    projected:
      defaultMode: 420
      sources:
      - serviceAccountToken:
          expirationSeconds: 3607
          path: token
      - configMap:
          items:
          - key: ca.crt
            path: ca.crt
          name: kube-root-ca.crt
      - downwardAPI:
          items:
          - fieldRef:
              apiVersion: v1
              fieldPath: metadata.namespace
            path: namespace
status:
  conditions:
  - lastProbeTime: null
    lastTransitionTime: "2022-02-17T21:51:01Z"
    status: "True"
    type: Initialized
  - lastProbeTime: null
    lastTransitionTime: "2022-02-17T21:51:06Z"
    status: "True"
    type: Ready
  - lastProbeTime: null
    lastTransitionTime: "2022-02-17T21:51:06Z"
    status: "True"
    type: ContainersReady
  - lastProbeTime: null
    lastTransitionTime: "2022-02-17T21:51:01Z"
    status: "True"
    type: PodScheduled
  containerStatuses:
  - containerID: containerd://5403af59a2b46ee5a23fb0ae4b1e077f7ca5c5fb7af16e1ab21c00e0e616462a
    image: docker.io/library/nginx:latest
    imageID: docker.io/library/nginx@sha256:2834dc507516af02784808c5f48b7cbe38b8ed5d0f4837f16e78d00deb7e7767
    lastState: {}
    name: nginx
    ready: true
    restartCount: 0
    started: true
    state:
      running:
        startedAt: "2022-02-17T21:51:05Z"
  hostIP: 192.168.0.113
  phase: Running
  podIP: 10.88.0.3
  podIPs:
  - ip: 10.88.0.3
  - ip: 2001:db8::1
  qosClass: Guaranteed
  startTime: "2022-02-17T21:51:01Z"

Examining pod logs

First, look at the logs of the affected container:

kubectl logs ${POD_NAME} ${CONTAINER_NAME}

If your container has previously crashed, you can access the previous container's crash log with:

kubectl logs --previous ${POD_NAME} ${CONTAINER_NAME}

Debugging with container exec

If the container image includes debugging utilities, as is the case with images built from Linux and Windows OS base images, you can run commands inside a specific container with kubectl exec:

kubectl exec ${POD_NAME} -c ${CONTAINER_NAME} -- ${CMD} ${ARG1} ${ARG2} ... ${ARGN}

Note:

-c ${CONTAINER_NAME} is optional. You can omit it for Pods that only contain a single container.

As an example, to look at the logs from a running Cassandra pod, you might run

kubectl exec cassandra -- cat /var/log/cassandra/system.log

You can run a shell that's connected to your terminal using the -i and -t arguments to kubectl exec, for example:

kubectl exec -it cassandra -- sh

For more details, see Get a Shell to a Running Container.

Debugging with an ephemeral debug container

FEATURE STATE: Kubernetes v1.25 [stable]

Ephemeral containers are useful for interactive troubleshooting when kubectl exec is insufficient because a container has crashed or a container image doesn't include debugging utilities, such as with distroless images.

Example debugging using ephemeral containers

You can use the kubectl debug command to add ephemeral containers to a running Pod. First, create a pod for the example:

kubectl run ephemeral-demo --image=registry.k8s.io/pause:3.1 --restart=Never

The examples in this section use the pause container image because it does not contain debugging utilities, but this method works with all container images.

If you attempt to use kubectl exec to create a shell you will see an error because there is no shell in this container image.

kubectl exec -it ephemeral-demo -- sh

OCI runtime exec failed: exec failed: container_linux.go:346: starting container process caused "exec: \"sh\": executable file not found in $PATH": unknown

You can instead add a debugging container using kubectl debug. If you specify the -i/--interactive argument, kubectl will automatically attach to the console of the Ephemeral Container.

kubectl debug -it ephemeral-demo --image=busybox:1.28 --target=ephemeral-demo

Defaulting debug container name to debugger-8xzrl.
If you don't see a command prompt, try pressing enter.
/ #

This command adds a new busybox container and attaches to it. The --target parameter targets the process namespace of another container. It's necessary here because kubectl run does not enable process namespace sharing in the pod it creates.

Note:

The --target parameter must be supported by the . When not supported, the Ephemeral Container may not be started, or it may be started with an isolated process namespace so that ps does not reveal processes in other containers.

You can view the state of the newly created ephemeral container using kubectl describe:

kubectl describe pod ephemeral-demo

...
Ephemeral Containers:
  debugger-8xzrl:
    Container ID:   docker://b888f9adfd15bd5739fefaa39e1df4dd3c617b9902082b1cfdc29c4028ffb2eb
    Image:          busybox
    Image ID:       docker-pullable://busybox@sha256:1828edd60c5efd34b2bf5dd3282ec0cc04d47b2ff9caa0b6d4f07a21d1c08084
    Port:           <none>
    Host Port:      <none>
    State:          Running
      Started:      Wed, 12 Feb 2020 14:25:42 +0100
    Ready:          False
    Restart Count:  0
    Environment:    <none>
    Mounts:         <none>
...

Use kubectl delete to remove the Pod when you're finished:

kubectl delete pod ephemeral-demo

Debugging using a copy of the Pod

Sometimes Pod configuration options make it difficult to troubleshoot in certain situations. For example, you can't run kubectl exec to troubleshoot your container if your container image does not include a shell or if your application crashes on startup. In these situations you can use kubectl debug to create a copy of the Pod with configuration values changed to aid debugging.

Copying a Pod while adding a new container

Adding a new container can be useful when your application is running but not behaving as you expect and you'd like to add additional troubleshooting utilities to the Pod.

For example, maybe your application's container images are built on busybox but you need debugging utilities not included in busybox. You can simulate this scenario using kubectl run:

kubectl run myapp --image=busybox:1.28 --restart=Never -- sleep 1d

Run this command to create a copy of myapp named myapp-debug that adds a new Ubuntu container for debugging:

kubectl debug myapp -it --image=ubuntu --share-processes --copy-to=myapp-debug

Defaulting debug container name to debugger-w7xmf.
If you don't see a command prompt, try pressing enter.
root@myapp-debug:/#

Note:

kubectl debug automatically generates a container name if you don't choose one using the --container flag.
The -i flag causes kubectl debug to attach to the new container by default. You can prevent this by specifying --attach=false. If your session becomes disconnected you can reattach using kubectl attach.
The --share-processes allows the containers in this Pod to see processes from the other containers in the Pod. For more information about how this works, see Share Process Namespace between Containers in a Pod.

Don't forget to clean up the debugging Pod when you're finished with it:

kubectl delete pod myapp myapp-debug

Copying a Pod while changing its command

Sometimes it's useful to change the command for a container, for example to add a debugging flag or because the application is crashing.

To simulate a crashing application, use kubectl run to create a container that immediately exits:

kubectl run --image=busybox:1.28 myapp -- false

You can see using kubectl describe pod myapp that this container is crashing:

Containers:
  myapp:
    Image:         busybox
    ...
    Args:
      false
    State:          Waiting
      Reason:       CrashLoopBackOff
    Last State:     Terminated
      Reason:       Error
      Exit Code:    1

You can use kubectl debug to create a copy of this Pod with the command changed to an interactive shell:

kubectl debug myapp -it --copy-to=myapp-debug --container=myapp -- sh

If you don't see a command prompt, try pressing enter.
/ #

Now you have an interactive shell that you can use to perform tasks like checking filesystem paths or running the container command manually.

Note:

To change the command of a specific container you must specify its name using --container or kubectl debug will instead create a new container to run the command you specified.
The -i flag causes kubectl debug to attach to the container by default. You can prevent this by specifying --attach=false. If your session becomes disconnected you can reattach using kubectl attach.

Don't forget to clean up the debugging Pod when you're finished with it:

kubectl delete pod myapp myapp-debug

Copying a Pod while changing container images

In some situations you may want to change a misbehaving Pod from its normal production container images to an image containing a debugging build or additional utilities.

As an example, create a Pod using kubectl run:

kubectl run myapp --image=busybox:1.28 --restart=Never -- sleep 1d

Now use kubectl debug to make a copy and change its container image to ubuntu:

kubectl debug myapp --copy-to=myapp-debug --set-image=*=ubuntu

The syntax of --set-image uses the same container_name=image syntax as kubectl set image. *=ubuntu means change the image of all containers to ubuntu.

Don't forget to clean up the debugging Pod when you're finished with it:

kubectl delete pod myapp myapp-debug

Debugging via a shell on the node

If none of these approaches work, you can find the Node on which the Pod is running and create a Pod running on the Node. To create an interactive shell on a Node using kubectl debug, run:

kubectl debug node/mynode -it --image=ubuntu

Creating debugging pod node-debugger-mynode-pdx84 with container debugger on node mynode.
If you don't see a command prompt, try pressing enter.
root@ek8s:/#

When creating a debugging session on a node, keep in mind that:

kubectl debug automatically generates the name of the new Pod based on the name of the Node.
The root filesystem of the Node will be mounted at /host.
The container runs in the host IPC, Network, and PID namespaces, although the pod isn't privileged, so reading some process information may fail, and chroot /host may fail.
If you need a privileged pod, create it manually or use the --profile=sysadmin flag.

Don't forget to clean up the debugging Pod when you're finished with it:

kubectl delete pod node-debugger-mynode-pdx84

Debugging Profiles

When using kubectl debug to debug a node via a debugging Pod, a Pod via an ephemeral container, or a copied Pod, you can apply a debugging profile to them using the --profile flag. By applying a profile, specific properties such as securityContext are set, allowing for adaptation to various scenarios.

The available profiles are as follows:

Profile	Description
legacy	A set of properties backwards compatibility with 1.22 behavior
general	A reasonable set of generic properties for each debugging journey
baseline	A set of properties compatible with PodSecurityStandard baseline policy
restricted	A set of properties compatible with PodSecurityStandard restricted policy
netadmin	A set of properties including Network Administrator privileges
sysadmin	A set of properties including System Administrator (root) privileges

Note:

If you don't specify --profile, the legacy profile is used by default, but it is planned to be deprecated in the near future. So it is recommended to use other profiles such as general.

Assume that you create a Pod and debug it. First, create a Pod named myapp as an example:

kubectl run myapp --image=busybox:1.28 --restart=Never -- sleep 1d

Then, debug the Pod using an ephemeral container. If the ephemeral container needs to have privilege, you can use the sysadmin profile:

kubectl debug -it myapp --image=busybox:1.28 --target=myapp --profile=sysadmin

Targeting container "myapp". If you don't see processes from this container it may be because the container runtime doesn't support this feature.
Defaulting debug container name to debugger-6kg4x.
If you don't see a command prompt, try pressing enter.
/ #

Check the capabilities of the ephemeral container process by running the following command inside the container:

/ # grep Cap /proc/$$/status

...
CapPrm:	000001ffffffffff
CapEff:	000001ffffffffff
...

This means the container process is granted full capabilities as a privileged container by applying sysadmin profile. See more details about capabilities.

You can also check that the ephemeral container was created as a privileged container:

kubectl get pod myapp -o jsonpath='{.spec.ephemeralContainers[0].securityContext}'

{"privileged":true}

Clean up the Pod when you're finished with it:

kubectl delete pod myapp

4.1.7 - Get a Shell to a Running Container

This page shows how to use kubectl exec to get a shell to a running container.

Before you begin

Getting a shell to a container

In this exercise, you create a Pod that has one container. The container runs the nginx image. Here is the configuration file for the Pod:

application/shell-demo.yaml

apiVersion: v1
kind: Pod
metadata:
  name: shell-demo
spec:
  volumes:
  - name: shared-data
    emptyDir: {}
  containers:
  - name: nginx
    image: nginx
    volumeMounts:
    - name: shared-data
      mountPath: /usr/share/nginx/html
  hostNetwork: true
  dnsPolicy: Default

Create the Pod:

kubectl apply -f https://k8s.io/examples/application/shell-demo.yaml

Verify that the container is running:

kubectl get pod shell-demo

Get a shell to the running container:

kubectl exec --stdin --tty shell-demo -- /bin/bash

Note:

The double dash (--) separates the arguments you want to pass to the command from the kubectl arguments.

In your shell, list the root directory:

# Run this inside the container
ls /

In your shell, experiment with other commands. Here are some examples:

# You can run these example commands inside the container
ls /
cat /proc/mounts
cat /proc/1/maps
apt-get update
apt-get install -y tcpdump
tcpdump
apt-get install -y lsof
lsof
apt-get install -y procps
ps aux
ps aux | grep nginx

Writing the root page for nginx

Look again at the configuration file for your Pod. The Pod has an emptyDir volume, and the container mounts the volume at /usr/share/nginx/html.

In your shell, create an index.html file in the /usr/share/nginx/html directory:

# Run this inside the container
echo 'Hello shell demo' > /usr/share/nginx/html/index.html

In your shell, send a GET request to the nginx server:

# Run this in the shell inside your container
apt-get update
apt-get install curl
curl http://localhost/

The output shows the text that you wrote to the index.html file:

Hello shell demo

When you are finished with your shell, enter exit.

exit # To quit the shell in the container

Running individual commands in a container

In an ordinary command window, not your shell, list the environment variables in the running container:

kubectl exec shell-demo -- env

Experiment with running other commands. Here are some examples:

kubectl exec shell-demo -- ps aux
kubectl exec shell-demo -- ls /
kubectl exec shell-demo -- cat /proc/1/mounts

Opening a shell when a Pod has more than one container

If a Pod has more than one container, use --container or -c to specify a container in the kubectl exec command. For example, suppose you have a Pod named my-pod, and the Pod has two containers named main-app and helper-app. The following command would open a shell to the main-app container.

kubectl exec -i -t my-pod --container main-app -- /bin/bash

Note:

The short options -i and -t are the same as the long options --stdin and --tty

What's next

Read about kubectl exec

4.2 - Troubleshooting Clusters

Debugging common cluster issues.

This doc is about cluster troubleshooting; we assume you have already ruled out your application as the root cause of the problem you are experiencing. See the application troubleshooting guide for tips on application debugging. You may also visit the troubleshooting overview document for more information.

For troubleshooting kubectl, refer to Troubleshooting kubectl.

Listing your cluster

The first thing to debug in your cluster is if your nodes are all registered correctly.

Run the following command:

kubectl get nodes

And verify that all of the nodes you expect to see are present and that they are all in the Ready state.

To get detailed information about the overall health of your cluster, you can run:

kubectl cluster-info dump

Example: debugging a down/unreachable node

Sometimes when debugging it can be useful to look at the status of a node -- for example, because you've noticed strange behavior of a Pod that's running on the node, or to find out why a Pod won't schedule onto the node. As with Pods, you can use kubectl describe node and kubectl get node -o yaml to retrieve detailed information about nodes. For example, here's what you'll see if a node is down (disconnected from the network, or kubelet dies and won't restart, etc.). Notice the events that show the node is NotReady, and also notice that the pods are no longer running (they are evicted after five minutes of NotReady status).

kubectl get nodes

NAME                     STATUS       ROLES     AGE     VERSION
kube-worker-1            NotReady     <none>    1h      v1.23.3
kubernetes-node-bols     Ready        <none>    1h      v1.23.3
kubernetes-node-st6x     Ready        <none>    1h      v1.23.3
kubernetes-node-unaj     Ready        <none>    1h      v1.23.3

kubectl describe node kube-worker-1

Name:               kube-worker-1
Roles:              <none>
Labels:             beta.kubernetes.io/arch=amd64
                    beta.kubernetes.io/os=linux
                    kubernetes.io/arch=amd64
                    kubernetes.io/hostname=kube-worker-1
                    kubernetes.io/os=linux
Annotations:        kubeadm.alpha.kubernetes.io/cri-socket: /run/containerd/containerd.sock
                    node.alpha.kubernetes.io/ttl: 0
                    volumes.kubernetes.io/controller-managed-attach-detach: true
CreationTimestamp:  Thu, 17 Feb 2022 16:46:30 -0500
Taints:             node.kubernetes.io/unreachable:NoExecute
                    node.kubernetes.io/unreachable:NoSchedule
Unschedulable:      false
Lease:
  HolderIdentity:  kube-worker-1
  AcquireTime:     <unset>
  RenewTime:       Thu, 17 Feb 2022 17:13:09 -0500
Conditions:
  Type                 Status    LastHeartbeatTime                 LastTransitionTime                Reason              Message
  ----                 ------    -----------------                 ------------------                ------              -------
  NetworkUnavailable   False     Thu, 17 Feb 2022 17:09:13 -0500   Thu, 17 Feb 2022 17:09:13 -0500   WeaveIsUp           Weave pod has set this
  MemoryPressure       Unknown   Thu, 17 Feb 2022 17:12:40 -0500   Thu, 17 Feb 2022 17:13:52 -0500   NodeStatusUnknown   Kubelet stopped posting node status.
  DiskPressure         Unknown   Thu, 17 Feb 2022 17:12:40 -0500   Thu, 17 Feb 2022 17:13:52 -0500   NodeStatusUnknown   Kubelet stopped posting node status.
  PIDPressure          Unknown   Thu, 17 Feb 2022 17:12:40 -0500   Thu, 17 Feb 2022 17:13:52 -0500   NodeStatusUnknown   Kubelet stopped posting node status.
  Ready                Unknown   Thu, 17 Feb 2022 17:12:40 -0500   Thu, 17 Feb 2022 17:13:52 -0500   NodeStatusUnknown   Kubelet stopped posting node status.
Addresses:
  InternalIP:  192.168.0.113
  Hostname:    kube-worker-1
Capacity:
  cpu:                2
  ephemeral-storage:  15372232Ki
  hugepages-2Mi:      0
  memory:             2025188Ki
  pods:               110
Allocatable:
  cpu:                2
  ephemeral-storage:  14167048988
  hugepages-2Mi:      0
  memory:             1922788Ki
  pods:               110
System Info:
  Machine ID:                 9384e2927f544209b5d7b67474bbf92b
  System UUID:                aa829ca9-73d7-064d-9019-df07404ad448
  Boot ID:                    5a295a03-aaca-4340-af20-1327fa5dab5c
  Kernel Version:             5.13.0-28-generic
  OS Image:                   Ubuntu 21.10
  Operating System:           linux
  Architecture:               amd64
  Container Runtime Version:  containerd://1.5.9
  Kubelet Version:            v1.23.3
  Kube-Proxy Version:         v1.23.3
Non-terminated Pods:          (4 in total)
  Namespace                   Name                                 CPU Requests  CPU Limits  Memory Requests  Memory Limits  Age
  ---------                   ----                                 ------------  ----------  ---------------  -------------  ---
  default                     nginx-deployment-67d4bdd6f5-cx2nz    500m (25%)    500m (25%)  128Mi (6%)       128Mi (6%)     23m
  default                     nginx-deployment-67d4bdd6f5-w6kd7    500m (25%)    500m (25%)  128Mi (6%)       128Mi (6%)     23m
  kube-system                 kube-proxy-dnxbz                     0 (0%)        0 (0%)      0 (0%)           0 (0%)         28m
  kube-system                 weave-net-gjxxp                      100m (5%)     0 (0%)      200Mi (10%)      0 (0%)         28m
Allocated resources:
  (Total limits may be over 100 percent, i.e., overcommitted.)
  Resource           Requests     Limits
  --------           --------     ------
  cpu                1100m (55%)  1 (50%)
  memory             456Mi (24%)  256Mi (13%)
  ephemeral-storage  0 (0%)       0 (0%)
  hugepages-2Mi      0 (0%)       0 (0%)
Events:
...

kubectl get node kube-worker-1 -o yaml

apiVersion: v1
kind: Node
metadata:
  annotations:
    kubeadm.alpha.kubernetes.io/cri-socket: /run/containerd/containerd.sock
    node.alpha.kubernetes.io/ttl: "0"
    volumes.kubernetes.io/controller-managed-attach-detach: "true"
  creationTimestamp: "2022-02-17T21:46:30Z"
  labels:
    beta.kubernetes.io/arch: amd64
    beta.kubernetes.io/os: linux
    kubernetes.io/arch: amd64
    kubernetes.io/hostname: kube-worker-1
    kubernetes.io/os: linux
  name: kube-worker-1
  resourceVersion: "4026"
  uid: 98efe7cb-2978-4a0b-842a-1a7bf12c05f8
spec: {}
status:
  addresses:
  - address: 192.168.0.113
    type: InternalIP
  - address: kube-worker-1
    type: Hostname
  allocatable:
    cpu: "2"
    ephemeral-storage: "14167048988"
    hugepages-2Mi: "0"
    memory: 1922788Ki
    pods: "110"
  capacity:
    cpu: "2"
    ephemeral-storage: 15372232Ki
    hugepages-2Mi: "0"
    memory: 2025188Ki
    pods: "110"
  conditions:
  - lastHeartbeatTime: "2022-02-17T22:20:32Z"
    lastTransitionTime: "2022-02-17T22:20:32Z"
    message: Weave pod has set this
    reason: WeaveIsUp
    status: "False"
    type: NetworkUnavailable
  - lastHeartbeatTime: "2022-02-17T22:20:15Z"
    lastTransitionTime: "2022-02-17T22:13:25Z"
    message: kubelet has sufficient memory available
    reason: KubeletHasSufficientMemory
    status: "False"
    type: MemoryPressure
  - lastHeartbeatTime: "2022-02-17T22:20:15Z"
    lastTransitionTime: "2022-02-17T22:13:25Z"
    message: kubelet has no disk pressure
    reason: KubeletHasNoDiskPressure
    status: "False"
    type: DiskPressure
  - lastHeartbeatTime: "2022-02-17T22:20:15Z"
    lastTransitionTime: "2022-02-17T22:13:25Z"
    message: kubelet has sufficient PID available
    reason: KubeletHasSufficientPID
    status: "False"
    type: PIDPressure
  - lastHeartbeatTime: "2022-02-17T22:20:15Z"
    lastTransitionTime: "2022-02-17T22:15:15Z"
    message: kubelet is posting ready status
    reason: KubeletReady
    status: "True"
    type: Ready
  daemonEndpoints:
    kubeletEndpoint:
      Port: 10250
  nodeInfo:
    architecture: amd64
    bootID: 22333234-7a6b-44d4-9ce1-67e31dc7e369
    containerRuntimeVersion: containerd://1.5.9
    kernelVersion: 5.13.0-28-generic
    kubeProxyVersion: v1.23.3
    kubeletVersion: v1.23.3
    machineID: 9384e2927f544209b5d7b67474bbf92b
    operatingSystem: linux
    osImage: Ubuntu 21.10
    systemUUID: aa829ca9-73d7-064d-9019-df07404ad448

Looking at logs

For now, digging deeper into the cluster requires logging into the relevant machines. Here are the locations of the relevant log files. On systemd-based systems, you may need to use journalctl instead of examining log files.

Control Plane nodes

/var/log/kube-apiserver.log - API Server, responsible for serving the API
/var/log/kube-scheduler.log - Scheduler, responsible for making scheduling decisions
/var/log/kube-controller-manager.log - a component that runs most Kubernetes built-in controllers, with the notable exception of scheduling (the kube-scheduler handles scheduling).

Worker Nodes

/var/log/kubelet.log - logs from the kubelet, responsible for running containers on the node
/var/log/kube-proxy.log - logs from kube-proxy, which is responsible for directing traffic to Service endpoints

Cluster failure modes

This is an incomplete list of things that could go wrong, and how to adjust your cluster setup to mitigate the problems.

Contributing causes

VM(s) shutdown
Network partition within cluster, or between cluster and users
Crashes in Kubernetes software
Data loss or unavailability of persistent storage (e.g. GCE PD or AWS EBS volume)
Operator error, for example, misconfigured Kubernetes software or application software

Specific scenarios

API server VM shutdown or apiserver crashing
- Results
  - unable to stop, update, or start new pods, services, replication controller
  - existing pods and services should continue to work normally unless they depend on the Kubernetes API
API server backing storage lost
- Results
  - the kube-apiserver component fails to start successfully and become healthy
  - kubelets will not be able to reach it but will continue to run the same pods and provide the same service proxying
  - manual recovery or recreation of apiserver state necessary before apiserver is restarted
Supporting services (node controller, replication controller manager, scheduler, etc) VM shutdown or crashes
- currently those are colocated with the apiserver, and their unavailability has similar consequences as apiserver
- in future, these will be replicated as well and may not be co-located
- they do not have their own persistent state
Individual node (VM or physical machine) shuts down
- Results
  - pods on that Node stop running
Network partition
- Results
  - partition A thinks the nodes in partition B are down; partition B thinks the apiserver is down. (Assuming the master VM ends up in partition A.)
Kubelet software fault
- Results
  - crashing kubelet cannot start new pods on the node
  - kubelet might delete the pods or not
  - node marked unhealthy
  - replication controllers start new pods elsewhere
Cluster operator error
- Results
  - loss of pods, services, etc
  - lost of apiserver backing store
  - users unable to read API
  - etc.

Mitigations

Action: Use the IaaS provider's automatic VM restarting feature for IaaS VMs
- Mitigates: Apiserver VM shutdown or apiserver crashing
- Mitigates: Supporting services VM shutdown or crashes
Action: Use IaaS providers reliable storage (e.g. GCE PD or AWS EBS volume) for VMs with apiserver+etcd
- Mitigates: Apiserver backing storage lost
Action: Use high-availability configuration
- Mitigates: Control plane node shutdown or control plane components (scheduler, API server, controller-manager) crashing
  - Will tolerate one or more simultaneous node or component failures
- Mitigates: API server backing storage (i.e., etcd's data directory) lost
  - Assumes HA (highly-available) etcd configuration
Action: Snapshot apiserver PDs/EBS-volumes periodically
- Mitigates: Apiserver backing storage lost
- Mitigates: Some cases of operator error
- Mitigates: Some cases of Kubernetes software fault
Action: use replication controller and services in front of pods
- Mitigates: Node shutdown
- Mitigates: Kubelet software fault
Action: applications (containers) designed to tolerate unexpected restarts
- Mitigates: Node shutdown
- Mitigates: Kubelet software fault

What's next

Learn about the metrics available in the Resource Metrics Pipeline
Discover additional tools for monitoring resource usage
Use Node Problem Detector to monitor node health
Use kubectl debug node to debug Kubernetes nodes
Use crictl to debug Kubernetes nodes
Get more information about Kubernetes auditing
Use telepresence to develop and debug services locally

4.2.1 - Troubleshooting kubectl

This documentation is about investigating and diagnosing kubectl related issues. If you encounter issues accessing kubectl or connecting to your cluster, this document outlines various common scenarios and potential solutions to help identify and address the likely cause.

Before you begin

You need to have a Kubernetes cluster.
You also need to have kubectl installed - see install tools

Verify kubectl setup

Make sure you have installed and configured kubectl correctly on your local machine. Check the kubectl version to ensure it is up-to-date and compatible with your cluster.

Check kubectl version:

kubectl version

You'll see a similar output:

Client Version: version.Info{Major:"1", Minor:"27", GitVersion:"v1.27.4",GitCommit:"fa3d7990104d7c1f16943a67f11b154b71f6a132", GitTreeState:"clean",BuildDate:"2023-07-19T12:20:54Z", GoVersion:"go1.20.6", Compiler:"gc", Platform:"linux/amd64"}
Kustomize Version: v5.0.1
Server Version: version.Info{Major:"1", Minor:"27", GitVersion:"v1.27.3",GitCommit:"25b4e43193bcda6c7328a6d147b1fb73a33f1598", GitTreeState:"clean",BuildDate:"2023-06-14T09:47:40Z", GoVersion:"go1.20.5", Compiler:"gc", Platform:"linux/amd64"}

If you see Unable to connect to the server: dial tcp <server-ip>:8443: i/o timeout, instead of Server Version, you need to troubleshoot kubectl connectivity with your cluster.

Make sure you have installed the kubectl by following the official documentation for installing kubectl, and you have properly configured the $PATH environment variable.

Check kubeconfig

The kubectl requires a kubeconfig file to connect to a Kubernetes cluster. The kubeconfig file is usually located under the ~/.kube/config directory. Make sure that you have a valid kubeconfig file. If you don't have a kubeconfig file, you can obtain it from your Kubernetes administrator, or you can copy it from your Kubernetes control plane's /etc/kubernetes/admin.conf directory. If you have deployed your Kubernetes cluster on a cloud platform and lost your kubeconfig file, you can re-generate it using your cloud provider's tools. Refer the cloud provider's documentation for re-generating a kubeconfig file.

Check if the $KUBECONFIG environment variable is configured correctly. You can set $KUBECONFIGenvironment variable or use the --kubeconfig parameter with the kubectl to specify the directory of a kubeconfig file.

Check VPN connectivity

If you are using a Virtual Private Network (VPN) to access your Kubernetes cluster, make sure that your VPN connection is active and stable. Sometimes, VPN disconnections can lead to connection issues with the cluster. Reconnect to the VPN and try accessing the cluster again.

Authentication and authorization

If you are using the token based authentication and the kubectl is returning an error regarding the authentication token or authentication server address, validate the Kubernetes authentication token and the authentication server address are configured properly.

If kubectl is returning an error regarding the authorization, make sure that you are using the valid user credentials. And you have the permission to access the resource that you have requested.

Verify contexts

Kubernetes supports multiple clusters and contexts. Ensure that you are using the correct context to interact with your cluster.

List available contexts:

kubectl config get-contexts

Switch to the appropriate context:

kubectl config use-context <context-name>

API server and load balancer

The kube-apiserver server is the central component of a Kubernetes cluster. If the API server or the load balancer that runs in front of your API servers is not reachable or not responding, you won't be able to interact with the cluster.

Check the if the API server's host is reachable by using ping command. Check cluster's network connectivity and firewall. If your are using a cloud provider for deploying the cluster, check your cloud provider's health check status for the cluster's API server.

Verify the status of the load balancer (if used) to ensure it is healthy and forwarding traffic to the API server.

TLS problems

Additional tools required - base64 and openssl version 3.0 or above.

The Kubernetes API server only serves HTTPS requests by default. In that case TLS problems may occur due to various reasons, such as certificate expiry or chain of trust validity.

You can find the TLS certificate in the kubeconfig file, located in the ~/.kube/config directory. The certificate-authority attribute contains the CA certificate and the client-certificate attribute contains the client certificate.

Verify the expiry of these certificates:

kubectl config view --flatten --output 'jsonpath={.clusters[0].cluster.certificate-authority-data}' | base64 -d | openssl x509 -noout -dates

output:

notBefore=Feb 13 05:57:47 2024 GMT
notAfter=Feb 10 06:02:47 2034 GMT

kubectl config view --flatten --output 'jsonpath={.users[0].user.client-certificate-data}'| base64 -d | openssl x509 -noout -dates

output:

notBefore=Feb 13 05:57:47 2024 GMT
notAfter=Feb 12 06:02:50 2025 GMT

Verify kubectl helpers

Some kubectl authentication helpers provide easy access to Kubernetes clusters. If you have used such helpers and are facing connectivity issues, ensure that the necessary configurations are still present.

Check kubectl configuration for authentication details:

kubectl config view

If you previously used a helper tool (for example, kubectl-oidc-login), ensure that it is still installed and configured correctly.

4.2.2 - Resource metrics pipeline

For Kubernetes, the Metrics API offers a basic set of metrics to support automatic scaling and similar use cases. This API makes information available about resource usage for node and pod, including metrics for CPU and memory. If you deploy the Metrics API into your cluster, clients of the Kubernetes API can then query for this information, and you can use Kubernetes' access control mechanisms to manage permissions to do so.

The HorizontalPodAutoscaler (HPA) and VerticalPodAutoscaler (VPA) use data from the metrics API to adjust workload replicas and resources to meet customer demand.

You can also view the resource metrics using the kubectl top command.

Note:

The Metrics API, and the metrics pipeline that it enables, only offers the minimum CPU and memory metrics to enable automatic scaling using HPA and / or VPA. If you would like to provide a more complete set of metrics, you can complement the simpler Metrics API by deploying a second metrics pipeline that uses the Custom Metrics API.

Figure 1 illustrates the architecture of the resource metrics pipeline.

flowchart RL subgraph cluster[Cluster] direction RL S[

] A[Metrics-
Server] subgraph B[Nodes] direction TB D[cAdvisor] --> C[kubelet] E[Container
runtime] --> D E1[Container
runtime] --> D P[pod data] -.- C end L[API
server] W[HPA] C ---->|node level
resource metrics| A -->|metrics
API| L --> W end L ---> K[kubectl
top] classDef box fill:#fff,stroke:#000,stroke-width:1px,color:#000; class W,B,P,K,cluster,D,E,E1 box classDef spacewhite fill:#ffffff,stroke:#fff,stroke-width:0px,color:#000 class S spacewhite classDef k8s fill:#326ce5,stroke:#fff,stroke-width:1px,color:#fff; class A,L,C k8s

JavaScript must be enabled to view this content

Figure 1. Resource Metrics Pipeline

The architecture components, from right to left in the figure, consist of the following:

cAdvisor: Daemon for collecting, aggregating and exposing container metrics included in Kubelet.
kubelet: Node agent for managing container resources. Resource metrics are accessible using the /metrics/resource and /stats kubelet API endpoints.
node level resource metrics: API provided by the kubelet for discovering and retrieving per-node summarized stats available through the /metrics/resource endpoint.
metrics-server: Cluster addon component that collects and aggregates resource metrics pulled from each kubelet. The API server serves Metrics API for use by HPA, VPA, and by the kubectl top command. Metrics Server is a reference implementation of the Metrics API.
Metrics API: Kubernetes API supporting access to CPU and memory used for workload autoscaling. To make this work in your cluster, you need an API extension server that provides the Metrics API.

Note:
cAdvisor supports reading metrics from cgroups, which works with typical container runtimes on Linux. If you use a container runtime that uses another resource isolation mechanism, for example virtualization, then that container runtime must support CRI Container Metrics in order for metrics to be available to the kubelet.

Metrics API

FEATURE STATE: Kubernetes 1.8 [beta]

The metrics-server implements the Metrics API. This API allows you to access CPU and memory usage for the nodes and pods in your cluster. Its primary role is to feed resource usage metrics to K8s autoscaler components.

Here is an example of the Metrics API request for a minikube node piped through jq for easier reading:

kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes/minikube" | jq '.'

Here is the same API call using curl:

curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/nodes/minikube

Sample response:

{
  "kind": "NodeMetrics",
  "apiVersion": "metrics.k8s.io/v1beta1",
  "metadata": {
    "name": "minikube",
    "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/minikube",
    "creationTimestamp": "2022-01-27T18:48:43Z"
  },
  "timestamp": "2022-01-27T18:48:33Z",
  "window": "30s",
  "usage": {
    "cpu": "487558164n",
    "memory": "732212Ki"
  }
}

Here is an example of the Metrics API request for a kube-scheduler-minikube pod contained in the kube-system namespace and piped through jq for easier reading:

kubectl get --raw "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube" | jq '.'

Here is the same API call using curl:

curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube

Sample response:

{
  "kind": "PodMetrics",
  "apiVersion": "metrics.k8s.io/v1beta1",
  "metadata": {
    "name": "kube-scheduler-minikube",
    "namespace": "kube-system",
    "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube",
    "creationTimestamp": "2022-01-27T19:25:00Z"
  },
  "timestamp": "2022-01-27T19:24:31Z",
  "window": "30s",
  "containers": [
    {
      "name": "kube-scheduler",
      "usage": {
        "cpu": "9559630n",
        "memory": "22244Ki"
      }
    }
  ]
}

The Metrics API is defined in the k8s.io/metrics repository. You must enable the API aggregation layer and register an APIService for the metrics.k8s.io API.

To learn more about the Metrics API, see resource metrics API design, the metrics-server repository and the resource metrics API.

Note:

You must deploy the metrics-server or alternative adapter that serves the Metrics API to be able to access it.

Measuring resource usage

CPU

CPU is reported as the average core usage measured in cpu units. One cpu, in Kubernetes, is equivalent to 1 vCPU/Core for cloud providers, and 1 hyper-thread on bare-metal Intel processors.

This value is derived by taking a rate over a cumulative CPU counter provided by the kernel (in both Linux and Windows kernels). The time window used to calculate CPU is shown under window field in Metrics API.

To learn more about how Kubernetes allocates and measures CPU resources, see meaning of CPU.

Memory

Memory is reported as the working set, measured in bytes, at the instant the metric was collected.

In an ideal world, the "working set" is the amount of memory in-use that cannot be freed under memory pressure. However, calculation of the working set varies by host OS, and generally makes heavy use of heuristics to produce an estimate.

The Kubernetes model for a container's working set expects that the container runtime counts anonymous memory associated with the container in question. The working set metric typically also includes some cached (file-backed) memory, because the host OS cannot always reclaim pages.

To learn more about how Kubernetes allocates and measures memory resources, see meaning of memory.

Metrics Server

The metrics-server fetches resource metrics from the kubelets and exposes them in the Kubernetes API server through the Metrics API for use by the HPA and VPA. You can also view these metrics using the kubectl top command.

The metrics-server uses the Kubernetes API to track nodes and pods in your cluster. The metrics-server queries each node over HTTP to fetch metrics. The metrics-server also builds an internal view of pod metadata, and keeps a cache of pod health. That cached pod health information is available via the extension API that the metrics-server makes available.

For example with an HPA query, the metrics-server needs to identify which pods fulfill the label selectors in the deployment.

The metrics-server calls the kubelet API to collect metrics from each node. Depending on the metrics-server version it uses:

Metrics resource endpoint /metrics/resource in version v0.6.0+ or
Summary API endpoint /stats/summary in older versions

What's next

To learn more about the metrics-server, see the metrics-server repository.

You can also check out the following:

To learn about how the kubelet serves node metrics, and how you can access those via the Kubernetes API, read Node Metrics Data.

4.2.3 - Tools for Monitoring Resources

To scale an application and provide a reliable service, you need to understand how the application behaves when it is deployed. You can examine application performance in a Kubernetes cluster by examining the containers, pods, services, and the characteristics of the overall cluster. Kubernetes provides detailed information about an application's resource usage at each of these levels. This information allows you to evaluate your application's performance and where bottlenecks can be removed to improve overall performance.

In Kubernetes, application monitoring does not depend on a single monitoring solution. On new clusters, you can use resource metrics or full metrics pipelines to collect monitoring statistics.

Resource metrics pipeline

The resource metrics pipeline provides a limited set of metrics related to cluster components such as the Horizontal Pod Autoscaler controller, as well as the kubectl top utility. These metrics are collected by the lightweight, short-term, in-memory metrics-server and are exposed via the metrics.k8s.io API.

metrics-server discovers all nodes on the cluster and queries each node's kubelet for CPU and memory usage. The kubelet acts as a bridge between the Kubernetes master and the nodes, managing the pods and containers running on a machine. The kubelet translates each pod into its constituent containers and fetches individual container usage statistics from the container runtime through the container runtime interface. If you use a container runtime that uses Linux cgroups and namespaces to implement containers, and the container runtime does not publish usage statistics, then the kubelet can look up those statistics directly (using code from cAdvisor). No matter how those statistics arrive, the kubelet then exposes the aggregated pod resource usage statistics through the metrics-server Resource Metrics API. This API is served at /metrics/resource/v1beta1 on the kubelet's authenticated and read-only ports.

Full metrics pipeline

A full metrics pipeline gives you access to richer metrics. Kubernetes can respond to these metrics by automatically scaling or adapting the cluster based on its current state, using mechanisms such as the Horizontal Pod Autoscaler. The monitoring pipeline fetches metrics from the kubelet and then exposes them to Kubernetes via an adapter by implementing either the custom.metrics.k8s.io or external.metrics.k8s.io API.

Kubernetes is designed to work with OpenMetrics, which is one of the CNCF Observability and Analysis - Monitoring Projects, built upon and carefully extending Prometheus exposition format in almost 100% backwards-compatible ways.

If you glance over at the CNCF Landscape, you can see a number of monitoring projects that can work with Kubernetes by scraping metric data and using that to help you observe your cluster. It is up to you to select the tool or tools that suit your needs. The CNCF landscape for observability and analytics includes a mix of open-source software, paid-for software-as-a-service, and other commercial products.

When you design and implement a full metrics pipeline you can make that monitoring data available back to Kubernetes. For example, a HorizontalPodAutoscaler can use the processed metrics to work out how many Pods to run for a component of your workload.

Integration of a full metrics pipeline into your Kubernetes implementation is outside the scope of Kubernetes documentation because of the very wide scope of possible solutions.

The choice of monitoring platform depends heavily on your needs, budget, and technical resources. Kubernetes does not recommend any specific metrics pipeline; many options are available. Your monitoring system should be capable of handling the OpenMetrics metrics transmission standard and needs to be chosen to best fit into your overall design and deployment of your infrastructure platform.

What's next

Learn about additional debugging tools, including:

4.2.4 - Monitor Node Health

Node Problem Detector is a daemon for monitoring and reporting about a node's health. You can run Node Problem Detector as a DaemonSet or as a standalone daemon. Node Problem Detector collects information about node problems from various daemons and reports these conditions to the API server as Node Conditions or as Events.

To learn how to install and use Node Problem Detector, see Node Problem Detector project documentation.

Before you begin

Limitations

Node Problem Detector uses the kernel log format for reporting kernel issues. To learn how to extend the kernel log format, see Add support for another log format.

Enabling Node Problem Detector

Some cloud providers enable Node Problem Detector as an Addon. You can also enable Node Problem Detector with kubectl or by creating an Addon DaemonSet.

Using kubectl to enable Node Problem Detector

kubectl provides the most flexible management of Node Problem Detector. You can overwrite the default configuration to fit it into your environment or to detect customized node problems. For example:

Create a Node Problem Detector configuration similar to node-problem-detector.yaml:

debug/node-problem-detector.yaml

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: node-problem-detector-v0.1
  namespace: kube-system
  labels:
    k8s-app: node-problem-detector
    version: v0.1
    kubernetes.io/cluster-service: "true"
spec:
  selector:
    matchLabels:
      k8s-app: node-problem-detector  
      version: v0.1
      kubernetes.io/cluster-service: "true"
  template:
    metadata:
      labels:
        k8s-app: node-problem-detector
        version: v0.1
        kubernetes.io/cluster-service: "true"
    spec:
      hostNetwork: true
      containers:
      - name: node-problem-detector
        image: registry.k8s.io/node-problem-detector:v0.1
        securityContext:
          privileged: true
        resources:
          limits:
            cpu: "200m"
            memory: "100Mi"
          requests:
            cpu: "20m"
            memory: "20Mi"
        volumeMounts:
        - name: log
          mountPath: /log
          readOnly: true
      volumes:
      - name: log
        hostPath:
          path: /var/log/

Note:

You should verify that the system log directory is right for your operating system distribution.

Start node problem detector with kubectl:

kubectl apply -f https://k8s.io/examples/debug/node-problem-detector.yaml

Using an Addon pod to enable Node Problem Detector

If you are using a custom cluster bootstrap solution and don't need to overwrite the default configuration, you can leverage the Addon pod to further automate the deployment.

Create node-problem-detector.yaml, and save the configuration in the Addon pod's directory /etc/kubernetes/addons/node-problem-detector on a control plane node.

Overwrite the configuration

The default configuration is embedded when building the Docker image of Node Problem Detector.

However, you can use a ConfigMap to overwrite the configuration:

Change the configuration files in config/

Create the ConfigMap node-problem-detector-config:

kubectl create configmap node-problem-detector-config --from-file=config/

Change the node-problem-detector.yaml to use the ConfigMap:

debug/node-problem-detector-configmap.yaml

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: node-problem-detector-v0.1
  namespace: kube-system
  labels:
    k8s-app: node-problem-detector
    version: v0.1
    kubernetes.io/cluster-service: "true"
spec:
  selector:
    matchLabels:
      k8s-app: node-problem-detector  
      version: v0.1
      kubernetes.io/cluster-service: "true"
  template:
    metadata:
      labels:
        k8s-app: node-problem-detector
        version: v0.1
        kubernetes.io/cluster-service: "true"
    spec:
      hostNetwork: true
      containers:
      - name: node-problem-detector
        image: registry.k8s.io/node-problem-detector:v0.1
        securityContext:
          privileged: true
        resources:
          limits:
            cpu: "200m"
            memory: "100Mi"
          requests:
            cpu: "20m"
            memory: "20Mi"
        volumeMounts:
        - name: log
          mountPath: /log
          readOnly: true
        - name: config # Overwrite the config/ directory with ConfigMap volume
          mountPath: /config
          readOnly: true
      volumes:
      - name: log
        hostPath:
          path: /var/log/
      - name: config # Define ConfigMap volume
        configMap:
          name: node-problem-detector-config

Recreate the Node Problem Detector with the new configuration file:

# If you have a node-problem-detector running, delete before recreating
kubectl delete -f https://k8s.io/examples/debug/node-problem-detector.yaml
kubectl apply -f https://k8s.io/examples/debug/node-problem-detector-configmap.yaml

Note:

This approach only applies to a Node Problem Detector started with kubectl.

Overwriting a configuration is not supported if a Node Problem Detector runs as a cluster Addon. The Addon manager does not support ConfigMap.

Problem Daemons

A problem daemon is a sub-daemon of the Node Problem Detector. It monitors specific kinds of node problems and reports them to the Node Problem Detector. There are several types of supported problem daemons.

A SystemLogMonitor type of daemon monitors the system logs and reports problems and metrics according to predefined rules. You can customize the configurations for different log sources such as filelog, kmsg, kernel, abrt, and systemd.
A SystemStatsMonitor type of daemon collects various health-related system stats as metrics. You can customize its behavior by updating its configuration file.
A CustomPluginMonitor type of daemon invokes and checks various node problems by running user-defined scripts. You can use different custom plugin monitors to monitor different problems and customize the daemon behavior by updating the configuration file.
A HealthChecker type of daemon checks the health of the kubelet and container runtime on a node.

Adding support for other log format

The system log monitor currently supports file-based logs, journald, and kmsg. Additional sources can be added by implementing a new log watcher.

Adding custom plugin monitors

You can extend the Node Problem Detector to execute any monitor scripts written in any language by developing a custom plugin. The monitor scripts must conform to the plugin protocol in exit code and standard output. For more information, please refer to the plugin interface proposal.

Exporter

An exporter reports the node problems and/or metrics to certain backends. The following exporters are supported:

Kubernetes exporter: this exporter reports node problems to the Kubernetes API server. Temporary problems are reported as Events and permanent problems are reported as Node Conditions.
Prometheus exporter: this exporter reports node problems and metrics locally as Prometheus (or OpenMetrics) metrics. You can specify the IP address and port for the exporter using command line arguments.
Stackdriver exporter: this exporter reports node problems and metrics to the Stackdriver Monitoring API. The exporting behavior can be customized using a configuration file.

Recommendations and restrictions

It is recommended to run the Node Problem Detector in your cluster to monitor node health. When running the Node Problem Detector, you can expect extra resource overhead on each node. Usually this is fine, because:

The kernel log grows relatively slowly.
A resource limit is set for the Node Problem Detector.
Even under high load, the resource usage is acceptable. For more information, see the Node Problem Detector benchmark result.

4.2.5 - Debugging Kubernetes nodes with crictl

FEATURE STATE: Kubernetes v1.11 [stable]

crictl is a command-line interface for CRI-compatible container runtimes. You can use it to inspect and debug container runtimes and applications on a Kubernetes node. crictl and its source are hosted in the cri-tools repository.

Before you begin

crictl requires a Linux operating system with a CRI runtime.

Installing crictl

You can download a compressed archive crictl from the cri-tools release page, for several different architectures. Download the version that corresponds to your version of Kubernetes. Extract it and move it to a location on your system path, such as /usr/local/bin/.

General usage

The crictl command has several subcommands and runtime flags. Use crictl help or crictl <subcommand> help for more details.

You can set the endpoint for crictl by doing one of the following:

Set the --runtime-endpoint and --image-endpoint flags.
Set the CONTAINER_RUNTIME_ENDPOINT and IMAGE_SERVICE_ENDPOINT environment variables.
Set the endpoint in the configuration file /etc/crictl.yaml. To specify a different file, use the --config=PATH_TO_FILE flag when you run crictl.

Note:

If you don't set an endpoint, crictl attempts to connect to a list of known endpoints, which might result in an impact to performance.

You can also specify timeout values when connecting to the server and enable or disable debugging, by specifying timeout or debug values in the configuration file or using the --timeout and --debug command-line flags.

To view or edit the current configuration, view or edit the contents of /etc/crictl.yaml. For example, the configuration when using the containerd container runtime would be similar to this:

runtime-endpoint: unix:///var/run/containerd/containerd.sock
image-endpoint: unix:///var/run/containerd/containerd.sock
timeout: 10
debug: true

To learn more about crictl, refer to the crictl documentation.

Example crictl commands

The following examples show some crictl commands and example output.

Warning:

If you use crictl to create pod sandboxes or containers on a running Kubernetes cluster, the Kubelet will eventually delete them. crictl is not a general purpose workflow tool, but a tool that is useful for debugging.

List pods

List all pods:

crictl pods

The output is similar to this:

POD ID              CREATED              STATE               NAME                         NAMESPACE           ATTEMPT
926f1b5a1d33a       About a minute ago   Ready               sh-84d7dcf559-4r2gq          default             0
4dccb216c4adb       About a minute ago   Ready               nginx-65899c769f-wv2gp       default             0
a86316e96fa89       17 hours ago         Ready               kube-proxy-gblk4             kube-system         0
919630b8f81f1       17 hours ago         Ready               nvidia-device-plugin-zgbbv   kube-system         0

List pods by name:

crictl pods --name nginx-65899c769f-wv2gp

The output is similar to this:

POD ID              CREATED             STATE               NAME                     NAMESPACE           ATTEMPT
4dccb216c4adb       2 minutes ago       Ready               nginx-65899c769f-wv2gp   default             0

List pods by label:

crictl pods --label run=nginx

The output is similar to this:

POD ID              CREATED             STATE               NAME                     NAMESPACE           ATTEMPT
4dccb216c4adb       2 minutes ago       Ready               nginx-65899c769f-wv2gp   default             0

List images

List all images:

crictl images

The output is similar to this:

IMAGE                                     TAG                 IMAGE ID            SIZE
busybox                                   latest              8c811b4aec35f       1.15MB
k8s-gcrio.azureedge.net/hyperkube-amd64   v1.10.3             e179bbfe5d238       665MB
k8s-gcrio.azureedge.net/pause-amd64       3.1                 da86e6ba6ca19       742kB
nginx                                     latest              cd5239a0906a6       109MB

List images by repository:

crictl images nginx

The output is similar to this:

IMAGE               TAG                 IMAGE ID            SIZE
nginx               latest              cd5239a0906a6       109MB

Only list image IDs:

crictl images -q

The output is similar to this:

sha256:8c811b4aec35f259572d0f79207bc0678df4c736eeec50bc9fec37ed936a472a
sha256:e179bbfe5d238de6069f3b03fccbecc3fb4f2019af741bfff1233c4d7b2970c5
sha256:da86e6ba6ca197bf6bc5e9d900febd906b133eaa4750e6bed647b0fbe50ed43e
sha256:cd5239a0906a6ccf0562354852fae04bc5b52d72a2aff9a871ddb6bd57553569

List containers

List all containers:

crictl ps -a

The output is similar to this:

CONTAINER ID        IMAGE                                                                                                             CREATED             STATE               NAME                       ATTEMPT
1f73f2d81bf98       busybox@sha256:141c253bc4c3fd0a201d32dc1f493bcf3fff003b6df416dea4f41046e0f37d47                                   7 minutes ago       Running             sh                         1
9c5951df22c78       busybox@sha256:141c253bc4c3fd0a201d32dc1f493bcf3fff003b6df416dea4f41046e0f37d47                                   8 minutes ago       Exited              sh                         0
87d3992f84f74       nginx@sha256:d0a8828cccb73397acb0073bf34f4d7d8aa315263f1e7806bf8c55d8ac139d5f                                     8 minutes ago       Running             nginx                      0
1941fb4da154f       k8s-gcrio.azureedge.net/hyperkube-amd64@sha256:00d814b1f7763f4ab5be80c58e98140dfc69df107f253d7fdd714b30a714260a   18 hours ago        Running             kube-proxy                 0

List running containers:

crictl ps

The output is similar to this:

CONTAINER ID        IMAGE                                                                                                             CREATED             STATE               NAME                       ATTEMPT
1f73f2d81bf98       busybox@sha256:141c253bc4c3fd0a201d32dc1f493bcf3fff003b6df416dea4f41046e0f37d47                                   6 minutes ago       Running             sh                         1
87d3992f84f74       nginx@sha256:d0a8828cccb73397acb0073bf34f4d7d8aa315263f1e7806bf8c55d8ac139d5f                                     7 minutes ago       Running             nginx                      0
1941fb4da154f       k8s-gcrio.azureedge.net/hyperkube-amd64@sha256:00d814b1f7763f4ab5be80c58e98140dfc69df107f253d7fdd714b30a714260a   17 hours ago        Running             kube-proxy                 0

Execute a command in a running container

crictl exec -i -t 1f73f2d81bf98 ls

The output is similar to this:

bin   dev   etc   home  proc  root  sys   tmp   usr   var

Get a container's logs

Get all container logs:

crictl logs 87d3992f84f74

The output is similar to this:

10.240.0.96 - - [06/Jun/2018:02:45:49 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-"
10.240.0.96 - - [06/Jun/2018:02:45:50 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-"
10.240.0.96 - - [06/Jun/2018:02:45:51 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-"

Get only the latest N lines of logs:

crictl logs --tail=1 87d3992f84f74

The output is similar to this:

10.240.0.96 - - [06/Jun/2018:02:45:51 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-"

Run a pod sandbox

Using crictl to run a pod sandbox is useful for debugging container runtimes. On a running Kubernetes cluster, the sandbox will eventually be stopped and deleted by the Kubelet.

Create a JSON file like the following:

{
  "metadata": {
    "name": "nginx-sandbox",
    "namespace": "default",
    "attempt": 1,
    "uid": "hdishd83djaidwnduwk28bcsb"
  },
  "log_directory": "/tmp",
  "linux": {
  }
}

Use the crictl runp command to apply the JSON and run the sandbox.
```
crictl runp pod-config.json
```
The ID of the sandbox is returned.

Create a container

Using crictl to create a container is useful for debugging container runtimes. On a running Kubernetes cluster, the container will eventually be stopped and deleted by the Kubelet.

Pull a busybox image

crictl pull busybox

Image is up to date for busybox@sha256:141c253bc4c3fd0a201d32dc1f493bcf3fff003b6df416dea4f41046e0f37d47

Create configs for the pod and the container:

Pod config:

{
  "metadata": {
    "name": "busybox-sandbox",
    "namespace": "default",
    "attempt": 1,
    "uid": "aewi4aeThua7ooShohbo1phoj"
  },
  "log_directory": "/tmp",
  "linux": {
  }
}

Container config:

{
  "metadata": {
    "name": "busybox"
  },
  "image":{
    "image": "busybox"
  },
  "command": [
    "top"
  ],
  "log_path":"busybox.log",
  "linux": {
  }
}

Create the container, passing the ID of the previously-created pod, the container config file, and the pod config file. The ID of the container is returned.
```
crictl create f84dd361f8dc51518ed291fbadd6db537b0496536c1d2d6c05ff943ce8c9a54f container-config.json pod-config.json
```

List all containers and verify that the newly-created container has its state set to Created.

crictl ps -a

The output is similar to this:

CONTAINER ID        IMAGE               CREATED             STATE               NAME                ATTEMPT
3e025dd50a72d       busybox             32 seconds ago      Created             busybox             0

Start a container

To start a container, pass its ID to crictl start:

crictl start 3e025dd50a72d956c4f14881fbb5b1080c9275674e95fb67f965f6478a957d60

The output is similar to this:

3e025dd50a72d956c4f14881fbb5b1080c9275674e95fb67f965f6478a957d60

Check the container has its state set to Running.

crictl ps

The output is similar to this:

CONTAINER ID   IMAGE    CREATED              STATE    NAME     ATTEMPT
3e025dd50a72d  busybox  About a minute ago   Running  busybox  0

What's next

4.2.6 - Auditing

Kubernetes auditing provides a security-relevant, chronological set of records documenting the sequence of actions in a cluster. The cluster audits the activities generated by users, by applications that use the Kubernetes API, and by the control plane itself.

Auditing allows cluster administrators to answer the following questions:

what happened?
when did it happen?
who initiated it?
on what did it happen?
where was it observed?
from where was it initiated?
to where was it going?

Audit records begin their lifecycle inside the kube-apiserver component. Each request on each stage of its execution generates an audit event, which is then pre-processed according to a certain policy and written to a backend. The policy determines what's recorded and the backends persist the records. The current backend implementations include logs files and webhooks.

Each request can be recorded with an associated stage. The defined stages are:

RequestReceived - The stage for events generated as soon as the audit handler receives the request, and before it is delegated down the handler chain.
ResponseStarted - Once the response headers are sent, but before the response body is sent. This stage is only generated for long-running requests (e.g. watch).
ResponseComplete - The response body has been completed and no more bytes will be sent.
Panic - Events generated when a panic occurred.

Note:

The configuration of an Audit Event configuration is different from the Event API object.

The audit logging feature increases the memory consumption of the API server because some context required for auditing is stored for each request. Memory consumption depends on the audit logging configuration.

Audit policy

Audit policy defines rules about what events should be recorded and what data they should include. The audit policy object structure is defined in the audit.k8s.io API group. When an event is processed, it's compared against the list of rules in order. The first matching rule sets the audit level of the event. The defined audit levels are:

None - don't log events that match this rule.
Metadata - log request metadata (requesting user, timestamp, resource, verb, etc.) but not request or response body.
Request - log event metadata and request body but not response body. This does not apply for non-resource requests.
RequestResponse - log event metadata, request and response bodies. This does not apply for non-resource requests.

You can pass a file with the policy to kube-apiserver using the --audit-policy-file flag. If the flag is omitted, no events are logged. Note that the rules field must be provided in the audit policy file. A policy with no (0) rules is treated as illegal.

Below is an example audit policy file:

audit/audit-policy.yaml

apiVersion: audit.k8s.io/v1 # This is required.
kind: Policy
# Don't generate audit events for all requests in RequestReceived stage.
omitStages:
  - "RequestReceived"
rules:
  # Log pod changes at RequestResponse level
  - level: RequestResponse
    resources:
    - group: ""
      # Resource "pods" doesn't match requests to any subresource of pods,
      # which is consistent with the RBAC policy.
      resources: ["pods"]
  # Log "pods/log", "pods/status" at Metadata level
  - level: Metadata
    resources:
    - group: ""
      resources: ["pods/log", "pods/status"]

  # Don't log requests to a configmap called "controller-leader"
  - level: None
    resources:
    - group: ""
      resources: ["configmaps"]
      resourceNames: ["controller-leader"]

  # Don't log watch requests by the "system:kube-proxy" on endpoints or services
  - level: None
    users: ["system:kube-proxy"]
    verbs: ["watch"]
    resources:
    - group: "" # core API group
      resources: ["endpoints", "services"]

  # Don't log authenticated requests to certain non-resource URL paths.
  - level: None
    userGroups: ["system:authenticated"]
    nonResourceURLs:
    - "/api*" # Wildcard matching.
    - "/version"

  # Log the request body of configmap changes in kube-system.
  - level: Request
    resources:
    - group: "" # core API group
      resources: ["configmaps"]
    # This rule only applies to resources in the "kube-system" namespace.
    # The empty string "" can be used to select non-namespaced resources.
    namespaces: ["kube-system"]

  # Log configmap and secret changes in all other namespaces at the Metadata level.
  - level: Metadata
    resources:
    - group: "" # core API group
      resources: ["secrets", "configmaps"]

  # Log all other resources in core and extensions at the Request level.
  - level: Request
    resources:
    - group: "" # core API group
    - group: "extensions" # Version of group should NOT be included.

  # A catch-all rule to log all other requests at the Metadata level.
  - level: Metadata
    # Long-running requests like watches that fall under this rule will not
    # generate an audit event in RequestReceived.
    omitStages:
      - "RequestReceived"

You can use a minimal audit policy file to log all requests at the Metadata level:

# Log all requests at the Metadata level.
apiVersion: audit.k8s.io/v1
kind: Policy
rules:
- level: Metadata

If you're crafting your own audit profile, you can use the audit profile for Google Container-Optimized OS as a starting point. You can check the configure-helper.sh script, which generates an audit policy file. You can see most of the audit policy file by looking directly at the script.

You can also refer to the Policy configuration reference for details about the fields defined.

Audit backends

Audit backends persist audit events to an external storage. Out of the box, the kube-apiserver provides two backends:

Log backend, which writes events into the filesystem
Webhook backend, which sends events to an external HTTP API

In all cases, audit events follow a structure defined by the Kubernetes API in the audit.k8s.io API group.

Note:

In case of patches, request body is a JSON array with patch operations, not a JSON object with an appropriate Kubernetes API object. For example, the following request body is a valid patch request to /apis/batch/v1/namespaces/some-namespace/jobs/some-job-name:

[
  {
    "op": "replace",
    "path": "/spec/parallelism",
    "value": 0
  },
  {
    "op": "remove",
    "path": "/spec/template/spec/containers/0/terminationMessagePolicy"
  }
]

Log backend

The log backend writes audit events to a file in JSONlines format. You can configure the log audit backend using the following kube-apiserver flags:

--audit-log-path specifies the log file path that log backend uses to write audit events. Not specifying this flag disables log backend. - means standard out
--audit-log-maxage defined the maximum number of days to retain old audit log files
--audit-log-maxbackup defines the maximum number of audit log files to retain
--audit-log-maxsize defines the maximum size in megabytes of the audit log file before it gets rotated

If your cluster's control plane runs the kube-apiserver as a Pod, remember to mount the hostPath to the location of the policy file and log file, so that audit records are persisted. For example:

  - --audit-policy-file=/etc/kubernetes/audit-policy.yaml
  - --audit-log-path=/var/log/kubernetes/audit/audit.log

then mount the volumes:

...
volumeMounts:
  - mountPath: /etc/kubernetes/audit-policy.yaml
    name: audit
    readOnly: true
  - mountPath: /var/log/kubernetes/audit/
    name: audit-log
    readOnly: false

and finally configure the hostPath:

...
volumes:
- name: audit
  hostPath:
    path: /etc/kubernetes/audit-policy.yaml
    type: File

- name: audit-log
  hostPath:
    path: /var/log/kubernetes/audit/
    type: DirectoryOrCreate

Webhook backend

The webhook audit backend sends audit events to a remote web API, which is assumed to be a form of the Kubernetes API, including means of authentication. You can configure a webhook audit backend using the following kube-apiserver flags:

--audit-webhook-config-file specifies the path to a file with a webhook configuration. The webhook configuration is effectively a specialized kubeconfig.
--audit-webhook-initial-backoff specifies the amount of time to wait after the first failed request before retrying. Subsequent requests are retried with exponential backoff.

The webhook config file uses the kubeconfig format to specify the remote address of the service and credentials used to connect to it.

Event batching

Both log and webhook backends support batching. Using webhook as an example, here's the list of available flags. To get the same flag for log backend, replace webhook with log in the flag name. By default, batching is enabled in webhook and disabled in log. Similarly, by default throttling is enabled in webhook and disabled in log.

--audit-webhook-mode defines the buffering strategy. One of the following:
- batch - buffer events and asynchronously process them in batches. This is the default.
- blocking - block API server responses on processing each individual event.
- blocking-strict - Same as blocking, but when there is a failure during audit logging at the RequestReceived stage, the whole request to the kube-apiserver fails.

The following flags are used only in the batch mode:

--audit-webhook-batch-buffer-size defines the number of events to buffer before batching. If the rate of incoming events overflows the buffer, events are dropped.
--audit-webhook-batch-max-size defines the maximum number of events in one batch.
--audit-webhook-batch-max-wait defines the maximum amount of time to wait before unconditionally batching events in the queue.
--audit-webhook-batch-throttle-qps defines the maximum average number of batches generated per second.
--audit-webhook-batch-throttle-burst defines the maximum number of batches generated at the same moment if the allowed QPS was underutilized previously.

Parameter tuning

Parameters should be set to accommodate the load on the API server.

For example, if kube-apiserver receives 100 requests each second, and each request is audited only on ResponseStarted and ResponseComplete stages, you should account for ≅200 audit events being generated each second. Assuming that there are up to 100 events in a batch, you should set throttling level at least 2 queries per second. Assuming that the backend can take up to 5 seconds to write events, you should set the buffer size to hold up to 5 seconds of events; that is: 10 batches, or 1000 events.

In most cases however, the default parameters should be sufficient and you don't have to worry about setting them manually. You can look at the following Prometheus metrics exposed by kube-apiserver and in the logs to monitor the state of the auditing subsystem.

apiserver_audit_event_total metric contains the total number of audit events exported.
apiserver_audit_error_total metric contains the total number of events dropped due to an error during exporting.

Log entry truncation

Both log and webhook backends support limiting the size of events that are logged. As an example, the following is the list of flags available for the log backend:

audit-log-truncate-enabled whether event and batch truncating is enabled.
audit-log-truncate-max-batch-size maximum size in bytes of the batch sent to the underlying backend.
audit-log-truncate-max-event-size maximum size in bytes of the audit event sent to the underlying backend.

By default truncate is disabled in both webhook and log, a cluster administrator should set audit-log-truncate-enabled or audit-webhook-truncate-enabled to enable the feature.

What's next

Learn about Mutating webhook auditing annotations.
Learn more about Event and the Policy resource types by reading the Audit configuration reference.

4.2.7 - Debugging Kubernetes Nodes With Kubectl

This page shows how to debug a node running on the Kubernetes cluster using kubectl debug command.

Before you begin

Your Kubernetes server must be at or later than version 1.2. To check the version, enter kubectl version.

You need to have permission to create Pods and to assign those new Pods to arbitrary nodes. You also need to be authorized to create Pods that access filesystems from the host.

Debugging a Node using `kubectl debug node`

Use the kubectl debug node command to deploy a Pod to a Node that you want to troubleshoot. This command is helpful in scenarios where you can't access your Node by using an SSH connection. When the Pod is created, the Pod opens an interactive shell on the Node. To create an interactive shell on a Node named “mynode”, run:

kubectl debug node/mynode -it --image=ubuntu

Creating debugging pod node-debugger-mynode-pdx84 with container debugger on node mynode.
If you don't see a command prompt, try pressing enter.
root@mynode:/#

The debug command helps to gather information and troubleshoot issues. Commands that you might use include ip, ifconfig, nc, ping, and ps and so on. You can also install other tools, such as mtr, tcpdump, and curl, from the respective package manager.

Note:

The debug commands may differ based on the image the debugging pod is using and these commands might need to be installed.

The debugging Pod can access the root filesystem of the Node, mounted at /host in the Pod. If you run your kubelet in a filesystem namespace, the debugging Pod sees the root for that namespace, not for the entire node. For a typical Linux node, you can look at the following paths to find relevant logs:

/host/var/log/kubelet.log: Logs from the kubelet, responsible for running containers on the node.
/host/var/log/kube-proxy.log: Logs from kube-proxy, which is responsible for directing traffic to Service endpoints.
/host/var/log/containerd.log: Logs from the containerd process running on the node.
/host/var/log/syslog: Shows general messages and information regarding the system.
/host/var/log/kern.log: Shows kernel logs.

When creating a debugging session on a Node, keep in mind that:

kubectl debug automatically generates the name of the new pod, based on the name of the node.
The root filesystem of the Node will be mounted at /host.
Although the container runs in the host IPC, Network, and PID namespaces, the pod isn't privileged. This means that reading some process information might fail because access to that information is restricted to superusers. For example, chroot /host will fail. If you need a privileged pod, create it manually or use the --profile=sysadmin flag.
By applying Debugging Profiles, you can set specific properties such as securityContext to a debugging Pod.

Cleaning up

When you finish using the debugging Pod, delete it:

kubectl get pods

NAME                          READY   STATUS       RESTARTS   AGE
node-debugger-mynode-pdx84    0/1     Completed    0          8m1s

# Change the pod name accordingly
kubectl delete pod node-debugger-mynode-pdx84 --now

pod "node-debugger-mynode-pdx84" deleted

Note:

The kubectl debug node command won't work if the Node is down (disconnected from the network, or kubelet dies and won't restart, etc.). Check debugging a down/unreachable node in that case.

4.2.8 - Developing and debugging services locally using telepresence

Kubernetes applications usually consist of multiple, separate services, each running in its own container. Developing and debugging these services on a remote Kubernetes cluster can be cumbersome, requiring you to get a shell on a running container in order to run debugging tools.

telepresence is a tool to ease the process of developing and debugging services locally while proxying the service to a remote Kubernetes cluster. Using telepresence allows you to use custom tools, such as a debugger and IDE, for a local service and provides the service full access to ConfigMap, secrets, and the services running on the remote cluster.

This document describes using telepresence to develop and debug services running on a remote cluster locally.

Before you begin

Kubernetes cluster is installed
kubectl is configured to communicate with the cluster
Telepresence is installed

Connecting your local machine to a remote Kubernetes cluster

After installing telepresence, run telepresence connect to launch its Daemon and connect your local workstation to the cluster.

$ telepresence connect
 
Launching Telepresence Daemon
...
Connected to context default (https://<cluster public IP>)

You can curl services using the Kubernetes syntax e.g. curl -ik https://kubernetes.default

Developing or debugging an existing service

When developing an application on Kubernetes, you typically program or debug a single service. The service might require access to other services for testing and debugging. One option is to use the continuous deployment pipeline, but even the fastest deployment pipeline introduces a delay in the program or debug cycle.

Use the telepresence intercept $SERVICE_NAME --port $LOCAL_PORT:$REMOTE_PORT command to create an "intercept" for rerouting remote service traffic.

Where:

$SERVICE_NAME is the name of your local service
$LOCAL_PORT is the port that your service is running on your local workstation
And $REMOTE_PORT is the port your service listens to in the cluster

Running this command tells Telepresence to send remote traffic to your local service instead of the service in the remote Kubernetes cluster. Make edits to your service source code locally, save, and see the corresponding changes when accessing your remote application take effect immediately. You can also run your local service using a debugger or any other local development tool.

How does Telepresence work?

Telepresence installs a traffic-agent sidecar next to your existing application's container running in the remote cluster. It then captures all traffic requests going into the Pod, and instead of forwarding this to the application in the remote cluster, it routes all traffic (when you create a global intercept or a subset of the traffic (when you create a personal intercept) to your local development environment.

What's next

If you're interested in a hands-on tutorial, check out this tutorial that walks through locally developing the Guestbook application on Google Kubernetes Engine.

For further reading, visit the Telepresence website.

4.2.9 - Windows debugging tips

Node-level troubleshooting

My Pods are stuck at "Container Creating" or restarting over and over

Ensure that your pause image is compatible with your Windows OS version. See Pause container to see the latest / recommended pause image and/or get more information.

Note:
If using containerd as your container runtime the pause image is specified in the plugins.plugins.cri.sandbox_image field of the of config.toml configuration file.
My pods show status as ErrImgPull or ImagePullBackOff

Ensure that your Pod is getting scheduled to a compatible Windows Node.

More information on how to specify a compatible node for your Pod can be found in this guide.

Network troubleshooting

My Windows Pods do not have network connectivity

If you are using virtual machines, ensure that MAC spoofing is enabled on all the VM network adapter(s).
My Windows Pods cannot ping external resources

Windows Pods do not have outbound rules programmed for the ICMP protocol. However, TCP/UDP is supported. When trying to demonstrate connectivity to resources outside of the cluster, substitute ping <IP> with corresponding curl <IP> commands.

If you are still facing problems, most likely your network configuration in cni.conf deserves some extra attention. You can always edit this static file. The configuration update will apply to any new Kubernetes resources.

One of the Kubernetes networking requirements (see Kubernetes model) is for cluster communication to occur without NAT internally. To honor this requirement, there is an ExceptionList for all the communication where you do not want outbound NAT to occur. However, this also means that you need to exclude the external IP you are trying to query from the ExceptionList. Only then will the traffic originating from your Windows pods be SNAT'ed correctly to receive a response from the outside world. In this regard, your ExceptionList in cni.conf should look as follows:
```
"ExceptionList": [
                "10.244.0.0/16",  # Cluster subnet
                "10.96.0.0/12",   # Service subnet
                "10.127.130.0/24" # Management (host) subnet
            ]
```
My Windows node cannot access NodePort type Services

Local NodePort access from the node itself fails. This is a known limitation. NodePort access works from other nodes or external clients.
vNICs and HNS endpoints of containers are being deleted

This issue can be caused when the hostname-override parameter is not passed to kube-proxy. To resolve it, users need to pass the hostname to kube-proxy as follows:
```
C:\k\kube-proxy.exe --hostname-override=$(hostname)
```
My Windows node cannot access my services using the service IP

This is a known limitation of the networking stack on Windows. However, Windows Pods can access the Service IP.
No network adapter is found when starting the kubelet

The Windows networking stack needs a virtual adapter for Kubernetes networking to work. If the following commands return no results (in an admin shell), virtual network creation — a necessary prerequisite for the kubelet to work — has failed:
```
Get-HnsNetwork | ? Name -ieq "cbr0"
Get-NetAdapter | ? Name -Like "vEthernet (Ethernet*"
```
Often it is worthwhile to modify the InterfaceName parameter of the start.ps1 script, in cases where the host's network adapter isn't "Ethernet". Otherwise, consult the output of the start-kubelet.ps1 script to see if there are errors during virtual network creation.
DNS resolution is not properly working

Check the DNS limitations for Windows in this section.
kubectl port-forward fails with "unable to do port forwarding: wincat not found"

This was implemented in Kubernetes 1.15 by including wincat.exe in the pause infrastructure container mcr.microsoft.com/oss/kubernetes/pause:3.6. Be sure to use a supported version of Kubernetes. If you would like to build your own pause infrastructure container be sure to include wincat.

My Kubernetes installation is failing because my Windows Server node is behind a proxy

If you are behind a proxy, the following PowerShell environment variables must be defined:

[Environment]::SetEnvironmentVariable("HTTP_PROXY", "http://proxy.example.com:80/", [EnvironmentVariableTarget]::Machine)
[Environment]::SetEnvironmentVariable("HTTPS_PROXY", "http://proxy.example.com:443/", [EnvironmentVariableTarget]::Machine)

Flannel troubleshooting

With Flannel, my nodes are having issues after rejoining a cluster

Whenever a previously deleted node is being re-joined to the cluster, flannelD tries to assign a new pod subnet to the node. Users should remove the old pod subnet configuration files in the following paths:
```
Remove-Item C:\k\SourceVip.json
Remove-Item C:\k\SourceVipRequest.json
```
Flanneld is stuck in "Waiting for the Network to be created"

There are numerous reports of this issue; most likely it is a timing issue for when the management IP of the flannel network is set. A workaround is to relaunch start.ps1 or relaunch it manually as follows:
```
[Environment]::SetEnvironmentVariable("NODE_NAME", "<Windows_Worker_Hostname>")
C:\flannel\flanneld.exe --kubeconfig-file=c:\k\config --iface=<Windows_Worker_Node_IP> --ip-masq=1 --kube-subnet-mgr=1
```
My Windows Pods cannot launch because of missing /run/flannel/subnet.env

This indicates that Flannel didn't launch correctly. You can either try to restart flanneld.exe or you can copy the files over manually from /run/flannel/subnet.env on the Kubernetes master to C:\run\flannel\subnet.env on the Windows worker node and modify the FLANNEL_SUBNET row to a different number. For example, if node subnet 10.244.4.1/24 is desired:
```
FLANNEL_NETWORK=10.244.0.0/16
FLANNEL_SUBNET=10.244.4.1/24
FLANNEL_MTU=1500
FLANNEL_IPMASQ=true
```

Further investigation

If these steps don't resolve your problem, you can get help running Windows containers on Windows nodes in Kubernetes through:

StackOverflow Windows Server Container topic
Kubernetes Official Forum discuss.kubernetes.io
Kubernetes Slack #SIG-Windows Channel

5 - Manage Kubernetes Objects

Declarative and imperative paradigms for interacting with the Kubernetes API.

5.1 - Declarative Management of Kubernetes Objects Using Configuration Files

Kubernetes objects can be created, updated, and deleted by storing multiple object configuration files in a directory and using kubectl apply to recursively create and update those objects as needed. This method retains writes made to live objects without merging the changes back into the object configuration files. kubectl diff also gives you a preview of what changes apply will make.

Before you begin

Install kubectl.

To check the version, enter kubectl version.

Trade-offs

The kubectl tool supports three kinds of object management:

Imperative commands
Imperative object configuration
Declarative object configuration

See Kubernetes Object Management for a discussion of the advantages and disadvantage of each kind of object management.

Overview

Declarative object configuration requires a firm understanding of the Kubernetes object definitions and configuration. Read and complete the following documents if you have not already:

Following are definitions for terms used in this document:

object configuration file / configuration file: A file that defines the configuration for a Kubernetes object. This topic shows how to pass configuration files to kubectl apply. Configuration files are typically stored in source control, such as Git.
live object configuration / live configuration: The live configuration values of an object, as observed by the Kubernetes cluster. These are kept in the Kubernetes cluster storage, typically etcd.
declarative configuration writer / declarative writer: A person or software component that makes updates to a live object. The live writers referred to in this topic make changes to object configuration files and run kubectl apply to write the changes.

How to create objects

Use kubectl apply to create all objects, except those that already exist, defined by configuration files in a specified directory:

kubectl apply -f <directory>

This sets the kubectl.kubernetes.io/last-applied-configuration: '{...}' annotation on each object. The annotation contains the contents of the object configuration file that was used to create the object.

Note:

Add the -R flag to recursively process directories.

Here's an example of an object configuration file:

application/simple_deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  minReadySeconds: 5
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

Run kubectl diff to print the object that will be created:

kubectl diff -f https://k8s.io/examples/application/simple_deployment.yaml

Note:

diff uses server-side dry-run, which needs to be enabled on kube-apiserver.

Since diff performs a server-side apply request in dry-run mode, it requires granting PATCH, CREATE, and UPDATE permissions. See Dry-Run Authorization for details.

Create the object using kubectl apply:

kubectl apply -f https://k8s.io/examples/application/simple_deployment.yaml

Print the live configuration using kubectl get:

kubectl get -f https://k8s.io/examples/application/simple_deployment.yaml -o yaml

The output shows that the kubectl.kubernetes.io/last-applied-configuration annotation was written to the live configuration, and it matches the configuration file:

kind: Deployment
metadata:
  annotations:
    # ...
    # This is the json representation of simple_deployment.yaml
    # It was written by kubectl apply when the object was created
    kubectl.kubernetes.io/last-applied-configuration: |
      {"apiVersion":"apps/v1","kind":"Deployment",
      "metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
      "spec":{"minReadySeconds":5,"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
      "spec":{"containers":[{"image":"nginx:1.14.2","name":"nginx",
      "ports":[{"containerPort":80}]}]}}}}      
  # ...
spec:
  # ...
  minReadySeconds: 5
  selector:
    matchLabels:
      # ...
      app: nginx
  template:
    metadata:
      # ...
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx:1.14.2
        # ...
        name: nginx
        ports:
        - containerPort: 80
        # ...
      # ...
    # ...
  # ...

How to update objects

You can also use kubectl apply to update all objects defined in a directory, even if those objects already exist. This approach accomplishes the following:

Sets fields that appear in the configuration file in the live configuration.
Clears fields removed from the configuration file in the live configuration.

kubectl diff -f <directory>
kubectl apply -f <directory>

Note:

Add the -R flag to recursively process directories.

Here's an example configuration file:

application/simple_deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  minReadySeconds: 5
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

Create the object using kubectl apply:

kubectl apply -f https://k8s.io/examples/application/simple_deployment.yaml

Note:

For purposes of illustration, the preceding command refers to a single configuration file instead of a directory.

Print the live configuration using kubectl get:

kubectl get -f https://k8s.io/examples/application/simple_deployment.yaml -o yaml

The output shows that the kubectl.kubernetes.io/last-applied-configuration annotation was written to the live configuration, and it matches the configuration file:

kind: Deployment
metadata:
  annotations:
    # ...
    # This is the json representation of simple_deployment.yaml
    # It was written by kubectl apply when the object was created
    kubectl.kubernetes.io/last-applied-configuration: |
      {"apiVersion":"apps/v1","kind":"Deployment",
      "metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
      "spec":{"minReadySeconds":5,"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
      "spec":{"containers":[{"image":"nginx:1.14.2","name":"nginx",
      "ports":[{"containerPort":80}]}]}}}}      
  # ...
spec:
  # ...
  minReadySeconds: 5
  selector:
    matchLabels:
      # ...
      app: nginx
  template:
    metadata:
      # ...
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx:1.14.2
        # ...
        name: nginx
        ports:
        - containerPort: 80
        # ...
      # ...
    # ...
  # ...

Directly update the replicas field in the live configuration by using kubectl scale. This does not use kubectl apply:

kubectl scale deployment/nginx-deployment --replicas=2

Print the live configuration using kubectl get:

kubectl get deployment nginx-deployment -o yaml

The output shows that the replicas field has been set to 2, and the last-applied-configuration annotation does not contain a replicas field:

apiVersion: apps/v1
kind: Deployment
metadata:
  annotations:
    # ...
    # note that the annotation does not contain replicas
    # because it was not updated through apply
    kubectl.kubernetes.io/last-applied-configuration: |
      {"apiVersion":"apps/v1","kind":"Deployment",
      "metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
      "spec":{"minReadySeconds":5,"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
      "spec":{"containers":[{"image":"nginx:1.14.2","name":"nginx",
      "ports":[{"containerPort":80}]}]}}}}      
  # ...
spec:
  replicas: 2 # written by scale
  # ...
  minReadySeconds: 5
  selector:
    matchLabels:
      # ...
      app: nginx
  template:
    metadata:
      # ...
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx:1.14.2
        # ...
        name: nginx
        ports:
        - containerPort: 80
      # ...

Update the simple_deployment.yaml configuration file to change the image from nginx:1.14.2 to nginx:1.16.1, and delete the minReadySeconds field:

application/update_deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.16.1 # update the image
        ports:
        - containerPort: 80

Apply the changes made to the configuration file:

kubectl diff -f https://k8s.io/examples/application/update_deployment.yaml
kubectl apply -f https://k8s.io/examples/application/update_deployment.yaml

Print the live configuration using kubectl get:

kubectl get -f https://k8s.io/examples/application/update_deployment.yaml -o yaml

The output shows the following changes to the live configuration:

The replicas field retains the value of 2 set by kubectl scale. This is possible because it is omitted from the configuration file.
The image field has been updated to nginx:1.16.1 from nginx:1.14.2.
The last-applied-configuration annotation has been updated with the new image.
The minReadySeconds field has been cleared.
The last-applied-configuration annotation no longer contains the minReadySeconds field.

apiVersion: apps/v1
kind: Deployment
metadata:
  annotations:
    # ...
    # The annotation contains the updated image to nginx 1.16.1,
    # but does not contain the updated replicas to 2
    kubectl.kubernetes.io/last-applied-configuration: |
      {"apiVersion":"apps/v1","kind":"Deployment",
      "metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
      "spec":{"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
      "spec":{"containers":[{"image":"nginx:1.16.1","name":"nginx",
      "ports":[{"containerPort":80}]}]}}}}      
    # ...
spec:
  replicas: 2 # Set by `kubectl scale`.  Ignored by `kubectl apply`.
  # minReadySeconds cleared by `kubectl apply`
  # ...
  selector:
    matchLabels:
      # ...
      app: nginx
  template:
    metadata:
      # ...
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx:1.16.1 # Set by `kubectl apply`
        # ...
        name: nginx
        ports:
        - containerPort: 80
        # ...
      # ...
    # ...
  # ...

Warning:

Mixing kubectl apply with the imperative object configuration commands create and replace is not supported. This is because create and replace do not retain the kubectl.kubernetes.io/last-applied-configuration that kubectl apply uses to compute updates.

How to delete objects

There are two approaches to delete objects managed by kubectl apply.

Recommended: `kubectl delete -f <filename>`

Manually deleting objects using the imperative command is the recommended approach, as it is more explicit about what is being deleted, and less likely to result in the user deleting something unintentionally:

kubectl delete -f <filename>

Alternative: `kubectl apply -f <directory> --prune`

As an alternative to kubectl delete, you can use kubectl apply to identify objects to be deleted after their manifests have been removed from a directory in the local filesystem.

In Kubernetes 1.30, there are two pruning modes available in kubectl apply:

Allowlist-based pruning: This mode has existed since kubectl v1.5 but is still in alpha due to usability, correctness and performance issues with its design. The ApplySet-based mode is designed to replace it.
ApplySet-based pruning: An apply set is a server-side object (by default, a Secret) that kubectl can use to accurately and efficiently track set membership across apply operations. This mode was introduced in alpha in kubectl v1.27 as a replacement for allowlist-based pruning.

Allow list
Apply set

FEATURE STATE: Kubernetes v1.5 [alpha]

Warning:

Take care when using --prune with kubectl apply in allow list mode. Which objects are pruned depends on the values of the --prune-allowlist, --selector and --namespace flags, and relies on dynamic discovery of the objects in scope. Especially if flag values are changed between invocations, this can lead to objects being unexpectedly deleted or retained.

To use allowlist-based pruning, add the following flags to your kubectl apply invocation:

--prune: Delete previously applied objects that are not in the set passed to the current invocation.
--prune-allowlist: A list of group-version-kinds (GVKs) to consider for pruning. This flag is optional but strongly encouraged, as its default value is a partial list of both namespaced and cluster-scoped types, which can lead to surprising results.
--selector/-l: Use a label selector to constrain the set of objects selected for pruning. This flag is optional but strongly encouraged.
--all: use instead of --selector/-l to explicitly select all previously applied objects of the allowlisted types.

Allowlist-based pruning queries the API server for all objects of the allowlisted GVKs that match the given labels (if any), and attempts to match the returned live object configurations against the object manifest files. If an object matches the query, and it does not have a manifest in the directory, and it has a kubectl.kubernetes.io/last-applied-configuration annotation, it is deleted.

kubectl apply -f <directory> --prune -l <labels> --prune-allowlist=<gvk-list>

Warning:

Apply with prune should only be run against the root directory containing the object manifests. Running against sub-directories can cause objects to be unintentionally deleted if they were previously applied, have the labels given (if any), and do not appear in the subdirectory.

FEATURE STATE: Kubernetes v1.27 [alpha]

Caution:

kubectl apply --prune --applyset is in alpha, and backwards incompatible changes might be introduced in subsequent releases.

To use ApplySet-based pruning, set the KUBECTL_APPLYSET=true environment variable, and add the following flags to your kubectl apply invocation:

--prune: Delete previously applied objects that are not in the set passed to the current invocation.
--applyset: The name of an object that kubectl can use to accurately and efficiently track set membership across apply operations.

KUBECTL_APPLYSET=true kubectl apply -f <directory> --prune --applyset=<name>

By default, the type of the ApplySet parent object used is a Secret. However, ConfigMaps can also be used in the format: --applyset=configmaps/<name>. When using a Secret or ConfigMap, kubectl will create the object if it does not already exist.

It is also possible to use custom resources as ApplySet parent objects. To enable this, label the Custom Resource Definition (CRD) that defines the resource you want to use with the following: applyset.kubernetes.io/is-parent-type: true. Then, create the object you want to use as an ApplySet parent (kubectl does not do this automatically for custom resources). Finally, refer to that object in the applyset flag as follows: --applyset=<resource>.<group>/<name> (for example, widgets.custom.example.com/widget-name).

With ApplySet-based pruning, kubectl adds the applyset.kubernetes.io/part-of=<parentID> label to each object in the set before they are sent to the server. For performance reasons, it also collects the list of resource types and namespaces that the set contains and adds these in annotations on the live parent object. Finally, at the end of the apply operation, it queries the API server for objects of those types in those namespaces (or in the cluster scope, as applicable) that belong to the set, as defined by the applyset.kubernetes.io/part-of=<parentID> label.

Caveats and restrictions:

Each object may be a member of at most one set.
The --namespace flag is required when using any namespaced parent, including the default Secret. This means that ApplySets spanning multiple namespaces must use a cluster-scoped custom resource as the parent object.
To safely use ApplySet-based pruning with multiple directories, use a unique ApplySet name for each.

How to view an object

You can use kubectl get with -o yaml to view the configuration of a live object:

kubectl get -f <filename|url> -o yaml

How apply calculates differences and merges changes

Caution:

A patch is an update operation that is scoped to specific fields of an object instead of the entire object. This enables updating only a specific set of fields on an object without reading the object first.

When kubectl apply updates the live configuration for an object, it does so by sending a patch request to the API server. The patch defines updates scoped to specific fields of the live object configuration. The kubectl apply command calculates this patch request using the configuration file, the live configuration, and the last-applied-configuration annotation stored in the live configuration.

Merge patch calculation

The kubectl apply command writes the contents of the configuration file to the kubectl.kubernetes.io/last-applied-configuration annotation. This is used to identify fields that have been removed from the configuration file and need to be cleared from the live configuration. Here are the steps used to calculate which fields should be deleted or set:

Calculate the fields to delete. These are the fields present in last-applied-configuration and missing from the configuration file.
Calculate the fields to add or set. These are the fields present in the configuration file whose values don't match the live configuration.

Here's an example. Suppose this is the configuration file for a Deployment object:

application/update_deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.16.1 # update the image
        ports:
        - containerPort: 80

Also, suppose this is the live configuration for the same Deployment object:

apiVersion: apps/v1
kind: Deployment
metadata:
  annotations:
    # ...
    # note that the annotation does not contain replicas
    # because it was not updated through apply
    kubectl.kubernetes.io/last-applied-configuration: |
      {"apiVersion":"apps/v1","kind":"Deployment",
      "metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
      "spec":{"minReadySeconds":5,"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
      "spec":{"containers":[{"image":"nginx:1.14.2","name":"nginx",
      "ports":[{"containerPort":80}]}]}}}}      
  # ...
spec:
  replicas: 2 # written by scale
  # ...
  minReadySeconds: 5
  selector:
    matchLabels:
      # ...
      app: nginx
  template:
    metadata:
      # ...
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx:1.14.2
        # ...
        name: nginx
        ports:
        - containerPort: 80
      # ...

Here are the merge calculations that would be performed by kubectl apply:

Calculate the fields to delete by reading values from last-applied-configuration and comparing them to values in the configuration file. Clear fields explicitly set to null in the local object configuration file regardless of whether they appear in the last-applied-configuration. In this example, minReadySeconds appears in the last-applied-configuration annotation, but does not appear in the configuration file. Action: Clear minReadySeconds from the live configuration.
Calculate the fields to set by reading values from the configuration file and comparing them to values in the live configuration. In this example, the value of image in the configuration file does not match the value in the live configuration. Action: Set the value of image in the live configuration.
Set the last-applied-configuration annotation to match the value of the configuration file.
Merge the results from 1, 2, 3 into a single patch request to the API server.

Here is the live configuration that is the result of the merge:

apiVersion: apps/v1
kind: Deployment
metadata:
  annotations:
    # ...
    # The annotation contains the updated image to nginx 1.16.1,
    # but does not contain the updated replicas to 2
    kubectl.kubernetes.io/last-applied-configuration: |
      {"apiVersion":"apps/v1","kind":"Deployment",
      "metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
      "spec":{"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
      "spec":{"containers":[{"image":"nginx:1.16.1","name":"nginx",
      "ports":[{"containerPort":80}]}]}}}}      
    # ...
spec:
  selector:
    matchLabels:
      # ...
      app: nginx
  replicas: 2 # Set by `kubectl scale`.  Ignored by `kubectl apply`.
  # minReadySeconds cleared by `kubectl apply`
  # ...
  template:
    metadata:
      # ...
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx:1.16.1 # Set by `kubectl apply`
        # ...
        name: nginx
        ports:
        - containerPort: 80
        # ...
      # ...
    # ...
  # ...

How different types of fields are merged

How a particular field in a configuration file is merged with the live configuration depends on the type of the field. There are several types of fields:

primitive: A field of type string, integer, or boolean. For example, image and replicas are primitive fields. Action: Replace.
map, also called object: A field of type map or a complex type that contains subfields. For example, labels, annotations,spec and metadata are all maps. Action: Merge elements or subfields.
list: A field containing a list of items that can be either primitive types or maps. For example, containers, ports, and args are lists. Action: Varies.

When kubectl apply updates a map or list field, it typically does not replace the entire field, but instead updates the individual subelements. For instance, when merging the spec on a Deployment, the entire spec is not replaced. Instead the subfields of spec, such as replicas, are compared and merged.

Merging changes to primitive fields

Primitive fields are replaced or cleared.

Note:

- is used for "not applicable" because the value is not used.

Field in object configuration file	Field in live object configuration	Field in last-applied-configuration	Action
Yes	Yes	-	Set live to configuration file value.
Yes	No	-	Set live to local configuration.
No	-	Yes	Clear from live configuration.
No	-	No	Do nothing. Keep live value.

Merging changes to map fields

Fields that represent maps are merged by comparing each of the subfields or elements of the map:

Note:

- is used for "not applicable" because the value is not used.

Key in object configuration file	Key in live object configuration	Field in last-applied-configuration	Action
Yes	Yes	-	Compare sub fields values.
Yes	No	-	Set live to local configuration.
No	-	Yes	Delete from live configuration.
No	-	No	Do nothing. Keep live value.

Merging changes for fields of type list

Merging changes to a list uses one of three strategies:

Replace the list if all its elements are primitives.
Merge individual elements in a list of complex elements.
Merge a list of primitive elements.

The choice of strategy is made on a per-field basis.

Replace the list if all its elements are primitives

Treat the list the same as a primitive field. Replace or delete the entire list. This preserves ordering.

Example: Use kubectl apply to update the args field of a Container in a Pod. This sets the value of args in the live configuration to the value in the configuration file. Any args elements that had previously been added to the live configuration are lost. The order of the args elements defined in the configuration file is retained in the live configuration.

# last-applied-configuration value
    args: ["a", "b"]

# configuration file value
    args: ["a", "c"]

# live configuration
    args: ["a", "b", "d"]

# result after merge
    args: ["a", "c"]

Explanation: The merge used the configuration file value as the new list value.

Merge individual elements of a list of complex elements:

Treat the list as a map, and treat a specific field of each element as a key. Add, delete, or update individual elements. This does not preserve ordering.

This merge strategy uses a special tag on each field called a patchMergeKey. The patchMergeKey is defined for each field in the Kubernetes source code: types.go When merging a list of maps, the field specified as the patchMergeKey for a given element is used like a map key for that element.

Example: Use kubectl apply to update the containers field of a PodSpec. This merges the list as though it was a map where each element is keyed by name.

# last-applied-configuration value
    containers:
    - name: nginx
      image: nginx:1.16
    - name: nginx-helper-a # key: nginx-helper-a; will be deleted in result
      image: helper:1.3
    - name: nginx-helper-b # key: nginx-helper-b; will be retained
      image: helper:1.3

# configuration file value
    containers:
    - name: nginx
      image: nginx:1.16
    - name: nginx-helper-b
      image: helper:1.3
    - name: nginx-helper-c # key: nginx-helper-c; will be added in result
      image: helper:1.3

# live configuration
    containers:
    - name: nginx
      image: nginx:1.16
    - name: nginx-helper-a
      image: helper:1.3
    - name: nginx-helper-b
      image: helper:1.3
      args: ["run"] # Field will be retained
    - name: nginx-helper-d # key: nginx-helper-d; will be retained
      image: helper:1.3

# result after merge
    containers:
    - name: nginx
      image: nginx:1.16
      # Element nginx-helper-a was deleted
    - name: nginx-helper-b
      image: helper:1.3
      args: ["run"] # Field was retained
    - name: nginx-helper-c # Element was added
      image: helper:1.3
    - name: nginx-helper-d # Element was ignored
      image: helper:1.3

Explanation:

The container named "nginx-helper-a" was deleted because no container named "nginx-helper-a" appeared in the configuration file.
The container named "nginx-helper-b" retained the changes to args in the live configuration. kubectl apply was able to identify that "nginx-helper-b" in the live configuration was the same "nginx-helper-b" as in the configuration file, even though their fields had different values (no args in the configuration file). This is because the patchMergeKey field value (name) was identical in both.
The container named "nginx-helper-c" was added because no container with that name appeared in the live configuration, but one with that name appeared in the configuration file.
The container named "nginx-helper-d" was retained because no element with that name appeared in the last-applied-configuration.

Merge a list of primitive elements

As of Kubernetes 1.5, merging lists of primitive elements is not supported.

Note:

Which of the above strategies is chosen for a given field is controlled by the patchStrategy tag in types.go If no patchStrategy is specified for a field of type list, then the list is replaced.

Default field values

The API server sets certain fields to default values in the live configuration if they are not specified when the object is created.

Here's a configuration file for a Deployment. The file does not specify strategy:

application/simple_deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  minReadySeconds: 5
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

Create the object using kubectl apply:

kubectl apply -f https://k8s.io/examples/application/simple_deployment.yaml

Print the live configuration using kubectl get:

kubectl get -f https://k8s.io/examples/application/simple_deployment.yaml -o yaml

The output shows that the API server set several fields to default values in the live configuration. These fields were not specified in the configuration file.

apiVersion: apps/v1
kind: Deployment
# ...
spec:
  selector:
    matchLabels:
      app: nginx
  minReadySeconds: 5
  replicas: 1 # defaulted by apiserver
  strategy:
    rollingUpdate: # defaulted by apiserver - derived from strategy.type
      maxSurge: 1
      maxUnavailable: 1
    type: RollingUpdate # defaulted by apiserver
  template:
    metadata:
      creationTimestamp: null
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx:1.14.2
        imagePullPolicy: IfNotPresent # defaulted by apiserver
        name: nginx
        ports:
        - containerPort: 80
          protocol: TCP # defaulted by apiserver
        resources: {} # defaulted by apiserver
        terminationMessagePath: /dev/termination-log # defaulted by apiserver
      dnsPolicy: ClusterFirst # defaulted by apiserver
      restartPolicy: Always # defaulted by apiserver
      securityContext: {} # defaulted by apiserver
      terminationGracePeriodSeconds: 30 # defaulted by apiserver
# ...

In a patch request, defaulted fields are not re-defaulted unless they are explicitly cleared as part of a patch request. This can cause unexpected behavior for fields that are defaulted based on the values of other fields. When the other fields are later changed, the values defaulted from them will not be updated unless they are explicitly cleared.

For this reason, it is recommended that certain fields defaulted by the server are explicitly defined in the configuration file, even if the desired values match the server defaults. This makes it easier to recognize conflicting values that will not be re-defaulted by the server.

Example:

# last-applied-configuration
spec:
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

# configuration file
spec:
  strategy:
    type: Recreate # updated value
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

# live configuration
spec:
  strategy:
    type: RollingUpdate # defaulted value
    rollingUpdate: # defaulted value derived from type
      maxSurge : 1
      maxUnavailable: 1
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

# result after merge - ERROR!
spec:
  strategy:
    type: Recreate # updated value: incompatible with rollingUpdate
    rollingUpdate: # defaulted value: incompatible with "type: Recreate"
      maxSurge : 1
      maxUnavailable: 1
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

Explanation:

The user creates a Deployment without defining strategy.type.
The server defaults strategy.type to RollingUpdate and defaults the strategy.rollingUpdate values.
The user changes strategy.type to Recreate. The strategy.rollingUpdate values remain at their defaulted values, though the server expects them to be cleared. If the strategy.rollingUpdate values had been defined initially in the configuration file, it would have been more clear that they needed to be deleted.
Apply fails because strategy.rollingUpdate is not cleared. The strategy.rollingupdate field cannot be defined with a strategy.type of Recreate.

Recommendation: These fields should be explicitly defined in the object configuration file:

Selectors and PodTemplate labels on workloads, such as Deployment, StatefulSet, Job, DaemonSet, ReplicaSet, and ReplicationController
Deployment rollout strategy

How to clear server-defaulted fields or fields set by other writers

Fields that do not appear in the configuration file can be cleared by setting their values to null and then applying the configuration file. For fields defaulted by the server, this triggers re-defaulting the values.

How to change ownership of a field between the configuration file and direct imperative writers

These are the only methods you should use to change an individual object field:

Use kubectl apply.
Write directly to the live configuration without modifying the configuration file: for example, use kubectl scale.

Changing the owner from a direct imperative writer to a configuration file

Add the field to the configuration file. For the field, discontinue direct updates to the live configuration that do not go through kubectl apply.

Changing the owner from a configuration file to a direct imperative writer

As of Kubernetes 1.5, changing ownership of a field from a configuration file to an imperative writer requires manual steps:

Remove the field from the configuration file.
Remove the field from the kubectl.kubernetes.io/last-applied-configuration annotation on the live object.

Changing management methods

Kubernetes objects should be managed using only one method at a time. Switching from one method to another is possible, but is a manual process.

Note:

It is OK to use imperative deletion with declarative management.

Migrating from imperative command management to declarative object configuration

Migrating from imperative command management to declarative object configuration involves several manual steps:

Export the live object to a local configuration file:

kubectl get <kind>/<name> -o yaml > <kind>_<name>.yaml

Manually remove the status field from the configuration file.

Note:
This step is optional, as kubectl apply does not update the status field even if it is present in the configuration file.
Set the kubectl.kubernetes.io/last-applied-configuration annotation on the object:
```
kubectl replace --save-config -f <kind>_<name>.yaml
```
Change processes to use kubectl apply for managing the object exclusively.

Migrating from imperative object configuration to declarative object configuration

Set the kubectl.kubernetes.io/last-applied-configuration annotation on the object:
```
kubectl replace --save-config -f <kind>_<name>.yaml
```
Change processes to use kubectl apply for managing the object exclusively.

Defining controller selectors and PodTemplate labels

Warning:

Updating selectors on controllers is strongly discouraged.

The recommended approach is to define a single, immutable PodTemplate label used only by the controller selector with no other semantic meaning.

Example:

selector:
  matchLabels:
      controller-selector: "apps/v1/deployment/nginx"
template:
  metadata:
    labels:
      controller-selector: "apps/v1/deployment/nginx"

What's next

5.2 - Declarative Management of Kubernetes Objects Using Kustomize

Kustomize is a standalone tool to customize Kubernetes objects through a kustomization file.

Since 1.14, Kubectl also supports the management of Kubernetes objects using a kustomization file. To view Resources found in a directory containing a kustomization file, run the following command:

kubectl kustomize <kustomization_directory>

To apply those Resources, run kubectl apply with --kustomize or -k flag:

kubectl apply -k <kustomization_directory>

Before you begin

Install kubectl.

To check the version, enter kubectl version.

Overview of Kustomize

Kustomize is a tool for customizing Kubernetes configurations. It has the following features to manage application configuration files:

generating resources from other sources
setting cross-cutting fields for resources
composing and customizing collections of resources

Generating Resources

ConfigMaps and Secrets hold configuration or sensitive data that are used by other Kubernetes objects, such as Pods. The source of truth of ConfigMaps or Secrets are usually external to a cluster, such as a .properties file or an SSH keyfile. Kustomize has secretGenerator and configMapGenerator, which generate Secret and ConfigMap from files or literals.

configMapGenerator

To generate a ConfigMap from a file, add an entry to the files list in configMapGenerator. Here is an example of generating a ConfigMap with a data item from a .properties file:

# Create a application.properties file
cat <<EOF >application.properties
FOO=Bar
EOF

cat <<EOF >./kustomization.yaml
configMapGenerator:
- name: example-configmap-1
  files:
  - application.properties
EOF

The generated ConfigMap can be examined with the following command:

kubectl kustomize ./

The generated ConfigMap is:

apiVersion: v1
data:
  application.properties: |
    FOO=Bar    
kind: ConfigMap
metadata:
  name: example-configmap-1-8mbdf7882g

To generate a ConfigMap from an env file, add an entry to the envs list in configMapGenerator. Here is an example of generating a ConfigMap with a data item from a .env file:

# Create a .env file
cat <<EOF >.env
FOO=Bar
EOF

cat <<EOF >./kustomization.yaml
configMapGenerator:
- name: example-configmap-1
  envs:
  - .env
EOF

The generated ConfigMap can be examined with the following command:

kubectl kustomize ./

The generated ConfigMap is:

apiVersion: v1
data:
  FOO: Bar
kind: ConfigMap
metadata:
  name: example-configmap-1-42cfbf598f

Note:

Each variable in the .env file becomes a separate key in the ConfigMap that you generate. This is different from the previous example which embeds a file named application.properties (and all its entries) as the value for a single key.

ConfigMaps can also be generated from literal key-value pairs. To generate a ConfigMap from a literal key-value pair, add an entry to the literals list in configMapGenerator. Here is an example of generating a ConfigMap with a data item from a key-value pair:

cat <<EOF >./kustomization.yaml
configMapGenerator:
- name: example-configmap-2
  literals:
  - FOO=Bar
EOF

The generated ConfigMap can be checked by the following command:

kubectl kustomize ./

The generated ConfigMap is:

apiVersion: v1
data:
  FOO: Bar
kind: ConfigMap
metadata:
  name: example-configmap-2-g2hdhfc6tk

To use a generated ConfigMap in a Deployment, reference it by the name of the configMapGenerator. Kustomize will automatically replace this name with the generated name.

This is an example deployment that uses a generated ConfigMap:

# Create a application.properties file
cat <<EOF >application.properties
FOO=Bar
EOF

cat <<EOF >deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  labels:
    app: my-app
spec:
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: app
        image: my-app
        volumeMounts:
        - name: config
          mountPath: /config
      volumes:
      - name: config
        configMap:
          name: example-configmap-1
EOF

cat <<EOF >./kustomization.yaml
resources:
- deployment.yaml
configMapGenerator:
- name: example-configmap-1
  files:
  - application.properties
EOF

Generate the ConfigMap and Deployment:

kubectl kustomize ./

The generated Deployment will refer to the generated ConfigMap by name:

apiVersion: v1
data:
  application.properties: |
    FOO=Bar    
kind: ConfigMap
metadata:
  name: example-configmap-1-g4hk9g2ff8
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: my-app
  name: my-app
spec:
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - image: my-app
        name: app
        volumeMounts:
        - mountPath: /config
          name: config
      volumes:
      - configMap:
          name: example-configmap-1-g4hk9g2ff8
        name: config

secretGenerator

You can generate Secrets from files or literal key-value pairs. To generate a Secret from a file, add an entry to the files list in secretGenerator. Here is an example of generating a Secret with a data item from a file:

# Create a password.txt file
cat <<EOF >./password.txt
username=admin
password=secret
EOF

cat <<EOF >./kustomization.yaml
secretGenerator:
- name: example-secret-1
  files:
  - password.txt
EOF

The generated Secret is as follows:

apiVersion: v1
data:
  password.txt: dXNlcm5hbWU9YWRtaW4KcGFzc3dvcmQ9c2VjcmV0Cg==
kind: Secret
metadata:
  name: example-secret-1-t2kt65hgtb
type: Opaque

To generate a Secret from a literal key-value pair, add an entry to literals list in secretGenerator. Here is an example of generating a Secret with a data item from a key-value pair:

cat <<EOF >./kustomization.yaml
secretGenerator:
- name: example-secret-2
  literals:
  - username=admin
  - password=secret
EOF

The generated Secret is as follows:

apiVersion: v1
data:
  password: c2VjcmV0
  username: YWRtaW4=
kind: Secret
metadata:
  name: example-secret-2-t52t6g96d8
type: Opaque

Like ConfigMaps, generated Secrets can be used in Deployments by referring to the name of the secretGenerator:

# Create a password.txt file
cat <<EOF >./password.txt
username=admin
password=secret
EOF

cat <<EOF >deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  labels:
    app: my-app
spec:
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: app
        image: my-app
        volumeMounts:
        - name: password
          mountPath: /secrets
      volumes:
      - name: password
        secret:
          secretName: example-secret-1
EOF

cat <<EOF >./kustomization.yaml
resources:
- deployment.yaml
secretGenerator:
- name: example-secret-1
  files:
  - password.txt
EOF

generatorOptions

The generated ConfigMaps and Secrets have a content hash suffix appended. This ensures that a new ConfigMap or Secret is generated when the contents are changed. To disable the behavior of appending a suffix, one can use generatorOptions. Besides that, it is also possible to specify cross-cutting options for generated ConfigMaps and Secrets.

cat <<EOF >./kustomization.yaml
configMapGenerator:
- name: example-configmap-3
  literals:
  - FOO=Bar
generatorOptions:
  disableNameSuffixHash: true
  labels:
    type: generated
  annotations:
    note: generated
EOF

Runkubectl kustomize ./ to view the generated ConfigMap:

apiVersion: v1
data:
  FOO: Bar
kind: ConfigMap
metadata:
  annotations:
    note: generated
  labels:
    type: generated
  name: example-configmap-3

Setting cross-cutting fields

It is quite common to set cross-cutting fields for all Kubernetes resources in a project. Some use cases for setting cross-cutting fields:

setting the same namespace for all Resources
adding the same name prefix or suffix
adding the same set of labels
adding the same set of annotations

Here is an example:

# Create a deployment.yaml
cat <<EOF >./deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
  labels:
    app: nginx
spec:
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx
EOF

cat <<EOF >./kustomization.yaml
namespace: my-namespace
namePrefix: dev-
nameSuffix: "-001"
commonLabels:
  app: bingo
commonAnnotations:
  oncallPager: 800-555-1212
resources:
- deployment.yaml
EOF

Run kubectl kustomize ./ to view those fields are all set in the Deployment Resource:

apiVersion: apps/v1
kind: Deployment
metadata:
  annotations:
    oncallPager: 800-555-1212
  labels:
    app: bingo
  name: dev-nginx-deployment-001
  namespace: my-namespace
spec:
  selector:
    matchLabels:
      app: bingo
  template:
    metadata:
      annotations:
        oncallPager: 800-555-1212
      labels:
        app: bingo
    spec:
      containers:
      - image: nginx
        name: nginx

Composing and Customizing Resources

It is common to compose a set of Resources in a project and manage them inside the same file or directory. Kustomize offers composing Resources from different files and applying patches or other customization to them.

Composing

Kustomize supports composition of different resources. The resources field, in the kustomization.yaml file, defines the list of resources to include in a configuration. Set the path to a resource's configuration file in the resources list. Here is an example of an NGINX application comprised of a Deployment and a Service:

# Create a deployment.yaml file
cat <<EOF > deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 2
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - name: my-nginx
        image: nginx
        ports:
        - containerPort: 80
EOF

# Create a service.yaml file
cat <<EOF > service.yaml
apiVersion: v1
kind: Service
metadata:
  name: my-nginx
  labels:
    run: my-nginx
spec:
  ports:
  - port: 80
    protocol: TCP
  selector:
    run: my-nginx
EOF

# Create a kustomization.yaml composing them
cat <<EOF >./kustomization.yaml
resources:
- deployment.yaml
- service.yaml
EOF

The Resources from kubectl kustomize ./ contain both the Deployment and the Service objects.

Customizing

Patches can be used to apply different customizations to Resources. Kustomize supports different patching mechanisms through patchesStrategicMerge and patchesJson6902. patchesStrategicMerge is a list of file paths. Each file should be resolved to a strategic merge patch. The names inside the patches must match Resource names that are already loaded. Small patches that do one thing are recommended. For example, create one patch for increasing the deployment replica number and another patch for setting the memory limit.

# Create a deployment.yaml file
cat <<EOF > deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 2
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - name: my-nginx
        image: nginx
        ports:
        - containerPort: 80
EOF

# Create a patch increase_replicas.yaml
cat <<EOF > increase_replicas.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  replicas: 3
EOF

# Create another patch set_memory.yaml
cat <<EOF > set_memory.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  template:
    spec:
      containers:
      - name: my-nginx
        resources:
          limits:
            memory: 512Mi
EOF

cat <<EOF >./kustomization.yaml
resources:
- deployment.yaml
patchesStrategicMerge:
- increase_replicas.yaml
- set_memory.yaml
EOF

Run kubectl kustomize ./ to view the Deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      run: my-nginx
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - image: nginx
        name: my-nginx
        ports:
        - containerPort: 80
        resources:
          limits:
            memory: 512Mi

Not all Resources or fields support strategic merge patches. To support modifying arbitrary fields in arbitrary Resources, Kustomize offers applying JSON patch through patchesJson6902. To find the correct Resource for a Json patch, the group, version, kind and name of that Resource need to be specified in kustomization.yaml. For example, increasing the replica number of a Deployment object can also be done through patchesJson6902.

# Create a deployment.yaml file
cat <<EOF > deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 2
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - name: my-nginx
        image: nginx
        ports:
        - containerPort: 80
EOF

# Create a json patch
cat <<EOF > patch.yaml
- op: replace
  path: /spec/replicas
  value: 3
EOF

# Create a kustomization.yaml
cat <<EOF >./kustomization.yaml
resources:
- deployment.yaml

patchesJson6902:
- target:
    group: apps
    version: v1
    kind: Deployment
    name: my-nginx
  path: patch.yaml
EOF

Run kubectl kustomize ./ to see the replicas field is updated:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      run: my-nginx
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - image: nginx
        name: my-nginx
        ports:
        - containerPort: 80

In addition to patches, Kustomize also offers customizing container images or injecting field values from other objects into containers without creating patches. For example, you can change the image used inside containers by specifying the new image in images field in kustomization.yaml.

cat <<EOF > deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 2
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - name: my-nginx
        image: nginx
        ports:
        - containerPort: 80
EOF

cat <<EOF >./kustomization.yaml
resources:
- deployment.yaml
images:
- name: nginx
  newName: my.image.registry/nginx
  newTag: 1.4.0
EOF

Run kubectl kustomize ./ to see that the image being used is updated:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  replicas: 2
  selector:
    matchLabels:
      run: my-nginx
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - image: my.image.registry/nginx:1.4.0
        name: my-nginx
        ports:
        - containerPort: 80

Sometimes, the application running in a Pod may need to use configuration values from other objects. For example, a Pod from a Deployment object need to read the corresponding Service name from Env or as a command argument. Since the Service name may change as namePrefix or nameSuffix is added in the kustomization.yaml file. It is not recommended to hard code the Service name in the command argument. For this usage, Kustomize can inject the Service name into containers through vars.

# Create a deployment.yaml file (quoting the here doc delimiter)
cat <<'EOF' > deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 2
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - name: my-nginx
        image: nginx
        command: ["start", "--host", "$(MY_SERVICE_NAME)"]
EOF

# Create a service.yaml file
cat <<EOF > service.yaml
apiVersion: v1
kind: Service
metadata:
  name: my-nginx
  labels:
    run: my-nginx
spec:
  ports:
  - port: 80
    protocol: TCP
  selector:
    run: my-nginx
EOF

cat <<EOF >./kustomization.yaml
namePrefix: dev-
nameSuffix: "-001"

resources:
- deployment.yaml
- service.yaml

vars:
- name: MY_SERVICE_NAME
  objref:
    kind: Service
    name: my-nginx
    apiVersion: v1
EOF

Run kubectl kustomize ./ to see that the Service name injected into containers is dev-my-nginx-001:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: dev-my-nginx-001
spec:
  replicas: 2
  selector:
    matchLabels:
      run: my-nginx
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - command:
        - start
        - --host
        - dev-my-nginx-001
        image: nginx
        name: my-nginx

Bases and Overlays

Kustomize has the concepts of bases and overlays. A base is a directory with a kustomization.yaml, which contains a set of resources and associated customization. A base could be either a local directory or a directory from a remote repo, as long as a kustomization.yaml is present inside. An overlay is a directory with a kustomization.yaml that refers to other kustomization directories as its bases. A base has no knowledge of an overlay and can be used in multiple overlays. An overlay may have multiple bases and it composes all resources from bases and may also have customization on top of them.

Here is an example of a base:

# Create a directory to hold the base
mkdir base
# Create a base/deployment.yaml
cat <<EOF > base/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 2
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - name: my-nginx
        image: nginx
EOF

# Create a base/service.yaml file
cat <<EOF > base/service.yaml
apiVersion: v1
kind: Service
metadata:
  name: my-nginx
  labels:
    run: my-nginx
spec:
  ports:
  - port: 80
    protocol: TCP
  selector:
    run: my-nginx
EOF
# Create a base/kustomization.yaml
cat <<EOF > base/kustomization.yaml
resources:
- deployment.yaml
- service.yaml
EOF

This base can be used in multiple overlays. You can add different namePrefix or other cross-cutting fields in different overlays. Here are two overlays using the same base.

mkdir dev
cat <<EOF > dev/kustomization.yaml
resources:
- ../base
namePrefix: dev-
EOF

mkdir prod
cat <<EOF > prod/kustomization.yaml
resources:
- ../base
namePrefix: prod-
EOF

How to apply/view/delete objects using Kustomize

Use --kustomize or -k in kubectl commands to recognize Resources managed by kustomization.yaml. Note that -k should point to a kustomization directory, such as

kubectl apply -k <kustomization directory>/

Given the following kustomization.yaml,

# Create a deployment.yaml file
cat <<EOF > deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 2
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - name: my-nginx
        image: nginx
        ports:
        - containerPort: 80
EOF

# Create a kustomization.yaml
cat <<EOF >./kustomization.yaml
namePrefix: dev-
commonLabels:
  app: my-nginx
resources:
- deployment.yaml
EOF

Run the following command to apply the Deployment object dev-my-nginx:

> kubectl apply -k ./
deployment.apps/dev-my-nginx created

Run one of the following commands to view the Deployment object dev-my-nginx:

kubectl get -k ./

kubectl describe -k ./

Run the following command to compare the Deployment object dev-my-nginx against the state that the cluster would be in if the manifest was applied:

kubectl diff -k ./

Run the following command to delete the Deployment object dev-my-nginx:

> kubectl delete -k ./
deployment.apps "dev-my-nginx" deleted

Kustomize Feature List

Field	Type	Explanation
namespace	string	add namespace to all resources
namePrefix	string	value of this field is prepended to the names of all resources
nameSuffix	string	value of this field is appended to the names of all resources
commonLabels	map[string]string	labels to add to all resources and selectors
commonAnnotations	map[string]string	annotations to add to all resources
resources	[]string	each entry in this list must resolve to an existing resource configuration file
configMapGenerator	[]ConfigMapArgs	Each entry in this list generates a ConfigMap
secretGenerator	[]SecretArgs	Each entry in this list generates a Secret
generatorOptions	GeneratorOptions	Modify behaviors of all ConfigMap and Secret generator
bases	[]string	Each entry in this list should resolve to a directory containing a kustomization.yaml file
patchesStrategicMerge	[]string	Each entry in this list should resolve a strategic merge patch of a Kubernetes object
patchesJson6902	[]Patch	Each entry in this list should resolve to a Kubernetes object and a Json Patch
vars	[]Var	Each entry is to capture text from one resource's field
images	[]Image	Each entry is to modify the name, tags and/or digest for one image without creating patches
configurations	[]string	Each entry in this list should resolve to a file containing Kustomize transformer configurations
crds	[]string	Each entry in this list should resolve to an OpenAPI definition file for Kubernetes types

What's next

5.3 - Managing Kubernetes Objects Using Imperative Commands

Kubernetes objects can quickly be created, updated, and deleted directly using imperative commands built into the kubectl command-line tool. This document explains how those commands are organized and how to use them to manage live objects.

Before you begin

Install kubectl.

To check the version, enter kubectl version.

Trade-offs

The kubectl tool supports three kinds of object management:

Imperative commands
Imperative object configuration
Declarative object configuration

See Kubernetes Object Management for a discussion of the advantages and disadvantage of each kind of object management.

How to create objects

The kubectl tool supports verb-driven commands for creating some of the most common object types. The commands are named to be recognizable to users unfamiliar with the Kubernetes object types.

run: Create a new Pod to run a Container.
expose: Create a new Service object to load balance traffic across Pods.
autoscale: Create a new Autoscaler object to automatically horizontally scale a controller, such as a Deployment.

The kubectl tool also supports creation commands driven by object type. These commands support more object types and are more explicit about their intent, but require users to know the type of objects they intend to create.

create <objecttype> [<subtype>] <instancename>

Some objects types have subtypes that you can specify in the create command. For example, the Service object has several subtypes including ClusterIP, LoadBalancer, and NodePort. Here's an example that creates a Service with subtype NodePort:

kubectl create service nodeport <myservicename>

In the preceding example, the create service nodeport command is called a subcommand of the create service command.

You can use the -h flag to find the arguments and flags supported by a subcommand:

kubectl create service nodeport -h

How to update objects

The kubectl command supports verb-driven commands for some common update operations. These commands are named to enable users unfamiliar with Kubernetes objects to perform updates without knowing the specific fields that must be set:

scale: Horizontally scale a controller to add or remove Pods by updating the replica count of the controller.
annotate: Add or remove an annotation from an object.
label: Add or remove a label from an object.

The kubectl command also supports update commands driven by an aspect of the object. Setting this aspect may set different fields for different object types:

set <field>: Set an aspect of an object.

Note:

In Kubernetes version 1.5, not every verb-driven command has an associated aspect-driven command.

The kubectl tool supports these additional ways to update a live object directly, however they require a better understanding of the Kubernetes object schema.

edit: Directly edit the raw configuration of a live object by opening its configuration in an editor.
patch: Directly modify specific fields of a live object by using a patch string. For more details on patch strings, see the patch section in API Conventions.

How to delete objects

You can use the delete command to delete an object from a cluster:

delete <type>/<name>

Note:

You can use kubectl delete for both imperative commands and imperative object configuration. The difference is in the arguments passed to the command. To use kubectl delete as an imperative command, pass the object to be deleted as an argument. Here's an example that passes a Deployment object named nginx:

kubectl delete deployment/nginx

How to view an object

There are several commands for printing information about an object:

get: Prints basic information about matching objects. Use get -h to see a list of options.
describe: Prints aggregated detailed information about matching objects.
logs: Prints the stdout and stderr for a container running in a Pod.

Using `set` commands to modify objects before creation

There are some object fields that don't have a flag you can use in a create command. In some of those cases, you can use a combination of set and create to specify a value for the field before object creation. This is done by piping the output of the create command to the set command, and then back to the create command. Here's an example:

kubectl create service clusterip my-svc --clusterip="None" -o yaml --dry-run=client | kubectl set selector --local -f - 'environment=qa' -o yaml | kubectl create -f -

The kubectl create service -o yaml --dry-run=client command creates the configuration for the Service, but prints it to stdout as YAML instead of sending it to the Kubernetes API server.
The kubectl set selector --local -f - -o yaml command reads the configuration from stdin, and writes the updated configuration to stdout as YAML.
The kubectl create -f - command creates the object using the configuration provided via stdin.

Using `--edit` to modify objects before creation

You can use kubectl create --edit to make arbitrary changes to an object before it is created. Here's an example:

kubectl create service clusterip my-svc --clusterip="None" -o yaml --dry-run=client > /tmp/srv.yaml
kubectl create --edit -f /tmp/srv.yaml

The kubectl create service command creates the configuration for the Service and saves it to /tmp/srv.yaml.
The kubectl create --edit command opens the configuration file for editing before it creates the object.

What's next

5.4 - Imperative Management of Kubernetes Objects Using Configuration Files

Kubernetes objects can be created, updated, and deleted by using the kubectl command-line tool along with an object configuration file written in YAML or JSON. This document explains how to define and manage objects using configuration files.

Before you begin

Install kubectl.

To check the version, enter kubectl version.

Trade-offs

The kubectl tool supports three kinds of object management:

Imperative commands
Imperative object configuration
Declarative object configuration

See Kubernetes Object Management for a discussion of the advantages and disadvantage of each kind of object management.

How to create objects

You can use kubectl create -f to create an object from a configuration file. Refer to the kubernetes API reference for details.

kubectl create -f <filename|url>

How to update objects

Warning:

Updating objects with the replace command drops all parts of the spec not specified in the configuration file. This should not be used with objects whose specs are partially managed by the cluster, such as Services of type LoadBalancer, where the externalIPs field is managed independently from the configuration file. Independently managed fields must be copied to the configuration file to prevent replace from dropping them.

You can use kubectl replace -f to update a live object according to a configuration file.

kubectl replace -f <filename|url>

How to delete objects

You can use kubectl delete -f to delete an object that is described in a configuration file.

kubectl delete -f <filename|url>

Note:

If configuration file has specified the generateName field in the metadata section instead of the name field, you cannot delete the object using kubectl delete -f <filename|url>. You will have to use other flags for deleting the object. For example:

kubectl delete <type> <name>
kubectl delete <type> -l <label>

How to view an object

You can use kubectl get -f to view information about an object that is described in a configuration file.

kubectl get -f <filename|url> -o yaml

The -o yaml flag specifies that the full object configuration is printed. Use kubectl get -h to see a list of options.

Limitations

The create, replace, and delete commands work well when each object's configuration is fully defined and recorded in its configuration file. However when a live object is updated, and the updates are not merged into its configuration file, the updates will be lost the next time a replace is executed. This can happen if a controller, such as a HorizontalPodAutoscaler, makes updates directly to a live object. Here's an example:

You create an object from a configuration file.
Another source updates the object by changing some field.
You replace the object from the configuration file. Changes made by the other source in step 2 are lost.

If you need to support multiple writers to the same object, you can use kubectl apply to manage the object.

Creating and editing an object from a URL without saving the configuration

Suppose you have the URL of an object configuration file. You can use kubectl create --edit to make changes to the configuration before the object is created. This is particularly useful for tutorials and tasks that point to a configuration file that could be modified by the reader.

kubectl create -f <url> --edit

Migrating from imperative commands to imperative object configuration

Migrating from imperative commands to imperative object configuration involves several manual steps.

Export the live object to a local object configuration file:
```
kubectl get <kind>/<name> -o yaml > <kind>_<name>.yaml
```
Manually remove the status field from the object configuration file.
For subsequent object management, use replace exclusively.
```
kubectl replace -f <kind>_<name>.yaml
```

Defining controller selectors and PodTemplate labels

Warning:

Updating selectors on controllers is strongly discouraged.

The recommended approach is to define a single, immutable PodTemplate label used only by the controller selector with no other semantic meaning.

Example label:

selector:
  matchLabels:
      controller-selector: "apps/v1/deployment/nginx"
template:
  metadata:
    labels:
      controller-selector: "apps/v1/deployment/nginx"

What's next

5.5 - Update API Objects in Place Using kubectl patch

Use kubectl patch to update Kubernetes API objects in place. Do a strategic merge patch or a JSON merge patch.

This task shows how to use kubectl patch to update an API object in place. The exercises in this task demonstrate a strategic merge patch and a JSON merge patch.

Before you begin

To check the version, enter kubectl version.

Use a strategic merge patch to update a Deployment

Here's the configuration file for a Deployment that has two replicas. Each replica is a Pod that has one container:

application/deployment-patch.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: patch-demo
spec:
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: patch-demo-ctr
        image: nginx
      tolerations:
      - effect: NoSchedule
        key: dedicated
        value: test-team

Create the Deployment:

kubectl apply -f https://k8s.io/examples/application/deployment-patch.yaml

View the Pods associated with your Deployment:

kubectl get pods

The output shows that the Deployment has two Pods. The 1/1 indicates that each Pod has one container:

NAME                        READY     STATUS    RESTARTS   AGE
patch-demo-28633765-670qr   1/1       Running   0          23s
patch-demo-28633765-j5qs3   1/1       Running   0          23s

Make a note of the names of the running Pods. Later, you will see that these Pods get terminated and replaced by new ones.

At this point, each Pod has one Container that runs the nginx image. Now suppose you want each Pod to have two containers: one that runs nginx and one that runs redis.

Create a file named patch-file.yaml that has this content:

spec:
  template:
    spec:
      containers:
      - name: patch-demo-ctr-2
        image: redis

Patch your Deployment:

kubectl patch deployment patch-demo --patch-file patch-file.yaml

View the patched Deployment:

kubectl get deployment patch-demo --output yaml

The output shows that the PodSpec in the Deployment has two Containers:

containers:
- image: redis
  imagePullPolicy: Always
  name: patch-demo-ctr-2
  ...
- image: nginx
  imagePullPolicy: Always
  name: patch-demo-ctr
  ...

View the Pods associated with your patched Deployment:

kubectl get pods

The output shows that the running Pods have different names from the Pods that were running previously. The Deployment terminated the old Pods and created two new Pods that comply with the updated Deployment spec. The 2/2 indicates that each Pod has two Containers:

NAME                          READY     STATUS    RESTARTS   AGE
patch-demo-1081991389-2wrn5   2/2       Running   0          1m
patch-demo-1081991389-jmg7b   2/2       Running   0          1m

Take a closer look at one of the patch-demo Pods:

kubectl get pod <your-pod-name> --output yaml

The output shows that the Pod has two Containers: one running nginx and one running redis:

containers:
- image: redis
  ...
- image: nginx
  ...

Notes on the strategic merge patch

The patch you did in the preceding exercise is called a strategic merge patch. Notice that the patch did not replace the containers list. Instead it added a new Container to the list. In other words, the list in the patch was merged with the existing list. This is not always what happens when you use a strategic merge patch on a list. In some cases, the list is replaced, not merged.

With a strategic merge patch, a list is either replaced or merged depending on its patch strategy. The patch strategy is specified by the value of the patchStrategy key in a field tag in the Kubernetes source code. For example, the Containers field of PodSpec struct has a patchStrategy of merge:

type PodSpec struct {
  ...
  Containers []Container `json:"containers" patchStrategy:"merge" patchMergeKey:"name" ...`
  ...
}

You can also see the patch strategy in the OpenApi spec:

"io.k8s.api.core.v1.PodSpec": {
    ...,
    "containers": {
        "description": "List of containers belonging to the pod.  ...."
    },
    "x-kubernetes-patch-merge-key": "name",
    "x-kubernetes-patch-strategy": "merge"
}

And you can see the patch strategy in the Kubernetes API documentation.

Create a file named patch-file-tolerations.yaml that has this content:

spec:
  template:
    spec:
      tolerations:
      - effect: NoSchedule
        key: disktype
        value: ssd

Patch your Deployment:

kubectl patch deployment patch-demo --patch-file patch-file-tolerations.yaml

View the patched Deployment:

kubectl get deployment patch-demo --output yaml

The output shows that the PodSpec in the Deployment has only one Toleration:

tolerations:
- effect: NoSchedule
  key: disktype
  value: ssd

Notice that the tolerations list in the PodSpec was replaced, not merged. This is because the Tolerations field of PodSpec does not have a patchStrategy key in its field tag. So the strategic merge patch uses the default patch strategy, which is replace.

type PodSpec struct {
  ...
  Tolerations []Toleration `json:"tolerations,omitempty" protobuf:"bytes,22,opt,name=tolerations"`
  ...
}

Use a JSON merge patch to update a Deployment

A strategic merge patch is different from a JSON merge patch. With a JSON merge patch, if you want to update a list, you have to specify the entire new list. And the new list completely replaces the existing list.

The kubectl patch command has a type parameter that you can set to one of these values:

Parameter value	Merge type
json	JSON Patch, RFC 6902
merge	JSON Merge Patch, RFC 7386
strategic	Strategic merge patch

For a comparison of JSON patch and JSON merge patch, see JSON Patch and JSON Merge Patch.

The default value for the type parameter is strategic. So in the preceding exercise, you did a strategic merge patch.

Next, do a JSON merge patch on your same Deployment. Create a file named patch-file-2.yaml that has this content:

spec:
  template:
    spec:
      containers:
      - name: patch-demo-ctr-3
        image: gcr.io/google-samples/hello-app:2.0

In your patch command, set type to merge:

kubectl patch deployment patch-demo --type merge --patch-file patch-file-2.yaml

View the patched Deployment:

kubectl get deployment patch-demo --output yaml

The containers list that you specified in the patch has only one Container. The output shows that your list of one Container replaced the existing containers list.

spec:
  containers:
  - image: gcr.io/google-samples/hello-app:2.0
    ...
    name: patch-demo-ctr-3

List the running Pods:

kubectl get pods

In the output, you can see that the existing Pods were terminated, and new Pods were created. The 1/1 indicates that each new Pod is running only one Container.

NAME                          READY     STATUS    RESTARTS   AGE
patch-demo-1307768864-69308   1/1       Running   0          1m
patch-demo-1307768864-c86dc   1/1       Running   0          1m

Use strategic merge patch to update a Deployment using the retainKeys strategy

Here's the configuration file for a Deployment that uses the RollingUpdate strategy:

application/deployment-retainkeys.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: retainkeys-demo
spec:
  selector:
    matchLabels:
      app: nginx
  strategy:
    rollingUpdate:
      maxSurge: 30%
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: retainkeys-demo-ctr
        image: nginx

Create the deployment:

kubectl apply -f https://k8s.io/examples/application/deployment-retainkeys.yaml

At this point, the deployment is created and is using the RollingUpdate strategy.

Create a file named patch-file-no-retainkeys.yaml that has this content:

spec:
  strategy:
    type: Recreate

Patch your Deployment:

kubectl patch deployment retainkeys-demo --type strategic --patch-file patch-file-no-retainkeys.yaml

In the output, you can see that it is not possible to set type as Recreate when a value is defined for spec.strategy.rollingUpdate:

The Deployment "retainkeys-demo" is invalid: spec.strategy.rollingUpdate: Forbidden: may not be specified when strategy `type` is 'Recreate'

The way to remove the value for spec.strategy.rollingUpdate when updating the value for type is to use the retainKeys strategy for the strategic merge.

Create another file named patch-file-retainkeys.yaml that has this content:

spec:
  strategy:
    $retainKeys:
    - type
    type: Recreate

With this patch, we indicate that we want to retain only the type key of the strategy object. Thus, the rollingUpdate will be removed during the patch operation.

Patch your Deployment again with this new patch:

kubectl patch deployment retainkeys-demo --type strategic --patch-file patch-file-retainkeys.yaml

Examine the content of the Deployment:

kubectl get deployment retainkeys-demo --output yaml

The output shows that the strategy object in the Deployment does not contain the rollingUpdate key anymore:

spec:
  strategy:
    type: Recreate
  template:

Notes on the strategic merge patch using the retainKeys strategy

The patch you did in the preceding exercise is called a strategic merge patch with retainKeys strategy. This method introduces a new directive $retainKeys that has the following strategies:

It contains a list of strings.
All fields needing to be preserved must be present in the $retainKeys list.
The fields that are present will be merged with live object.
All of the missing fields will be cleared when patching.
All fields in the $retainKeys list must be a superset or the same as the fields present in the patch.

The retainKeys strategy does not work for all objects. It only works when the value of the patchStrategy key in a field tag in the Kubernetes source code contains retainKeys. For example, the Strategy field of the DeploymentSpec struct has a patchStrategy of retainKeys:

type DeploymentSpec struct {
  ...
  // +patchStrategy=retainKeys
  Strategy DeploymentStrategy `json:"strategy,omitempty" patchStrategy:"retainKeys" ...`
  ...
}

You can also see the retainKeys strategy in the OpenApi spec:

"io.k8s.api.apps.v1.DeploymentSpec": {
    ...,
    "strategy": {
        "$ref": "#/definitions/io.k8s.api.apps.v1.DeploymentStrategy",
        "description": "The deployment strategy to use to replace existing pods with new ones.",
        "x-kubernetes-patch-strategy": "retainKeys"
    },
    ....
}

And you can see the retainKeys strategy in the Kubernetes API documentation.

Alternate forms of the kubectl patch command

The kubectl patch command takes YAML or JSON. It can take the patch as a file or directly on the command line.

Create a file named patch-file.json that has this content:

{
   "spec": {
      "template": {
         "spec": {
            "containers": [
               {
                  "name": "patch-demo-ctr-2",
                  "image": "redis"
               }
            ]
         }
      }
   }
}

The following commands are equivalent:

kubectl patch deployment patch-demo --patch-file patch-file.yaml
kubectl patch deployment patch-demo --patch 'spec:\n template:\n  spec:\n   containers:\n   - name: patch-demo-ctr-2\n     image: redis'

kubectl patch deployment patch-demo --patch-file patch-file.json
kubectl patch deployment patch-demo --patch '{"spec": {"template": {"spec": {"containers": [{"name": "patch-demo-ctr-2","image": "redis"}]}}}}'

Update an object's replica count using `kubectl patch` with `--subresource`

FEATURE STATE: Kubernetes v1.24 [alpha]

The flag --subresource=[subresource-name] is used with kubectl commands like get, patch, edit and replace to fetch and update status and scale subresources of the resources (applicable for kubectl version v1.24 or more). This flag is used with all the API resources (built-in and CRs) that have status or scale subresource. Deployment is one of the examples which supports these subresources.

Here's a manifest for a Deployment that has two replicas:

application/deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  replicas: 2 # tells deployment to run 2 pods matching the template
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

Create the Deployment:

kubectl apply -f https://k8s.io/examples/application/deployment.yaml

View the Pods associated with your Deployment:

kubectl get pods -l app=nginx

In the output, you can see that Deployment has two Pods. For example:

NAME                                READY   STATUS    RESTARTS   AGE
nginx-deployment-7fb96c846b-22567   1/1     Running   0          47s
nginx-deployment-7fb96c846b-mlgns   1/1     Running   0          47s

Now, patch that Deployment with --subresource=[subresource-name] flag:

kubectl patch deployment nginx-deployment --subresource='scale' --type='merge' -p '{"spec":{"replicas":3}}'

The output is:

scale.autoscaling/nginx-deployment patched

View the Pods associated with your patched Deployment:

kubectl get pods -l app=nginx

In the output, you can see one new pod is created, so now you have 3 running pods.

NAME                                READY   STATUS    RESTARTS   AGE
nginx-deployment-7fb96c846b-22567   1/1     Running   0          107s
nginx-deployment-7fb96c846b-lxfr2   1/1     Running   0          14s
nginx-deployment-7fb96c846b-mlgns   1/1     Running   0          107s

View the patched Deployment:

kubectl get deployment nginx-deployment -o yaml

...
spec:
  replicas: 3
  ...
status:
  ...
  availableReplicas: 3
  readyReplicas: 3
  replicas: 3

Note:

If you run kubectl patch and specify --subresource flag for resource that doesn't support that particular subresource, the API server returns a 404 Not Found error.

Summary

In this exercise, you used kubectl patch to change the live configuration of a Deployment object. You did not change the configuration file that you originally used to create the Deployment object. Other commands for updating API objects include kubectl annotate, kubectl edit, kubectl replace, kubectl scale, and kubectl apply.

Note:

Strategic merge patch is not supported for custom resources.

What's next

5.6 - Migrate Kubernetes Objects Using Storage Version Migration

FEATURE STATE: Kubernetes v1.30 [alpha]

Kubernetes relies on API data being actively re-written, to support some maintenance activities related to at rest storage. Two prominent examples are the versioned schema of stored resources (that is, the preferred storage schema changing from v1 to v2 for a given resource) and encryption at rest (that is, rewriting stale data based on a change in how the data should be encrypted).

Before you begin

Install kubectl.

Your Kubernetes server must be version v1.30. To check the version, enter kubectl version.

Ensure that your cluster has the StorageVersionMigrator and InformerResourceVersion feature gates enabled. You will need control plane administrator access to make that change.

Enable storage version migration REST api by setting runtime config storagemigration.k8s.io/v1alpha1 to true for the API server. For more information on how to do that, read enable or disable a Kubernetes API.

Re-encrypt Kubernetes secrets using storage version migration

To begin with, configure KMS provider to encrypt data at rest in etcd using following encryption configuration.
```
kind: EncryptionConfiguration
apiVersion: apiserver.config.k8s.io/v1
resources:
- resources:
  - secrets
  providers:
  - aescbc:
    keys:
    - name: key1
      secret: c2VjcmV0IGlzIHNlY3VyZQ==
```
Make sure to enable automatic reload of encryption configuration file by setting --encryption-provider-config-automatic-reload to true.

Create a Secret using kubectl.

kubectl create secret generic my-secret --from-literal=key1=supersecret

Verify the serialized data for that Secret object is prefixed with k8s:enc:aescbc:v1:key1.

Update the encryption configuration file as follows to rotate the encryption key.

kind: EncryptionConfiguration
apiVersion: apiserver.config.k8s.io/v1
resources:
- resources:
  - secrets
  providers:
  - aescbc:
    keys:
    - name: key2
      secret: c2VjcmV0IGlzIHNlY3VyZSwgaXMgaXQ/
  - aescbc:
    keys:
    - name: key1
      secret: c2VjcmV0IGlzIHNlY3VyZQ==

To ensure that previously created secret my-secert is re-encrypted with new key key2, you will use Storage Version Migration.

Create a StorageVersionMigration manifest named migrate-secret.yaml as follows:

kind: StorageVersionMigration
apiVersion: storagemigration.k8s.io/v1alpha1
metadata:
  name: secrets-migration
spec:
  resource:
    group: ""
    version: v1
    resource: secrets

Create the object using kubectl as follows:

kubectl apply -f migrate-secret.yaml

Monitor migration of Secrets by checking the .status of the StorageVersionMigration. A successful migration should have its Succeeded condition set to true. Get the StorageVersionMigration object as follows:

kubectl get storageversionmigration.storagemigration.k8s.io/secrets-migration -o yaml

The output is similar to:

kind: StorageVersionMigration
apiVersion: storagemigration.k8s.io/v1alpha1
metadata:
  name: secrets-migration
  uid: 628f6922-a9cb-4514-b076-12d3c178967c
  resourceVersion: "90"
  creationTimestamp: "2024-03-12T20:29:45Z"
spec:
  resource:
    group: ""
    version: v1
    resource: secrets
status:
  conditions:
  - type: Running
    status: "False"
    lastUpdateTime: "2024-03-12T20:29:46Z"
    reason: StorageVersionMigrationInProgress
  - type: Succeeded
    status: "True"
    lastUpdateTime: "2024-03-12T20:29:46Z"
    reason: StorageVersionMigrationSucceeded
  resourceVersion: "84"

Verify the stored secret is now prefixed with k8s:enc:aescbc:v1:key2.

Update the preferred storage schema of a CRD

Consider a scenario where a CustomResourceDefinition (CRD) is created to serve custom resources (CRs) and is set as the preferred storage schema. When it's time to introduce v2 of the CRD, it can be added for serving only with a conversion webhook. This enables a smoother transition where users can create CRs using either the v1 or v2 schema, with the webhook in place to perform the necessary schema conversion between them. Before setting v2 as the preferred storage schema version, it's important to ensure that all existing CRs stored as v1 are migrated to v2. This migration can be achieved through Storage Version Migration to migrate all CRs from v1 to v2.

Create a manifest for the CRD, named test-crd.yaml, as follows:

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: selfierequests.stable.example.com
spec:
  group: stable.example.com
  names:
    plural: SelfieRequests
    singular: SelfieRequest
    kind: SelfieRequest
    listKind: SelfieRequestList
  scope: Namespaced
  versions:
  - name: v1
    served: true
    storage: true
    schema:
      openAPIV3Schema:
        type: object
        properties:
          hostPort:
            type: string
  conversion:
    strategy: Webhook
    webhook:
      clientConfig:
        url: "https://127.0.0.1:9443/crdconvert"
        caBundle: <CABundle info>
    conversionReviewVersions:
    - v1
    - v2

Create CRD using kubectl:

kubectl apply -f test-crd.yaml

Create a manifest for an example testcrd. Name the manifest cr1.yaml and use these contents:

apiVersion: stable.example.com/v1
kind: SelfieRequest
metadata:
  name: cr1
  namespace: default

Create CR using kubectl:

kubectl apply -f cr1.yaml

Verify that CR is written and stored as v1 by getting the object from etcd.
```
ETCDCTL_API=3 etcdctl get /kubernetes.io/stable.example.com/testcrds/default/cr1 [...] | hexdump -C
```
where [...] contains the additional arguments for connecting to the etcd server.

Update the CRD test-crd.yaml to include v2 version for serving and storage and v1 as serving only, as follows:

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
name: selfierequests.stable.example.com
spec:
  group: stable.example.com
  names:
    plural: SelfieRequests
    singular: SelfieRequest
    kind: SelfieRequest
    listKind: SelfieRequestList
  scope: Namespaced
  versions:
    - name: v2
      served: true
      storage: true
      schema:
        openAPIV3Schema:
          type: object
          properties:
            host:
              type: string
            port:
              type: string
    - name: v1
      served: true
      storage: false
      schema:
        openAPIV3Schema:
          type: object
          properties:
            hostPort:
              type: string
  conversion:
    strategy: Webhook
    webhook:
      clientConfig:
        url: "https://127.0.0.1:9443/crdconvert"
        caBundle: <CABundle info>
      conversionReviewVersions:
        - v1
        - v2

Update CRD using kubectl:

kubectl apply -f test-crd.yaml

Create CR resource file with name cr2.yaml as follows:

apiVersion: stable.example.com/v2
kind: SelfieRequest
metadata:
  name: cr2
  namespace: default

Create CR using kubectl:
```
kubectl apply -f cr2.yaml
```
Verify that CR is written and stored as v2 by getting the object from etcd.
```
ETCDCTL_API=3 etcdctl get /kubernetes.io/stable.example.com/testcrds/default/cr2 [...] | hexdump -C
```
where [...] contains the additional arguments for connecting to the etcd server.

Create a StorageVersionMigration manifest named migrate-crd.yaml, with the contents as follows:

kind: StorageVersionMigration
apiVersion: storagemigration.k8s.io/v1alpha1
metadata:
  name: crdsvm
spec:
  resource:
    group: stable.example.com
    version: v1
    resource: SelfieRequest

Create the object using kubectl as follows:

kubectl apply -f migrate-crd.yaml

Monitor migration of secrets using status. Successful migration should have Succeeded condition set to "True" in the status field. Get the migration resource as follows:

kubectl get storageversionmigration.storagemigration.k8s.io/crdsvm -o yaml

The output is similar to:

kind: StorageVersionMigration
apiVersion: storagemigration.k8s.io/v1alpha1
metadata:
  name: crdsvm
  uid: 13062fe4-32d7-47cc-9528-5067fa0c6ac8
  resourceVersion: "111"
  creationTimestamp: "2024-03-12T22:40:01Z"
spec:
  resource:
    group: stable.example.com
    version: v1
    resource: testcrds
status:
  conditions:
    - type: Running
      status: "False"
      lastUpdateTime: "2024-03-12T22:40:03Z"
      reason: StorageVersionMigrationInProgress
    - type: Succeeded
      status: "True"
      lastUpdateTime: "2024-03-12T22:40:03Z"
      reason: StorageVersionMigrationSucceeded
  resourceVersion: "106"

Verify that previously created cr1 is now written and stored as v2 by getting the object from etcd.
```
ETCDCTL_API=3 etcdctl get /kubernetes.io/stable.example.com/testcrds/default/cr1 [...] | hexdump -C
```
where [...] contains the additional arguments for connecting to the etcd server.

6 - Managing Secrets

Managing confidential settings data using Secrets.

6.1 - Managing Secrets using kubectl

Creating Secret objects using kubectl command line.

This page shows you how to create, edit, manage, and delete Kubernetes Secrets using the kubectl command-line tool.

Before you begin

Create a Secret

A Secret object stores sensitive data such as credentials used by Pods to access services. For example, you might need a Secret to store the username and password needed to access a database.

You can create the Secret by passing the raw data in the command, or by storing the credentials in files that you pass in the command. The following commands create a Secret that stores the username admin and the password S!B\*d$zDsb=.

Use raw data

Run the following command:

kubectl create secret generic db-user-pass \
    --from-literal=username=admin \
    --from-literal=password='S!B\*d$zDsb='

You must use single quotes '' to escape special characters such as $, \, *, =, and ! in your strings. If you don't, your shell will interpret these characters.

Note:

The stringData field for a Secret does not work well with server-side apply.

Use source files

Store the credentials in files:
```
echo -n 'admin' > ./username.txt
echo -n 'S!B\*d$zDsb=' > ./password.txt
```
The -n flag ensures that the generated files do not have an extra newline character at the end of the text. This is important because when kubectl reads a file and encodes the content into a base64 string, the extra newline character gets encoded too. You do not need to escape special characters in strings that you include in a file.

Pass the file paths in the kubectl command:

kubectl create secret generic db-user-pass \
    --from-file=./username.txt \
    --from-file=./password.txt

The default key name is the file name. You can optionally set the key name using --from-file=[key=]source. For example:

kubectl create secret generic db-user-pass \
    --from-file=username=./username.txt \
    --from-file=password=./password.txt

With either method, the output is similar to:

secret/db-user-pass created

Verify the Secret

Check that the Secret was created:

kubectl get secrets

The output is similar to:

NAME              TYPE       DATA      AGE
db-user-pass      Opaque     2         51s

View the details of the Secret:

kubectl describe secret db-user-pass

The output is similar to:

Name:            db-user-pass
Namespace:       default
Labels:          <none>
Annotations:     <none>

Type:            Opaque

Data
====
password:    12 bytes
username:    5 bytes

The commands kubectl get and kubectl describe avoid showing the contents of a Secret by default. This is to protect the Secret from being exposed accidentally, or from being stored in a terminal log.

Decode the Secret

View the contents of the Secret you created:

kubectl get secret db-user-pass -o jsonpath='{.data}'

The output is similar to:

{ "password": "UyFCXCpkJHpEc2I9", "username": "YWRtaW4=" }

Decode the password data:
```
echo 'UyFCXCpkJHpEc2I9' | base64 --decode
```
The output is similar to:
```
S!B\*d$zDsb=
```
Caution:
This is an example for documentation purposes. In practice, this method could cause the command with the encoded data to be stored in your shell history. Anyone with access to your computer could find the command and decode the secret. A better approach is to combine the view and decode commands.
```
kubectl get secret db-user-pass -o jsonpath='{.data.password}' | base64 --decode
```

Edit a Secret

You can edit an existing Secret object unless it is immutable. To edit a Secret, run the following command:

kubectl edit secrets <secret-name>

This opens your default editor and allows you to update the base64 encoded Secret values in the data field, such as in the following example:

# Please edit the object below. Lines beginning with a '#' will be ignored,
# and an empty file will abort the edit. If an error occurs while saving this file, it will be
# reopened with the relevant failures.
#
apiVersion: v1
data:
  password: UyFCXCpkJHpEc2I9
  username: YWRtaW4=
kind: Secret
metadata:
  creationTimestamp: "2022-06-28T17:44:13Z"
  name: db-user-pass
  namespace: default
  resourceVersion: "12708504"
  uid: 91becd59-78fa-4c85-823f-6d44436242ac
type: Opaque

Clean up

To delete a Secret, run the following command:

kubectl delete secret db-user-pass

What's next

Read more about the Secret concept
Learn how to manage Secrets using config file
Learn how to manage Secrets using kustomize

6.2 - Managing Secrets using Configuration File

Creating Secret objects using resource configuration file.

Before you begin

Create the Secret

You can define the Secret object in a manifest first, in JSON or YAML format, and then create that object. The Secret resource contains two maps: data and stringData. The data field is used to store arbitrary data, encoded using base64. The stringData field is provided for convenience, and it allows you to provide the same data as unencoded strings. The keys of data and stringData must consist of alphanumeric characters, -, _ or ..

The following example stores two strings in a Secret using the data field.

Convert the strings to base64:
```
echo -n 'admin' | base64
echo -n '1f2d1e2e67df' | base64
```
Note:
The serialized JSON and YAML values of Secret data are encoded as base64 strings. Newlines are not valid within these strings and must be omitted. When using the base64 utility on Darwin/macOS, users should avoid using the -b option to split long lines. Conversely, Linux users should add the option -w 0 to base64 commands or the pipeline base64 | tr -d '\n' if the -w option is not available.

The output is similar to:
```
YWRtaW4=
MWYyZDFlMmU2N2Rm
```

Create the manifest:

apiVersion: v1
kind: Secret
metadata:
  name: mysecret
type: Opaque
data:
  username: YWRtaW4=
  password: MWYyZDFlMmU2N2Rm

Note that the name of a Secret object must be a valid DNS subdomain name.

Create the Secret using kubectl apply:
```
kubectl apply -f ./secret.yaml
```
The output is similar to:
```
secret/mysecret created
```

To verify that the Secret was created and to decode the Secret data, refer to Managing Secrets using kubectl.

Specify unencoded data when creating a Secret

For certain scenarios, you may wish to use the stringData field instead. This field allows you to put a non-base64 encoded string directly into the Secret, and the string will be encoded for you when the Secret is created or updated.

A practical example of this might be where you are deploying an application that uses a Secret to store a configuration file, and you want to populate parts of that configuration file during your deployment process.

For example, if your application uses the following configuration file:

apiUrl: "https://my.api.com/api/v1"
username: "<user>"
password: "<password>"

You could store this in a Secret using the following definition:

apiVersion: v1
kind: Secret
metadata:
  name: mysecret
type: Opaque
stringData:
  config.yaml: |
    apiUrl: "https://my.api.com/api/v1"
    username: <user>
    password: <password>

Note:

The stringData field for a Secret does not work well with server-side apply.

When you retrieve the Secret data, the command returns the encoded values, and not the plaintext values you provided in stringData.

For example, if you run the following command:

kubectl get secret mysecret -o yaml

The output is similar to:

apiVersion: v1
data:
  config.yaml: YXBpVXJsOiAiaHR0cHM6Ly9teS5hcGkuY29tL2FwaS92MSIKdXNlcm5hbWU6IHt7dXNlcm5hbWV9fQpwYXNzd29yZDoge3twYXNzd29yZH19
kind: Secret
metadata:
  creationTimestamp: 2018-11-15T20:40:59Z
  name: mysecret
  namespace: default
  resourceVersion: "7225"
  uid: c280ad2e-e916-11e8-98f2-025000000001
type: Opaque

Specify both `data` and `stringData`

If you specify a field in both data and stringData, the value from stringData is used.

For example, if you define the following Secret:

apiVersion: v1
kind: Secret
metadata:
  name: mysecret
type: Opaque
data:
  username: YWRtaW4=
stringData:
  username: administrator

Note:

The stringData field for a Secret does not work well with server-side apply.

The Secret object is created as follows:

apiVersion: v1
data:
  username: YWRtaW5pc3RyYXRvcg==
kind: Secret
metadata:
  creationTimestamp: 2018-11-15T20:46:46Z
  name: mysecret
  namespace: default
  resourceVersion: "7579"
  uid: 91460ecb-e917-11e8-98f2-025000000001
type: Opaque

YWRtaW5pc3RyYXRvcg== decodes to administrator.

Edit a Secret

To edit the data in the Secret you created using a manifest, modify the data or stringData field in your manifest and apply the file to your cluster. You can edit an existing Secret object unless it is immutable.

For example, if you want to change the password from the previous example to birdsarentreal, do the following:

Encode the new password string:

echo -n 'birdsarentreal' | base64

The output is similar to:

YmlyZHNhcmVudHJlYWw=

Update the data field with your new password string:

apiVersion: v1
kind: Secret
metadata:
  name: mysecret
type: Opaque
data:
  username: YWRtaW4=
  password: YmlyZHNhcmVudHJlYWw=

Apply the manifest to your cluster:

kubectl apply -f ./secret.yaml

The output is similar to:

secret/mysecret configured

Kubernetes updates the existing Secret object. In detail, the kubectl tool notices that there is an existing Secret object with the same name. kubectl fetches the existing object, plans changes to it, and submits the changed Secret object to your cluster control plane.

If you specified kubectl apply --server-side instead, kubectl uses Server Side Apply instead.

Clean up

To delete the Secret you have created:

kubectl delete secret mysecret

What's next

Read more about the Secret concept
Learn how to manage Secrets using kubectl
Learn how to manage Secrets using kustomize

6.3 - Managing Secrets using Kustomize

Creating Secret objects using kustomization.yaml file.

kubectl supports using the Kustomize object management tool to manage Secrets and ConfigMaps. You create a resource generator using Kustomize, which generates a Secret that you can apply to the API server using kubectl.

Before you begin

Create a Secret

You can generate a Secret by defining a secretGenerator in a kustomization.yaml file that references other existing files, .env files, or literal values. For example, the following instructions create a kustomization file for the username admin and the password 1f2d1e2e67df.

Note:

The stringData field for a Secret does not work well with server-side apply.

Create the kustomization file


secretGenerator:
- name: database-creds
  literals:
  - username=admin
  - password=1f2d1e2e67df

Store the credentials in files. The filenames are the keys of the secret:
```
echo -n 'admin' > ./username.txt
echo -n '1f2d1e2e67df' > ./password.txt
```
The -n flag ensures that there's no newline character at the end of your files.

Create the kustomization.yaml file:

secretGenerator:
- name: database-creds
  files:
  - username.txt
  - password.txt

You can also define the secretGenerator in the kustomization.yaml file by providing .env files. For example, the following kustomization.yaml file pulls in data from an .env.secret file:

secretGenerator:
- name: db-user-pass
  envs:
  - .env.secret

In all cases, you don't need to encode the values in base64. The name of the YAML file must be kustomization.yaml or kustomization.yml.

Apply the kustomization file

To create the Secret, apply the directory that contains the kustomization file:

kubectl apply -k <directory-path>

The output is similar to:

secret/database-creds-5hdh7hhgfk created

When a Secret is generated, the Secret name is created by hashing the Secret data and appending the hash value to the name. This ensures that a new Secret is generated each time the data is modified.

To verify that the Secret was created and to decode the Secret data,

kubectl get -k <directory-path> -o jsonpath='{.data}'

The output is similar to:

{ "password": "MWYyZDFlMmU2N2Rm", "username": "YWRtaW4=" }

echo 'MWYyZDFlMmU2N2Rm' | base64 --decode

The output is similar to:

1f2d1e2e67df

For more information, refer to Managing Secrets using kubectl and Declarative Management of Kubernetes Objects Using Kustomize.

Edit a Secret

In your kustomization.yaml file, modify the data, such as the password.
Apply the directory that contains the kustomization file:
```
kubectl apply -k <directory-path>
```
The output is similar to:
```
secret/db-user-pass-6f24b56cc8 created
```

The edited Secret is created as a new Secret object, instead of updating the existing Secret object. You might need to update references to the Secret in your Pods.

Clean up

To delete a Secret, use kubectl:

kubectl delete secret db-user-pass

What's next

Read more about the Secret concept
Learn how to manage Secrets using kubectl
Learn how to manage Secrets using config file

7 - Inject Data Into Applications

Specify configuration and other data for the Pods that run your workload.

7.1 - Define a Command and Arguments for a Container

This page shows how to define commands and arguments when you run a container in a Pod.

Before you begin

To check the version, enter kubectl version.

Define a command and arguments when you create a Pod

When you create a Pod, you can define a command and arguments for the containers that run in the Pod. To define a command, include the command field in the configuration file. To define arguments for the command, include the args field in the configuration file. The command and arguments that you define cannot be changed after the Pod is created.

The command and arguments that you define in the configuration file override the default command and arguments provided by the container image. If you define args, but do not define a command, the default command is used with your new arguments.

Note:

The command field corresponds to ENTRYPOINT, and the args field corresponds to CMD in some container runtimes.

In this exercise, you create a Pod that runs one container. The configuration file for the Pod defines a command and two arguments:

pods/commands.yaml

apiVersion: v1
kind: Pod
metadata:
  name: command-demo
  labels:
    purpose: demonstrate-command
spec:
  containers:
  - name: command-demo-container
    image: debian
    command: ["printenv"]
    args: ["HOSTNAME", "KUBERNETES_PORT"]
  restartPolicy: OnFailure

Create a Pod based on the YAML configuration file:

kubectl apply -f https://k8s.io/examples/pods/commands.yaml

List the running Pods:
```
kubectl get pods
```
The output shows that the container that ran in the command-demo Pod has completed.
To see the output of the command that ran in the container, view the logs from the Pod:
```
kubectl logs command-demo
```
The output shows the values of the HOSTNAME and KUBERNETES_PORT environment variables:
```
command-demo
tcp://10.3.240.1:443
```

Use environment variables to define arguments

In the preceding example, you defined the arguments directly by providing strings. As an alternative to providing strings directly, you can define arguments by using environment variables:

env:
- name: MESSAGE
  value: "hello world"
command: ["/bin/echo"]
args: ["$(MESSAGE)"]

This means you can define an argument for a Pod using any of the techniques available for defining environment variables, including ConfigMaps and Secrets.

Note:

The environment variable appears in parentheses, "$(VAR)". This is required for the variable to be expanded in the command or args field.

Run a command in a shell

In some cases, you need your command to run in a shell. For example, your command might consist of several commands piped together, or it might be a shell script. To run your command in a shell, wrap it like this:

command: ["/bin/sh"]
args: ["-c", "while true; do echo hello; sleep 10;done"]

What's next

Learn more about configuring pods and containers.
Learn more about running commands in a container.
See Container.

7.2 - Define Dependent Environment Variables

This page shows how to define dependent environment variables for a container in a Kubernetes Pod.

Before you begin

Define an environment dependent variable for a container

When you create a Pod, you can set dependent environment variables for the containers that run in the Pod. To set dependent environment variables, you can use $(VAR_NAME) in the value of env in the configuration file.

In this exercise, you create a Pod that runs one container. The configuration file for the Pod defines a dependent environment variable with common usage defined. Here is the configuration manifest for the Pod:

pods/inject/dependent-envars.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dependent-envars-demo
spec:
  containers:
    - name: dependent-envars-demo
      args:
        - while true; do echo -en '\n'; printf UNCHANGED_REFERENCE=$UNCHANGED_REFERENCE'\n'; printf SERVICE_ADDRESS=$SERVICE_ADDRESS'\n';printf ESCAPED_REFERENCE=$ESCAPED_REFERENCE'\n'; sleep 30; done;
      command:
        - sh
        - -c
      image: busybox:1.28
      env:
        - name: SERVICE_PORT
          value: "80"
        - name: SERVICE_IP
          value: "172.17.0.1"
        - name: UNCHANGED_REFERENCE
          value: "$(PROTOCOL)://$(SERVICE_IP):$(SERVICE_PORT)"
        - name: PROTOCOL
          value: "https"
        - name: SERVICE_ADDRESS
          value: "$(PROTOCOL)://$(SERVICE_IP):$(SERVICE_PORT)"
        - name: ESCAPED_REFERENCE
          value: "$$(PROTOCOL)://$(SERVICE_IP):$(SERVICE_PORT)"

Create a Pod based on that manifest:

kubectl apply -f https://k8s.io/examples/pods/inject/dependent-envars.yaml

pod/dependent-envars-demo created

List the running Pods:

kubectl get pods dependent-envars-demo

NAME                      READY     STATUS    RESTARTS   AGE
dependent-envars-demo     1/1       Running   0          9s

Check the logs for the container running in your Pod:

kubectl logs pod/dependent-envars-demo


UNCHANGED_REFERENCE=$(PROTOCOL)://172.17.0.1:80
SERVICE_ADDRESS=https://172.17.0.1:80
ESCAPED_REFERENCE=$(PROTOCOL)://172.17.0.1:80

As shown above, you have defined the correct dependency reference of SERVICE_ADDRESS, bad dependency reference of UNCHANGED_REFERENCE and skip dependent references of ESCAPED_REFERENCE.

When an environment variable is already defined when being referenced, the reference can be correctly resolved, such as in the SERVICE_ADDRESS case.

Note that order matters in the env list. An environment variable is not considered "defined" if it is specified further down the list. That is why UNCHANGED_REFERENCE fails to resolve $(PROTOCOL) in the example above.

When the environment variable is undefined or only includes some variables, the undefined environment variable is treated as a normal string, such as UNCHANGED_REFERENCE. Note that incorrectly parsed environment variables, in general, will not block the container from starting.

The $(VAR_NAME) syntax can be escaped with a double $, ie: $$(VAR_NAME). Escaped references are never expanded, regardless of whether the referenced variable is defined or not. This can be seen from the ESCAPED_REFERENCE case above.

What's next

Learn more about environment variables.
See EnvVarSource.

7.3 - Define Environment Variables for a Container

This page shows how to define environment variables for a container in a Kubernetes Pod.

Before you begin

Define an environment variable for a container

When you create a Pod, you can set environment variables for the containers that run in the Pod. To set environment variables, include the env or envFrom field in the configuration file.

The env and envFrom fields have different effects.

env: allows you to set environment variables for a container, specifying a value directly for each variable that you name.
envFrom: allows you to set environment variables for a container by referencing either a ConfigMap or a Secret. When you use envFrom, all the key-value pairs in the referenced ConfigMap or Secret are set as environment variables for the container. You can also specify a common prefix string.

You can read more about ConfigMap and Secret.

This page explains how to use env.

In this exercise, you create a Pod that runs one container. The configuration file for the Pod defines an environment variable with name DEMO_GREETING and value "Hello from the environment". Here is the configuration manifest for the Pod:

pods/inject/envars.yaml

apiVersion: v1
kind: Pod
metadata:
  name: envar-demo
  labels:
    purpose: demonstrate-envars
spec:
  containers:
  - name: envar-demo-container
    image: gcr.io/google-samples/hello-app:2.0
    env:
    - name: DEMO_GREETING
      value: "Hello from the environment"
    - name: DEMO_FAREWELL
      value: "Such a sweet sorrow"

Create a Pod based on that manifest:

kubectl apply -f https://k8s.io/examples/pods/inject/envars.yaml

List the running Pods:

kubectl get pods -l purpose=demonstrate-envars

The output is similar to:

NAME            READY     STATUS    RESTARTS   AGE
envar-demo      1/1       Running   0          9s

List the Pod's container environment variables:

kubectl exec envar-demo -- printenv

The output is similar to this:

NODE_VERSION=4.4.2
EXAMPLE_SERVICE_PORT_8080_TCP_ADDR=10.3.245.237
HOSTNAME=envar-demo
...
DEMO_GREETING=Hello from the environment
DEMO_FAREWELL=Such a sweet sorrow

Note:

The environment variables set using the env or envFrom field override any environment variables specified in the container image.

Note:

Environment variables may reference each other, however ordering is important. Variables making use of others defined in the same context must come later in the list. Similarly, avoid circular references.

Using environment variables inside of your config

Environment variables that you define in a Pod's configuration under .spec.containers[*].env[*] can be used elsewhere in the configuration, for example in commands and arguments that you set for the Pod's containers. In the example configuration below, the GREETING, HONORIFIC, and NAME environment variables are set to Warm greetings to, The Most Honorable, and Kubernetes, respectively. The environment variable MESSAGE combines the set of all these environment variables and then uses it as a CLI argument passed to the env-print-demo container.

Environment variable names consist of letters, numbers, underscores, dots, or hyphens, but the first character cannot be a digit. If the RelaxedEnvironmentVariableValidation feature gate is enabled, all printable ASCII characters except "=" may be used for environment variable names.

apiVersion: v1
kind: Pod
metadata:
  name: print-greeting
spec:
  containers:
  - name: env-print-demo
    image: bash
    env:
    - name: GREETING
      value: "Warm greetings to"
    - name: HONORIFIC
      value: "The Most Honorable"
    - name: NAME
      value: "Kubernetes"
    - name: MESSAGE
      value: "$(GREETING) $(HONORIFIC) $(NAME)"
    command: ["echo"]
    args: ["$(MESSAGE)"]

Upon creation, the command echo Warm greetings to The Most Honorable Kubernetes is run on the container.

What's next

Learn more about environment variables.
Learn about using secrets as environment variables.
See EnvVarSource.

7.4 - Expose Pod Information to Containers Through Environment Variables

This page shows how a Pod can use environment variables to expose information about itself to containers running in the Pod, using the downward API. You can use environment variables to expose Pod fields, container fields, or both.

In Kubernetes, there are two ways to expose Pod and container fields to a running container:

Environment variables, as explained in this task
Volume files

Together, these two ways of exposing Pod and container fields are called the downward API.

As Services are the primary mode of communication between containerized applications managed by Kubernetes, it is helpful to be able to discover them at runtime.

Read more about accessing Services here.

Before you begin

Use Pod fields as values for environment variables

In this part of exercise, you create a Pod that has one container, and you project Pod-level fields into the running container as environment variables.

pods/inject/dapi-envars-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dapi-envars-fieldref
spec:
  containers:
    - name: test-container
      image: registry.k8s.io/busybox
      command: [ "sh", "-c"]
      args:
      - while true; do
          echo -en '\n';
          printenv MY_NODE_NAME MY_POD_NAME MY_POD_NAMESPACE;
          printenv MY_POD_IP MY_POD_SERVICE_ACCOUNT;
          sleep 10;
        done;
      env:
        - name: MY_NODE_NAME
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName
        - name: MY_POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: MY_POD_NAMESPACE
          valueFrom:
            fieldRef:
              fieldPath: metadata.namespace
        - name: MY_POD_IP
          valueFrom:
            fieldRef:
              fieldPath: status.podIP
        - name: MY_POD_SERVICE_ACCOUNT
          valueFrom:
            fieldRef:
              fieldPath: spec.serviceAccountName
  restartPolicy: Never

In that manifest, you can see five environment variables. The env field is an array of environment variable definitions. The first element in the array specifies that the MY_NODE_NAME environment variable gets its value from the Pod's spec.nodeName field. Similarly, the other environment variables get their names from Pod fields.

Note:

The fields in this example are Pod fields. They are not fields of the container in the Pod.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/inject/dapi-envars-pod.yaml

Verify that the container in the Pod is running:

# If the new Pod isn't yet healthy, rerun this command a few times.
kubectl get pods

View the container's logs:

kubectl logs dapi-envars-fieldref

The output shows the values of selected environment variables:

minikube
dapi-envars-fieldref
default
172.17.0.4
default

To see why these values are in the log, look at the command and args fields in the configuration file. When the container starts, it writes the values of five environment variables to stdout. It repeats this every ten seconds.

Next, get a shell into the container that is running in your Pod:

kubectl exec -it dapi-envars-fieldref -- sh

In your shell, view the environment variables:

# Run this in a shell inside the container
printenv

The output shows that certain environment variables have been assigned the values of Pod fields:

MY_POD_SERVICE_ACCOUNT=default
...
MY_POD_NAMESPACE=default
MY_POD_IP=172.17.0.4
...
MY_NODE_NAME=minikube
...
MY_POD_NAME=dapi-envars-fieldref

Use container fields as values for environment variables

In the preceding exercise, you used information from Pod-level fields as the values for environment variables. In this next exercise, you are going to pass fields that are part of the Pod definition, but taken from the specific container rather than from the Pod overall.

Here is a manifest for another Pod that again has just one container:

pods/inject/dapi-envars-container.yaml

apiVersion: v1
kind: Pod
metadata:
  name: dapi-envars-resourcefieldref
spec:
  containers:
    - name: test-container
      image: registry.k8s.io/busybox:1.24
      command: [ "sh", "-c"]
      args:
      - while true; do
          echo -en '\n';
          printenv MY_CPU_REQUEST MY_CPU_LIMIT;
          printenv MY_MEM_REQUEST MY_MEM_LIMIT;
          sleep 10;
        done;
      resources:
        requests:
          memory: "32Mi"
          cpu: "125m"
        limits:
          memory: "64Mi"
          cpu: "250m"
      env:
        - name: MY_CPU_REQUEST
          valueFrom:
            resourceFieldRef:
              containerName: test-container
              resource: requests.cpu
        - name: MY_CPU_LIMIT
          valueFrom:
            resourceFieldRef:
              containerName: test-container
              resource: limits.cpu
        - name: MY_MEM_REQUEST
          valueFrom:
            resourceFieldRef:
              containerName: test-container
              resource: requests.memory
        - name: MY_MEM_LIMIT
          valueFrom:
            resourceFieldRef:
              containerName: test-container
              resource: limits.memory
  restartPolicy: Never

In this manifest, you can see four environment variables. The env field is an array of environment variable definitions. The first element in the array specifies that the MY_CPU_REQUEST environment variable gets its value from the requests.cpu field of a container named test-container. Similarly, the other environment variables get their values from fields that are specific to this container.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/inject/dapi-envars-container.yaml

Verify that the container in the Pod is running:

# If the new Pod isn't yet healthy, rerun this command a few times.
kubectl get pods

View the container's logs:

kubectl logs dapi-envars-resourcefieldref

The output shows the values of selected environment variables:

What's next

Read Defining Environment Variables for a Container
Read the spec API definition for Pod. This includes the definition of Container (part of Pod).
Read the list of available fields that you can expose using the downward API.

Read about Pods, containers and environment variables in the legacy API reference:

7.5 - Expose Pod Information to Containers Through Files

This page shows how a Pod can use a downwardAPI volume, to expose information about itself to containers running in the Pod. A downwardAPI volume can expose Pod fields and container fields.

In Kubernetes, there are two ways to expose Pod and container fields to a running container:

Environment variables
Volume files, as explained in this task

Together, these two ways of exposing Pod and container fields are called the downward API.

Before you begin

Store Pod fields

In this part of exercise, you create a Pod that has one container, and you project Pod-level fields into the running container as files. Here is the manifest for the Pod:

pods/inject/dapi-volume.yaml

apiVersion: v1
kind: Pod
metadata:
  name: kubernetes-downwardapi-volume-example
  labels:
    zone: us-est-coast
    cluster: test-cluster1
    rack: rack-22
  annotations:
    build: two
    builder: john-doe
spec:
  containers:
    - name: client-container
      image: registry.k8s.io/busybox
      command: ["sh", "-c"]
      args:
      - while true; do
          if [[ -e /etc/podinfo/labels ]]; then
            echo -en '\n\n'; cat /etc/podinfo/labels; fi;
          if [[ -e /etc/podinfo/annotations ]]; then
            echo -en '\n\n'; cat /etc/podinfo/annotations; fi;
          sleep 5;
        done;
      volumeMounts:
        - name: podinfo
          mountPath: /etc/podinfo
  volumes:
    - name: podinfo
      downwardAPI:
        items:
          - path: "labels"
            fieldRef:
              fieldPath: metadata.labels
          - path: "annotations"
            fieldRef:
              fieldPath: metadata.annotations

In the manifest, you can see that the Pod has a downwardAPI Volume, and the container mounts the volume at /etc/podinfo.

Look at the items array under downwardAPI. Each element of the array defines a downwardAPI volume. The first element specifies that the value of the Pod's metadata.labels field should be stored in a file named labels. The second element specifies that the value of the Pod's annotations field should be stored in a file named annotations.

Note:

The fields in this example are Pod fields. They are not fields of the container in the Pod.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/inject/dapi-volume.yaml

Verify that the container in the Pod is running:

kubectl get pods

View the container's logs:

kubectl logs kubernetes-downwardapi-volume-example

The output shows the contents of the labels file and the annotations file:

cluster="test-cluster1"
rack="rack-22"
zone="us-est-coast"

build="two"
builder="john-doe"

Get a shell into the container that is running in your Pod:

kubectl exec -it kubernetes-downwardapi-volume-example -- sh

In your shell, view the labels file:

/# cat /etc/podinfo/labels

The output shows that all of the Pod's labels have been written to the labels file:

cluster="test-cluster1"
rack="rack-22"
zone="us-est-coast"

Similarly, view the annotations file:

/# cat /etc/podinfo/annotations

View the files in the /etc/podinfo directory:

/# ls -laR /etc/podinfo

In the output, you can see that the labels and annotations files are in a temporary subdirectory: in this example, ..2982_06_02_21_47_53.299460680. In the /etc/podinfo directory, ..data is a symbolic link to the temporary subdirectory. Also in the /etc/podinfo directory, labels and annotations are symbolic links.

drwxr-xr-x  ... Feb 6 21:47 ..2982_06_02_21_47_53.299460680
lrwxrwxrwx  ... Feb 6 21:47 ..data -> ..2982_06_02_21_47_53.299460680
lrwxrwxrwx  ... Feb 6 21:47 annotations -> ..data/annotations
lrwxrwxrwx  ... Feb 6 21:47 labels -> ..data/labels

/etc/..2982_06_02_21_47_53.299460680:
total 8
-rw-r--r--  ... Feb  6 21:47 annotations
-rw-r--r--  ... Feb  6 21:47 labels

Using symbolic links enables dynamic atomic refresh of the metadata; updates are written to a new temporary directory, and the ..data symlink is updated atomically using rename(2).

Note:

A container using Downward API as a subPath volume mount will not receive Downward API updates.

Exit the shell:

/# exit

Store container fields

The preceding exercise, you made Pod-level fields accessible using the downward API. In this next exercise, you are going to pass fields that are part of the Pod definition, but taken from the specific container rather than from the Pod overall. Here is a manifest for a Pod that again has just one container:

pods/inject/dapi-volume-resources.yaml

apiVersion: v1
kind: Pod
metadata:
  name: kubernetes-downwardapi-volume-example-2
spec:
  containers:
    - name: client-container
      image: registry.k8s.io/busybox:1.24
      command: ["sh", "-c"]
      args:
      - while true; do
          echo -en '\n';
          if [[ -e /etc/podinfo/cpu_limit ]]; then
            echo -en '\n'; cat /etc/podinfo/cpu_limit; fi;
          if [[ -e /etc/podinfo/cpu_request ]]; then
            echo -en '\n'; cat /etc/podinfo/cpu_request; fi;
          if [[ -e /etc/podinfo/mem_limit ]]; then
            echo -en '\n'; cat /etc/podinfo/mem_limit; fi;
          if [[ -e /etc/podinfo/mem_request ]]; then
            echo -en '\n'; cat /etc/podinfo/mem_request; fi;
          sleep 5;
        done;
      resources:
        requests:
          memory: "32Mi"
          cpu: "125m"
        limits:
          memory: "64Mi"
          cpu: "250m"
      volumeMounts:
        - name: podinfo
          mountPath: /etc/podinfo
  volumes:
    - name: podinfo
      downwardAPI:
        items:
          - path: "cpu_limit"
            resourceFieldRef:
              containerName: client-container
              resource: limits.cpu
              divisor: 1m
          - path: "cpu_request"
            resourceFieldRef:
              containerName: client-container
              resource: requests.cpu
              divisor: 1m
          - path: "mem_limit"
            resourceFieldRef:
              containerName: client-container
              resource: limits.memory
              divisor: 1Mi
          - path: "mem_request"
            resourceFieldRef:
              containerName: client-container
              resource: requests.memory
              divisor: 1Mi

In the manifest, you can see that the Pod has a downwardAPI volume, and that the single container in that Pod mounts the volume at /etc/podinfo.

Look at the items array under downwardAPI. Each element of the array defines a file in the downward API volume.

The first element specifies that in the container named client-container, the value of the limits.cpu field in the format specified by 1m should be published as a file named cpu_limit. The divisor field is optional and has the default value of 1. A divisor of 1 means cores for cpu resources, or bytes for memory resources.

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/inject/dapi-volume-resources.yaml

Get a shell into the container that is running in your Pod:

kubectl exec -it kubernetes-downwardapi-volume-example-2 -- sh

In your shell, view the cpu_limit file:

# Run this in a shell inside the container
cat /etc/podinfo/cpu_limit

You can use similar commands to view the cpu_request, mem_limit and mem_request files.

Project keys to specific paths and file permissions

You can project keys to specific paths and specific permissions on a per-file basis. For more information, see Secrets.

What's next

Read the spec API definition for Pod. This includes the definition of Container (part of Pod).
Read the list of available fields that you can expose using the downward API.

Read about volumes in the legacy API reference:

Check the Volume API definition which defines a generic volume in a Pod for containers to access.
Check the DownwardAPIVolumeSource API definition which defines a volume that contains Downward API information.
Check the DownwardAPIVolumeFile API definition which contains references to object or resource fields for populating a file in the Downward API volume.
Check the ResourceFieldSelector API definition which specifies the container resources and their output format.

7.6 - Distribute Credentials Securely Using Secrets

This page shows how to securely inject sensitive data, such as passwords and encryption keys, into Pods.

Before you begin

Convert your secret data to a base-64 representation

Suppose you want to have two pieces of secret data: a username my-app and a password 39528$vdg7Jb. First, use a base64 encoding tool to convert your username and password to a base64 representation. Here's an example using the commonly available base64 program:

echo -n 'my-app' | base64
echo -n '39528$vdg7Jb' | base64

The output shows that the base-64 representation of your username is bXktYXBw, and the base-64 representation of your password is Mzk1MjgkdmRnN0pi.

Caution:

Use a local tool trusted by your OS to decrease the security risks of external tools.

Create a Secret

Here is a configuration file you can use to create a Secret that holds your username and password:

pods/inject/secret.yaml

apiVersion: v1
kind: Secret
metadata:
  name: test-secret
data:
  username: bXktYXBw
  password: Mzk1MjgkdmRnN0pi

Create the Secret

kubectl apply -f https://k8s.io/examples/pods/inject/secret.yaml

View information about the Secret:

kubectl get secret test-secret

Output:

NAME          TYPE      DATA      AGE
test-secret   Opaque    2         1m

View more detailed information about the Secret:

kubectl describe secret test-secret

Output:

Name:       test-secret
Namespace:  default
Labels:     <none>
Annotations:    <none>

Type:   Opaque

Data
====
password:   13 bytes
username:   7 bytes

Create a Secret directly with kubectl

If you want to skip the Base64 encoding step, you can create the same Secret using the kubectl create secret command. For example:

kubectl create secret generic test-secret --from-literal='username=my-app' --from-literal='password=39528$vdg7Jb'

This is more convenient. The detailed approach shown earlier runs through each step explicitly to demonstrate what is happening.

Create a Pod that has access to the secret data through a Volume

Here is a configuration file you can use to create a Pod:

pods/inject/secret-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: secret-test-pod
spec:
  containers:
    - name: test-container
      image: nginx
      volumeMounts:
        # name must match the volume name below
        - name: secret-volume
          mountPath: /etc/secret-volume
          readOnly: true
  # The secret data is exposed to Containers in the Pod through a Volume.
  volumes:
    - name: secret-volume
      secret:
        secretName: test-secret

Create the Pod:

kubectl apply -f https://k8s.io/examples/pods/inject/secret-pod.yaml

Verify that your Pod is running:

kubectl get pod secret-test-pod

Output:

NAME              READY     STATUS    RESTARTS   AGE
secret-test-pod   1/1       Running   0          42m

Get a shell into the Container that is running in your Pod:
```
kubectl exec -i -t secret-test-pod -- /bin/bash
```
The secret data is exposed to the Container through a Volume mounted under /etc/secret-volume.

In your shell, list the files in the /etc/secret-volume directory:
```
# Run this in the shell inside the container
ls /etc/secret-volume
```
The output shows two files, one for each piece of secret data:
```
password username
```

In your shell, display the contents of the username and password files:

# Run this in the shell inside the container
echo "$( cat /etc/secret-volume/username )"
echo "$( cat /etc/secret-volume/password )"

The output is your username and password:

my-app
39528$vdg7Jb

Modify your image or command line so that the program looks for files in the mountPath directory. Each key in the Secret data map becomes a file name in this directory.

Project Secret keys to specific file paths

You can also control the paths within the volume where Secret keys are projected. Use the .spec.volumes[].secret.items field to change the target path of each key:

apiVersion: v1
kind: Pod
metadata:
  name: mypod
spec:
  containers:
  - name: mypod
    image: redis
    volumeMounts:
    - name: foo
      mountPath: "/etc/foo"
      readOnly: true
  volumes:
  - name: foo
    secret:
      secretName: mysecret
      items:
      - key: username
        path: my-group/my-username

When you deploy this Pod, the following happens:

The username key from mysecret is available to the container at the path /etc/foo/my-group/my-username instead of at /etc/foo/username.
The password key from that Secret object is not projected.

If you list keys explicitly using .spec.volumes[].secret.items, consider the following:

Only keys specified in items are projected.
To consume all keys from the Secret, all of them must be listed in the items field.
All listed keys must exist in the corresponding Secret. Otherwise, the volume is not created.

Set POSIX permissions for Secret keys

You can set the POSIX file access permission bits for a single Secret key. If you don't specify any permissions, 0644 is used by default. You can also set a default POSIX file mode for the entire Secret volume, and you can override per key if needed.

For example, you can specify a default mode like this:

apiVersion: v1
kind: Pod
metadata:
  name: mypod
spec:
  containers:
  - name: mypod
    image: redis
    volumeMounts:
    - name: foo
      mountPath: "/etc/foo"
  volumes:
  - name: foo
    secret:
      secretName: mysecret
      defaultMode: 0400

The Secret is mounted on /etc/foo; all the files created by the secret volume mount have permission 0400.

Note:

If you're defining a Pod or a Pod template using JSON, beware that the JSON specification doesn't support octal literals for numbers because JSON considers 0400 to be the decimal value 400. In JSON, use decimal values for the defaultMode instead. If you're writing YAML, you can write the defaultMode in octal.

Define container environment variables using Secret data

You can consume the data in Secrets as environment variables in your containers.

If a container already consumes a Secret in an environment variable, a Secret update will not be seen by the container unless it is restarted. There are third party solutions for triggering restarts when secrets change.

Define a container environment variable with data from a single Secret

Define an environment variable as a key-value pair in a Secret:

kubectl create secret generic backend-user --from-literal=backend-username='backend-admin'

Assign the backend-username value defined in the Secret to the SECRET_USERNAME environment variable in the Pod specification.

pods/inject/pod-single-secret-env-variable.yaml

apiVersion: v1
kind: Pod
metadata:
  name: env-single-secret
spec:
  containers:
  - name: envars-test-container
    image: nginx
    env:
    - name: SECRET_USERNAME
      valueFrom:
        secretKeyRef:
          name: backend-user
          key: backend-username

Create the Pod:

kubectl create -f https://k8s.io/examples/pods/inject/pod-single-secret-env-variable.yaml

In your shell, display the content of SECRET_USERNAME container environment variable.
```
kubectl exec -i -t env-single-secret -- /bin/sh -c 'echo $SECRET_USERNAME'
```
The output is similar to:
```
backend-admin
```

Define container environment variables with data from multiple Secrets

As with the previous example, create the Secrets first.

kubectl create secret generic backend-user --from-literal=backend-username='backend-admin'
kubectl create secret generic db-user --from-literal=db-username='db-admin'

Define the environment variables in the Pod specification.

pods/inject/pod-multiple-secret-env-variable.yaml

apiVersion: v1
kind: Pod
metadata:
  name: envvars-multiple-secrets
spec:
  containers:
  - name: envars-test-container
    image: nginx
    env:
    - name: BACKEND_USERNAME
      valueFrom:
        secretKeyRef:
          name: backend-user
          key: backend-username
    - name: DB_USERNAME
      valueFrom:
        secretKeyRef:
          name: db-user
          key: db-username

Create the Pod:

kubectl create -f https://k8s.io/examples/pods/inject/pod-multiple-secret-env-variable.yaml

In your shell, display the container environment variables.

kubectl exec -i -t envvars-multiple-secrets -- /bin/sh -c 'env | grep _USERNAME'

The output is similar to:

DB_USERNAME=db-admin
BACKEND_USERNAME=backend-admin

Configure all key-value pairs in a Secret as container environment variables

Note:

This functionality is available in Kubernetes v1.6 and later.

Create a Secret containing multiple key-value pairs

kubectl create secret generic test-secret --from-literal=username='my-app' --from-literal=password='39528$vdg7Jb'

Use envFrom to define all of the Secret's data as container environment variables. The key from the Secret becomes the environment variable name in the Pod.

pods/inject/pod-secret-envFrom.yaml

apiVersion: v1
kind: Pod
metadata:
  name: envfrom-secret
spec:
  containers:
  - name: envars-test-container
    image: nginx
    envFrom:
    - secretRef:
        name: test-secret

Create the Pod:

kubectl create -f https://k8s.io/examples/pods/inject/pod-secret-envFrom.yaml

In your shell, display username and password container environment variables.

kubectl exec -i -t envfrom-secret -- /bin/sh -c 'echo "username: $username\npassword: $password\n"'

The output is similar to:

username: my-app
password: 39528$vdg7Jb

Example: Provide prod/test credentials to Pods using Secrets

This example illustrates a Pod which consumes a secret containing production credentials and another Pod which consumes a secret with test environment credentials.

Create a secret for prod environment credentials:

kubectl create secret generic prod-db-secret --from-literal=username=produser --from-literal=password=Y4nys7f11

The output is similar to:

secret "prod-db-secret" created

Create a secret for test environment credentials.
```
kubectl create secret generic test-db-secret --from-literal=username=testuser --from-literal=password=iluvtests
```
The output is similar to:
```
secret "test-db-secret" created
```
Note:
Special characters such as $, \, *, =, and ! will be interpreted by your shell and require escaping.

In most shells, the easiest way to escape the password is to surround it with single quotes ('). For example, if your actual password is S!B\*d$zDsb=, you should execute the command as follows:
```
kubectl create secret generic dev-db-secret --from-literal=username=devuser --from-literal=password='S!B\*d$zDsb='
```
You do not need to escape special characters in passwords from files (--from-file).

Create the Pod manifests:

cat <<EOF > pod.yaml
apiVersion: v1
kind: List
items:
- kind: Pod
  apiVersion: v1
  metadata:
    name: prod-db-client-pod
    labels:
      name: prod-db-client
  spec:
    volumes:
    - name: secret-volume
      secret:
        secretName: prod-db-secret
    containers:
    - name: db-client-container
      image: myClientImage
      volumeMounts:
      - name: secret-volume
        readOnly: true
        mountPath: "/etc/secret-volume"
- kind: Pod
  apiVersion: v1
  metadata:
    name: test-db-client-pod
    labels:
      name: test-db-client
  spec:
    volumes:
    - name: secret-volume
      secret:
        secretName: test-db-secret
    containers:
    - name: db-client-container
      image: myClientImage
      volumeMounts:
      - name: secret-volume
        readOnly: true
        mountPath: "/etc/secret-volume"
EOF

Note:

How the specs for the two Pods differ only in one field; this facilitates creating Pods with different capabilities from a common Pod template.

Apply all those objects on the API server by running:
```
kubectl create -f pod.yaml
```

Both containers will have the following files present on their filesystems with the values for each container's environment:

/etc/secret-volume/username
/etc/secret-volume/password

You could further simplify the base Pod specification by using two service accounts:

prod-user with the prod-db-secret
test-user with the test-db-secret

The Pod specification is shortened to:

apiVersion: v1
kind: Pod
metadata:
  name: prod-db-client-pod
  labels:
    name: prod-db-client
spec:
  serviceAccount: prod-db-client
  containers:
  - name: db-client-container
    image: myClientImage

References

What's next

Learn more about Secrets.
Learn about Volumes.

8 - Run Applications

Run and manage both stateless and stateful applications.

8.1 - Run a Stateless Application Using a Deployment

This page shows how to run an application using a Kubernetes Deployment object.

Objectives

Create an nginx deployment.
Use kubectl to list information about the deployment.
Update the deployment.

Before you begin

Your Kubernetes server must be at or later than version v1.9. To check the version, enter kubectl version.

Creating and exploring an nginx deployment

You can run an application by creating a Kubernetes Deployment object, and you can describe a Deployment in a YAML file. For example, this YAML file describes a Deployment that runs the nginx:1.14.2 Docker image:

application/deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  replicas: 2 # tells deployment to run 2 pods matching the template
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

Create a Deployment based on the YAML file:

kubectl apply -f https://k8s.io/examples/application/deployment.yaml

Display information about the Deployment:

kubectl describe deployment nginx-deployment

The output is similar to this:

Name:     nginx-deployment
Namespace:    default
CreationTimestamp:  Tue, 30 Aug 2016 18:11:37 -0700
Labels:     app=nginx
Annotations:    deployment.kubernetes.io/revision=1
Selector:   app=nginx
Replicas:   2 desired | 2 updated | 2 total | 2 available | 0 unavailable
StrategyType:   RollingUpdate
MinReadySeconds:  0
RollingUpdateStrategy:  1 max unavailable, 1 max surge
Pod Template:
  Labels:       app=nginx
  Containers:
    nginx:
    Image:              nginx:1.14.2
    Port:               80/TCP
    Environment:        <none>
    Mounts:             <none>
  Volumes:              <none>
Conditions:
  Type          Status  Reason
  ----          ------  ------
  Available     True    MinimumReplicasAvailable
  Progressing   True    NewReplicaSetAvailable
OldReplicaSets:   <none>
NewReplicaSet:    nginx-deployment-1771418926 (2/2 replicas created)
No events.

List the Pods created by the deployment:

kubectl get pods -l app=nginx

The output is similar to this:

NAME                                READY     STATUS    RESTARTS   AGE
nginx-deployment-1771418926-7o5ns   1/1       Running   0          16h
nginx-deployment-1771418926-r18az   1/1       Running   0          16h

Display information about a Pod:
```
kubectl describe pod <pod-name>
```
where <pod-name> is the name of one of your Pods.

Updating the deployment

You can update the deployment by applying a new YAML file. This YAML file specifies that the deployment should be updated to use nginx 1.16.1.

application/deployment-update.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  replicas: 2
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.16.1 # Update the version of nginx from 1.14.2 to 1.16.1
        ports:
        - containerPort: 80

Apply the new YAML file:

kubectl apply -f https://k8s.io/examples/application/deployment-update.yaml

Watch the deployment create pods with new names and delete the old pods:
```
kubectl get pods -l app=nginx
```

Scaling the application by increasing the replica count

You can increase the number of Pods in your Deployment by applying a new YAML file. This YAML file sets replicas to 4, which specifies that the Deployment should have four Pods:

application/deployment-scale.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  replicas: 4 # Update the replicas from 2 to 4
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.16.1
        ports:
        - containerPort: 80

Apply the new YAML file:

kubectl apply -f https://k8s.io/examples/application/deployment-scale.yaml

Verify that the Deployment has four Pods:

kubectl get pods -l app=nginx

The output is similar to this:

NAME                               READY     STATUS    RESTARTS   AGE
nginx-deployment-148880595-4zdqq   1/1       Running   0          25s
nginx-deployment-148880595-6zgi1   1/1       Running   0          25s
nginx-deployment-148880595-fxcez   1/1       Running   0          2m
nginx-deployment-148880595-rwovn   1/1       Running   0          2m

Deleting a deployment

Delete the deployment by name:

kubectl delete deployment nginx-deployment

ReplicationControllers -- the Old Way

The preferred way to create a replicated application is to use a Deployment, which in turn uses a ReplicaSet. Before the Deployment and ReplicaSet were added to Kubernetes, replicated applications were configured using a ReplicationController.

What's next

Learn more about Deployment objects.

8.2 - Run a Single-Instance Stateful Application

This page shows you how to run a single-instance stateful application in Kubernetes using a PersistentVolume and a Deployment. The application is MySQL.

Objectives

Create a PersistentVolume referencing a disk in your environment.
Create a MySQL Deployment.
Expose MySQL to other pods in the cluster at a known DNS name.

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes
To check the version, enter kubectl version.
You need to either have a dynamic PersistentVolume provisioner with a default StorageClass, or statically provision PersistentVolumes yourself to satisfy the PersistentVolumeClaims used here.

Deploy MySQL

You can run a stateful application by creating a Kubernetes Deployment and connecting it to an existing PersistentVolume using a PersistentVolumeClaim. For example, this YAML file describes a Deployment that runs MySQL and references the PersistentVolumeClaim. The file defines a volume mount for /var/lib/mysql, and then creates a PersistentVolumeClaim that looks for a 20G volume. This claim is satisfied by any existing volume that meets the requirements, or by a dynamic provisioner.

Note: The password is defined in the config yaml, and this is insecure. See Kubernetes Secrets for a secure solution.

application/mysql/mysql-deployment.yaml

apiVersion: v1
kind: Service
metadata:
  name: mysql
spec:
  ports:
  - port: 3306
  selector:
    app: mysql
  clusterIP: None
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mysql
spec:
  selector:
    matchLabels:
      app: mysql
  strategy:
    type: Recreate
  template:
    metadata:
      labels:
        app: mysql
    spec:
      containers:
      - image: mysql:5.6
        name: mysql
        env:
          # Use secret in real usage
        - name: MYSQL_ROOT_PASSWORD
          value: password
        ports:
        - containerPort: 3306
          name: mysql
        volumeMounts:
        - name: mysql-persistent-storage
          mountPath: /var/lib/mysql
      volumes:
      - name: mysql-persistent-storage
        persistentVolumeClaim:
          claimName: mysql-pv-claim

application/mysql/mysql-pv.yaml

apiVersion: v1
kind: PersistentVolume
metadata:
  name: mysql-pv-volume
  labels:
    type: local
spec:
  storageClassName: manual
  capacity:
    storage: 20Gi
  accessModes:
    - ReadWriteOnce
  hostPath:
    path: "/mnt/data"
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: mysql-pv-claim
spec:
  storageClassName: manual
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 20Gi

Deploy the PV and PVC of the YAML file:

kubectl apply -f https://k8s.io/examples/application/mysql/mysql-pv.yaml

Deploy the contents of the YAML file:

kubectl apply -f https://k8s.io/examples/application/mysql/mysql-deployment.yaml

Display information about the Deployment:

kubectl describe deployment mysql

The output is similar to this:

Name:                 mysql
Namespace:            default
CreationTimestamp:    Tue, 01 Nov 2016 11:18:45 -0700
Labels:               app=mysql
Annotations:          deployment.kubernetes.io/revision=1
Selector:             app=mysql
Replicas:             1 desired | 1 updated | 1 total | 0 available | 1 unavailable
StrategyType:         Recreate
MinReadySeconds:      0
Pod Template:
  Labels:       app=mysql
  Containers:
    mysql:
    Image:      mysql:5.6
    Port:       3306/TCP
    Environment:
      MYSQL_ROOT_PASSWORD:      password
    Mounts:
      /var/lib/mysql from mysql-persistent-storage (rw)
  Volumes:
    mysql-persistent-storage:
    Type:       PersistentVolumeClaim (a reference to a PersistentVolumeClaim in the same namespace)
    ClaimName:  mysql-pv-claim
    ReadOnly:   false
Conditions:
  Type          Status  Reason
  ----          ------  ------
  Available     False   MinimumReplicasUnavailable
  Progressing   True    ReplicaSetUpdated
OldReplicaSets:       <none>
NewReplicaSet:        mysql-63082529 (1/1 replicas created)
Events:
  FirstSeen    LastSeen    Count    From                SubobjectPath    Type        Reason            Message
  ---------    --------    -----    ----                -------------    --------    ------            -------
  33s          33s         1        {deployment-controller }             Normal      ScalingReplicaSet Scaled up replica set mysql-63082529 to 1

List the pods created by the Deployment:

kubectl get pods -l app=mysql

The output is similar to this:

NAME                   READY     STATUS    RESTARTS   AGE
mysql-63082529-2z3ki   1/1       Running   0          3m

Inspect the PersistentVolumeClaim:

kubectl describe pvc mysql-pv-claim

The output is similar to this:

Name:         mysql-pv-claim
Namespace:    default
StorageClass:
Status:       Bound
Volume:       mysql-pv-volume
Labels:       <none>
Annotations:    pv.kubernetes.io/bind-completed=yes
                pv.kubernetes.io/bound-by-controller=yes
Capacity:     20Gi
Access Modes: RWO
Events:       <none>

Accessing the MySQL instance

The preceding YAML file creates a service that allows other Pods in the cluster to access the database. The Service option clusterIP: None lets the Service DNS name resolve directly to the Pod's IP address. This is optimal when you have only one Pod behind a Service and you don't intend to increase the number of Pods.

Run a MySQL client to connect to the server:

kubectl run -it --rm --image=mysql:5.6 --restart=Never mysql-client -- mysql -h mysql -ppassword

This command creates a new Pod in the cluster running a MySQL client and connects it to the server through the Service. If it connects, you know your stateful MySQL database is up and running.

Waiting for pod default/mysql-client-274442439-zyp6i to be running, status is Pending, pod ready: false
If you don't see a command prompt, try pressing enter.

mysql>

Updating

The image or any other part of the Deployment can be updated as usual with the kubectl apply command. Here are some precautions that are specific to stateful apps:

Don't scale the app. This setup is for single-instance apps only. The underlying PersistentVolume can only be mounted to one Pod. For clustered stateful apps, see the StatefulSet documentation.
Use strategy: type: Recreate in the Deployment configuration YAML file. This instructs Kubernetes to not use rolling updates. Rolling updates will not work, as you cannot have more than one Pod running at a time. The Recreate strategy will stop the first pod before creating a new one with the updated configuration.

Deleting a deployment

Delete the deployed objects by name:

kubectl delete deployment,svc mysql
kubectl delete pvc mysql-pv-claim
kubectl delete pv mysql-pv-volume

If you manually provisioned a PersistentVolume, you also need to manually delete it, as well as release the underlying resource. If you used a dynamic provisioner, it automatically deletes the PersistentVolume when it sees that you deleted the PersistentVolumeClaim. Some dynamic provisioners (such as those for EBS and PD) also release the underlying resource upon deleting the PersistentVolume.

What's next

8.3 - Run a Replicated Stateful Application

This page shows how to run a replicated stateful application using a StatefulSet. This application is a replicated MySQL database. The example topology has a single primary server and multiple replicas, using asynchronous row-based replication.

Note:

This is not a production configuration. MySQL settings remain on insecure defaults to keep the focus on general patterns for running stateful applications in Kubernetes.

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes
You need to either have a dynamic PersistentVolume provisioner with a default StorageClass, or statically provision PersistentVolumes yourself to satisfy the PersistentVolumeClaims used here.
This tutorial assumes you are familiar with PersistentVolumes and StatefulSets, as well as other core concepts like Pods, Services, and ConfigMaps.
Some familiarity with MySQL helps, but this tutorial aims to present general patterns that should be useful for other systems.
You are using the default namespace or another namespace that does not contain any conflicting objects.

Objectives

Deploy a replicated MySQL topology with a StatefulSet.
Send MySQL client traffic.
Observe resistance to downtime.
Scale the StatefulSet up and down.

Deploy MySQL

The example MySQL deployment consists of a ConfigMap, two Services, and a StatefulSet.

Create a ConfigMap

Create the ConfigMap from the following YAML configuration file:

application/mysql/mysql-configmap.yaml

apiVersion: v1
kind: ConfigMap
metadata:
  name: mysql
  labels:
    app: mysql
    app.kubernetes.io/name: mysql
data:
  primary.cnf: |
    # Apply this config only on the primary.
    [mysqld]
    log-bin    
  replica.cnf: |
    # Apply this config only on replicas.
    [mysqld]
    super-read-only

kubectl apply -f https://k8s.io/examples/application/mysql/mysql-configmap.yaml

This ConfigMap provides my.cnf overrides that let you independently control configuration on the primary MySQL server and its replicas. In this case, you want the primary server to be able to serve replication logs to replicas and you want replicas to reject any writes that don't come via replication.

There's nothing special about the ConfigMap itself that causes different portions to apply to different Pods. Each Pod decides which portion to look at as it's initializing, based on information provided by the StatefulSet controller.

Create Services

Create the Services from the following YAML configuration file:

application/mysql/mysql-services.yaml

# Headless service for stable DNS entries of StatefulSet members.
apiVersion: v1
kind: Service
metadata:
  name: mysql
  labels:
    app: mysql
    app.kubernetes.io/name: mysql
spec:
  ports:
  - name: mysql
    port: 3306
  clusterIP: None
  selector:
    app: mysql
---
# Client service for connecting to any MySQL instance for reads.
# For writes, you must instead connect to the primary: mysql-0.mysql.
apiVersion: v1
kind: Service
metadata:
  name: mysql-read
  labels:
    app: mysql
    app.kubernetes.io/name: mysql
    readonly: "true"
spec:
  ports:
  - name: mysql
    port: 3306
  selector:
    app: mysql

kubectl apply -f https://k8s.io/examples/application/mysql/mysql-services.yaml

The headless Service provides a home for the DNS entries that the StatefulSet controllers creates for each Pod that's part of the set. Because the headless Service is named mysql, the Pods are accessible by resolving <pod-name>.mysql from within any other Pod in the same Kubernetes cluster and namespace.

The client Service, called mysql-read, is a normal Service with its own cluster IP that distributes connections across all MySQL Pods that report being Ready. The set of potential endpoints includes the primary MySQL server and all replicas.

Note that only read queries can use the load-balanced client Service. Because there is only one primary MySQL server, clients should connect directly to the primary MySQL Pod (through its DNS entry within the headless Service) to execute writes.

Create the StatefulSet

Finally, create the StatefulSet from the following YAML configuration file:

application/mysql/mysql-statefulset.yaml

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: mysql
spec:
  selector:
    matchLabels:
      app: mysql
      app.kubernetes.io/name: mysql
  serviceName: mysql
  replicas: 3
  template:
    metadata:
      labels:
        app: mysql
        app.kubernetes.io/name: mysql
    spec:
      initContainers:
      - name: init-mysql
        image: mysql:5.7
        command:
        - bash
        - "-c"
        - |
          set -ex
          # Generate mysql server-id from pod ordinal index.
          [[ $HOSTNAME =~ -([0-9]+)$ ]] || exit 1
          ordinal=${BASH_REMATCH[1]}
          echo [mysqld] > /mnt/conf.d/server-id.cnf
          # Add an offset to avoid reserved server-id=0 value.
          echo server-id=$((100 + $ordinal)) >> /mnt/conf.d/server-id.cnf
          # Copy appropriate conf.d files from config-map to emptyDir.
          if [[ $ordinal -eq 0 ]]; then
            cp /mnt/config-map/primary.cnf /mnt/conf.d/
          else
            cp /mnt/config-map/replica.cnf /mnt/conf.d/
          fi          
        volumeMounts:
        - name: conf
          mountPath: /mnt/conf.d
        - name: config-map
          mountPath: /mnt/config-map
      - name: clone-mysql
        image: gcr.io/google-samples/xtrabackup:1.0
        command:
        - bash
        - "-c"
        - |
          set -ex
          # Skip the clone if data already exists.
          [[ -d /var/lib/mysql/mysql ]] && exit 0
          # Skip the clone on primary (ordinal index 0).
          [[ `hostname` =~ -([0-9]+)$ ]] || exit 1
          ordinal=${BASH_REMATCH[1]}
          [[ $ordinal -eq 0 ]] && exit 0
          # Clone data from previous peer.
          ncat --recv-only mysql-$(($ordinal-1)).mysql 3307 | xbstream -x -C /var/lib/mysql
          # Prepare the backup.
          xtrabackup --prepare --target-dir=/var/lib/mysql          
        volumeMounts:
        - name: data
          mountPath: /var/lib/mysql
          subPath: mysql
        - name: conf
          mountPath: /etc/mysql/conf.d
      containers:
      - name: mysql
        image: mysql:5.7
        env:
        - name: MYSQL_ALLOW_EMPTY_PASSWORD
          value: "1"
        ports:
        - name: mysql
          containerPort: 3306
        volumeMounts:
        - name: data
          mountPath: /var/lib/mysql
          subPath: mysql
        - name: conf
          mountPath: /etc/mysql/conf.d
        resources:
          requests:
            cpu: 500m
            memory: 1Gi
        livenessProbe:
          exec:
            command: ["mysqladmin", "ping"]
          initialDelaySeconds: 30
          periodSeconds: 10
          timeoutSeconds: 5
        readinessProbe:
          exec:
            # Check we can execute queries over TCP (skip-networking is off).
            command: ["mysql", "-h", "127.0.0.1", "-e", "SELECT 1"]
          initialDelaySeconds: 5
          periodSeconds: 2
          timeoutSeconds: 1
      - name: xtrabackup
        image: gcr.io/google-samples/xtrabackup:1.0
        ports:
        - name: xtrabackup
          containerPort: 3307
        command:
        - bash
        - "-c"
        - |
          set -ex
          cd /var/lib/mysql

          # Determine binlog position of cloned data, if any.
          if [[ -f xtrabackup_slave_info && "x$(<xtrabackup_slave_info)" != "x" ]]; then
            # XtraBackup already generated a partial "CHANGE MASTER TO" query
            # because we're cloning from an existing replica. (Need to remove the tailing semicolon!)
            cat xtrabackup_slave_info | sed -E 's/;$//g' > change_master_to.sql.in
            # Ignore xtrabackup_binlog_info in this case (it's useless).
            rm -f xtrabackup_slave_info xtrabackup_binlog_info
          elif [[ -f xtrabackup_binlog_info ]]; then
            # We're cloning directly from primary. Parse binlog position.
            [[ `cat xtrabackup_binlog_info` =~ ^(.*?)[[:space:]]+(.*?)$ ]] || exit 1
            rm -f xtrabackup_binlog_info xtrabackup_slave_info
            echo "CHANGE MASTER TO MASTER_LOG_FILE='${BASH_REMATCH[1]}',\
                  MASTER_LOG_POS=${BASH_REMATCH[2]}" > change_master_to.sql.in
          fi

          # Check if we need to complete a clone by starting replication.
          if [[ -f change_master_to.sql.in ]]; then
            echo "Waiting for mysqld to be ready (accepting connections)"
            until mysql -h 127.0.0.1 -e "SELECT 1"; do sleep 1; done

            echo "Initializing replication from clone position"
            mysql -h 127.0.0.1 \
                  -e "$(<change_master_to.sql.in), \
                          MASTER_HOST='mysql-0.mysql', \
                          MASTER_USER='root', \
                          MASTER_PASSWORD='', \
                          MASTER_CONNECT_RETRY=10; \
                        START SLAVE;" || exit 1
            # In case of container restart, attempt this at-most-once.
            mv change_master_to.sql.in change_master_to.sql.orig
          fi

          # Start a server to send backups when requested by peers.
          exec ncat --listen --keep-open --send-only --max-conns=1 3307 -c \
            "xtrabackup --backup --slave-info --stream=xbstream --host=127.0.0.1 --user=root"          
        volumeMounts:
        - name: data
          mountPath: /var/lib/mysql
          subPath: mysql
        - name: conf
          mountPath: /etc/mysql/conf.d
        resources:
          requests:
            cpu: 100m
            memory: 100Mi
      volumes:
      - name: conf
        emptyDir: {}
      - name: config-map
        configMap:
          name: mysql
  volumeClaimTemplates:
  - metadata:
      name: data
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 10Gi

kubectl apply -f https://k8s.io/examples/application/mysql/mysql-statefulset.yaml

You can watch the startup progress by running:

kubectl get pods -l app=mysql --watch

After a while, you should see all 3 Pods become Running:

NAME      READY     STATUS    RESTARTS   AGE
mysql-0   2/2       Running   0          2m
mysql-1   2/2       Running   0          1m
mysql-2   2/2       Running   0          1m

Press Ctrl+C to cancel the watch.

Note:

If you don't see any progress, make sure you have a dynamic PersistentVolume provisioner enabled, as mentioned in the prerequisites.

This manifest uses a variety of techniques for managing stateful Pods as part of a StatefulSet. The next section highlights some of these techniques to explain what happens as the StatefulSet creates Pods.

Understanding stateful Pod initialization

The StatefulSet controller starts Pods one at a time, in order by their ordinal index. It waits until each Pod reports being Ready before starting the next one.

In addition, the controller assigns each Pod a unique, stable name of the form <statefulset-name>-<ordinal-index>, which results in Pods named mysql-0, mysql-1, and mysql-2.

The Pod template in the above StatefulSet manifest takes advantage of these properties to perform orderly startup of MySQL replication.

Generating configuration

Before starting any of the containers in the Pod spec, the Pod first runs any init containers in the order defined.

The first init container, named init-mysql, generates special MySQL config files based on the ordinal index.

The script determines its own ordinal index by extracting it from the end of the Pod name, which is returned by the hostname command. Then it saves the ordinal (with a numeric offset to avoid reserved values) into a file called server-id.cnf in the MySQL conf.d directory. This translates the unique, stable identity provided by the StatefulSet into the domain of MySQL server IDs, which require the same properties.

The script in the init-mysql container also applies either primary.cnf or replica.cnf from the ConfigMap by copying the contents into conf.d. Because the example topology consists of a single primary MySQL server and any number of replicas, the script assigns ordinal 0 to be the primary server, and everyone else to be replicas. Combined with the StatefulSet controller's deployment order guarantee, this ensures the primary MySQL server is Ready before creating replicas, so they can begin replicating.

Cloning existing data

In general, when a new Pod joins the set as a replica, it must assume the primary MySQL server might already have data on it. It also must assume that the replication logs might not go all the way back to the beginning of time. These conservative assumptions are the key to allow a running StatefulSet to scale up and down over time, rather than being fixed at its initial size.

The second init container, named clone-mysql, performs a clone operation on a replica Pod the first time it starts up on an empty PersistentVolume. That means it copies all existing data from another running Pod, so its local state is consistent enough to begin replicating from the primary server.

MySQL itself does not provide a mechanism to do this, so the example uses a popular open-source tool called Percona XtraBackup. During the clone, the source MySQL server might suffer reduced performance. To minimize impact on the primary MySQL server, the script instructs each Pod to clone from the Pod whose ordinal index is one lower. This works because the StatefulSet controller always ensures Pod N is Ready before starting Pod N+1.

Starting replication

After the init containers complete successfully, the regular containers run. The MySQL Pods consist of a mysql container that runs the actual mysqld server, and an xtrabackup container that acts as a sidecar.

The xtrabackup sidecar looks at the cloned data files and determines if it's necessary to initialize MySQL replication on the replica. If so, it waits for mysqld to be ready and then executes the CHANGE MASTER TO and START SLAVE commands with replication parameters extracted from the XtraBackup clone files.

Once a replica begins replication, it remembers its primary MySQL server and reconnects automatically if the server restarts or the connection dies. Also, because replicas look for the primary server at its stable DNS name (mysql-0.mysql), they automatically find the primary server even if it gets a new Pod IP due to being rescheduled.

Lastly, after starting replication, the xtrabackup container listens for connections from other Pods requesting a data clone. This server remains up indefinitely in case the StatefulSet scales up, or in case the next Pod loses its PersistentVolumeClaim and needs to redo the clone.

Sending client traffic

You can send test queries to the primary MySQL server (hostname mysql-0.mysql) by running a temporary container with the mysql:5.7 image and running the mysql client binary.

kubectl run mysql-client --image=mysql:5.7 -i --rm --restart=Never --\
  mysql -h mysql-0.mysql <<EOF
CREATE DATABASE test;
CREATE TABLE test.messages (message VARCHAR(250));
INSERT INTO test.messages VALUES ('hello');
EOF

Use the hostname mysql-read to send test queries to any server that reports being Ready:

kubectl run mysql-client --image=mysql:5.7 -i -t --rm --restart=Never --\
  mysql -h mysql-read -e "SELECT * FROM test.messages"

You should get output like this:

Waiting for pod default/mysql-client to be running, status is Pending, pod ready: false
+---------+
| message |
+---------+
| hello   |
+---------+
pod "mysql-client" deleted

To demonstrate that the mysql-read Service distributes connections across servers, you can run SELECT @@server_id in a loop:

kubectl run mysql-client-loop --image=mysql:5.7 -i -t --rm --restart=Never --\
  bash -ic "while sleep 1; do mysql -h mysql-read -e 'SELECT @@server_id,NOW()'; done"

You should see the reported @@server_id change randomly, because a different endpoint might be selected upon each connection attempt:

+-------------+---------------------+
| @@server_id | NOW()               |
+-------------+---------------------+
|         100 | 2006-01-02 15:04:05 |
+-------------+---------------------+
+-------------+---------------------+
| @@server_id | NOW()               |
+-------------+---------------------+
|         102 | 2006-01-02 15:04:06 |
+-------------+---------------------+
+-------------+---------------------+
| @@server_id | NOW()               |
+-------------+---------------------+
|         101 | 2006-01-02 15:04:07 |
+-------------+---------------------+

You can press Ctrl+C when you want to stop the loop, but it's useful to keep it running in another window so you can see the effects of the following steps.

Simulate Pod and Node failure

To demonstrate the increased availability of reading from the pool of replicas instead of a single server, keep the SELECT @@server_id loop from above running while you force a Pod out of the Ready state.

Break the Readiness probe

The readiness probe for the mysql container runs the command mysql -h 127.0.0.1 -e 'SELECT 1' to make sure the server is up and able to execute queries.

One way to force this readiness probe to fail is to break that command:

kubectl exec mysql-2 -c mysql -- mv /usr/bin/mysql /usr/bin/mysql.off

This reaches into the actual container's filesystem for Pod mysql-2 and renames the mysql command so the readiness probe can't find it. After a few seconds, the Pod should report one of its containers as not Ready, which you can check by running:

kubectl get pod mysql-2

Look for 1/2 in the READY column:

NAME      READY     STATUS    RESTARTS   AGE
mysql-2   1/2       Running   0          3m

At this point, you should see your SELECT @@server_id loop continue to run, although it never reports 102 anymore. Recall that the init-mysql script defined server-id as 100 + $ordinal, so server ID 102 corresponds to Pod mysql-2.

Now repair the Pod and it should reappear in the loop output after a few seconds:

kubectl exec mysql-2 -c mysql -- mv /usr/bin/mysql.off /usr/bin/mysql

Delete Pods

The StatefulSet also recreates Pods if they're deleted, similar to what a ReplicaSet does for stateless Pods.

kubectl delete pod mysql-2

The StatefulSet controller notices that no mysql-2 Pod exists anymore, and creates a new one with the same name and linked to the same PersistentVolumeClaim. You should see server ID 102 disappear from the loop output for a while and then return on its own.

Drain a Node

If your Kubernetes cluster has multiple Nodes, you can simulate Node downtime (such as when Nodes are upgraded) by issuing a drain.

First determine which Node one of the MySQL Pods is on:

kubectl get pod mysql-2 -o wide

The Node name should show up in the last column:

NAME      READY     STATUS    RESTARTS   AGE       IP            NODE
mysql-2   2/2       Running   0          15m       10.244.5.27   kubernetes-node-9l2t

Then, drain the Node by running the following command, which cordons it so no new Pods may schedule there, and then evicts any existing Pods. Replace <node-name> with the name of the Node you found in the last step.

Caution:

Draining a Node can impact other workloads and applications running on the same node. Only perform the following step in a test cluster.

# See above advice about impact on other workloads
kubectl drain <node-name> --force --delete-emptydir-data --ignore-daemonsets

Now you can watch as the Pod reschedules on a different Node:

kubectl get pod mysql-2 -o wide --watch

It should look something like this:

NAME      READY   STATUS          RESTARTS   AGE       IP            NODE
mysql-2   2/2     Terminating     0          15m       10.244.1.56   kubernetes-node-9l2t
[...]
mysql-2   0/2     Pending         0          0s        <none>        kubernetes-node-fjlm
mysql-2   0/2     Init:0/2        0          0s        <none>        kubernetes-node-fjlm
mysql-2   0/2     Init:1/2        0          20s       10.244.5.32   kubernetes-node-fjlm
mysql-2   0/2     PodInitializing 0          21s       10.244.5.32   kubernetes-node-fjlm
mysql-2   1/2     Running         0          22s       10.244.5.32   kubernetes-node-fjlm
mysql-2   2/2     Running         0          30s       10.244.5.32   kubernetes-node-fjlm

And again, you should see server ID 102 disappear from the SELECT @@server_id loop output for a while and then return.

Now uncordon the Node to return it to a normal state:

kubectl uncordon <node-name>

Scaling the number of replicas

When you use MySQL replication, you can scale your read query capacity by adding replicas. For a StatefulSet, you can achieve this with a single command:

kubectl scale statefulset mysql  --replicas=5

Watch the new Pods come up by running:

kubectl get pods -l app=mysql --watch

Once they're up, you should see server IDs 103 and 104 start appearing in the SELECT @@server_id loop output.

You can also verify that these new servers have the data you added before they existed:

kubectl run mysql-client --image=mysql:5.7 -i -t --rm --restart=Never --\
  mysql -h mysql-3.mysql -e "SELECT * FROM test.messages"

Waiting for pod default/mysql-client to be running, status is Pending, pod ready: false
+---------+
| message |
+---------+
| hello   |
+---------+
pod "mysql-client" deleted

Scaling back down is also seamless:

kubectl scale statefulset mysql --replicas=3

Note:

Although scaling up creates new PersistentVolumeClaims automatically, scaling down does not automatically delete these PVCs.

This gives you the choice to keep those initialized PVCs around to make scaling back up quicker, or to extract data before deleting them.

You can see this by running:

kubectl get pvc -l app=mysql

Which shows that all 5 PVCs still exist, despite having scaled the StatefulSet down to 3:

NAME           STATUS    VOLUME                                     CAPACITY   ACCESSMODES   AGE
data-mysql-0   Bound     pvc-8acbf5dc-b103-11e6-93fa-42010a800002   10Gi       RWO           20m
data-mysql-1   Bound     pvc-8ad39820-b103-11e6-93fa-42010a800002   10Gi       RWO           20m
data-mysql-2   Bound     pvc-8ad69a6d-b103-11e6-93fa-42010a800002   10Gi       RWO           20m
data-mysql-3   Bound     pvc-50043c45-b1c5-11e6-93fa-42010a800002   10Gi       RWO           2m
data-mysql-4   Bound     pvc-500a9957-b1c5-11e6-93fa-42010a800002   10Gi       RWO           2m

If you don't intend to reuse the extra PVCs, you can delete them:

kubectl delete pvc data-mysql-3
kubectl delete pvc data-mysql-4

Cleaning up

Cancel the SELECT @@server_id loop by pressing Ctrl+C in its terminal, or running the following from another terminal:
```
kubectl delete pod mysql-client-loop --now
```
Delete the StatefulSet. This also begins terminating the Pods.
```
kubectl delete statefulset mysql
```
Verify that the Pods disappear. They might take some time to finish terminating.
```
kubectl get pods -l app=mysql
```
You'll know the Pods have terminated when the above returns:
```
No resources found.
```
Delete the ConfigMap, Services, and PersistentVolumeClaims.
```
kubectl delete configmap,service,pvc -l app=mysql
```
If you manually provisioned PersistentVolumes, you also need to manually delete them, as well as release the underlying resources. If you used a dynamic provisioner, it automatically deletes the PersistentVolumes when it sees that you deleted the PersistentVolumeClaims. Some dynamic provisioners (such as those for EBS and PD) also release the underlying resources upon deleting the PersistentVolumes.

What's next

Learn more about scaling a StatefulSet.
Learn more about debugging a StatefulSet.
Learn more about deleting a StatefulSet.
Learn more about force deleting StatefulSet Pods.
Look in the Helm Charts repository for other stateful application examples.

8.4 - Scale a StatefulSet

This task shows how to scale a StatefulSet. Scaling a StatefulSet refers to increasing or decreasing the number of replicas.

Before you begin

StatefulSets are only available in Kubernetes version 1.5 or later. To check your version of Kubernetes, run kubectl version.
Not all stateful applications scale nicely. If you are unsure about whether to scale your StatefulSets, see StatefulSet concepts or StatefulSet tutorial for further information.
You should perform scaling only when you are confident that your stateful application cluster is completely healthy.

Scaling StatefulSets

Use kubectl to scale StatefulSets

First, find the StatefulSet you want to scale.

kubectl get statefulsets <stateful-set-name>

Change the number of replicas of your StatefulSet:

kubectl scale statefulsets <stateful-set-name> --replicas=<new-replicas>

Make in-place updates on your StatefulSets

Alternatively, you can do in-place updates on your StatefulSets.

If your StatefulSet was initially created with kubectl apply, update .spec.replicas of the StatefulSet manifests, and then do a kubectl apply:

kubectl apply -f <stateful-set-file-updated>

Otherwise, edit that field with kubectl edit:

kubectl edit statefulsets <stateful-set-name>

Or use kubectl patch:

kubectl patch statefulsets <stateful-set-name> -p '{"spec":{"replicas":<new-replicas>}}'

Troubleshooting

Scaling down does not work right

You cannot scale down a StatefulSet when any of the stateful Pods it manages is unhealthy. Scaling down only takes place after those stateful Pods become running and ready.

If spec.replicas > 1, Kubernetes cannot determine the reason for an unhealthy Pod. It might be the result of a permanent fault or of a transient fault. A transient fault can be caused by a restart required by upgrading or maintenance.

If the Pod is unhealthy due to a permanent fault, scaling without correcting the fault may lead to a state where the StatefulSet membership drops below a certain minimum number of replicas that are needed to function correctly. This may cause your StatefulSet to become unavailable.

If the Pod is unhealthy due to a transient fault and the Pod might become available again, the transient error may interfere with your scale-up or scale-down operation. Some distributed databases have issues when nodes join and leave at the same time. It is better to reason about scaling operations at the application level in these cases, and perform scaling only when you are sure that your stateful application cluster is completely healthy.

What's next

Learn more about deleting a StatefulSet.

8.5 - Delete a StatefulSet

This task shows you how to delete a StatefulSet.

Before you begin

This task assumes you have an application running on your cluster represented by a StatefulSet.

Deleting a StatefulSet

You can delete a StatefulSet in the same way you delete other resources in Kubernetes: use the kubectl delete command, and specify the StatefulSet either by file or by name.

kubectl delete -f <file.yaml>

kubectl delete statefulsets <statefulset-name>

You may need to delete the associated headless service separately after the StatefulSet itself is deleted.

kubectl delete service <service-name>

When deleting a StatefulSet through kubectl, the StatefulSet scales down to 0. All Pods that are part of this workload are also deleted. If you want to delete only the StatefulSet and not the Pods, use --cascade=orphan. For example:

kubectl delete -f <file.yaml> --cascade=orphan

By passing --cascade=orphan to kubectl delete, the Pods managed by the StatefulSet are left behind even after the StatefulSet object itself is deleted. If the pods have a label app.kubernetes.io/name=MyApp, you can then delete them as follows:

kubectl delete pods -l app.kubernetes.io/name=MyApp

Persistent Volumes

Deleting the Pods in a StatefulSet will not delete the associated volumes. This is to ensure that you have the chance to copy data off the volume before deleting it. Deleting the PVC after the pods have terminated might trigger deletion of the backing Persistent Volumes depending on the storage class and reclaim policy. You should never assume ability to access a volume after claim deletion.

Note:

Use caution when deleting a PVC, as it may lead to data loss.

Complete deletion of a StatefulSet

To delete everything in a StatefulSet, including the associated pods, you can run a series of commands similar to the following:

grace=$(kubectl get pods <stateful-set-pod> --template '{{.spec.terminationGracePeriodSeconds}}')
kubectl delete statefulset -l app.kubernetes.io/name=MyApp
sleep $grace
kubectl delete pvc -l app.kubernetes.io/name=MyApp

In the example above, the Pods have the label app.kubernetes.io/name=MyApp; substitute your own label as appropriate.

Force deletion of StatefulSet pods

If you find that some pods in your StatefulSet are stuck in the 'Terminating' or 'Unknown' states for an extended period of time, you may need to manually intervene to forcefully delete the pods from the apiserver. This is a potentially dangerous task. Refer to Force Delete StatefulSet Pods for details.

What's next

Learn more about force deleting StatefulSet Pods.

8.6 - Force Delete StatefulSet Pods

This page shows how to delete Pods which are part of a stateful set, and explains the considerations to keep in mind when doing so.

Before you begin

This is a fairly advanced task and has the potential to violate some of the properties inherent to StatefulSet.
Before proceeding, make yourself familiar with the considerations enumerated below.

StatefulSet considerations

In normal operation of a StatefulSet, there is never a need to force delete a StatefulSet Pod. The StatefulSet controller is responsible for creating, scaling and deleting members of the StatefulSet. It tries to ensure that the specified number of Pods from ordinal 0 through N-1 are alive and ready. StatefulSet ensures that, at any time, there is at most one Pod with a given identity running in a cluster. This is referred to as at most one semantics provided by a StatefulSet.

Manual force deletion should be undertaken with caution, as it has the potential to violate the at most one semantics inherent to StatefulSet. StatefulSets may be used to run distributed and clustered applications which have a need for a stable network identity and stable storage. These applications often have configuration which relies on an ensemble of a fixed number of members with fixed identities. Having multiple members with the same identity can be disastrous and may lead to data loss (e.g. split brain scenario in quorum-based systems).

Delete Pods

You can perform a graceful pod deletion with the following command:

kubectl delete pods <pod>

For the above to lead to graceful termination, the Pod must not specify a pod.Spec.TerminationGracePeriodSeconds of 0. The practice of setting a pod.Spec.TerminationGracePeriodSeconds of 0 seconds is unsafe and strongly discouraged for StatefulSet Pods. Graceful deletion is safe and will ensure that the Pod shuts down gracefully before the kubelet deletes the name from the apiserver.

A Pod is not deleted automatically when a node is unreachable. The Pods running on an unreachable Node enter the 'Terminating' or 'Unknown' state after a timeout. Pods may also enter these states when the user attempts graceful deletion of a Pod on an unreachable Node. The only ways in which a Pod in such a state can be removed from the apiserver are as follows:

The Node object is deleted (either by you, or by the Node Controller).
The kubelet on the unresponsive Node starts responding, kills the Pod and removes the entry from the apiserver.
Force deletion of the Pod by the user.

The recommended best practice is to use the first or second approach. If a Node is confirmed to be dead (e.g. permanently disconnected from the network, powered down, etc), then delete the Node object. If the Node is suffering from a network partition, then try to resolve this or wait for it to resolve. When the partition heals, the kubelet will complete the deletion of the Pod and free up its name in the apiserver.

Normally, the system completes the deletion once the Pod is no longer running on a Node, or the Node is deleted by an administrator. You may override this by force deleting the Pod.

Force Deletion

Force deletions do not wait for confirmation from the kubelet that the Pod has been terminated. Irrespective of whether a force deletion is successful in killing a Pod, it will immediately free up the name from the apiserver. This would let the StatefulSet controller create a replacement Pod with that same identity; this can lead to the duplication of a still-running Pod, and if said Pod can still communicate with the other members of the StatefulSet, will violate the at most one semantics that StatefulSet is designed to guarantee.

When you force delete a StatefulSet pod, you are asserting that the Pod in question will never again make contact with other Pods in the StatefulSet and its name can be safely freed up for a replacement to be created.

If you want to delete a Pod forcibly using kubectl version >= 1.5, do the following:

kubectl delete pods <pod> --grace-period=0 --force

If you're using any version of kubectl <= 1.4, you should omit the --force option and use:

kubectl delete pods <pod> --grace-period=0

If even after these commands the pod is stuck on Unknown state, use the following command to remove the pod from the cluster:

kubectl patch pod <pod> -p '{"metadata":{"finalizers":null}}'

Always perform force deletion of StatefulSet Pods carefully and with complete knowledge of the risks involved.

What's next

Learn more about debugging a StatefulSet.

8.7 - Horizontal Pod Autoscaling

In Kubernetes, a HorizontalPodAutoscaler automatically updates a workload resource (such as a Deployment or StatefulSet), with the aim of automatically scaling the workload to match demand.

Horizontal scaling means that the response to increased load is to deploy more Pods. This is different from vertical scaling, which for Kubernetes would mean assigning more resources (for example: memory or CPU) to the Pods that are already running for the workload.

If the load decreases, and the number of Pods is above the configured minimum, the HorizontalPodAutoscaler instructs the workload resource (the Deployment, StatefulSet, or other similar resource) to scale back down.

Horizontal pod autoscaling does not apply to objects that can't be scaled (for example: a DaemonSet.)

The HorizontalPodAutoscaler is implemented as a Kubernetes API resource and a controller. The resource determines the behavior of the controller. The horizontal pod autoscaling controller, running within the Kubernetes control plane, periodically adjusts the desired scale of its target (for example, a Deployment) to match observed metrics such as average CPU utilization, average memory utilization, or any other custom metric you specify.

There is walkthrough example of using horizontal pod autoscaling.

How does a HorizontalPodAutoscaler work?

graph BT hpa[Horizontal Pod Autoscaler] --> scale[Scale] subgraph rc[RC / Deployment] scale end scale -.-> pod1[Pod 1] scale -.-> pod2[Pod 2] scale -.-> pod3[Pod N] classDef hpa fill:#D5A6BD,stroke:#1E1E1D,stroke-width:1px,color:#1E1E1D; classDef rc fill:#F9CB9C,stroke:#1E1E1D,stroke-width:1px,color:#1E1E1D; classDef scale fill:#B6D7A8,stroke:#1E1E1D,stroke-width:1px,color:#1E1E1D; classDef pod fill:#9FC5E8,stroke:#1E1E1D,stroke-width:1px,color:#1E1E1D; class hpa hpa; class rc rc; class scale scale; class pod1,pod2,pod3 pod

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Figure 1. HorizontalPodAutoscaler controls the scale of a Deployment and its ReplicaSet

Kubernetes implements horizontal pod autoscaling as a control loop that runs intermittently (it is not a continuous process). The interval is set by the --horizontal-pod-autoscaler-sync-period parameter to the kube-controller-manager (and the default interval is 15 seconds).

Once during each period, the controller manager queries the resource utilization against the metrics specified in each HorizontalPodAutoscaler definition. The controller manager finds the target resource defined by the scaleTargetRef, then selects the pods based on the target resource's .spec.selector labels, and obtains the metrics from either the resource metrics API (for per-pod resource metrics), or the custom metrics API (for all other metrics).

For per-pod resource metrics (like CPU), the controller fetches the metrics from the resource metrics API for each Pod targeted by the HorizontalPodAutoscaler. Then, if a target utilization value is set, the controller calculates the utilization value as a percentage of the equivalent resource request on the containers in each Pod. If a target raw value is set, the raw metric values are used directly. The controller then takes the mean of the utilization or the raw value (depending on the type of target specified) across all targeted Pods, and produces a ratio used to scale the number of desired replicas.

Please note that if some of the Pod's containers do not have the relevant resource request set, CPU utilization for the Pod will not be defined and the autoscaler will not take any action for that metric. See the algorithm details section below for more information about how the autoscaling algorithm works.
For per-pod custom metrics, the controller functions similarly to per-pod resource metrics, except that it works with raw values, not utilization values.
For object metrics and external metrics, a single metric is fetched, which describes the object in question. This metric is compared to the target value, to produce a ratio as above. In the autoscaling/v2 API version, this value can optionally be divided by the number of Pods before the comparison is made.

The common use for HorizontalPodAutoscaler is to configure it to fetch metrics from aggregated APIs (metrics.k8s.io, custom.metrics.k8s.io, or external.metrics.k8s.io). The metrics.k8s.io API is usually provided by an add-on named Metrics Server, which needs to be launched separately. For more information about resource metrics, see Metrics Server.

Support for metrics APIs explains the stability guarantees and support status for these different APIs.

The HorizontalPodAutoscaler controller accesses corresponding workload resources that support scaling (such as Deployments and StatefulSet). These resources each have a subresource named scale, an interface that allows you to dynamically set the number of replicas and examine each of their current states. For general information about subresources in the Kubernetes API, see Kubernetes API Concepts.

Algorithm details

From the most basic perspective, the HorizontalPodAutoscaler controller operates on the ratio between desired metric value and current metric value:

desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]

For example, if the current metric value is 200m, and the desired value is 100m, the number of replicas will be doubled, since 200.0 / 100.0 == 2.0 If the current value is instead 50m, you'll halve the number of replicas, since 50.0 / 100.0 == 0.5. The control plane skips any scaling action if the ratio is sufficiently close to 1.0 (within a globally-configurable tolerance, 0.1 by default).

When a targetAverageValue or targetAverageUtilization is specified, the currentMetricValue is computed by taking the average of the given metric across all Pods in the HorizontalPodAutoscaler's scale target.

Before checking the tolerance and deciding on the final values, the control plane also considers whether any metrics are missing, and how many Pods are Ready. All Pods with a deletion timestamp set (objects with a deletion timestamp are in the process of being shut down / removed) are ignored, and all failed Pods are discarded.

If a particular Pod is missing metrics, it is set aside for later; Pods with missing metrics will be used to adjust the final scaling amount.

When scaling on CPU, if any pod has yet to become ready (it's still initializing, or possibly is unhealthy) or the most recent metric point for the pod was before it became ready, that pod is set aside as well.

Due to technical constraints, the HorizontalPodAutoscaler controller cannot exactly determine the first time a pod becomes ready when determining whether to set aside certain CPU metrics. Instead, it considers a Pod "not yet ready" if it's unready and transitioned to ready within a short, configurable window of time since it started. This value is configured with the --horizontal-pod-autoscaler-initial-readiness-delay flag, and its default is 30 seconds. Once a pod has become ready, it considers any transition to ready to be the first if it occurred within a longer, configurable time since it started. This value is configured with the --horizontal-pod-autoscaler-cpu-initialization-period flag, and its default is 5 minutes.

The currentMetricValue / desiredMetricValue base scale ratio is then calculated using the remaining pods not set aside or discarded from above.

If there were any missing metrics, the control plane recomputes the average more conservatively, assuming those pods were consuming 100% of the desired value in case of a scale down, and 0% in case of a scale up. This dampens the magnitude of any potential scale.

Furthermore, if any not-yet-ready pods were present, and the workload would have scaled up without factoring in missing metrics or not-yet-ready pods, the controller conservatively assumes that the not-yet-ready pods are consuming 0% of the desired metric, further dampening the magnitude of a scale up.

After factoring in the not-yet-ready pods and missing metrics, the controller recalculates the usage ratio. If the new ratio reverses the scale direction, or is within the tolerance, the controller doesn't take any scaling action. In other cases, the new ratio is used to decide any change to the number of Pods.

Note that the original value for the average utilization is reported back via the HorizontalPodAutoscaler status, without factoring in the not-yet-ready pods or missing metrics, even when the new usage ratio is used.

If multiple metrics are specified in a HorizontalPodAutoscaler, this calculation is done for each metric, and then the largest of the desired replica counts is chosen. If any of these metrics cannot be converted into a desired replica count (e.g. due to an error fetching the metrics from the metrics APIs) and a scale down is suggested by the metrics which can be fetched, scaling is skipped. This means that the HPA is still capable of scaling up if one or more metrics give a desiredReplicas greater than the current value.

Finally, right before HPA scales the target, the scale recommendation is recorded. The controller considers all recommendations within a configurable window choosing the highest recommendation from within that window. This value can be configured using the --horizontal-pod-autoscaler-downscale-stabilization flag, which defaults to 5 minutes. This means that scaledowns will occur gradually, smoothing out the impact of rapidly fluctuating metric values.

API Object

The Horizontal Pod Autoscaler is an API resource in the Kubernetes autoscaling API group. The current stable version can be found in the autoscaling/v2 API version which includes support for scaling on memory and custom metrics. The new fields introduced in autoscaling/v2 are preserved as annotations when working with autoscaling/v1.

When you create a HorizontalPodAutoscaler API object, make sure the name specified is a valid DNS subdomain name. More details about the API object can be found at HorizontalPodAutoscaler Object.

Stability of workload scale

When managing the scale of a group of replicas using the HorizontalPodAutoscaler, it is possible that the number of replicas keeps fluctuating frequently due to the dynamic nature of the metrics evaluated. This is sometimes referred to as thrashing, or flapping. It's similar to the concept of hysteresis in cybernetics.

Autoscaling during rolling update

Kubernetes lets you perform a rolling update on a Deployment. In that case, the Deployment manages the underlying ReplicaSets for you. When you configure autoscaling for a Deployment, you bind a HorizontalPodAutoscaler to a single Deployment. The HorizontalPodAutoscaler manages the replicas field of the Deployment. The deployment controller is responsible for setting the replicas of the underlying ReplicaSets so that they add up to a suitable number during the rollout and also afterwards.

If you perform a rolling update of a StatefulSet that has an autoscaled number of replicas, the StatefulSet directly manages its set of Pods (there is no intermediate resource similar to ReplicaSet).

Support for resource metrics

Any HPA target can be scaled based on the resource usage of the pods in the scaling target. When defining the pod specification the resource requests like cpu and memory should be specified. This is used to determine the resource utilization and used by the HPA controller to scale the target up or down. To use resource utilization based scaling specify a metric source like this:

type: Resource
resource:
  name: cpu
  target:
    type: Utilization
    averageUtilization: 60

With this metric the HPA controller will keep the average utilization of the pods in the scaling target at 60%. Utilization is the ratio between the current usage of resource to the requested resources of the pod. See Algorithm for more details about how the utilization is calculated and averaged.

Note:

Since the resource usages of all the containers are summed up the total pod utilization may not accurately represent the individual container resource usage. This could lead to situations where a single container might be running with high usage and the HPA will not scale out because the overall pod usage is still within acceptable limits.

Container resource metrics

FEATURE STATE: Kubernetes v1.30 [stable]

The HorizontalPodAutoscaler API also supports a container metric source where the HPA can track the resource usage of individual containers across a set of Pods, in order to scale the target resource. This lets you configure scaling thresholds for the containers that matter most in a particular Pod. For example, if you have a web application and a sidecar container that provides logging, you can scale based on the resource use of the web application, ignoring the sidecar container and its resource use.

If you revise the target resource to have a new Pod specification with a different set of containers, you should revise the HPA spec if that newly added container should also be used for scaling. If the specified container in the metric source is not present or only present in a subset of the pods then those pods are ignored and the recommendation is recalculated. See Algorithm for more details about the calculation. To use container resources for autoscaling define a metric source as follows:

type: ContainerResource
containerResource:
  name: cpu
  container: application
  target:
    type: Utilization
    averageUtilization: 60

In the above example the HPA controller scales the target such that the average utilization of the cpu in the application container of all the pods is 60%.

Note:

If you change the name of a container that a HorizontalPodAutoscaler is tracking, you can make that change in a specific order to ensure scaling remains available and effective whilst the change is being applied. Before you update the resource that defines the container (such as a Deployment), you should update the associated HPA to track both the new and old container names. This way, the HPA is able to calculate a scaling recommendation throughout the update process.

Once you have rolled out the container name change to the workload resource, tidy up by removing the old container name from the HPA specification.

Scaling on custom metrics

FEATURE STATE: Kubernetes v1.23 [stable]

(the autoscaling/v2beta2 API version previously provided this ability as a beta feature)

Provided that you use the autoscaling/v2 API version, you can configure a HorizontalPodAutoscaler to scale based on a custom metric (that is not built in to Kubernetes or any Kubernetes component). The HorizontalPodAutoscaler controller then queries for these custom metrics from the Kubernetes API.

See Support for metrics APIs for the requirements.

Scaling on multiple metrics

FEATURE STATE: Kubernetes v1.23 [stable]

(the autoscaling/v2beta2 API version previously provided this ability as a beta feature)

Provided that you use the autoscaling/v2 API version, you can specify multiple metrics for a HorizontalPodAutoscaler to scale on. Then, the HorizontalPodAutoscaler controller evaluates each metric, and proposes a new scale based on that metric. The HorizontalPodAutoscaler takes the maximum scale recommended for each metric and sets the workload to that size (provided that this isn't larger than the overall maximum that you configured).

Support for metrics APIs

By default, the HorizontalPodAutoscaler controller retrieves metrics from a series of APIs. In order for it to access these APIs, cluster administrators must ensure that:

The API aggregation layer is enabled.
The corresponding APIs are registered:
- For resource metrics, this is the metrics.k8s.io API, generally provided by metrics-server. It can be launched as a cluster add-on.
- For custom metrics, this is the custom.metrics.k8s.io API. It's provided by "adapter" API servers provided by metrics solution vendors. Check with your metrics pipeline to see if there is a Kubernetes metrics adapter available.
- For external metrics, this is the external.metrics.k8s.io API. It may be provided by the custom metrics adapters provided above.

For more information on these different metrics paths and how they differ please see the relevant design proposals for the HPA V2, custom.metrics.k8s.io and external.metrics.k8s.io.

For examples of how to use them see the walkthrough for using custom metrics and the walkthrough for using external metrics.

Configurable scaling behavior

FEATURE STATE: Kubernetes v1.23 [stable]

(the autoscaling/v2beta2 API version previously provided this ability as a beta feature)

If you use the v2 HorizontalPodAutoscaler API, you can use the behavior field (see the API reference) to configure separate scale-up and scale-down behaviors. You specify these behaviours by setting scaleUp and / or scaleDown under the behavior field.

You can specify a stabilization window that prevents flapping the replica count for a scaling target. Scaling policies also let you control the rate of change of replicas while scaling.

Scaling policies

One or more scaling policies can be specified in the behavior section of the spec. When multiple policies are specified the policy which allows the highest amount of change is the policy which is selected by default. The following example shows this behavior while scaling down:

behavior:
  scaleDown:
    policies:
    - type: Pods
      value: 4
      periodSeconds: 60
    - type: Percent
      value: 10
      periodSeconds: 60

periodSeconds indicates the length of time in the past for which the policy must hold true. The maximum value that you can set for periodSeconds is 1800 (half an hour). The first policy (Pods) allows at most 4 replicas to be scaled down in one minute. The second policy (Percent) allows at most 10% of the current replicas to be scaled down in one minute.

Since by default the policy which allows the highest amount of change is selected, the second policy will only be used when the number of pod replicas is more than 40. With 40 or less replicas, the first policy will be applied. For instance if there are 80 replicas and the target has to be scaled down to 10 replicas then during the first step 8 replicas will be reduced. In the next iteration when the number of replicas is 72, 10% of the pods is 7.2 but the number is rounded up to 8. On each loop of the autoscaler controller the number of pods to be change is re-calculated based on the number of current replicas. When the number of replicas falls below 40 the first policy (Pods) is applied and 4 replicas will be reduced at a time.

The policy selection can be changed by specifying the selectPolicy field for a scaling direction. By setting the value to Min which would select the policy which allows the smallest change in the replica count. Setting the value to Disabled completely disables scaling in that direction.

Stabilization window

The stabilization window is used to restrict the flapping of replica count when the metrics used for scaling keep fluctuating. The autoscaling algorithm uses this window to infer a previous desired state and avoid unwanted changes to workload scale.

For example, in the following example snippet, a stabilization window is specified for scaleDown.

behavior:
  scaleDown:
    stabilizationWindowSeconds: 300

When the metrics indicate that the target should be scaled down the algorithm looks into previously computed desired states, and uses the highest value from the specified interval. In the above example, all desired states from the past 5 minutes will be considered.

This approximates a rolling maximum, and avoids having the scaling algorithm frequently remove Pods only to trigger recreating an equivalent Pod just moments later.

Default Behavior

To use the custom scaling not all fields have to be specified. Only values which need to be customized can be specified. These custom values are merged with default values. The default values match the existing behavior in the HPA algorithm.

behavior:
  scaleDown:
    stabilizationWindowSeconds: 300
    policies:
    - type: Percent
      value: 100
      periodSeconds: 15
  scaleUp:
    stabilizationWindowSeconds: 0
    policies:
    - type: Percent
      value: 100
      periodSeconds: 15
    - type: Pods
      value: 4
      periodSeconds: 15
    selectPolicy: Max

For scaling down the stabilization window is 300 seconds (or the value of the --horizontal-pod-autoscaler-downscale-stabilization flag if provided). There is only a single policy for scaling down which allows a 100% of the currently running replicas to be removed which means the scaling target can be scaled down to the minimum allowed replicas. For scaling up there is no stabilization window. When the metrics indicate that the target should be scaled up the target is scaled up immediately. There are 2 policies where 4 pods or a 100% of the currently running replicas may at most be added every 15 seconds till the HPA reaches its steady state.

Example: change downscale stabilization window

To provide a custom downscale stabilization window of 1 minute, the following behavior would be added to the HPA:

behavior:
  scaleDown:
    stabilizationWindowSeconds: 60

Example: limit scale down rate

To limit the rate at which pods are removed by the HPA to 10% per minute, the following behavior would be added to the HPA:

behavior:
  scaleDown:
    policies:
    - type: Percent
      value: 10
      periodSeconds: 60

To ensure that no more than 5 Pods are removed per minute, you can add a second scale-down policy with a fixed size of 5, and set selectPolicy to minimum. Setting selectPolicy to Min means that the autoscaler chooses the policy that affects the smallest number of Pods:

behavior:
  scaleDown:
    policies:
    - type: Percent
      value: 10
      periodSeconds: 60
    - type: Pods
      value: 5
      periodSeconds: 60
    selectPolicy: Min

Example: disable scale down

The selectPolicy value of Disabled turns off scaling the given direction. So to prevent downscaling the following policy would be used:

behavior:
  scaleDown:
    selectPolicy: Disabled

Support for HorizontalPodAutoscaler in kubectl

HorizontalPodAutoscaler, like every API resource, is supported in a standard way by kubectl. You can create a new autoscaler using kubectl create command. You can list autoscalers by kubectl get hpa or get detailed description by kubectl describe hpa. Finally, you can delete an autoscaler using kubectl delete hpa.

In addition, there is a special kubectl autoscale command for creating a HorizontalPodAutoscaler object. For instance, executing kubectl autoscale rs foo --min=2 --max=5 --cpu-percent=80 will create an autoscaler for ReplicaSet foo, with target CPU utilization set to 80% and the number of replicas between 2 and 5.

Implicit maintenance-mode deactivation

You can implicitly deactivate the HPA for a target without the need to change the HPA configuration itself. If the target's desired replica count is set to 0, and the HPA's minimum replica count is greater than 0, the HPA stops adjusting the target (and sets the ScalingActive Condition on itself to false) until you reactivate it by manually adjusting the target's desired replica count or HPA's minimum replica count.

Migrating Deployments and StatefulSets to horizontal autoscaling

When an HPA is enabled, it is recommended that the value of spec.replicas of the Deployment and / or StatefulSet be removed from their manifest(s). If this isn't done, any time a change to that object is applied, for example via kubectl apply -f deployment.yaml, this will instruct Kubernetes to scale the current number of Pods to the value of the spec.replicas key. This may not be desired and could be troublesome when an HPA is active.

Keep in mind that the removal of spec.replicas may incur a one-time degradation of Pod counts as the default value of this key is 1 (reference Deployment Replicas). Upon the update, all Pods except 1 will begin their termination procedures. Any deployment application afterwards will behave as normal and respect a rolling update configuration as desired. You can avoid this degradation by choosing one of the following two methods based on how you are modifying your deployments:

Client Side Apply (this is the default)
Server Side Apply

kubectl apply edit-last-applied deployment/<deployment_name>
In the editor, remove spec.replicas. When you save and exit the editor, kubectl applies the update. No changes to Pod counts happen at this step.
You can now remove spec.replicas from the manifest. If you use source code management, also commit your changes or take whatever other steps for revising the source code are appropriate for how you track updates.
From here on out you can run kubectl apply -f deployment.yaml

When using the Server-Side Apply you can follow the transferring ownership guidelines, which cover this exact use case.

What's next

If you configure autoscaling in your cluster, you may also want to consider using cluster autoscaling to ensure you are running the right number of nodes.

For more information on HorizontalPodAutoscaler:

Read a walkthrough example for horizontal pod autoscaling.
Read documentation for kubectl autoscale.
If you would like to write your own custom metrics adapter, check out the boilerplate to get started.
Read the API reference for HorizontalPodAutoscaler.

8.8 - HorizontalPodAutoscaler Walkthrough

A HorizontalPodAutoscaler (HPA for short) automatically updates a workload resource (such as a Deployment or StatefulSet), with the aim of automatically scaling the workload to match demand.

This document walks you through an example of enabling HorizontalPodAutoscaler to automatically manage scale for an example web app. This example workload is Apache httpd running some PHP code.

Before you begin

Your Kubernetes server must be at or later than version 1.23. To check the version, enter kubectl version. If you're running an older release of Kubernetes, refer to the version of the documentation for that release (see available documentation versions).

To follow this walkthrough, you also need to use a cluster that has a Metrics Server deployed and configured. The Kubernetes Metrics Server collects resource metrics from the kubelets in your cluster, and exposes those metrics through the Kubernetes API, using an APIService to add new kinds of resource that represent metric readings.

To learn how to deploy the Metrics Server, see the metrics-server documentation.

If you are running Minikube, run the following command to enable metrics-server:

minikube addons enable metrics-server

Run and expose php-apache server

To demonstrate a HorizontalPodAutoscaler, you will first start a Deployment that runs a container using the hpa-example image, and expose it as a Service using the following manifest:

application/php-apache.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: php-apache
spec:
  selector:
    matchLabels:
      run: php-apache
  template:
    metadata:
      labels:
        run: php-apache
    spec:
      containers:
      - name: php-apache
        image: registry.k8s.io/hpa-example
        ports:
        - containerPort: 80
        resources:
          limits:
            cpu: 500m
          requests:
            cpu: 200m
---
apiVersion: v1
kind: Service
metadata:
  name: php-apache
  labels:
    run: php-apache
spec:
  ports:
  - port: 80
  selector:
    run: php-apache

To do so, run the following command:

kubectl apply -f https://k8s.io/examples/application/php-apache.yaml

deployment.apps/php-apache created
service/php-apache created

Create the HorizontalPodAutoscaler

Now that the server is running, create the autoscaler using kubectl. The kubectl autoscale subcommand, part of kubectl, helps you do this.

You will shortly run a command that creates a HorizontalPodAutoscaler that maintains between 1 and 10 replicas of the Pods controlled by the php-apache Deployment that you created in the first step of these instructions.

Roughly speaking, the HPA controller will increase and decrease the number of replicas (by updating the Deployment) to maintain an average CPU utilization across all Pods of 50%. The Deployment then updates the ReplicaSet - this is part of how all Deployments work in Kubernetes - and then the ReplicaSet either adds or removes Pods based on the change to its .spec.

Since each pod requests 200 milli-cores by kubectl run, this means an average CPU usage of 100 milli-cores. See Algorithm details for more details on the algorithm.

Create the HorizontalPodAutoscaler:

kubectl autoscale deployment php-apache --cpu-percent=50 --min=1 --max=10

horizontalpodautoscaler.autoscaling/php-apache autoscaled

You can check the current status of the newly-made HorizontalPodAutoscaler, by running:

# You can use "hpa" or "horizontalpodautoscaler"; either name works OK.
kubectl get hpa

The output is similar to:

NAME         REFERENCE                     TARGET    MINPODS   MAXPODS   REPLICAS   AGE
php-apache   Deployment/php-apache/scale   0% / 50%  1         10        1          18s

(if you see other HorizontalPodAutoscalers with different names, that means they already existed, and isn't usually a problem).

Please note that the current CPU consumption is 0% as there are no clients sending requests to the server (the TARGET column shows the average across all the Pods controlled by the corresponding deployment).

Increase the load

Next, see how the autoscaler reacts to increased load. To do this, you'll start a different Pod to act as a client. The container within the client Pod runs in an infinite loop, sending queries to the php-apache service.

# Run this in a separate terminal
# so that the load generation continues and you can carry on with the rest of the steps
kubectl run -i --tty load-generator --rm --image=busybox:1.28 --restart=Never -- /bin/sh -c "while sleep 0.01; do wget -q -O- http://php-apache; done"

Now run:

# type Ctrl+C to end the watch when you're ready
kubectl get hpa php-apache --watch

Within a minute or so, you should see the higher CPU load; for example:

NAME         REFERENCE                     TARGET      MINPODS   MAXPODS   REPLICAS   AGE
php-apache   Deployment/php-apache/scale   305% / 50%  1         10        1          3m

and then, more replicas. For example:

NAME         REFERENCE                     TARGET      MINPODS   MAXPODS   REPLICAS   AGE
php-apache   Deployment/php-apache/scale   305% / 50%  1         10        7          3m

Here, CPU consumption has increased to 305% of the request. As a result, the Deployment was resized to 7 replicas:

kubectl get deployment php-apache

You should see the replica count matching the figure from the HorizontalPodAutoscaler

NAME         READY   UP-TO-DATE   AVAILABLE   AGE
php-apache   7/7      7           7           19m

Note:

It may take a few minutes to stabilize the number of replicas. Since the amount of load is not controlled in any way it may happen that the final number of replicas will differ from this example.

Stop generating load

To finish the example, stop sending the load.

In the terminal where you created the Pod that runs a busybox image, terminate the load generation by typing <Ctrl> + C.

Then verify the result state (after a minute or so):

# type Ctrl+C to end the watch when you're ready
kubectl get hpa php-apache --watch

The output is similar to:

NAME         REFERENCE                     TARGET       MINPODS   MAXPODS   REPLICAS   AGE
php-apache   Deployment/php-apache/scale   0% / 50%     1         10        1          11m

and the Deployment also shows that it has scaled down:

kubectl get deployment php-apache

NAME         READY   UP-TO-DATE   AVAILABLE   AGE
php-apache   1/1     1            1           27m

Once CPU utilization dropped to 0, the HPA automatically scaled the number of replicas back down to 1.

Autoscaling the replicas may take a few minutes.

Autoscaling on multiple metrics and custom metrics

You can introduce additional metrics to use when autoscaling the php-apache Deployment by making use of the autoscaling/v2 API version.

First, get the YAML of your HorizontalPodAutoscaler in the autoscaling/v2 form:

kubectl get hpa php-apache -o yaml > /tmp/hpa-v2.yaml

Open the /tmp/hpa-v2.yaml file in an editor, and you should see YAML which looks like this:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: php-apache
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: php-apache
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50
status:
  observedGeneration: 1
  lastScaleTime: <some-time>
  currentReplicas: 1
  desiredReplicas: 1
  currentMetrics:
  - type: Resource
    resource:
      name: cpu
      current:
        averageUtilization: 0
        averageValue: 0

Notice that the targetCPUUtilizationPercentage field has been replaced with an array called metrics. The CPU utilization metric is a resource metric, since it is represented as a percentage of a resource specified on pod containers. Notice that you can specify other resource metrics besides CPU. By default, the only other supported resource metric is memory. These resources do not change names from cluster to cluster, and should always be available, as long as the metrics.k8s.io API is available.

You can also specify resource metrics in terms of direct values, instead of as percentages of the requested value, by using a target.type of AverageValue instead of Utilization, and setting the corresponding target.averageValue field instead of the target.averageUtilization.

  metrics:
  - type: Resource
    resource:
      name: memory
      target:
        type: AverageValue
        averageValue: 500Mi

There are two other types of metrics, both of which are considered custom metrics: pod metrics and object metrics. These metrics may have names which are cluster specific, and require a more advanced cluster monitoring setup.

The first of these alternative metric types is pod metrics. These metrics describe Pods, and are averaged together across Pods and compared with a target value to determine the replica count. They work much like resource metrics, except that they only support a target type of AverageValue.

Pod metrics are specified using a metric block like this:

type: Pods
pods:
  metric:
    name: packets-per-second
  target:
    type: AverageValue
    averageValue: 1k

The second alternative metric type is object metrics. These metrics describe a different object in the same namespace, instead of describing Pods. The metrics are not necessarily fetched from the object; they only describe it. Object metrics support target types of both Value and AverageValue. With Value, the target is compared directly to the returned metric from the API. With AverageValue, the value returned from the custom metrics API is divided by the number of Pods before being compared to the target. The following example is the YAML representation of the requests-per-second metric.

type: Object
object:
  metric:
    name: requests-per-second
  describedObject:
    apiVersion: networking.k8s.io/v1
    kind: Ingress
    name: main-route
  target:
    type: Value
    value: 2k

If you provide multiple such metric blocks, the HorizontalPodAutoscaler will consider each metric in turn. The HorizontalPodAutoscaler will calculate proposed replica counts for each metric, and then choose the one with the highest replica count.

For example, if you had your monitoring system collecting metrics about network traffic, you could update the definition above using kubectl edit to look like this:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: php-apache
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: php-apache
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50
  - type: Pods
    pods:
      metric:
        name: packets-per-second
      target:
        type: AverageValue
        averageValue: 1k
  - type: Object
    object:
      metric:
        name: requests-per-second
      describedObject:
        apiVersion: networking.k8s.io/v1
        kind: Ingress
        name: main-route
      target:
        type: Value
        value: 10k
status:
  observedGeneration: 1
  lastScaleTime: <some-time>
  currentReplicas: 1
  desiredReplicas: 1
  currentMetrics:
  - type: Resource
    resource:
      name: cpu
    current:
      averageUtilization: 0
      averageValue: 0
  - type: Object
    object:
      metric:
        name: requests-per-second
      describedObject:
        apiVersion: networking.k8s.io/v1
        kind: Ingress
        name: main-route
      current:
        value: 10k

Then, your HorizontalPodAutoscaler would attempt to ensure that each pod was consuming roughly 50% of its requested CPU, serving 1000 packets per second, and that all pods behind the main-route Ingress were serving a total of 10000 requests per second.

Autoscaling on more specific metrics

Many metrics pipelines allow you to describe metrics either by name or by a set of additional descriptors called labels. For all non-resource metric types (pod, object, and external, described below), you can specify an additional label selector which is passed to your metric pipeline. For instance, if you collect a metric http_requests with the verb label, you can specify the following metric block to scale only on GET requests:

type: Object
object:
  metric:
    name: http_requests
    selector: {matchLabels: {verb: GET}}

This selector uses the same syntax as the full Kubernetes label selectors. The monitoring pipeline determines how to collapse multiple series into a single value, if the name and selector match multiple series. The selector is additive, and cannot select metrics that describe objects that are not the target object (the target pods in the case of the Pods type, and the described object in the case of the Object type).

Applications running on Kubernetes may need to autoscale based on metrics that don't have an obvious relationship to any object in the Kubernetes cluster, such as metrics describing a hosted service with no direct correlation to Kubernetes namespaces. In Kubernetes 1.10 and later, you can address this use case with external metrics.

Using external metrics requires knowledge of your monitoring system; the setup is similar to that required when using custom metrics. External metrics allow you to autoscale your cluster based on any metric available in your monitoring system. Provide a metric block with a name and selector, as above, and use the External metric type instead of Object. If multiple time series are matched by the metricSelector, the sum of their values is used by the HorizontalPodAutoscaler. External metrics support both the Value and AverageValue target types, which function exactly the same as when you use the Object type.

For example if your application processes tasks from a hosted queue service, you could add the following section to your HorizontalPodAutoscaler manifest to specify that you need one worker per 30 outstanding tasks.

- type: External
  external:
    metric:
      name: queue_messages_ready
      selector:
        matchLabels:
          queue: "worker_tasks"
    target:
      type: AverageValue
      averageValue: 30

When possible, it's preferable to use the custom metric target types instead of external metrics, since it's easier for cluster administrators to secure the custom metrics API. The external metrics API potentially allows access to any metric, so cluster administrators should take care when exposing it.

Appendix: Horizontal Pod Autoscaler Status Conditions

When using the autoscaling/v2 form of the HorizontalPodAutoscaler, you will be able to see status conditions set by Kubernetes on the HorizontalPodAutoscaler. These status conditions indicate whether or not the HorizontalPodAutoscaler is able to scale, and whether or not it is currently restricted in any way.

The conditions appear in the status.conditions field. To see the conditions affecting a HorizontalPodAutoscaler, we can use kubectl describe hpa:

kubectl describe hpa cm-test

Name:                           cm-test
Namespace:                      prom
Labels:                         <none>
Annotations:                    <none>
CreationTimestamp:              Fri, 16 Jun 2017 18:09:22 +0000
Reference:                      ReplicationController/cm-test
Metrics:                        ( current / target )
  "http_requests" on pods:      66m / 500m
Min replicas:                   1
Max replicas:                   4
ReplicationController pods:     1 current / 1 desired
Conditions:
  Type                  Status  Reason                  Message
  ----                  ------  ------                  -------
  AbleToScale           True    ReadyForNewScale        the last scale time was sufficiently old as to warrant a new scale
  ScalingActive         True    ValidMetricFound        the HPA was able to successfully calculate a replica count from pods metric http_requests
  ScalingLimited        False   DesiredWithinRange      the desired replica count is within the acceptable range
Events:

For this HorizontalPodAutoscaler, you can see several conditions in a healthy state. The first, AbleToScale, indicates whether or not the HPA is able to fetch and update scales, as well as whether or not any backoff-related conditions would prevent scaling. The second, ScalingActive, indicates whether or not the HPA is enabled (i.e. the replica count of the target is not zero) and is able to calculate desired scales. When it is False, it generally indicates problems with fetching metrics. Finally, the last condition, ScalingLimited, indicates that the desired scale was capped by the maximum or minimum of the HorizontalPodAutoscaler. This is an indication that you may wish to raise or lower the minimum or maximum replica count constraints on your HorizontalPodAutoscaler.

Quantities

All metrics in the HorizontalPodAutoscaler and metrics APIs are specified using a special whole-number notation known in Kubernetes as a quantity. For example, the quantity 10500m would be written as 10.5 in decimal notation. The metrics APIs will return whole numbers without a suffix when possible, and will generally return quantities in milli-units otherwise. This means you might see your metric value fluctuate between 1 and 1500m, or 1 and 1.5 when written in decimal notation.

Other possible scenarios

Creating the autoscaler declaratively

Instead of using kubectl autoscale command to create a HorizontalPodAutoscaler imperatively we can use the following manifest to create it declaratively:

application/hpa/php-apache.yaml

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: php-apache
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: php-apache
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

Then, create the autoscaler by executing the following command:

kubectl create -f https://k8s.io/examples/application/hpa/php-apache.yaml

horizontalpodautoscaler.autoscaling/php-apache created

8.9 - Specifying a Disruption Budget for your Application

FEATURE STATE: Kubernetes v1.21 [stable]

This page shows how to limit the number of concurrent disruptions that your application experiences, allowing for higher availability while permitting the cluster administrator to manage the clusters nodes.

Before you begin

Your Kubernetes server must be at or later than version v1.21. To check the version, enter kubectl version.

You are the owner of an application running on a Kubernetes cluster that requires high availability.
You should know how to deploy Replicated Stateless Applications and/or Replicated Stateful Applications.
You should have read about Pod Disruptions.
You should confirm with your cluster owner or service provider that they respect Pod Disruption Budgets.

Protecting an Application with a PodDisruptionBudget

Identify what application you want to protect with a PodDisruptionBudget (PDB).
Think about how your application reacts to disruptions.
Create a PDB definition as a YAML file.
Create the PDB object from the YAML file.

Identify an Application to Protect

The most common use case when you want to protect an application specified by one of the built-in Kubernetes controllers:

Deployment
ReplicationController
ReplicaSet
StatefulSet

In this case, make a note of the controller's .spec.selector; the same selector goes into the PDBs .spec.selector.

From version 1.15 PDBs support custom controllers where the scale subresource is enabled.

You can also use PDBs with pods which are not controlled by one of the above controllers, or arbitrary groups of pods, but there are some restrictions, described in Arbitrary workloads and arbitrary selectors.

Think about how your application reacts to disruptions

Decide how many instances can be down at the same time for a short period due to a voluntary disruption.

Stateless frontends:
- Concern: don't reduce serving capacity by more than 10%.
  - Solution: use PDB with minAvailable 90% for example.
Single-instance Stateful Application:
- Concern: do not terminate this application without talking to me.
  - Possible Solution 1: Do not use a PDB and tolerate occasional downtime.
  - Possible Solution 2: Set PDB with maxUnavailable=0. Have an understanding (outside of Kubernetes) that the cluster operator needs to consult you before termination. When the cluster operator contacts you, prepare for downtime, and then delete the PDB to indicate readiness for disruption. Recreate afterwards.
Multiple-instance Stateful application such as Consul, ZooKeeper, or etcd:
- Concern: Do not reduce number of instances below quorum, otherwise writes fail.
  - Possible Solution 1: set maxUnavailable to 1 (works with varying scale of application).
  - Possible Solution 2: set minAvailable to quorum-size (e.g. 3 when scale is 5). (Allows more disruptions at once).
Restartable Batch Job:
- Concern: Job needs to complete in case of voluntary disruption.
  - Possible solution: Do not create a PDB. The Job controller will create a replacement pod.

Rounding logic when specifying percentages

Values for minAvailable or maxUnavailable can be expressed as integers or as a percentage.

When you specify an integer, it represents a number of Pods. For instance, if you set minAvailable to 10, then 10 Pods must always be available, even during a disruption.
When you specify a percentage by setting the value to a string representation of a percentage (eg. "50%"), it represents a percentage of total Pods. For instance, if you set minAvailable to "50%", then at least 50% of the Pods remain available during a disruption.

When you specify the value as a percentage, it may not map to an exact number of Pods. For example, if you have 7 Pods and you set minAvailable to "50%", it's not immediately obvious whether that means 3 Pods or 4 Pods must be available. Kubernetes rounds up to the nearest integer, so in this case, 4 Pods must be available. When you specify the value maxUnavailable as a percentage, Kubernetes rounds up the number of Pods that may be disrupted. Thereby a disruption can exceed your defined maxUnavailable percentage. You can examine the code that controls this behavior.

Specifying a PodDisruptionBudget

A PodDisruptionBudget has three fields:

A label selector .spec.selector to specify the set of pods to which it applies. This field is required.
.spec.minAvailable which is a description of the number of pods from that set that must still be available after the eviction, even in the absence of the evicted pod. minAvailable can be either an absolute number or a percentage.
.spec.maxUnavailable (available in Kubernetes 1.7 and higher) which is a description of the number of pods from that set that can be unavailable after the eviction. It can be either an absolute number or a percentage.

Note:

The behavior for an empty selector differs between the policy/v1beta1 and policy/v1 APIs for PodDisruptionBudgets. For policy/v1beta1 an empty selector matches zero pods, while for policy/v1 an empty selector matches every pod in the namespace.

You can specify only one of maxUnavailable and minAvailable in a single PodDisruptionBudget. maxUnavailable can only be used to control the eviction of pods that have an associated controller managing them. In the examples below, "desired replicas" is the scale of the controller managing the pods being selected by the PodDisruptionBudget.

Example 1: With a minAvailable of 5, evictions are allowed as long as they leave behind 5 or more healthy pods among those selected by the PodDisruptionBudget's selector.

Example 2: With a minAvailable of 30%, evictions are allowed as long as at least 30% of the number of desired replicas are healthy.

Example 3: With a maxUnavailable of 5, evictions are allowed as long as there are at most 5 unhealthy replicas among the total number of desired replicas.

Example 4: With a maxUnavailable of 30%, evictions are allowed as long as the number of unhealthy replicas does not exceed 30% of the total number of desired replica rounded up to the nearest integer. If the total number of desired replicas is just one, that single replica is still allowed for disruption, leading to an effective unavailability of 100%.

In typical usage, a single budget would be used for a collection of pods managed by a controller—for example, the pods in a single ReplicaSet or StatefulSet.

Note:

A disruption budget does not truly guarantee that the specified number/percentage of pods will always be up. For example, a node that hosts a pod from the collection may fail when the collection is at the minimum size specified in the budget, thus bringing the number of available pods from the collection below the specified size. The budget can only protect against voluntary evictions, not all causes of unavailability.

If you set maxUnavailable to 0% or 0, or you set minAvailable to 100% or the number of replicas, you are requiring zero voluntary evictions. When you set zero voluntary evictions for a workload object such as ReplicaSet, then you cannot successfully drain a Node running one of those Pods. If you try to drain a Node where an unevictable Pod is running, the drain never completes. This is permitted as per the semantics of PodDisruptionBudget.

You can find examples of pod disruption budgets defined below. They match pods with the label app: zookeeper.

Example PDB Using minAvailable:

policy/zookeeper-pod-disruption-budget-minavailable.yaml

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: zk-pdb
spec:
  minAvailable: 2
  selector:
    matchLabels:
      app: zookeeper

Example PDB Using maxUnavailable:

policy/zookeeper-pod-disruption-budget-maxunavailable.yaml

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: zk-pdb
spec:
  maxUnavailable: 1
  selector:
    matchLabels:
      app: zookeeper

For example, if the above zk-pdb object selects the pods of a StatefulSet of size 3, both specifications have the exact same meaning. The use of maxUnavailable is recommended as it automatically responds to changes in the number of replicas of the corresponding controller.

Create the PDB object

You can create or update the PDB object using kubectl.

kubectl apply -f mypdb.yaml

Check the status of the PDB

Use kubectl to check that your PDB is created.

Assuming you don't actually have pods matching app: zookeeper in your namespace, then you'll see something like this:

kubectl get poddisruptionbudgets

NAME     MIN AVAILABLE   MAX UNAVAILABLE   ALLOWED DISRUPTIONS   AGE
zk-pdb   2               N/A               0                     7s

If there are matching pods (say, 3), then you would see something like this:

kubectl get poddisruptionbudgets

NAME     MIN AVAILABLE   MAX UNAVAILABLE   ALLOWED DISRUPTIONS   AGE
zk-pdb   2               N/A               1                     7s

The non-zero value for ALLOWED DISRUPTIONS means that the disruption controller has seen the pods, counted the matching pods, and updated the status of the PDB.

You can get more information about the status of a PDB with this command:

kubectl get poddisruptionbudgets zk-pdb -o yaml

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  annotations:
…
  creationTimestamp: "2020-03-04T04:22:56Z"
  generation: 1
  name: zk-pdb
…
status:
  currentHealthy: 3
  desiredHealthy: 2
  disruptionsAllowed: 1
  expectedPods: 3
  observedGeneration: 1

Healthiness of a Pod

The current implementation considers healthy pods, as pods that have .status.conditions item with type="Ready" and status="True". These pods are tracked via .status.currentHealthy field in the PDB status.

Unhealthy Pod Eviction Policy

FEATURE STATE: Kubernetes v1.27 [beta]

Note:

This feature is enabled by default. You can disable it by disabling the PDBUnhealthyPodEvictionPolicy feature gate on the API server.

PodDisruptionBudget guarding an application ensures that .status.currentHealthy number of pods does not fall below the number specified in .status.desiredHealthy by disallowing eviction of healthy pods. By using .spec.unhealthyPodEvictionPolicy, you can also define the criteria when unhealthy pods should be considered for eviction. The default behavior when no policy is specified corresponds to the IfHealthyBudget policy.

Policies:

IfHealthyBudget: Running pods (.status.phase="Running"), but not yet healthy can be evicted only if the guarded application is not disrupted (.status.currentHealthy is at least equal to .status.desiredHealthy).; This policy ensures that running pods of an already disrupted application have the best chance to become healthy. This has negative implications for draining nodes, which can be blocked by misbehaving applications that are guarded by a PDB. More specifically applications with pods in CrashLoopBackOff state (due to a bug or misconfiguration), or pods that are just failing to report the Ready condition.
AlwaysAllow: Running pods (.status.phase="Running"), but not yet healthy are considered disrupted and can be evicted regardless of whether the criteria in a PDB is met.; This means prospective running pods of a disrupted application might not get a chance to become healthy. By using this policy, cluster managers can easily evict misbehaving applications that are guarded by a PDB. More specifically applications with pods in CrashLoopBackOff state (due to a bug or misconfiguration), or pods that are just failing to report the Ready condition.

Note:

Pods in Pending, Succeeded or Failed phase are always considered for eviction.

Arbitrary workloads and arbitrary selectors

You can skip this section if you only use PDBs with the built-in workload resources (Deployment, ReplicaSet, StatefulSet and ReplicationController) or with custom resources that implement a scale subresource, and where the PDB selector exactly matches the selector of the Pod's owning resource.

You can use a PDB with pods controlled by another resource, by an "operator", or bare pods, but with these restrictions:

only .spec.minAvailable can be used, not .spec.maxUnavailable.
only an integer value can be used with .spec.minAvailable, not a percentage.

It is not possible to use other availability configurations, because Kubernetes cannot derive a total number of pods without a supported owning resource.

You can use a selector which selects a subset or superset of the pods belonging to a workload resource. The eviction API will disallow eviction of any pod covered by multiple PDBs, so most users will want to avoid overlapping selectors. One reasonable use of overlapping PDBs is when pods are being transitioned from one PDB to another.

8.10 - Accessing the Kubernetes API from a Pod

This guide demonstrates how to access the Kubernetes API from within a pod.

Before you begin

Accessing the API from within a Pod

When accessing the API from within a Pod, locating and authenticating to the API server are slightly different to the external client case.

The easiest way to use the Kubernetes API from a Pod is to use one of the official client libraries. These libraries can automatically discover the API server and authenticate.

Using Official Client Libraries

From within a Pod, the recommended ways to connect to the Kubernetes API are:

For a Go client, use the official Go client library. The rest.InClusterConfig() function handles API host discovery and authentication automatically. See an example here.
For a Python client, use the official Python client library. The config.load_incluster_config() function handles API host discovery and authentication automatically. See an example here.
There are a number of other libraries available, please refer to the Client Libraries page.

In each case, the service account credentials of the Pod are used to communicate securely with the API server.

Directly accessing the REST API

While running in a Pod, your container can create an HTTPS URL for the Kubernetes API server by fetching the KUBERNETES_SERVICE_HOST and KUBERNETES_SERVICE_PORT_HTTPS environment variables. The API server's in-cluster address is also published to a Service named kubernetes in the default namespace so that pods may reference kubernetes.default.svc as a DNS name for the local API server.

Note:

Kubernetes does not guarantee that the API server has a valid certificate for the hostname kubernetes.default.svc; however, the control plane is expected to present a valid certificate for the hostname or IP address that $KUBERNETES_SERVICE_HOST represents.

The recommended way to authenticate to the API server is with a service account credential. By default, a Pod is associated with a service account, and a credential (token) for that service account is placed into the filesystem tree of each container in that Pod, at /var/run/secrets/kubernetes.io/serviceaccount/token.

If available, a certificate bundle is placed into the filesystem tree of each container at /var/run/secrets/kubernetes.io/serviceaccount/ca.crt, and should be used to verify the serving certificate of the API server.

Finally, the default namespace to be used for namespaced API operations is placed in a file at /var/run/secrets/kubernetes.io/serviceaccount/namespace in each container.

Using kubectl proxy

If you would like to query the API without an official client library, you can run kubectl proxy as the command of a new sidecar container in the Pod. This way, kubectl proxy will authenticate to the API and expose it on the localhost interface of the Pod, so that other containers in the Pod can use it directly.

Without using a proxy

It is possible to avoid using the kubectl proxy by passing the authentication token directly to the API server. The internal certificate secures the connection.

# Point to the internal API server hostname
APISERVER=https://kubernetes.default.svc

# Path to ServiceAccount token
SERVICEACCOUNT=/var/run/secrets/kubernetes.io/serviceaccount

# Read this Pod's namespace
NAMESPACE=$(cat ${SERVICEACCOUNT}/namespace)

# Read the ServiceAccount bearer token
TOKEN=$(cat ${SERVICEACCOUNT}/token)

# Reference the internal certificate authority (CA)
CACERT=${SERVICEACCOUNT}/ca.crt

# Explore the API with TOKEN
curl --cacert ${CACERT} --header "Authorization: Bearer ${TOKEN}" -X GET ${APISERVER}/api

The output will be similar to this:

{
  "kind": "APIVersions",
  "versions": ["v1"],
  "serverAddressByClientCIDRs": [
    {
      "clientCIDR": "0.0.0.0/0",
      "serverAddress": "10.0.1.149:443"
    }
  ]
}

9 - Run Jobs

Run Jobs using parallel processing.

9.1 - Running Automated Tasks with a CronJob

This page shows how to run automated tasks using Kubernetes CronJob object.

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes

Creating a CronJob

Cron jobs require a config file. Here is a manifest for a CronJob that runs a simple demonstration task every minute:

application/job/cronjob.yaml

apiVersion: batch/v1
kind: CronJob
metadata:
  name: hello
spec:
  schedule: "* * * * *"
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: hello
            image: busybox:1.28
            imagePullPolicy: IfNotPresent
            command:
            - /bin/sh
            - -c
            - date; echo Hello from the Kubernetes cluster
          restartPolicy: OnFailure

Run the example CronJob by using this command:

kubectl create -f https://k8s.io/examples/application/job/cronjob.yaml

The output is similar to this:

cronjob.batch/hello created

After creating the cron job, get its status using this command:

kubectl get cronjob hello

The output is similar to this:

NAME    SCHEDULE      SUSPEND   ACTIVE   LAST SCHEDULE   AGE
hello   */1 * * * *   False     0        <none>          10s

As you can see from the results of the command, the cron job has not scheduled or run any jobs yet. Watch for the job to be created in around one minute:

kubectl get jobs --watch

The output is similar to this:

NAME               COMPLETIONS   DURATION   AGE
hello-4111706356   0/1                      0s
hello-4111706356   0/1           0s         0s
hello-4111706356   1/1           5s         5s

Now you've seen one running job scheduled by the "hello" cron job. You can stop watching the job and view the cron job again to see that it scheduled the job:

kubectl get cronjob hello

The output is similar to this:

NAME    SCHEDULE      SUSPEND   ACTIVE   LAST SCHEDULE   AGE
hello   */1 * * * *   False     0        50s             75s

You should see that the cron job hello successfully scheduled a job at the time specified in LAST SCHEDULE. There are currently 0 active jobs, meaning that the job has completed or failed.

Now, find the pods that the last scheduled job created and view the standard output of one of the pods.

Note:

The job name is different from the pod name.

# Replace "hello-4111706356" with the job name in your system
pods=$(kubectl get pods --selector=job-name=hello-4111706356 --output=jsonpath={.items[*].metadata.name})

Show the pod log:

kubectl logs $pods

The output is similar to this:

Fri Feb 22 11:02:09 UTC 2019
Hello from the Kubernetes cluster

Deleting a CronJob

When you don't need a cron job any more, delete it with kubectl delete cronjob <cronjob name>:

kubectl delete cronjob hello

Deleting the cron job removes all the jobs and pods it created and stops it from creating additional jobs. You can read more about removing jobs in garbage collection.

9.2 - Coarse Parallel Processing Using a Work Queue

In this example, you will run a Kubernetes Job with multiple parallel worker processes.

In this example, as each pod is created, it picks up one unit of work from a task queue, completes it, deletes it from the queue, and exits.

Here is an overview of the steps in this example:

Start a message queue service. In this example, you use RabbitMQ, but you could use another one. In practice you would set up a message queue service once and reuse it for many jobs.
Create a queue, and fill it with messages. Each message represents one task to be done. In this example, a message is an integer that we will do a lengthy computation on.
Start a Job that works on tasks from the queue. The Job starts several pods. Each pod takes one task from the message queue, processes it, and exits.

Before you begin

You should already be familiar with the basic, non-parallel, use of Job.

You will need a container image registry where you can upload images to run in your cluster.

This task example also assumes that you have Docker installed locally.

Starting a message queue service

This example uses RabbitMQ, however, you can adapt the example to use another AMQP-type message service.

In practice you could set up a message queue service once in a cluster and reuse it for many jobs, as well as for long-running services.

Start RabbitMQ as follows:

# make a Service for the StatefulSet to use
kubectl create -f https://kubernetes.io/examples/application/job/rabbitmq/rabbitmq-service.yaml

service "rabbitmq-service" created

kubectl create -f https://kubernetes.io/examples/application/job/rabbitmq/rabbitmq-statefulset.yaml

statefulset "rabbitmq" created

Testing the message queue service

Now, we can experiment with accessing the message queue. We will create a temporary interactive pod, install some tools on it, and experiment with queues.

First create a temporary interactive Pod.

# Create a temporary interactive container
kubectl run -i --tty temp --image ubuntu:22.04

Waiting for pod default/temp-loe07 to be running, status is Pending, pod ready: false
... [ previous line repeats several times .. hit return when it stops ] ...

Note that your pod name and command prompt will be different.

Next install the amqp-tools so you can work with message queues. The next commands show what you need to run inside the interactive shell in that Pod:

apt-get update && apt-get install -y curl ca-certificates amqp-tools python3 dnsutils

Later, you will make a container image that includes these packages.

Next, you will check that you can discover the Service for RabbitMQ:

# Run these commands inside the Pod
# Note the rabbitmq-service has a DNS name, provided by Kubernetes:
nslookup rabbitmq-service

Server:        10.0.0.10
Address:    10.0.0.10#53

Name:    rabbitmq-service.default.svc.cluster.local
Address: 10.0.147.152

(the IP addresses will vary)

If the kube-dns addon is not set up correctly, the previous step may not work for you. You can also find the IP address for that Service in an environment variable:

# run this check inside the Pod
env | grep RABBITMQ_SERVICE | grep HOST

RABBITMQ_SERVICE_SERVICE_HOST=10.0.147.152

(the IP address will vary)

Next you will verify that you can create a queue, and publish and consume messages.

# Run these commands inside the Pod
# In the next line, rabbitmq-service is the hostname where the rabbitmq-service
# can be reached.  5672 is the standard port for rabbitmq.
export BROKER_URL=amqp://guest:guest@rabbitmq-service:5672
# If you could not resolve "rabbitmq-service" in the previous step,
# then use this command instead:
BROKER_URL=amqp://guest:guest@$RABBITMQ_SERVICE_SERVICE_HOST:5672

# Now create a queue:

/usr/bin/amqp-declare-queue --url=$BROKER_URL -q foo -d

foo

Publish one message to the queue:

/usr/bin/amqp-publish --url=$BROKER_URL -r foo -p -b Hello

# And get it back.

/usr/bin/amqp-consume --url=$BROKER_URL -q foo -c 1 cat && echo 1>&2

Hello

In the last command, the amqp-consume tool took one message (-c 1) from the queue, and passes that message to the standard input of an arbitrary command. In this case, the program cat prints out the characters read from standard input, and the echo adds a carriage return so the example is readable.

Fill the queue with tasks

Now, fill the queue with some simulated tasks. In this example, the tasks are strings to be printed.

In a practice, the content of the messages might be:

names of files to that need to be processed
extra flags to the program
ranges of keys in a database table
configuration parameters to a simulation
frame numbers of a scene to be rendered

If there is large data that is needed in a read-only mode by all pods of the Job, you typically put that in a shared file system like NFS and mount that readonly on all the pods, or write the program in the pod so that it can natively read data from a cluster file system (for example: HDFS).

For this example, you will create the queue and fill it using the AMQP command line tools. In practice, you might write a program to fill the queue using an AMQP client library.

# Run this on your computer, not in the Pod
/usr/bin/amqp-declare-queue --url=$BROKER_URL -q job1  -d

job1

Add items to the queue:

for f in apple banana cherry date fig grape lemon melon
do
  /usr/bin/amqp-publish --url=$BROKER_URL -r job1 -p -b $f
done

You added 8 messages to the queue.

Create a container image

Now you are ready to create an image that you will run as a Job.

The job will use the amqp-consume utility to read the message from the queue and run the actual work. Here is a very simple example program:

application/job/rabbitmq/worker.py

#!/usr/bin/env python

# Just prints standard out and sleeps for 10 seconds.
import sys
import time
print("Processing " + sys.stdin.readlines()[0])
time.sleep(10)

Give the script execution permission:

chmod +x worker.py

Now, build an image. Make a temporary directory, change to it, download the Dockerfile, and worker.py. In either case, build the image with this command:

docker build -t job-wq-1 .

For the Docker Hub, tag your app image with your username and push to the Hub with the below commands. Replace <username> with your Hub username.

docker tag job-wq-1 <username>/job-wq-1
docker push <username>/job-wq-1

If you are using an alternative container image registry, tag the image and push it there instead.

Defining a Job

Here is a manifest for a Job. You'll need to make a copy of the Job manifest (call it ./job.yaml), and edit the name of the container image to match the name you used.

application/job/rabbitmq/job.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: job-wq-1
spec:
  completions: 8
  parallelism: 2
  template:
    metadata:
      name: job-wq-1
    spec:
      containers:
      - name: c
        image: gcr.io/<project>/job-wq-1
        env:
        - name: BROKER_URL
          value: amqp://guest:guest@rabbitmq-service:5672
        - name: QUEUE
          value: job1
      restartPolicy: OnFailure

In this example, each pod works on one item from the queue and then exits. So, the completion count of the Job corresponds to the number of work items done. That is why the example manifest has .spec.completions set to 8.

Running the Job

Now, run the Job:

# this assumes you downloaded and then edited the manifest already
kubectl apply -f ./job.yaml

You can wait for the Job to succeed, with a timeout:

# The check for condition name is case insensitive
kubectl wait --for=condition=complete --timeout=300s job/job-wq-1

Next, check on the Job:

kubectl describe jobs/job-wq-1

Name:             job-wq-1
Namespace:        default
Selector:         controller-uid=41d75705-92df-11e7-b85e-fa163ee3c11f
Labels:           controller-uid=41d75705-92df-11e7-b85e-fa163ee3c11f
                  job-name=job-wq-1
Annotations:      <none>
Parallelism:      2
Completions:      8
Start Time:       Wed, 06 Sep 2022 16:42:02 +0000
Pods Statuses:    0 Running / 8 Succeeded / 0 Failed
Pod Template:
  Labels:       controller-uid=41d75705-92df-11e7-b85e-fa163ee3c11f
                job-name=job-wq-1
  Containers:
   c:
    Image:      container-registry.example/causal-jigsaw-637/job-wq-1
    Port:
    Environment:
      BROKER_URL:       amqp://guest:guest@rabbitmq-service:5672
      QUEUE:            job1
    Mounts:             <none>
  Volumes:              <none>
Events:
  FirstSeen  LastSeen   Count    From    SubobjectPath    Type      Reason              Message
  ─────────  ────────   ─────    ────    ─────────────    ──────    ──────              ───────
  27s        27s        1        {job }                   Normal    SuccessfulCreate    Created pod: job-wq-1-hcobb
  27s        27s        1        {job }                   Normal    SuccessfulCreate    Created pod: job-wq-1-weytj
  27s        27s        1        {job }                   Normal    SuccessfulCreate    Created pod: job-wq-1-qaam5
  27s        27s        1        {job }                   Normal    SuccessfulCreate    Created pod: job-wq-1-b67sr
  26s        26s        1        {job }                   Normal    SuccessfulCreate    Created pod: job-wq-1-xe5hj
  15s        15s        1        {job }                   Normal    SuccessfulCreate    Created pod: job-wq-1-w2zqe
  14s        14s        1        {job }                   Normal    SuccessfulCreate    Created pod: job-wq-1-d6ppa
  14s        14s        1        {job }                   Normal    SuccessfulCreate    Created pod: job-wq-1-p17e0

All the pods for that Job succeeded! You're done.

Alternatives

This approach has the advantage that you do not need to modify your "worker" program to be aware that there is a work queue. You can include the worker program unmodified in your container image.

Using this approach does require that you run a message queue service. If running a queue service is inconvenient, you may want to consider one of the other job patterns.

This approach creates a pod for every work item. If your work items only take a few seconds, though, creating a Pod for every work item may add a lot of overhead. Consider another design, such as in the fine parallel work queue example, that executes multiple work items per Pod.

In this example, you used the amqp-consume utility to read the message from the queue and run the actual program. This has the advantage that you do not need to modify your program to be aware of the queue. The fine parallel work queue example shows how to communicate with the work queue using a client library.

Caveats

If the number of completions is set to less than the number of items in the queue, then not all items will be processed.

If the number of completions is set to more than the number of items in the queue, then the Job will not appear to be completed, even though all items in the queue have been processed. It will start additional pods which will block waiting for a message. You would need to make your own mechanism to spot when there is work to do and measure the size of the queue, setting the number of completions to match.

There is an unlikely race with this pattern. If the container is killed in between the time that the message is acknowledged by the amqp-consume command and the time that the container exits with success, or if the node crashes before the kubelet is able to post the success of the pod back to the API server, then the Job will not appear to be complete, even though all items in the queue have been processed.

9.3 - Fine Parallel Processing Using a Work Queue

In this example, you will run a Kubernetes Job that runs multiple parallel tasks as worker processes, each running as a separate Pod.

In this example, as each pod is created, it picks up one unit of work from a task queue, processes it, and repeats until the end of the queue is reached.

Here is an overview of the steps in this example:

Start a storage service to hold the work queue. In this example, you will use Redis to store work items. In the previous example, you used RabbitMQ. In this example, you will use Redis and a custom work-queue client library; this is because AMQP does not provide a good way for clients to detect when a finite-length work queue is empty. In practice you would set up a store such as Redis once and reuse it for the work queues of many jobs, and other things.
Create a queue, and fill it with messages. Each message represents one task to be done. In this example, a message is an integer that we will do a lengthy computation on.
Start a Job that works on tasks from the queue. The Job starts several pods. Each pod takes one task from the message queue, processes it, and repeats until the end of the queue is reached.

Before you begin

You will need a container image registry where you can upload images to run in your cluster. The example uses Docker Hub, but you could adapt it to a different container image registry.

This task example also assumes that you have Docker installed locally. You use Docker to build container images.

Be familiar with the basic, non-parallel, use of Job.

Starting Redis

For this example, for simplicity, you will start a single instance of Redis. See the Redis Example for an example of deploying Redis scalably and redundantly.

You could also download the following files directly:

To start a single instance of Redis, you need to create the redis pod and redis service:

kubectl apply -f https://k8s.io/examples/application/job/redis/redis-pod.yaml
kubectl apply -f https://k8s.io/examples/application/job/redis/redis-service.yaml

Filling the queue with tasks

Now let's fill the queue with some "tasks". In this example, the tasks are strings to be printed.

Start a temporary interactive pod for running the Redis CLI.

kubectl run -i --tty temp --image redis --command "/bin/sh"

Waiting for pod default/redis2-c7h78 to be running, status is Pending, pod ready: false
Hit enter for command prompt

Now hit enter, start the Redis CLI, and create a list with some work items in it.

redis-cli -h redis

redis:6379> rpush job2 "apple"
(integer) 1
redis:6379> rpush job2 "banana"
(integer) 2
redis:6379> rpush job2 "cherry"
(integer) 3
redis:6379> rpush job2 "date"
(integer) 4
redis:6379> rpush job2 "fig"
(integer) 5
redis:6379> rpush job2 "grape"
(integer) 6
redis:6379> rpush job2 "lemon"
(integer) 7
redis:6379> rpush job2 "melon"
(integer) 8
redis:6379> rpush job2 "orange"
(integer) 9
redis:6379> lrange job2 0 -1
1) "apple"
2) "banana"
3) "cherry"
4) "date"
5) "fig"
6) "grape"
7) "lemon"
8) "melon"
9) "orange"

So, the list with key job2 will be the work queue.

Note: if you do not have Kube DNS setup correctly, you may need to change the first step of the above block to redis-cli -h $REDIS_SERVICE_HOST.

Create a container image

Now you are ready to create an image that will process the work in that queue.

You're going to use a Python worker program with a Redis client to read the messages from the message queue.

A simple Redis work queue client library is provided, called rediswq.py (Download).

The "worker" program in each Pod of the Job uses the work queue client library to get work. Here it is:

application/job/redis/worker.py

#!/usr/bin/env python

import time
import rediswq

host="redis"
# Uncomment next two lines if you do not have Kube-DNS working.
# import os
# host = os.getenv("REDIS_SERVICE_HOST")

q = rediswq.RedisWQ(name="job2", host=host)
print("Worker with sessionID: " +  q.sessionID())
print("Initial queue state: empty=" + str(q.empty()))
while not q.empty():
  item = q.lease(lease_secs=10, block=True, timeout=2) 
  if item is not None:
    itemstr = item.decode("utf-8")
    print("Working on " + itemstr)
    time.sleep(10) # Put your actual work here instead of sleep.
    q.complete(item)
  else:
    print("Waiting for work")
print("Queue empty, exiting")

You could also download worker.py, rediswq.py, and Dockerfile files, then build the container image. Here's an example using Docker to do the image build:

docker build -t job-wq-2 .

Push the image

For the Docker Hub, tag your app image with your username and push to the Hub with the below commands. Replace <username> with your Hub username.

docker tag job-wq-2 <username>/job-wq-2
docker push <username>/job-wq-2

You need to push to a public repository or configure your cluster to be able to access your private repository.

Defining a Job

Here is a manifest for the Job you will create:

application/job/redis/job.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: job-wq-2
spec:
  parallelism: 2
  template:
    metadata:
      name: job-wq-2
    spec:
      containers:
      - name: c
        image: gcr.io/myproject/job-wq-2
      restartPolicy: OnFailure

Note:

Be sure to edit the manifest to change gcr.io/myproject to your own path.

In this example, each pod works on several items from the queue and then exits when there are no more items. Since the workers themselves detect when the workqueue is empty, and the Job controller does not know about the workqueue, it relies on the workers to signal when they are done working. The workers signal that the queue is empty by exiting with success. So, as soon as any worker exits with success, the controller knows the work is done, and that the Pods will exit soon. So, you need to leave the completion count of the Job unset. The job controller will wait for the other pods to complete too.

Running the Job

So, now run the Job:

# this assumes you downloaded and then edited the manifest already
kubectl apply -f ./job.yaml

Now wait a bit, then check on the Job:

kubectl describe jobs/job-wq-2

Name:             job-wq-2
Namespace:        default
Selector:         controller-uid=b1c7e4e3-92e1-11e7-b85e-fa163ee3c11f
Labels:           controller-uid=b1c7e4e3-92e1-11e7-b85e-fa163ee3c11f
                  job-name=job-wq-2
Annotations:      <none>
Parallelism:      2
Completions:      <unset>
Start Time:       Mon, 11 Jan 2022 17:07:59 +0000
Pods Statuses:    1 Running / 0 Succeeded / 0 Failed
Pod Template:
  Labels:       controller-uid=b1c7e4e3-92e1-11e7-b85e-fa163ee3c11f
                job-name=job-wq-2
  Containers:
   c:
    Image:              container-registry.example/exampleproject/job-wq-2
    Port:
    Environment:        <none>
    Mounts:             <none>
  Volumes:              <none>
Events:
  FirstSeen    LastSeen    Count    From            SubobjectPath    Type        Reason            Message
  ---------    --------    -----    ----            -------------    --------    ------            -------
  33s          33s         1        {job-controller }                Normal      SuccessfulCreate  Created pod: job-wq-2-lglf8

You can wait for the Job to succeed, with a timeout:

# The check for condition name is case insensitive
kubectl wait --for=condition=complete --timeout=300s job/job-wq-2

kubectl logs pods/job-wq-2-7r7b2

Worker with sessionID: bbd72d0a-9e5c-4dd6-abf6-416cc267991f
Initial queue state: empty=False
Working on banana
Working on date
Working on lemon

As you can see, one of the pods for this Job worked on several work units.

Alternatives

If running a queue service or modifying your containers to use a work queue is inconvenient, you may want to consider one of the other job patterns.

If you have a continuous stream of background processing work to run, then consider running your background workers with a ReplicaSet instead, and consider running a background processing library such as https://github.com/resque/resque.

9.4 - Indexed Job for Parallel Processing with Static Work Assignment

FEATURE STATE: Kubernetes v1.24 [stable]

In this example, you will run a Kubernetes Job that uses multiple parallel worker processes. Each worker is a different container running in its own Pod. The Pods have an index number that the control plane sets automatically, which allows each Pod to identify which part of the overall task to work on.

The pod index is available in the annotation batch.kubernetes.io/job-completion-index as a string representing its decimal value. In order for the containerized task process to obtain this index, you can publish the value of the annotation using the downward API mechanism. For convenience, the control plane automatically sets the downward API to expose the index in the JOB_COMPLETION_INDEX environment variable.

Here is an overview of the steps in this example:

Define a Job manifest using indexed completion. The downward API allows you to pass the pod index annotation as an environment variable or file to the container.
Start an Indexed Job based on that manifest.

Before you begin

You should already be familiar with the basic, non-parallel, use of Job.

Your Kubernetes server must be at or later than version v1.21. To check the version, enter kubectl version.

Choose an approach

To access the work item from the worker program, you have a few options:

Read the JOB_COMPLETION_INDEX environment variable. The Job controller automatically links this variable to the annotation containing the completion index.
Read a file that contains the completion index.
Assuming that you can't modify the program, you can wrap it with a script that reads the index using any of the methods above and converts it into something that the program can use as input.

For this example, imagine that you chose option 3 and you want to run the rev utility. This program accepts a file as an argument and prints its content reversed.

rev data.txt

You'll use the rev tool from the busybox container image.

As this is only an example, each Pod only does a tiny piece of work (reversing a short string). In a real workload you might, for example, create a Job that represents the task of producing 60 seconds of video based on scene data. Each work item in the video rendering Job would be to render a particular frame of that video clip. Indexed completion would mean that each Pod in the Job knows which frame to render and publish, by counting frames from the start of the clip.

Define an Indexed Job

Here is a sample Job manifest that uses Indexed completion mode:

application/job/indexed-job.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: 'indexed-job'
spec:
  completions: 5
  parallelism: 3
  completionMode: Indexed
  template:
    spec:
      restartPolicy: Never
      initContainers:
      - name: 'input'
        image: 'docker.io/library/bash'
        command:
        - "bash"
        - "-c"
        - |
          items=(foo bar baz qux xyz)
          echo ${items[$JOB_COMPLETION_INDEX]} > /input/data.txt          
        volumeMounts:
        - mountPath: /input
          name: input
      containers:
      - name: 'worker'
        image: 'docker.io/library/busybox'
        command:
        - "rev"
        - "/input/data.txt"
        volumeMounts:
        - mountPath: /input
          name: input
      volumes:
      - name: input
        emptyDir: {}

In the example above, you use the builtin JOB_COMPLETION_INDEX environment variable set by the Job controller for all containers. An init container maps the index to a static value and writes it to a file that is shared with the container running the worker through an emptyDir volume. Optionally, you can define your own environment variable through the downward API to publish the index to containers. You can also choose to load a list of values from a ConfigMap as an environment variable or file.

Alternatively, you can directly use the downward API to pass the annotation value as a volume file, like shown in the following example:

application/job/indexed-job-vol.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: 'indexed-job'
spec:
  completions: 5
  parallelism: 3
  completionMode: Indexed
  template:
    spec:
      restartPolicy: Never
      containers:
      - name: 'worker'
        image: 'docker.io/library/busybox'
        command:
        - "rev"
        - "/input/data.txt"
        volumeMounts:
        - mountPath: /input
          name: input
      volumes:
      - name: input
        downwardAPI:
          items:
          - path: "data.txt"
            fieldRef:
              fieldPath: metadata.annotations['batch.kubernetes.io/job-completion-index']

Running the Job

Now run the Job:

# This uses the first approach (relying on $JOB_COMPLETION_INDEX)
kubectl apply -f https://kubernetes.io/examples/application/job/indexed-job.yaml

When you create this Job, the control plane creates a series of Pods, one for each index you specified. The value of .spec.parallelism determines how many can run at once whereas .spec.completions determines how many Pods the Job creates in total.

Because .spec.parallelism is less than .spec.completions, the control plane waits for some of the first Pods to complete before starting more of them.

You can wait for the Job to succeed, with a timeout:

# The check for condition name is case insensitive
kubectl wait --for=condition=complete --timeout=300s job/indexed-job

Now, describe the Job and check that it was successful.

kubectl describe jobs/indexed-job

The output is similar to:

Name:              indexed-job
Namespace:         default
Selector:          controller-uid=bf865e04-0b67-483b-9a90-74cfc4c3e756
Labels:            controller-uid=bf865e04-0b67-483b-9a90-74cfc4c3e756
                   job-name=indexed-job
Annotations:       <none>
Parallelism:       3
Completions:       5
Start Time:        Thu, 11 Mar 2021 15:47:34 +0000
Pods Statuses:     2 Running / 3 Succeeded / 0 Failed
Completed Indexes: 0-2
Pod Template:
  Labels:  controller-uid=bf865e04-0b67-483b-9a90-74cfc4c3e756
           job-name=indexed-job
  Init Containers:
   input:
    Image:      docker.io/library/bash
    Port:       <none>
    Host Port:  <none>
    Command:
      bash
      -c
      items=(foo bar baz qux xyz)
      echo ${items[$JOB_COMPLETION_INDEX]} > /input/data.txt

    Environment:  <none>
    Mounts:
      /input from input (rw)
  Containers:
   worker:
    Image:      docker.io/library/busybox
    Port:       <none>
    Host Port:  <none>
    Command:
      rev
      /input/data.txt
    Environment:  <none>
    Mounts:
      /input from input (rw)
  Volumes:
   input:
    Type:       EmptyDir (a temporary directory that shares a pod's lifetime)
    Medium:
    SizeLimit:  <unset>
Events:
  Type    Reason            Age   From            Message
  ----    ------            ----  ----            -------
  Normal  SuccessfulCreate  4s    job-controller  Created pod: indexed-job-njkjj
  Normal  SuccessfulCreate  4s    job-controller  Created pod: indexed-job-9kd4h
  Normal  SuccessfulCreate  4s    job-controller  Created pod: indexed-job-qjwsz
  Normal  SuccessfulCreate  1s    job-controller  Created pod: indexed-job-fdhq5
  Normal  SuccessfulCreate  1s    job-controller  Created pod: indexed-job-ncslj

In this example, you run the Job with custom values for each index. You can inspect the output of one of the pods:

kubectl logs indexed-job-fdhq5 # Change this to match the name of a Pod from that Job

The output is similar to:

xuq

9.5 - Job with Pod-to-Pod Communication

In this example, you will run a Job in Indexed completion mode configured such that the pods created by the Job can communicate with each other using pod hostnames rather than pod IP addresses.

Pods within a Job might need to communicate among themselves. The user workload running in each pod could query the Kubernetes API server to learn the IPs of the other Pods, but it's much simpler to rely on Kubernetes' built-in DNS resolution.

Jobs in Indexed completion mode automatically set the pods' hostname to be in the format of ${jobName}-${completionIndex}. You can use this format to deterministically build pod hostnames and enable pod communication without needing to create a client connection to the Kubernetes control plane to obtain pod hostnames/IPs via API requests.

This configuration is useful for use cases where pod networking is required but you don't want to depend on a network connection with the Kubernetes API server.

Before you begin

You should already be familiar with the basic use of Job.

Your Kubernetes server must be at or later than version v1.21. To check the version, enter kubectl version.

Note:

If you are using MiniKube or a similar tool, you may need to take extra steps to ensure you have DNS.

Starting a Job with Pod-to-Pod Communication

To enable pod-to-pod communication using pod hostnames in a Job, you must do the following:

Set up a headless Service with a valid label selector for the pods created by your Job. The headless service must be in the same namespace as the Job. One easy way to do this is to use the job-name: <your-job-name> selector, since the job-name label will be automatically added by Kubernetes. This configuration will trigger the DNS system to create records of the hostnames of the pods running your Job.
Configure the headless service as subdomain service for the Job pods by including the following value in your Job template spec:
```
subdomain: <headless-svc-name>
```

Example

Below is a working example of a Job with pod-to-pod communication via pod hostnames enabled. The Job is completed only after all pods successfully ping each other using hostnames.

Note:

In the Bash script executed on each pod in the example below, the pod hostnames can be prefixed by the namespace as well if the pod needs to be reached from outside the namespace.


apiVersion: v1
kind: Service
metadata:
  name: headless-svc
spec:
  clusterIP: None # clusterIP must be None to create a headless service
  selector:
    job-name: example-job # must match Job name
---
apiVersion: batch/v1
kind: Job
metadata:
  name: example-job
spec:
  completions: 3
  parallelism: 3
  completionMode: Indexed
  template:
    spec:
      subdomain: headless-svc # has to match Service name
      restartPolicy: Never
      containers:
      - name: example-workload
        image: bash:latest
        command:
        - bash
        - -c
        - |
          for i in 0 1 2
          do
            gotStatus="-1"
            wantStatus="0"             
            while [ $gotStatus -ne $wantStatus ]
            do                                       
              ping -c 1 example-job-${i}.headless-svc > /dev/null 2>&1
              gotStatus=$?                
              if [ $gotStatus -ne $wantStatus ]; then
                echo "Failed to ping pod example-job-${i}.headless-svc, retrying in 1 second..."
                sleep 1
              fi
            done                                                         
            echo "Successfully pinged pod: example-job-${i}.headless-svc"
          done

After applying the example above, reach each other over the network using: <pod-hostname>.<headless-service-name>. You should see output similar to the following:

kubectl logs example-job-0-qws42

Failed to ping pod example-job-0.headless-svc, retrying in 1 second...
Successfully pinged pod: example-job-0.headless-svc
Successfully pinged pod: example-job-1.headless-svc
Successfully pinged pod: example-job-2.headless-svc

Note:

Keep in mind that the <pod-hostname>.<headless-service-name> name format used in this example would not work with DNS policy set to None or Default. You can learn more about pod DNS policies here.

9.6 - Parallel Processing using Expansions

This task demonstrates running multiple Jobs based on a common template. You can use this approach to process batches of work in parallel.

For this example there are only three items: apple, banana, and cherry. The sample Jobs process each item by printing a string then pausing.

See using Jobs in real workloads to learn about how this pattern fits more realistic use cases.

Before you begin

You should be familiar with the basic, non-parallel, use of Job.

For basic templating you need the command-line utility sed.

To follow the advanced templating example, you need a working installation of Python, and the Jinja2 template library for Python.

Once you have Python set up, you can install Jinja2 by running:

pip install --user jinja2

Create Jobs based on a template

First, download the following template of a Job to a file called job-tmpl.yaml. Here's what you'll download:

application/job/job-tmpl.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: process-item-$ITEM
  labels:
    jobgroup: jobexample
spec:
  template:
    metadata:
      name: jobexample
      labels:
        jobgroup: jobexample
    spec:
      containers:
      - name: c
        image: busybox:1.28
        command: ["sh", "-c", "echo Processing item $ITEM && sleep 5"]
      restartPolicy: Never

# Use curl to download job-tmpl.yaml
curl -L -s -O https://k8s.io/examples/application/job/job-tmpl.yaml

The file you downloaded is not yet a valid Kubernetes manifest. Instead that template is a YAML representation of a Job object with some placeholders that need to be filled in before it can be used. The $ITEM syntax is not meaningful to Kubernetes.

Create manifests from the template

The following shell snippet uses sed to replace the string $ITEM with the loop variable, writing into a temporary directory named jobs. Run this now:

# Expand the template into multiple files, one for each item to be processed.
mkdir ./jobs
for i in apple banana cherry
do
  cat job-tmpl.yaml | sed "s/\$ITEM/$i/" > ./jobs/job-$i.yaml
done

Check if it worked:

ls jobs/

The output is similar to this:

job-apple.yaml
job-banana.yaml
job-cherry.yaml

You could use any type of template language (for example: Jinja2; ERB), or write a program to generate the Job manifests.

Create Jobs from the manifests

Next, create all the Jobs with one kubectl command:

kubectl create -f ./jobs

The output is similar to this:

job.batch/process-item-apple created
job.batch/process-item-banana created
job.batch/process-item-cherry created

Now, check on the jobs:

kubectl get jobs -l jobgroup=jobexample

The output is similar to this:

NAME                  COMPLETIONS   DURATION   AGE
process-item-apple    1/1           14s        22s
process-item-banana   1/1           12s        21s
process-item-cherry   1/1           12s        20s

Using the -l option to kubectl selects only the Jobs that are part of this group of jobs (there might be other unrelated jobs in the system).

You can check on the Pods as well using the same label selector:

kubectl get pods -l jobgroup=jobexample

The output is similar to:

NAME                        READY     STATUS      RESTARTS   AGE
process-item-apple-kixwv    0/1       Completed   0          4m
process-item-banana-wrsf7   0/1       Completed   0          4m
process-item-cherry-dnfu9   0/1       Completed   0          4m

We can use this single command to check on the output of all jobs at once:

kubectl logs -f -l jobgroup=jobexample

The output should be:

Processing item apple
Processing item banana
Processing item cherry

Clean up

# Remove the Jobs you created
# Your cluster automatically cleans up their Pods
kubectl delete job -l jobgroup=jobexample

Use advanced template parameters

In the first example, each instance of the template had one parameter, and that parameter was also used in the Job's name. However, names are restricted to contain only certain characters.

This slightly more complex example uses the Jinja template language to generate manifests and then objects from those manifests, with a multiple parameters for each Job.

For this part of the task, you are going to use a one-line Python script to convert the template to a set of manifests.

First, copy and paste the following template of a Job object, into a file called job.yaml.jinja2:

{% set params = [{ "name": "apple", "url": "http://dbpedia.org/resource/Apple", },
                  { "name": "banana", "url": "http://dbpedia.org/resource/Banana", },
                  { "name": "cherry", "url": "http://dbpedia.org/resource/Cherry" }]
%}
{% for p in params %}
{% set name = p["name"] %}
{% set url = p["url"] %}
---
apiVersion: batch/v1
kind: Job
metadata:
  name: jobexample-{{ name }}
  labels:
    jobgroup: jobexample
spec:
  template:
    metadata:
      name: jobexample
      labels:
        jobgroup: jobexample
    spec:
      containers:
      - name: c
        image: busybox:1.28
        command: ["sh", "-c", "echo Processing URL {{ url }} && sleep 5"]
      restartPolicy: Never
{% endfor %}

The above template defines two parameters for each Job object using a list of python dicts (lines 1-4). A for loop emits one Job manifest for each set of parameters (remaining lines).

This example relies on a feature of YAML. One YAML file can contain multiple documents (Kubernetes manifests, in this case), separated by --- on a line by itself. You can pipe the output directly to kubectl to create the Jobs.

Next, use this one-line Python program to expand the template:

alias render_template='python -c "from jinja2 import Template; import sys; print(Template(sys.stdin.read()).render());"'

Use render_template to convert the parameters and template into a single YAML file containing Kubernetes manifests:

# This requires the alias you defined earlier
cat job.yaml.jinja2 | render_template > jobs.yaml

You can view jobs.yaml to verify that the render_template script worked correctly.

Once you are happy that render_template is working how you intend, you can pipe its output into kubectl:

cat job.yaml.jinja2 | render_template | kubectl apply -f -

Kubernetes accepts and runs the Jobs you created.

Clean up

# Remove the Jobs you created
# Your cluster automatically cleans up their Pods
kubectl delete job -l jobgroup=jobexample

Using Jobs in real workloads

In a real use case, each Job performs some substantial computation, such as rendering a frame of a movie, or processing a range of rows in a database. If you were rendering a movie you would set $ITEM to the frame number. If you were processing rows from a database table, you would set $ITEM to represent the range of database rows to process.

In the task, you ran a command to collect the output from Pods by fetching their logs. In a real use case, each Pod for a Job writes its output to durable storage before completing. You can use a PersistentVolume for each Job, or an external storage service. For example, if you are rendering frames for a movie, use HTTP to PUT the rendered frame data to a URL, using a different URL for each frame.

Labels on Jobs and Pods

After you create a Job, Kubernetes automatically adds additional labels that distinguish one Job's pods from another Job's pods.

In this example, each Job and its Pod template have a label: jobgroup=jobexample.

Kubernetes itself pays no attention to labels named jobgroup. Setting a label for all the Jobs you create from a template makes it convenient to operate on all those Jobs at once. In the first example you used a template to create several Jobs. The template ensures that each Pod also gets the same label, so you can check on all Pods for these templated Jobs with a single command.

Note:

The label key jobgroup is not special or reserved. You can pick your own labelling scheme. There are recommended labels that you can use if you wish.

Alternatives

If you plan to create a large number of Job objects, you may find that:

Even using labels, managing so many Jobs is cumbersome.
If you create many Jobs in a batch, you might place high load on the Kubernetes control plane. Alternatively, the Kubernetes API server could rate limit you, temporarily rejecting your requests with a 429 status.
You are limited by a resource quota on Jobs: the API server permanently rejects some of your requests when you create a great deal of work in one batch.

There are other job patterns that you can use to process large amounts of work without creating very many Job objects.

You could also consider writing your own controller to manage Job objects automatically.

9.7 - Handling retriable and non-retriable pod failures with Pod failure policy

FEATURE STATE: Kubernetes v1.26 [beta]

This document shows you how to use the Pod failure policy, in combination with the default Pod backoff failure policy, to improve the control over the handling of container- or Pod-level failure within a Job.

The definition of Pod failure policy may help you to:

better utilize the computational resources by avoiding unnecessary Pod retries.
avoid Job failures due to Pod disruptions (such preemption, API-initiated eviction or taint-based eviction).

Before you begin

You should already be familiar with the basic use of Job.

Your Kubernetes server must be at or later than version v1.25. To check the version, enter kubectl version.

Ensure that the feature gates PodDisruptionConditions and JobPodFailurePolicy are both enabled in your cluster.

Using Pod failure policy to avoid unnecessary Pod retries

With the following example, you can learn how to use Pod failure policy to avoid unnecessary Pod restarts when a Pod failure indicates a non-retriable software bug.

First, create a Job based on the config:

/controllers/job-pod-failure-policy-failjob.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: job-pod-failure-policy-failjob
spec:
  completions: 8
  parallelism: 2
  template:
    spec:
      restartPolicy: Never
      containers:
      - name: main
        image: docker.io/library/bash:5
        command: ["bash"]
        args:
        - -c
        - echo "Hello world! I'm going to exit with 42 to simulate a software bug." && sleep 30 && exit 42
  backoffLimit: 6
  podFailurePolicy:
    rules:
    - action: FailJob
      onExitCodes:
        containerName: main
        operator: In
        values: [42]

by running:

kubectl create -f job-pod-failure-policy-failjob.yaml

After around 30s the entire Job should be terminated. Inspect the status of the Job by running:

kubectl get jobs -l job-name=job-pod-failure-policy-failjob -o yaml

In the Job status, see a job Failed condition with the field reason equal PodFailurePolicy. Additionally, the message field contains a more detailed information about the Job termination, such as: Container main for pod default/job-pod-failure-policy-failjob-8ckj8 failed with exit code 42 matching FailJob rule at index 0.

For comparison, if the Pod failure policy was disabled it would take 6 retries of the Pod, taking at least 2 minutes.

Clean up

Delete the Job you created:

kubectl delete jobs/job-pod-failure-policy-failjob

The cluster automatically cleans up the Pods.

Using Pod failure policy to ignore Pod disruptions

With the following example, you can learn how to use Pod failure policy to ignore Pod disruptions from incrementing the Pod retry counter towards the .spec.backoffLimit limit.

Caution:

Timing is important for this example, so you may want to read the steps before execution. In order to trigger a Pod disruption it is important to drain the node while the Pod is running on it (within 90s since the Pod is scheduled).

Create a Job based on the config:

/controllers/job-pod-failure-policy-ignore.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: job-pod-failure-policy-ignore
spec:
  completions: 4
  parallelism: 2
  template:
    spec:
      restartPolicy: Never
      containers:
      - name: main
        image: docker.io/library/bash:5
        command: ["bash"]
        args:
        - -c
        - echo "Hello world! I'm going to exit with 0 (success)." && sleep 90 && exit 0
  backoffLimit: 0
  podFailurePolicy:
    rules:
    - action: Ignore
      onPodConditions:
      - type: DisruptionTarget

by running:

kubectl create -f job-pod-failure-policy-ignore.yaml

Run this command to check the nodeName the Pod is scheduled to:

nodeName=$(kubectl get pods -l job-name=job-pod-failure-policy-ignore -o jsonpath='{.items[0].spec.nodeName}')

Drain the node to evict the Pod before it completes (within 90s):

kubectl drain nodes/$nodeName --ignore-daemonsets --grace-period=0

Inspect the .status.failed to check the counter for the Job is not incremented:
```
kubectl get jobs -l job-name=job-pod-failure-policy-ignore -o yaml
```
Uncordon the node:
```
kubectl uncordon nodes/$nodeName
```

The Job resumes and succeeds.

For comparison, if the Pod failure policy was disabled the Pod disruption would result in terminating the entire Job (as the .spec.backoffLimit is set to 0).

Cleaning up

Delete the Job you created:

kubectl delete jobs/job-pod-failure-policy-ignore

The cluster automatically cleans up the Pods.

Using Pod failure policy to avoid unnecessary Pod retries based on custom Pod Conditions

With the following example, you can learn how to use Pod failure policy to avoid unnecessary Pod restarts based on custom Pod Conditions.

Note:

The example below works since version 1.27 as it relies on transitioning of deleted pods, in the Pending phase, to a terminal phase (see: Pod Phase).

First, create a Job based on the config:

/controllers/job-pod-failure-policy-config-issue.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: job-pod-failure-policy-config-issue
spec:
  completions: 8
  parallelism: 2
  template:
    spec:
      restartPolicy: Never
      containers:
      - name: main
        image: "non-existing-repo/non-existing-image:example"
  backoffLimit: 6
  podFailurePolicy:
    rules:
    - action: FailJob
      onPodConditions:
      - type: ConfigIssue

by running:

kubectl create -f job-pod-failure-policy-config-issue.yaml

Note that, the image is misconfigured, as it does not exist.

Inspect the status of the job's Pods by running:

kubectl get pods -l job-name=job-pod-failure-policy-config-issue -o yaml

You will see output similar to this:

containerStatuses:
- image: non-existing-repo/non-existing-image:example
   ...
   state:
   waiting:
      message: Back-off pulling image "non-existing-repo/non-existing-image:example"
      reason: ImagePullBackOff
      ...
phase: Pending

Note that the pod remains in the Pending phase as it fails to pull the misconfigured image. This, in principle, could be a transient issue and the image could get pulled. However, in this case, the image does not exist so we indicate this fact by a custom condition.

Add the custom condition. First prepare the patch by running:

cat <<EOF > patch.yaml
status:
  conditions:
  - type: ConfigIssue
    status: "True"
    reason: "NonExistingImage"
    lastTransitionTime: "$(date -u +"%Y-%m-%dT%H:%M:%SZ")"
EOF

Second, select one of the pods created by the job by running:

podName=$(kubectl get pods -l job-name=job-pod-failure-policy-config-issue -o jsonpath='{.items[0].metadata.name}')

Then, apply the patch on one of the pods by running the following command:

kubectl patch pod $podName --subresource=status --patch-file=patch.yaml

If applied successfully, you will get a notification like this:

pod/job-pod-failure-policy-config-issue-k6pvp patched

Delete the pod to transition it to Failed phase, by running the command:
```
kubectl delete pods/$podName
```
Inspect the status of the Job by running:
```
kubectl get jobs -l job-name=job-pod-failure-policy-config-issue -o yaml
```
In the Job status, see a job Failed condition with the field reason equal PodFailurePolicy. Additionally, the message field contains a more detailed information about the Job termination, such as: Pod default/job-pod-failure-policy-config-issue-k6pvp has condition ConfigIssue matching FailJob rule at index 0.

Note:

In a production environment, the steps 3 and 4 should be automated by a user-provided controller.

Cleaning up

Delete the Job you created:

kubectl delete jobs/job-pod-failure-policy-config-issue

The cluster automatically cleans up the Pods.

Alternatives

You could rely solely on the Pod backoff failure policy, by specifying the Job's .spec.backoffLimit field. However, in many situations it is problematic to find a balance between setting a low value for .spec.backoffLimit to avoid unnecessary Pod retries, yet high enough to make sure the Job would not be terminated by Pod disruptions.

10 - Access Applications in a Cluster

Configure load balancing, port forwarding, or setup firewall or DNS configurations to access applications in a cluster.

10.1 - Deploy and Access the Kubernetes Dashboard

Deploy the web UI (Kubernetes Dashboard) and access it.

Dashboard is a web-based Kubernetes user interface. You can use Dashboard to deploy containerized applications to a Kubernetes cluster, troubleshoot your containerized application, and manage the cluster resources. You can use Dashboard to get an overview of applications running on your cluster, as well as for creating or modifying individual Kubernetes resources (such as Deployments, Jobs, DaemonSets, etc). For example, you can scale a Deployment, initiate a rolling update, restart a pod or deploy new applications using a deploy wizard.

Dashboard also provides information on the state of Kubernetes resources in your cluster and on any errors that may have occurred.

Kubernetes Dashboard UI

Deploying the Dashboard UI

Note:

Kubernetes Dashboard supports only Helm-based installation currently as it is faster and gives us better control over all dependencies required by Dashboard to run.

The Dashboard UI is not deployed by default. To deploy it, run the following command:

# Add kubernetes-dashboard repository
helm repo add kubernetes-dashboard https://kubernetes.github.io/dashboard/
# Deploy a Helm Release named "kubernetes-dashboard" using the kubernetes-dashboard chart
helm upgrade --install kubernetes-dashboard kubernetes-dashboard/kubernetes-dashboard --create-namespace --namespace kubernetes-dashboard

Accessing the Dashboard UI

To protect your cluster data, Dashboard deploys with a minimal RBAC configuration by default. Currently, Dashboard only supports logging in with a Bearer Token. To create a token for this demo, you can follow our guide on creating a sample user.

Warning:

The sample user created in the tutorial will have administrative privileges and is for educational purposes only.

Command line proxy

You can enable access to the Dashboard using the kubectl command-line tool, by running the following command:

kubectl proxy

Kubectl will make Dashboard available at http://localhost:8001/api/v1/namespaces/kubernetes-dashboard/services/https:kubernetes-dashboard:/proxy/.

The UI can only be accessed from the machine where the command is executed. See kubectl proxy --help for more options.

Note:

The kubeconfig authentication method does not support external identity providers or X.509 certificate-based authentication.

Welcome view

When you access Dashboard on an empty cluster, you'll see the welcome page. This page contains a link to this document as well as a button to deploy your first application. In addition, you can view which system applications are running by default in the kube-system namespace of your cluster, for example the Dashboard itself.

Kubernetes Dashboard welcome page

Deploying containerized applications

Dashboard lets you create and deploy a containerized application as a Deployment and optional Service with a simple wizard. You can either manually specify application details, or upload a YAML or JSON manifest file containing application configuration.

Click the CREATE button in the upper right corner of any page to begin.

Specifying application details

The deploy wizard expects that you provide the following information:

App name (mandatory): Name for your application. A label with the name will be added to the Deployment and Service, if any, that will be deployed.

The application name must be unique within the selected Kubernetes namespace. It must start with a lowercase character, and end with a lowercase character or a number, and contain only lowercase letters, numbers and dashes (-). It is limited to 24 characters. Leading and trailing spaces are ignored.
Container image (mandatory): The URL of a public Docker container image on any registry, or a private image (commonly hosted on the Google Container Registry or Docker Hub). The container image specification must end with a colon.
Number of pods (mandatory): The target number of Pods you want your application to be deployed in. The value must be a positive integer.

A Deployment will be created to maintain the desired number of Pods across your cluster.
Service (optional): For some parts of your application (e.g. frontends) you may want to expose a Service onto an external, maybe public IP address outside of your cluster (external Service).

Note:
For external Services, you may need to open up one or more ports to do so.

Other Services that are only visible from inside the cluster are called internal Services.

Irrespective of the Service type, if you choose to create a Service and your container listens on a port (incoming), you need to specify two ports. The Service will be created mapping the port (incoming) to the target port seen by the container. This Service will route to your deployed Pods. Supported protocols are TCP and UDP. The internal DNS name for this Service will be the value you specified as application name above.

If needed, you can expand the Advanced options section where you can specify more settings:

Description: The text you enter here will be added as an annotation to the Deployment and displayed in the application's details.
Labels: Default labels to be used for your application are application name and version. You can specify additional labels to be applied to the Deployment, Service (if any), and Pods, such as release, environment, tier, partition, and release track.

Example:
```
release=1.0
tier=frontend
environment=pod
track=stable
```
Namespace: Kubernetes supports multiple virtual clusters backed by the same physical cluster. These virtual clusters are called namespaces. They let you partition resources into logically named groups.

Dashboard offers all available namespaces in a dropdown list, and allows you to create a new namespace. The namespace name may contain a maximum of 63 alphanumeric characters and dashes (-) but can not contain capital letters. Namespace names should not consist of only numbers. If the name is set as a number, such as 10, the pod will be put in the default namespace.

In case the creation of the namespace is successful, it is selected by default. If the creation fails, the first namespace is selected.
Image Pull Secret: In case the specified Docker container image is private, it may require pull secret credentials.

Dashboard offers all available secrets in a dropdown list, and allows you to create a new secret. The secret name must follow the DNS domain name syntax, for example new.image-pull.secret. The content of a secret must be base64-encoded and specified in a .dockercfg file. The secret name may consist of a maximum of 253 characters.

In case the creation of the image pull secret is successful, it is selected by default. If the creation fails, no secret is applied.
CPU requirement (cores) and Memory requirement (MiB): You can specify the minimum resource limits for the container. By default, Pods run with unbounded CPU and memory limits.
Run command and Run command arguments: By default, your containers run the specified Docker image's default entrypoint command. You can use the command options and arguments to override the default.
Run as privileged: This setting determines whether processes in privileged containers are equivalent to processes running as root on the host. Privileged containers can make use of capabilities like manipulating the network stack and accessing devices.
Environment variables: Kubernetes exposes Services through environment variables. You can compose environment variable or pass arguments to your commands using the values of environment variables. They can be used in applications to find a Service. Values can reference other variables using the $(VAR_NAME) syntax.

Uploading a YAML or JSON file

Kubernetes supports declarative configuration. In this style, all configuration is stored in manifests (YAML or JSON configuration files). The manifests use Kubernetes API resource schemas.

As an alternative to specifying application details in the deploy wizard, you can define your application in one or more manifests, and upload the files using Dashboard.

Using Dashboard

Following sections describe views of the Kubernetes Dashboard UI; what they provide and how can they be used.

When there are Kubernetes objects defined in the cluster, Dashboard shows them in the initial view. By default only objects from the default namespace are shown and this can be changed using the namespace selector located in the navigation menu.

Dashboard shows most Kubernetes object kinds and groups them in a few menu categories.

Admin overview

For cluster and namespace administrators, Dashboard lists Nodes, Namespaces and PersistentVolumes and has detail views for them. Node list view contains CPU and memory usage metrics aggregated across all Nodes. The details view shows the metrics for a Node, its specification, status, allocated resources, events and pods running on the node.

Workloads

Shows all applications running in the selected namespace. The view lists applications by workload kind (for example: Deployments, ReplicaSets, StatefulSets). Each workload kind can be viewed separately. The lists summarize actionable information about the workloads, such as the number of ready pods for a ReplicaSet or current memory usage for a Pod.

Detail views for workloads show status and specification information and surface relationships between objects. For example, Pods that ReplicaSet is controlling or new ReplicaSets and HorizontalPodAutoscalers for Deployments.

Services

Shows Kubernetes resources that allow for exposing services to external world and discovering them within a cluster. For that reason, Service and Ingress views show Pods targeted by them, internal endpoints for cluster connections and external endpoints for external users.

Storage

Storage view shows PersistentVolumeClaim resources which are used by applications for storing data.

ConfigMaps and Secrets

Shows all Kubernetes resources that are used for live configuration of applications running in clusters. The view allows for editing and managing config objects and displays secrets hidden by default.

Logs viewer

Pod lists and detail pages link to a logs viewer that is built into Dashboard. The viewer allows for drilling down logs from containers belonging to a single Pod.

Logs viewer

What's next

For more information, see the Kubernetes Dashboard project page.

10.2 - Accessing Clusters

This topic discusses multiple ways to interact with clusters.

Accessing for the first time with kubectl

When accessing the Kubernetes API for the first time, we suggest using the Kubernetes CLI, kubectl.

Check the location and credentials that kubectl knows about with this command:

kubectl config view

Many of the examples provide an introduction to using kubectl, and complete documentation is found in the kubectl reference.

Directly accessing the REST API

Kubectl handles locating and authenticating to the apiserver. If you want to directly access the REST API with an http client like curl or wget, or a browser, there are several ways to locate and authenticate:

Run kubectl in proxy mode.
- Recommended approach.
- Uses stored apiserver location.
- Verifies identity of apiserver using self-signed cert. No MITM possible.
- Authenticates to apiserver.
- In future, may do intelligent client-side load-balancing and failover.
Provide the location and credentials directly to the http client.
- Alternate approach.
- Works with some types of client code that are confused by using a proxy.
- Need to import a root cert into your browser to protect against MITM.

Using kubectl proxy

The following command runs kubectl in a mode where it acts as a reverse proxy. It handles locating the apiserver and authenticating. Run it like this:

kubectl proxy --port=8080

See kubectl proxy for more details.

Then you can explore the API with curl, wget, or a browser, replacing localhost with [::1] for IPv6, like so:

curl http://localhost:8080/api/

The output is similar to this:

{
  "kind": "APIVersions",
  "versions": [
    "v1"
  ],
  "serverAddressByClientCIDRs": [
    {
      "clientCIDR": "0.0.0.0/0",
      "serverAddress": "10.0.1.149:443"
    }
  ]
}

Without kubectl proxy

Use kubectl apply and kubectl describe secret... to create a token for the default service account with grep/cut:

First, create the Secret, requesting a token for the default ServiceAccount:

kubectl apply -f - <<EOF
apiVersion: v1
kind: Secret
metadata:
  name: default-token
  annotations:
    kubernetes.io/service-account.name: default
type: kubernetes.io/service-account-token
EOF

Next, wait for the token controller to populate the Secret with a token:

while ! kubectl describe secret default-token | grep -E '^token' >/dev/null; do
  echo "waiting for token..." >&2
  sleep 1
done

Capture and use the generated token:

APISERVER=$(kubectl config view --minify | grep server | cut -f 2- -d ":" | tr -d " ")
TOKEN=$(kubectl describe secret default-token | grep -E '^token' | cut -f2 -d':' | tr -d " ")

curl $APISERVER/api --header "Authorization: Bearer $TOKEN" --insecure

The output is similar to this:

{
  "kind": "APIVersions",
  "versions": [
    "v1"
  ],
  "serverAddressByClientCIDRs": [
    {
      "clientCIDR": "0.0.0.0/0",
      "serverAddress": "10.0.1.149:443"
    }
  ]
}

Using jsonpath:

APISERVER=$(kubectl config view --minify -o jsonpath='{.clusters[0].cluster.server}')
TOKEN=$(kubectl get secret default-token -o jsonpath='{.data.token}' | base64 --decode)

curl $APISERVER/api --header "Authorization: Bearer $TOKEN" --insecure

The output is similar to this:

{
  "kind": "APIVersions",
  "versions": [
    "v1"
  ],
  "serverAddressByClientCIDRs": [
    {
      "clientCIDR": "0.0.0.0/0",
      "serverAddress": "10.0.1.149:443"
    }
  ]
}

The above examples use the --insecure flag. This leaves it subject to MITM attacks. When kubectl accesses the cluster it uses a stored root certificate and client certificates to access the server. (These are installed in the ~/.kube directory). Since cluster certificates are typically self-signed, it may take special configuration to get your http client to use root certificate.

On some clusters, the apiserver does not require authentication; it may serve on localhost, or be protected by a firewall. There is not a standard for this. Controlling Access to the API describes how a cluster admin can configure this.

Programmatic access to the API

Kubernetes officially supports Go and Python client libraries.

Go client

To get the library, run the following command: go get k8s.io/client-go@kubernetes-<kubernetes-version-number>, see INSTALL.md for detailed installation instructions. See https://github.com/kubernetes/client-go to see which versions are supported.
Write an application atop of the client-go clients. Note that client-go defines its own API objects, so if needed, please import API definitions from client-go rather than from the main repository, e.g., import "k8s.io/client-go/kubernetes" is correct.

The Go client can use the same kubeconfig file as the kubectl CLI does to locate and authenticate to the apiserver. See this example.

If the application is deployed as a Pod in the cluster, please refer to the next section.

Python client

To use Python client, run the following command: pip install kubernetes. See Python Client Library page for more installation options.

The Python client can use the same kubeconfig file as the kubectl CLI does to locate and authenticate to the apiserver. See this example.

Other languages

There are client libraries for accessing the API from other languages. See documentation for other libraries for how they authenticate.

Accessing the API from a Pod

When accessing the API from a pod, locating and authenticating to the API server are somewhat different.

Please check Accessing the API from within a Pod for more details.

Accessing services running on the cluster

The previous section describes how to connect to the Kubernetes API server. For information about connecting to other services running on a Kubernetes cluster, see Access Cluster Services.

Requesting redirects

The redirect capabilities have been deprecated and removed. Please use a proxy (see below) instead.

So many proxies

There are several different proxies you may encounter when using Kubernetes:

The kubectl proxy:
- runs on a user's desktop or in a pod
- proxies from a localhost address to the Kubernetes apiserver
- client to proxy uses HTTP
- proxy to apiserver uses HTTPS
- locates apiserver
- adds authentication headers
The apiserver proxy:
- is a bastion built into the apiserver
- connects a user outside of the cluster to cluster IPs which otherwise might not be reachable
- runs in the apiserver processes
- client to proxy uses HTTPS (or http if apiserver so configured)
- proxy to target may use HTTP or HTTPS as chosen by proxy using available information
- can be used to reach a Node, Pod, or Service
- does load balancing when used to reach a Service
The kube proxy:
- runs on each node
- proxies UDP and TCP
- does not understand HTTP
- provides load balancing
- is only used to reach services
A Proxy/Load-balancer in front of apiserver(s):
- existence and implementation varies from cluster to cluster (e.g. nginx)
- sits between all clients and one or more apiservers
- acts as load balancer if there are several apiservers.
Cloud Load Balancers on external services:
- are provided by some cloud providers (e.g. AWS ELB, Google Cloud Load Balancer)
- are created automatically when the Kubernetes service has type LoadBalancer
- use UDP/TCP only
- implementation varies by cloud provider.

Kubernetes users will typically not need to worry about anything other than the first two types. The cluster admin will typically ensure that the latter types are set up correctly.

10.3 - Configure Access to Multiple Clusters

This page shows how to configure access to multiple clusters by using configuration files. After your clusters, users, and contexts are defined in one or more configuration files, you can quickly switch between clusters by using the kubectl config use-context command.

Note:

A file that is used to configure access to a cluster is sometimes called a kubeconfig file. This is a generic way of referring to configuration files. It does not mean that there is a file named kubeconfig.

Warning:

Only use kubeconfig files from trusted sources. Using a specially-crafted kubeconfig file could result in malicious code execution or file exposure. If you must use an untrusted kubeconfig file, inspect it carefully first, much as you would a shell script.

Before you begin

To check that kubectl is installed, run kubectl version --client. The kubectl version should be within one minor version of your cluster's API server.

Define clusters, users, and contexts

Suppose you have two clusters, one for development work and one for test work. In the development cluster, your frontend developers work in a namespace called frontend, and your storage developers work in a namespace called storage. In your test cluster, developers work in the default namespace, or they create auxiliary namespaces as they see fit. Access to the development cluster requires authentication by certificate. Access to the test cluster requires authentication by username and password.

Create a directory named config-exercise. In your config-exercise directory, create a file named config-demo with this content:

apiVersion: v1
kind: Config
preferences: {}

clusters:
- cluster:
  name: development
- cluster:
  name: test

users:
- name: developer
- name: experimenter

contexts:
- context:
  name: dev-frontend
- context:
  name: dev-storage
- context:
  name: exp-test

A configuration file describes clusters, users, and contexts. Your config-demo file has the framework to describe two clusters, two users, and three contexts.

Go to your config-exercise directory. Enter these commands to add cluster details to your configuration file:

kubectl config --kubeconfig=config-demo set-cluster development --server=https://1.2.3.4 --certificate-authority=fake-ca-file
kubectl config --kubeconfig=config-demo set-cluster test --server=https://5.6.7.8 --insecure-skip-tls-verify

Add user details to your configuration file:

Caution:

Storing passwords in Kubernetes client config is risky. A better alternative would be to use a credential plugin and store them separately. See: client-go credential plugins

kubectl config --kubeconfig=config-demo set-credentials developer --client-certificate=fake-cert-file --client-key=fake-key-seefile
kubectl config --kubeconfig=config-demo set-credentials experimenter --username=exp --password=some-password

Note:

To delete a user you can run kubectl --kubeconfig=config-demo config unset users.<name>
To remove a cluster, you can run kubectl --kubeconfig=config-demo config unset clusters.<name>
To remove a context, you can run kubectl --kubeconfig=config-demo config unset contexts.<name>

Add context details to your configuration file:

kubectl config --kubeconfig=config-demo set-context dev-frontend --cluster=development --namespace=frontend --user=developer
kubectl config --kubeconfig=config-demo set-context dev-storage --cluster=development --namespace=storage --user=developer
kubectl config --kubeconfig=config-demo set-context exp-test --cluster=test --namespace=default --user=experimenter

Open your config-demo file to see the added details. As an alternative to opening the config-demo file, you can use the config view command.

kubectl config --kubeconfig=config-demo view

The output shows the two clusters, two users, and three contexts:

apiVersion: v1
clusters:
- cluster:
    certificate-authority: fake-ca-file
    server: https://1.2.3.4
  name: development
- cluster:
    insecure-skip-tls-verify: true
    server: https://5.6.7.8
  name: test
contexts:
- context:
    cluster: development
    namespace: frontend
    user: developer
  name: dev-frontend
- context:
    cluster: development
    namespace: storage
    user: developer
  name: dev-storage
- context:
    cluster: test
    namespace: default
    user: experimenter
  name: exp-test
current-context: ""
kind: Config
preferences: {}
users:
- name: developer
  user:
    client-certificate: fake-cert-file
    client-key: fake-key-file
- name: experimenter
  user:
    # Documentation note (this comment is NOT part of the command output).
    # Storing passwords in Kubernetes client config is risky.
    # A better alternative would be to use a credential plugin
    # and store the credentials separately.
    # See https://kubernetes.io/docs/reference/access-authn-authz/authentication/#client-go-credential-plugins
    password: some-password
    username: exp

The fake-ca-file, fake-cert-file and fake-key-file above are the placeholders for the pathnames of the certificate files. You need to change these to the actual pathnames of certificate files in your environment.

Sometimes you may want to use Base64-encoded data embedded here instead of separate certificate files; in that case you need to add the suffix -data to the keys, for example, certificate-authority-data, client-certificate-data, client-key-data.

Each context is a triple (cluster, user, namespace). For example, the dev-frontend context says, "Use the credentials of the developer user to access the frontend namespace of the development cluster".

Set the current context:

kubectl config --kubeconfig=config-demo use-context dev-frontend

Now whenever you enter a kubectl command, the action will apply to the cluster, and namespace listed in the dev-frontend context. And the command will use the credentials of the user listed in the dev-frontend context.

To see only the configuration information associated with the current context, use the --minify flag.

kubectl config --kubeconfig=config-demo view --minify

The output shows configuration information associated with the dev-frontend context:

apiVersion: v1
clusters:
- cluster:
    certificate-authority: fake-ca-file
    server: https://1.2.3.4
  name: development
contexts:
- context:
    cluster: development
    namespace: frontend
    user: developer
  name: dev-frontend
current-context: dev-frontend
kind: Config
preferences: {}
users:
- name: developer
  user:
    client-certificate: fake-cert-file
    client-key: fake-key-file

Now suppose you want to work for a while in the test cluster.

Change the current context to exp-test:

kubectl config --kubeconfig=config-demo use-context exp-test

Now any kubectl command you give will apply to the default namespace of the test cluster. And the command will use the credentials of the user listed in the exp-test context.

View configuration associated with the new current context, exp-test.

kubectl config --kubeconfig=config-demo view --minify

Finally, suppose you want to work for a while in the storage namespace of the development cluster.

Change the current context to dev-storage:

kubectl config --kubeconfig=config-demo use-context dev-storage

View configuration associated with the new current context, dev-storage.

kubectl config --kubeconfig=config-demo view --minify

Create a second configuration file

In your config-exercise directory, create a file named config-demo-2 with this content:

apiVersion: v1
kind: Config
preferences: {}

contexts:
- context:
    cluster: development
    namespace: ramp
    user: developer
  name: dev-ramp-up

The preceding configuration file defines a new context named dev-ramp-up.

Set the KUBECONFIG environment variable

See whether you have an environment variable named KUBECONFIG. If so, save the current value of your KUBECONFIG environment variable, so you can restore it later. For example:

Linux

export KUBECONFIG_SAVED="$KUBECONFIG"

Windows PowerShell

$Env:KUBECONFIG_SAVED=$ENV:KUBECONFIG

The KUBECONFIG environment variable is a list of paths to configuration files. The list is colon-delimited for Linux and Mac, and semicolon-delimited for Windows. If you have a KUBECONFIG environment variable, familiarize yourself with the configuration files in the list.

Temporarily append two paths to your KUBECONFIG environment variable. For example:

Linux

export KUBECONFIG="${KUBECONFIG}:config-demo:config-demo-2"

Windows PowerShell

$Env:KUBECONFIG=("config-demo;config-demo-2")

In your config-exercise directory, enter this command:

kubectl config view

The output shows merged information from all the files listed in your KUBECONFIG environment variable. In particular, notice that the merged information has the dev-ramp-up context from the config-demo-2 file and the three contexts from the config-demo file:

contexts:
- context:
    cluster: development
    namespace: frontend
    user: developer
  name: dev-frontend
- context:
    cluster: development
    namespace: ramp
    user: developer
  name: dev-ramp-up
- context:
    cluster: development
    namespace: storage
    user: developer
  name: dev-storage
- context:
    cluster: test
    namespace: default
    user: experimenter
  name: exp-test

For more information about how kubeconfig files are merged, see Organizing Cluster Access Using kubeconfig Files

Explore the $HOME/.kube directory

If you already have a cluster, and you can use kubectl to interact with the cluster, then you probably have a file named config in the $HOME/.kube directory.

Go to $HOME/.kube, and see what files are there. Typically, there is a file named config. There might also be other configuration files in this directory. Briefly familiarize yourself with the contents of these files.

Append $HOME/.kube/config to your KUBECONFIG environment variable

If you have a $HOME/.kube/config file, and it's not already listed in your KUBECONFIG environment variable, append it to your KUBECONFIG environment variable now. For example:

Linux

export KUBECONFIG="${KUBECONFIG}:${HOME}/.kube/config"

Windows Powershell

$Env:KUBECONFIG="$Env:KUBECONFIG;$HOME\.kube\config"

View configuration information merged from all the files that are now listed in your KUBECONFIG environment variable. In your config-exercise directory, enter:

kubectl config view

Clean up

Return your KUBECONFIG environment variable to its original value. For example:

Linux

export KUBECONFIG="$KUBECONFIG_SAVED"

Windows PowerShell

$Env:KUBECONFIG=$ENV:KUBECONFIG_SAVED

Check the subject represented by the kubeconfig

It is not always obvious what attributes (username, groups) you will get after authenticating to the cluster. It can be even more challenging if you are managing more than one cluster at the same time.

There is a kubectl subcommand to check subject attributes, such as username, for your selected Kubernetes client context: kubectl auth whoami.

Read API access to authentication information for a client to learn about this in more detail.

What's next

10.4 - Use Port Forwarding to Access Applications in a Cluster

This page shows how to use kubectl port-forward to connect to a MongoDB server running in a Kubernetes cluster. This type of connection can be useful for database debugging.

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
- Killercoda
- Play with Kubernetes
Your Kubernetes server must be at or later than version v1.10. To check the version, enter kubectl version.
Install MongoDB Shell.

Creating MongoDB deployment and service

Create a Deployment that runs MongoDB:

kubectl apply -f https://k8s.io/examples/application/mongodb/mongo-deployment.yaml

The output of a successful command verifies that the deployment was created:

deployment.apps/mongo created

View the pod status to check that it is ready:

kubectl get pods

The output displays the pod created:

NAME                     READY   STATUS    RESTARTS   AGE
mongo-75f59d57f4-4nd6q   1/1     Running   0          2m4s

View the Deployment's status:

kubectl get deployment

The output displays that the Deployment was created:

NAME    READY   UP-TO-DATE   AVAILABLE   AGE
mongo   1/1     1            1           2m21s

The Deployment automatically manages a ReplicaSet. View the ReplicaSet status using:

kubectl get replicaset

The output displays that the ReplicaSet was created:

NAME               DESIRED   CURRENT   READY   AGE
mongo-75f59d57f4   1         1         1       3m12s

Create a Service to expose MongoDB on the network:

kubectl apply -f https://k8s.io/examples/application/mongodb/mongo-service.yaml

The output of a successful command verifies that the Service was created:

service/mongo created

Check the Service created:

kubectl get service mongo

The output displays the service created:

NAME    TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)     AGE
mongo   ClusterIP   10.96.41.183   <none>        27017/TCP   11s

Verify that the MongoDB server is running in the Pod, and listening on port 27017:

# Change mongo-75f59d57f4-4nd6q to the name of the Pod
kubectl get pod mongo-75f59d57f4-4nd6q --template='{{(index (index .spec.containers 0).ports 0).containerPort}}{{"\n"}}'

The output displays the port for MongoDB in that Pod:

27017 is the official TCP port for MongoDB.

Forward a local port to a port on the Pod

kubectl port-forward allows using resource name, such as a pod name, to select a matching pod to port forward to.

# Change mongo-75f59d57f4-4nd6q to the name of the Pod
kubectl port-forward mongo-75f59d57f4-4nd6q 28015:27017

which is the same as

kubectl port-forward pods/mongo-75f59d57f4-4nd6q 28015:27017

kubectl port-forward deployment/mongo 28015:27017

kubectl port-forward replicaset/mongo-75f59d57f4 28015:27017

kubectl port-forward service/mongo 28015:27017

Any of the above commands works. The output is similar to this:

Forwarding from 127.0.0.1:28015 -> 27017
Forwarding from [::1]:28015 -> 27017

Note:

kubectl port-forward does not return. To continue with the exercises, you will need to open another terminal.

Start the MongoDB command line interface:
```
mongosh --port 28015
```
At the MongoDB command line prompt, enter the ping command:
```
db.runCommand( { ping: 1 } )
```
A successful ping request returns:
```
{ ok: 1 }
```

Optionally let kubectl choose the local port

If you don't need a specific local port, you can let kubectl choose and allocate the local port and thus relieve you from having to manage local port conflicts, with the slightly simpler syntax:

kubectl port-forward deployment/mongo :27017

The kubectl tool finds a local port number that is not in use (avoiding low ports numbers, because these might be used by other applications). The output is similar to:

Forwarding from 127.0.0.1:63753 -> 27017
Forwarding from [::1]:63753 -> 27017

Discussion

Connections made to local port 28015 are forwarded to port 27017 of the Pod that is running the MongoDB server. With this connection in place, you can use your local workstation to debug the database that is running in the Pod.

Note:

kubectl port-forward is implemented for TCP ports only. The support for UDP protocol is tracked in issue 47862.

What's next

Learn more about kubectl port-forward.

10.5 - Use a Service to Access an Application in a Cluster

This page shows how to create a Kubernetes Service object that external clients can use to access an application running in a cluster. The Service provides load balancing for an application that has two running instances.

Before you begin

Objectives

Run two instances of a Hello World application.
Create a Service object that exposes a node port.
Use the Service object to access the running application.

Creating a service for an application running in two pods

Here is the configuration file for the application Deployment:

service/access/hello-application.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: hello-world
spec:
  selector:
    matchLabels:
      run: load-balancer-example
  replicas: 2
  template:
    metadata:
      labels:
        run: load-balancer-example
    spec:
      containers:
        - name: hello-world
          image: us-docker.pkg.dev/google-samples/containers/gke/hello-app:2.0
          ports:
            - containerPort: 8080
              protocol: TCP

Run a Hello World application in your cluster: Create the application Deployment using the file above:
```
kubectl apply -f https://k8s.io/examples/service/access/hello-application.yaml
```
The preceding command creates a Deployment and an associated ReplicaSet. The ReplicaSet has two Pods each of which runs the Hello World application.

Display information about the Deployment:

kubectl get deployments hello-world
kubectl describe deployments hello-world

Display information about your ReplicaSet objects:

kubectl get replicasets
kubectl describe replicasets

Create a Service object that exposes the deployment:

kubectl expose deployment hello-world --type=NodePort --name=example-service

Display information about the Service:

kubectl describe services example-service

The output is similar to this:

Name:                   example-service
Namespace:              default
Labels:                 run=load-balancer-example
Annotations:            <none>
Selector:               run=load-balancer-example
Type:                   NodePort
IP:                     10.32.0.16
Port:                   <unset> 8080/TCP
TargetPort:             8080/TCP
NodePort:               <unset> 31496/TCP
Endpoints:              10.200.1.4:8080,10.200.2.5:8080
Session Affinity:       None
Events:                 <none>

Make a note of the NodePort value for the Service. For example, in the preceding output, the NodePort value is 31496.

List the pods that are running the Hello World application:

kubectl get pods --selector="run=load-balancer-example" --output=wide

The output is similar to this:

NAME                           READY   STATUS    ...  IP           NODE
hello-world-2895499144-bsbk5   1/1     Running   ...  10.200.1.4   worker1
hello-world-2895499144-m1pwt   1/1     Running   ...  10.200.2.5   worker2

Get the public IP address of one of your nodes that is running a Hello World pod. How you get this address depends on how you set up your cluster. For example, if you are using Minikube, you can see the node address by running kubectl cluster-info. If you are using Google Compute Engine instances, you can use the gcloud compute instances list command to see the public addresses of your nodes.
On your chosen node, create a firewall rule that allows TCP traffic on your node port. For example, if your Service has a NodePort value of 31568, create a firewall rule that allows TCP traffic on port 31568. Different cloud providers offer different ways of configuring firewall rules.
Use the node address and node port to access the Hello World application:
```
curl http://<public-node-ip>:<node-port>
```
where <public-node-ip> is the public IP address of your node, and <node-port> is the NodePort value for your service. The response to a successful request is a hello message:
```
Hello, world!
Version: 2.0.0
Hostname: hello-world-cdd4458f4-m47c8
```

Using a service configuration file

As an alternative to using kubectl expose, you can use a service configuration file to create a Service.

Cleaning up

To delete the Service, enter this command:

kubectl delete services example-service

To delete the Deployment, the ReplicaSet, and the Pods that are running the Hello World application, enter this command:

kubectl delete deployment hello-world

What's next

Follow the Connecting Applications with Services tutorial.

10.6 - Connect a Frontend to a Backend Using Services

This task shows how to create a frontend and a backend microservice. The backend microservice is a hello greeter. The frontend exposes the backend using nginx and a Kubernetes Service object.

Objectives

Create and run a sample hello backend microservice using a Deployment object.
Use a Service object to send traffic to the backend microservice's multiple replicas.
Create and run a nginx frontend microservice, also using a Deployment object.
Configure the frontend microservice to send traffic to the backend microservice.
Use a Service object of type=LoadBalancer to expose the frontend microservice outside the cluster.

Before you begin

To check the version, enter kubectl version.

This task uses Services with external load balancers, which require a supported environment. If your environment does not support this, you can use a Service of type NodePort instead.

Creating the backend using a Deployment

The backend is a simple hello greeter microservice. Here is the configuration file for the backend Deployment:

service/access/backend-deployment.yaml

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: backend
spec:
  selector:
    matchLabels:
      app: hello
      tier: backend
      track: stable
  replicas: 3
  template:
    metadata:
      labels:
        app: hello
        tier: backend
        track: stable
    spec:
      containers:
        - name: hello
          image: "gcr.io/google-samples/hello-go-gke:1.0"
          ports:
            - name: http
              containerPort: 80
...

Create the backend Deployment:

kubectl apply -f https://k8s.io/examples/service/access/backend-deployment.yaml

View information about the backend Deployment:

kubectl describe deployment backend

The output is similar to this:

Name:                           backend
Namespace:                      default
CreationTimestamp:              Mon, 24 Oct 2016 14:21:02 -0700
Labels:                         app=hello
                                tier=backend
                                track=stable
Annotations:                    deployment.kubernetes.io/revision=1
Selector:                       app=hello,tier=backend,track=stable
Replicas:                       3 desired | 3 updated | 3 total | 3 available | 0 unavailable
StrategyType:                   RollingUpdate
MinReadySeconds:                0
RollingUpdateStrategy:          1 max unavailable, 1 max surge
Pod Template:
  Labels:       app=hello
                tier=backend
                track=stable
  Containers:
   hello:
    Image:              "gcr.io/google-samples/hello-go-gke:1.0"
    Port:               80/TCP
    Environment:        <none>
    Mounts:             <none>
  Volumes:              <none>
Conditions:
  Type          Status  Reason
  ----          ------  ------
  Available     True    MinimumReplicasAvailable
  Progressing   True    NewReplicaSetAvailable
OldReplicaSets:                 <none>
NewReplicaSet:                  hello-3621623197 (3/3 replicas created)
Events:
...

Creating the `hello` Service object

The key to sending requests from a frontend to a backend is the backend Service. A Service creates a persistent IP address and DNS name entry so that the backend microservice can always be reached. A Service uses selectors to find the Pods that it routes traffic to.

First, explore the Service configuration file:

service/access/backend-service.yaml

---
apiVersion: v1
kind: Service
metadata:
  name: hello
spec:
  selector:
    app: hello
    tier: backend
  ports:
  - protocol: TCP
    port: 80
    targetPort: http
...

In the configuration file, you can see that the Service, named hello routes traffic to Pods that have the labels app: hello and tier: backend.

Create the backend Service:

kubectl apply -f https://k8s.io/examples/service/access/backend-service.yaml

At this point, you have a backend Deployment running three replicas of your hello application, and you have a Service that can route traffic to them. However, this service is neither available nor resolvable outside the cluster.

Creating the frontend

Now that you have your backend running, you can create a frontend that is accessible outside the cluster, and connects to the backend by proxying requests to it.

The frontend sends requests to the backend worker Pods by using the DNS name given to the backend Service. The DNS name is hello, which is the value of the name field in the examples/service/access/backend-service.yaml configuration file.

The Pods in the frontend Deployment run a nginx image that is configured to proxy requests to the hello backend Service. Here is the nginx configuration file:

service/access/frontend-nginx.conf

# The identifier Backend is internal to nginx, and used to name this specific upstream
upstream Backend {
    # hello is the internal DNS name used by the backend Service inside Kubernetes
    server hello;
}
server {
listen 80;
location / {
    # The following statement will proxy traffic to the upstream named Backend
    proxy_pass http://Backend;
}

}

Similar to the backend, the frontend has a Deployment and a Service. An important difference to notice between the backend and frontend services, is that the configuration for the frontend Service has type: LoadBalancer, which means that the Service uses a load balancer provisioned by your cloud provider and will be accessible from outside the cluster.

service/access/frontend-service.yaml

---
apiVersion: v1
kind: Service
metadata:
  name: frontend
spec:
  selector:
    app: hello
    tier: frontend
  ports:
  - protocol: "TCP"
    port: 80
    targetPort: 80
  type: LoadBalancer
...

service/access/frontend-deployment.yaml

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: frontend
spec:
  selector:
    matchLabels:
      app: hello
      tier: frontend
      track: stable
  replicas: 1
  template:
    metadata:
      labels:
        app: hello
        tier: frontend
        track: stable
    spec:
      containers:
        - name: nginx
          image: "gcr.io/google-samples/hello-frontend:1.0"
          lifecycle:
            preStop:
              exec:
                command: ["/usr/sbin/nginx","-s","quit"]
...

Create the frontend Deployment and Service:

kubectl apply -f https://k8s.io/examples/service/access/frontend-deployment.yaml
kubectl apply -f https://k8s.io/examples/service/access/frontend-service.yaml

The output verifies that both resources were created:

deployment.apps/frontend created
service/frontend created

Note:

The nginx configuration is baked into the container image. A better way to do this would be to use a ConfigMap, so that you can change the configuration more easily.

Interact with the frontend Service

Once you've created a Service of type LoadBalancer, you can use this command to find the external IP:

kubectl get service frontend --watch

This displays the configuration for the frontend Service and watches for changes. Initially, the external IP is listed as <pending>:

NAME       TYPE           CLUSTER-IP      EXTERNAL-IP   PORT(S)  AGE
frontend   LoadBalancer   10.51.252.116   <pending>     80/TCP   10s

As soon as an external IP is provisioned, however, the configuration updates to include the new IP under the EXTERNAL-IP heading:

NAME       TYPE           CLUSTER-IP      EXTERNAL-IP        PORT(S)  AGE
frontend   LoadBalancer   10.51.252.116   XXX.XXX.XXX.XXX    80/TCP   1m

That IP can now be used to interact with the frontend service from outside the cluster.

Send traffic through the frontend

The frontend and backend are now connected. You can hit the endpoint by using the curl command on the external IP of your frontend Service.

curl http://${EXTERNAL_IP} # replace this with the EXTERNAL-IP you saw earlier

The output shows the message generated by the backend:

{"message":"Hello"}

Cleaning up

To delete the Services, enter this command:

kubectl delete services frontend backend

To delete the Deployments, the ReplicaSets and the Pods that are running the backend and frontend applications, enter this command:

kubectl delete deployment frontend backend

What's next

Learn more about Services
Learn more about ConfigMaps
Learn more about DNS for Service and Pods

10.7 - Create an External Load Balancer

This page shows how to create an external load balancer.

When creating a Service, you have the option of automatically creating a cloud load balancer. This provides an externally-accessible IP address that sends traffic to the correct port on your cluster nodes, provided your cluster runs in a supported environment and is configured with the correct cloud load balancer provider package.

You can also use an Ingress in place of Service. For more information, check the Ingress documentation.

Before you begin

Your cluster must be running in a cloud or other environment that already has support for configuring external load balancers.

Create a Service

Create a Service from a manifest

To create an external load balancer, add the following line to your Service manifest:

    type: LoadBalancer

Your manifest might then look like:

apiVersion: v1
kind: Service
metadata:
  name: example-service
spec:
  selector:
    app: example
  ports:
    - port: 8765
      targetPort: 9376
  type: LoadBalancer

Create a Service using kubectl

You can alternatively create the service with the kubectl expose command and its --type=LoadBalancer flag:

kubectl expose deployment example --port=8765 --target-port=9376 \
        --name=example-service --type=LoadBalancer

This command creates a new Service using the same selectors as the referenced resource (in the case of the example above, a Deployment named example).

For more information, including optional flags, refer to the kubectl expose reference.

Finding your IP address

You can find the IP address created for your service by getting the service information through kubectl:

kubectl describe services example-service

which should produce output similar to:

Name:                     example-service
Namespace:                default
Labels:                   app=example
Annotations:              <none>
Selector:                 app=example
Type:                     LoadBalancer
IP Families:              <none>
IP:                       10.3.22.96
IPs:                      10.3.22.96
LoadBalancer Ingress:     192.0.2.89
Port:                     <unset>  8765/TCP
TargetPort:               9376/TCP
NodePort:                 <unset>  30593/TCP
Endpoints:                172.17.0.3:9376
Session Affinity:         None
External Traffic Policy:  Cluster
Events:                   <none>

The load balancer's IP address is listed next to LoadBalancer Ingress.

Note:

If you are running your service on Minikube, you can find the assigned IP address and port with:

minikube service example-service --url

Preserving the client source IP

By default, the source IP seen in the target container is not the original source IP of the client. To enable preservation of the client IP, the following fields can be configured in the .spec of the Service:

.spec.externalTrafficPolicy - denotes if this Service desires to route external traffic to node-local or cluster-wide endpoints. There are two available options: Cluster (default) and Local. Cluster obscures the client source IP and may cause a second hop to another node, but should have good overall load-spreading. Local preserves the client source IP and avoids a second hop for LoadBalancer and NodePort type Services, but risks potentially imbalanced traffic spreading.
.spec.healthCheckNodePort - specifies the health check node port (numeric port number) for the service. If you don't specify healthCheckNodePort, the service controller allocates a port from your cluster's NodePort range.
You can configure that range by setting an API server command line option, --service-node-port-range. The Service will use the user-specified healthCheckNodePort value if you specify it, provided that the Service type is set to LoadBalancer and externalTrafficPolicy is set to Local.

Setting externalTrafficPolicy to Local in the Service manifest activates this feature. For example:

apiVersion: v1
kind: Service
metadata:
  name: example-service
spec:
  selector:
    app: example
  ports:
    - port: 8765
      targetPort: 9376
  externalTrafficPolicy: Local
  type: LoadBalancer

Caveats and limitations when preserving source IPs

Load balancing services from some cloud providers do not let you configure different weights for each target.

With each target weighted equally in terms of sending traffic to Nodes, external traffic is not equally load balanced across different Pods. The external load balancer is unaware of the number of Pods on each node that are used as a target.

Where NumServicePods << NumNodes or NumServicePods >> NumNodes, a fairly close-to-equal distribution will be seen, even without weights.

Internal pod to pod traffic should behave similar to ClusterIP services, with equal probability across all pods.

Garbage collecting load balancers

FEATURE STATE: Kubernetes v1.17 [stable]

In usual case, the correlating load balancer resources in cloud provider should be cleaned up soon after a LoadBalancer type Service is deleted. But it is known that there are various corner cases where cloud resources are orphaned after the associated Service is deleted. Finalizer Protection for Service LoadBalancers was introduced to prevent this from happening. By using finalizers, a Service resource will never be deleted until the correlating load balancer resources are also deleted.

Specifically, if a Service has type LoadBalancer, the service controller will attach a finalizer named service.kubernetes.io/load-balancer-cleanup. The finalizer will only be removed after the load balancer resource is cleaned up. This prevents dangling load balancer resources even in corner cases such as the service controller crashing.

External load balancer providers

It is important to note that the datapath for this functionality is provided by a load balancer external to the Kubernetes cluster.

When the Service type is set to LoadBalancer, Kubernetes provides functionality equivalent to type equals ClusterIP to pods within the cluster and extends it by programming the (external to Kubernetes) load balancer with entries for the nodes hosting the relevant Kubernetes pods. The Kubernetes control plane automates the creation of the external load balancer, health checks (if needed), and packet filtering rules (if needed). Once the cloud provider allocates an IP address for the load balancer, the control plane looks up that external IP address and populates it into the Service object.

What's next

Follow the Connecting Applications with Services tutorial
Read about Service
Read about Ingress

10.8 - List All Container Images Running in a Cluster

This page shows how to use kubectl to list all of the Container images for Pods running in a cluster.

Before you begin

To check the version, enter kubectl version.

In this exercise you will use kubectl to fetch all of the Pods running in a cluster, and format the output to pull out the list of Containers for each.

List all Container images in all namespaces

Fetch all Pods in all namespaces using kubectl get pods --all-namespaces
Format the output to include only the list of Container image names using -o jsonpath={.items[*].spec['initContainers', 'containers'][*].image}. This will recursively parse out the image field from the returned json.
- See the jsonpath reference for further information on how to use jsonpath.
Format the output using standard tools: tr, sort, uniq
- Use tr to replace spaces with newlines
- Use sort to sort the results
- Use uniq to aggregate image counts

kubectl get pods --all-namespaces -o jsonpath="{.items[*].spec['initContainers', 'containers'][*].image}" |\
tr -s '[[:space:]]' '\n' |\
sort |\
uniq -c

The jsonpath is interpreted as follows:

.items[*]: for each returned value
.spec: get the spec
['initContainers', 'containers'][*]: for each container
.image: get the image

Note:

When fetching a single Pod by name, for example kubectl get pod nginx, the .items[*] portion of the path should be omitted because a single Pod is returned instead of a list of items.

List Container images by Pod

The formatting can be controlled further by using the range operation to iterate over elements individually.

kubectl get pods --all-namespaces -o jsonpath='{range .items[*]}{"\n"}{.metadata.name}{":\t"}{range .spec.containers[*]}{.image}{", "}{end}{end}' |\
sort

List Container images filtering by Pod label

To target only Pods matching a specific label, use the -l flag. The following matches only Pods with labels matching app=nginx.

kubectl get pods --all-namespaces -o jsonpath="{.items[*].spec.containers[*].image}" -l app=nginx

List Container images filtering by Pod namespace

To target only pods in a specific namespace, use the namespace flag. The following matches only Pods in the kube-system namespace.

kubectl get pods --namespace kube-system -o jsonpath="{.items[*].spec.containers[*].image}"

List Container images using a go-template instead of jsonpath

As an alternative to jsonpath, Kubectl supports using go-templates for formatting the output:

kubectl get pods --all-namespaces -o go-template --template="{{range .items}}{{range .spec.containers}}{{.image}} {{end}}{{end}}"

What's next

Reference

Jsonpath reference guide
Go template reference guide

10.9 - Set up Ingress on Minikube with the NGINX Ingress Controller

An Ingress is an API object that defines rules which allow external access to services in a cluster. An Ingress controller fulfills the rules set in the Ingress.

This page shows you how to set up a simple Ingress which routes requests to Service 'web' or 'web2' depending on the HTTP URI.

Before you begin

This tutorial assumes that you are using minikube to run a local Kubernetes cluster. Visit Install tools to learn how to install minikube.

Note:

This tutorial uses a container that requires the AMD64 architecture. If you are using minikube on a computer with a different CPU architecture, you could try using minikube with a driver that can emulate AMD64. For example, the Docker Desktop driver can do this.

Your Kubernetes server must be at or later than version 1.19. To check the version, enter kubectl version. If you are using an older Kubernetes version, switch to the documentation for that version.

Create a minikube cluster

If you haven't already set up a cluster locally, run minikube start to create a cluster.

Enable the Ingress controller

To enable the NGINX Ingress controller, run the following command:
```
minikube addons enable ingress
```

Verify that the NGINX Ingress controller is running

kubectl get pods -n ingress-nginx

Note:

It can take up to a minute before you see these pods running OK.

The output is similar to:

NAME                                        READY   STATUS      RESTARTS    AGE
ingress-nginx-admission-create-g9g49        0/1     Completed   0          11m
ingress-nginx-admission-patch-rqp78         0/1     Completed   1          11m
ingress-nginx-controller-59b45fb494-26npt   1/1     Running     0          11m

Deploy a hello, world app

Create a Deployment using the following command:

kubectl create deployment web --image=gcr.io/google-samples/hello-app:1.0

The output should be:

deployment.apps/web created

Verify that the Deployment is in a Ready state:

kubectl get deployment web

The output should be similar to:

NAME   READY   UP-TO-DATE   AVAILABLE   AGE
web    1/1     1            1           53s

Expose the Deployment:

kubectl expose deployment web --type=NodePort --port=8080

The output should be:

service/web exposed

Verify the Service is created and is available on a node port:

kubectl get service web

The output is similar to:

NAME      TYPE       CLUSTER-IP       EXTERNAL-IP   PORT(S)          AGE
web       NodePort   10.104.133.249   <none>        8080:31637/TCP   12m

Visit the Service via NodePort, using the minikube service command. Follow the instructions for your platform:
- Linux
- MacOS
```
minikube service web --url
```
The output is similar to:
```
http://172.17.0.15:31637
```
Invoke the URL obtained in the output of the previous step:
```
curl http://172.17.0.15:31637 
```
```
# The command must be run in a separate terminal.
minikube service web --url 
```
The output is similar to:
```
http://127.0.0.1:62445
! Because you are using a Docker driver on darwin, the terminal needs to be open to run it.
```
From a different terminal, invoke the URL obtained in the output of the previous step:
```
curl http://127.0.0.1:62445 
```
The output is similar to:
```
Hello, world!
Version: 1.0.0
Hostname: web-55b8c6998d-8k564
```
You can now access the sample application via the Minikube IP address and NodePort. The next step lets you access the application using the Ingress resource.

Create an Ingress

The following manifest defines an Ingress that sends traffic to your Service via hello-world.example.

Create example-ingress.yaml from the following file:

service/networking/example-ingress.yaml

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: example-ingress
spec:
  ingressClassName: nginx
  rules:
    - host: hello-world.example
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: web
                port:
                  number: 8080

Create the Ingress object by running the following command:

kubectl apply -f https://k8s.io/examples/service/networking/example-ingress.yaml

The output should be:

ingress.networking.k8s.io/example-ingress created

Verify the IP address is set:

kubectl get ingress

Note:

This can take a couple of minutes.

You should see an IPv4 address in the ADDRESS column; for example:

NAME              CLASS   HOSTS                 ADDRESS        PORTS   AGE
example-ingress   nginx   hello-world.example   172.17.0.15    80      38s

Verify that the Ingress controller is directing traffic, by following the instructions for your platform:

Note:

The network is limited if using the Docker driver on MacOS (Darwin) and the Node IP is not reachable directly. To get ingress to work you’ll need to open a new terminal and run minikube tunnel.
sudo permission is required for it, so provide the password when prompted.

Linux
MacOS

curl --resolve "hello-world.example:80:$( minikube ip )" -i http://hello-world.example

minikube tunnel

The output is similar to:

Tunnel successfully started

NOTE: Please do not close this terminal as this process must stay alive for the tunnel to be accessible ...

The service/ingress example-ingress requires privileged ports to be exposed: [80 443]
sudo permission will be asked for it.
Starting tunnel for service example-ingress.

From within a new terminal, invoke the following command:

curl --resolve "hello-world.example:80:127.0.0.1" -i http://hello-world.example

You should see:

Hello, world!
Version: 1.0.0
Hostname: web-55b8c6998d-8k564

Optionally, you can also visit hello-world.example from your browser.

Add a line to the bottom of the /etc/hosts file on your computer (you will need administrator access):
- Linux
- MacOS
Look up the external IP address as reported by minikube
```
  minikube ip 
```
```
  172.17.0.15 hello-world.example
```
Note:
Change the IP address to match the output from minikube ip.
```
127.0.0.1 hello-world.example
```
After you make this change, your web browser sends requests for hello-world.example URLs to Minikube.

Create a second Deployment

Create another Deployment using the following command:

kubectl create deployment web2 --image=gcr.io/google-samples/hello-app:2.0

The output should be:

deployment.apps/web2 created

Verify that the Deployment is in a Ready state:

kubectl get deployment web2

The output should be similar to:

NAME   READY   UP-TO-DATE   AVAILABLE   AGE
web2   1/1     1            1           16s

Expose the second Deployment:

kubectl expose deployment web2 --port=8080 --type=NodePort

The output should be:

service/web2 exposed

Edit the existing Ingress

Edit the existing example-ingress.yaml manifest, and add the following lines at the end:

- path: /v2
  pathType: Prefix
  backend:
    service:
      name: web2
      port:
        number: 8080

Apply the changes:

kubectl apply -f example-ingress.yaml

You should see:

ingress.networking/example-ingress configured

Test your Ingress

Access the 1st version of the Hello World app.

Linux
MacOS

curl --resolve "hello-world.example:80:$( minikube ip )" -i http://hello-world.example

minikube tunnel

The output is similar to:

Tunnel successfully started

NOTE: Please do not close this terminal as this process must stay alive for the tunnel to be accessible ...

The service/ingress example-ingress requires privileged ports to be exposed: [80 443]
sudo permission will be asked for it.
Starting tunnel for service example-ingress.

From within a new terminal, invoke the following command:

curl --resolve "hello-world.example:80:127.0.0.1" -i http://hello-world.example

The output is similar to:

Hello, world!
Version: 1.0.0
Hostname: web-55b8c6998d-8k564

Access the 2nd version of the Hello World app.

Linux
MacOS

curl --resolve "hello-world.example:80:$( minikube ip )" -i http://hello-world.example/v2

minikube tunnel

The output is similar to:

Tunnel successfully started

NOTE: Please do not close this terminal as this process must stay alive for the tunnel to be accessible ...

The service/ingress example-ingress requires privileged ports to be exposed: [80 443]
sudo permission will be asked for it.
Starting tunnel for service example-ingress.

From within a new terminal, invoke the following command:

curl --resolve "hello-world.example:80:127.0.0.1" -i http://hello-world.example/v2

The output is similar to:

Hello, world!
Version: 2.0.0
Hostname: web2-75cd47646f-t8cjk

Note:

If you did the optional step to update /etc/hosts, you can also visit hello-world.example and hello-world.example/v2 from your browser.

What's next

Read more about Ingress
Read more about Ingress Controllers
Read more about Services

10.10 - Communicate Between Containers in the Same Pod Using a Shared Volume

This page shows how to use a Volume to communicate between two Containers running in the same Pod. See also how to allow processes to communicate by sharing process namespace between containers.

Before you begin

To check the version, enter kubectl version.

Creating a Pod that runs two Containers

In this exercise, you create a Pod that runs two Containers. The two containers share a Volume that they can use to communicate. Here is the configuration file for the Pod:

pods/two-container-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: two-containers
spec:

  restartPolicy: Never

  volumes:
  - name: shared-data
    emptyDir: {}

  containers:

  - name: nginx-container
    image: nginx
    volumeMounts:
    - name: shared-data
      mountPath: /usr/share/nginx/html

  - name: debian-container
    image: debian
    volumeMounts:
    - name: shared-data
      mountPath: /pod-data
    command: ["/bin/sh"]
    args: ["-c", "echo Hello from the debian container > /pod-data/index.html"]

In the configuration file, you can see that the Pod has a Volume named shared-data.

The first container listed in the configuration file runs an nginx server. The mount path for the shared Volume is /usr/share/nginx/html. The second container is based on the debian image, and has a mount path of /pod-data. The second container runs the following command and then terminates.

echo Hello from the debian container > /pod-data/index.html

Notice that the second container writes the index.html file in the root directory of the nginx server.

Create the Pod and the two Containers:

kubectl apply -f https://k8s.io/examples/pods/two-container-pod.yaml

View information about the Pod and the Containers:

kubectl get pod two-containers --output=yaml

Here is a portion of the output:

apiVersion: v1
kind: Pod
metadata:
  ...
  name: two-containers
  namespace: default
  ...
spec:
  ...
  containerStatuses:

  - containerID: docker://c1d8abd1 ...
    image: debian
    ...
    lastState:
      terminated:
        ...
    name: debian-container
    ...

  - containerID: docker://96c1ff2c5bb ...
    image: nginx
    ...
    name: nginx-container
    ...
    state:
      running:
    ...

You can see that the debian Container has terminated, and the nginx Container is still running.

Get a shell to nginx Container:

kubectl exec -it two-containers -c nginx-container -- /bin/bash

In your shell, verify that nginx is running:

root@two-containers:/# apt-get update
root@two-containers:/# apt-get install curl procps
root@two-containers:/# ps aux

The output is similar to this:

USER       PID  ...  STAT START   TIME COMMAND
root         1  ...  Ss   21:12   0:00 nginx: master process nginx -g daemon off;

Recall that the debian Container created the index.html file in the nginx root directory. Use curl to send a GET request to the nginx server:

root@two-containers:/# curl localhost

The output shows that nginx serves a web page written by the debian container:

Hello from the debian container

Discussion

The primary reason that Pods can have multiple containers is to support helper applications that assist a primary application. Typical examples of helper applications are data pullers, data pushers, and proxies. Helper and primary applications often need to communicate with each other. Typically this is done through a shared filesystem, as shown in this exercise, or through the loopback network interface, localhost. An example of this pattern is a web server along with a helper program that polls a Git repository for new updates.

The Volume in this exercise provides a way for Containers to communicate during the life of the Pod. If the Pod is deleted and recreated, any data stored in the shared Volume is lost.

What's next

Learn more about patterns for composite containers.
Learn about composite containers for modular architecture.
See Configuring a Pod to Use a Volume for Storage.
See Configure a Pod to share process namespace between containers in a Pod
See Volume.
See Pod.

10.11 - Configure DNS for a Cluster

Kubernetes offers a DNS cluster addon, which most of the supported environments enable by default. In Kubernetes version 1.11 and later, CoreDNS is recommended and is installed by default with kubeadm.

For more information on how to configure CoreDNS for a Kubernetes cluster, see the Customizing DNS Service. An example demonstrating how to use Kubernetes DNS with kube-dns, see the Kubernetes DNS sample plugin.

10.12 - Access Services Running on Clusters

This page shows how to connect to services running on the Kubernetes cluster.

Before you begin

To check the version, enter kubectl version.

Accessing services running on the cluster

In Kubernetes, nodes, pods and services all have their own IPs. In many cases, the node IPs, pod IPs, and some service IPs on a cluster will not be routable, so they will not be reachable from a machine outside the cluster, such as your desktop machine.

Ways to connect

You have several options for connecting to nodes, pods and services from outside the cluster:

Access services through public IPs.
- Use a service with type NodePort or LoadBalancer to make the service reachable outside the cluster. See the services and kubectl expose documentation.
- Depending on your cluster environment, this may only expose the service to your corporate network, or it may expose it to the internet. Think about whether the service being exposed is secure. Does it do its own authentication?
- Place pods behind services. To access one specific pod from a set of replicas, such as for debugging, place a unique label on the pod and create a new service which selects this label.
- In most cases, it should not be necessary for application developer to directly access nodes via their nodeIPs.
Access services, nodes, or pods using the Proxy Verb.
- Does apiserver authentication and authorization prior to accessing the remote service. Use this if the services are not secure enough to expose to the internet, or to gain access to ports on the node IP, or for debugging.
- Proxies may cause problems for some web applications.
- Only works for HTTP/HTTPS.
- Described here.
Access from a node or pod in the cluster.
- Run a pod, and then connect to a shell in it using kubectl exec. Connect to other nodes, pods, and services from that shell.
- Some clusters may allow you to ssh to a node in the cluster. From there you may be able to access cluster services. This is a non-standard method, and will work on some clusters but not others. Browsers and other tools may or may not be installed. Cluster DNS may not work.

Discovering builtin services

Typically, there are several services which are started on a cluster by kube-system. Get a list of these with the kubectl cluster-info command:

kubectl cluster-info

The output is similar to this:

Kubernetes master is running at https://192.0.2.1
elasticsearch-logging is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/elasticsearch-logging/proxy
kibana-logging is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/kibana-logging/proxy
kube-dns is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/kube-dns/proxy
grafana is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/monitoring-grafana/proxy
heapster is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/monitoring-heapster/proxy

This shows the proxy-verb URL for accessing each service. For example, this cluster has cluster-level logging enabled (using Elasticsearch), which can be reached at https://192.0.2.1/api/v1/namespaces/kube-system/services/elasticsearch-logging/proxy/ if suitable credentials are passed, or through a kubectl proxy at, for example: http://localhost:8080/api/v1/namespaces/kube-system/services/elasticsearch-logging/proxy/.

Note:

See Access Clusters Using the Kubernetes API for how to pass credentials or use kubectl proxy.

Manually constructing apiserver proxy URLs

As mentioned above, you use the kubectl cluster-info command to retrieve the service's proxy URL. To create proxy URLs that include service endpoints, suffixes, and parameters, you append to the service's proxy URL: http://kubernetes_master_address/api/v1/namespaces/namespace_name/services/[https:]service_name[:port_name]/proxy

If you haven't specified a name for your port, you don't have to specify port_name in the URL. You can also use the port number in place of the port_name for both named and unnamed ports.

By default, the API server proxies to your service using HTTP. To use HTTPS, prefix the service name with https:: http://<kubernetes_master_address>/api/v1/namespaces/<namespace_name>/services/<service_name>/proxy

The supported formats for the <service_name> segment of the URL are:

<service_name> - proxies to the default or unnamed port using http
<service_name>:<port_name> - proxies to the specified port name or port number using http
https:<service_name>: - proxies to the default or unnamed port using https (note the trailing colon)
https:<service_name>:<port_name> - proxies to the specified port name or port number using https

Examples

To access the Elasticsearch service endpoint _search?q=user:kimchy, you would use:

http://192.0.2.1/api/v1/namespaces/kube-system/services/elasticsearch-logging/proxy/_search?q=user:kimchy

To access the Elasticsearch cluster health information _cluster/health?pretty=true, you would use:

https://192.0.2.1/api/v1/namespaces/kube-system/services/elasticsearch-logging/proxy/_cluster/health?pretty=true

The health information is similar to this:

{
  "cluster_name" : "kubernetes_logging",
  "status" : "yellow",
  "timed_out" : false,
  "number_of_nodes" : 1,
  "number_of_data_nodes" : 1,
  "active_primary_shards" : 5,
  "active_shards" : 5,
  "relocating_shards" : 0,
  "initializing_shards" : 0,
  "unassigned_shards" : 5
}

To access the https Elasticsearch service health information _cluster/health?pretty=true, you would use:

https://192.0.2.1/api/v1/namespaces/kube-system/services/https:elasticsearch-logging:/proxy/_cluster/health?pretty=true

Using web browsers to access services running on the cluster

You may be able to put an apiserver proxy URL into the address bar of a browser. However:

Web browsers cannot usually pass tokens, so you may need to use basic (password) auth. Apiserver can be configured to accept basic auth, but your cluster may not be configured to accept basic auth.
Some web apps may not work, particularly those with client side javascript that construct URLs in a way that is unaware of the proxy path prefix.

11 - Extend Kubernetes

Understand advanced ways to adapt your Kubernetes cluster to the needs of your work environment.

11.1 - Configure the Aggregation Layer

Configuring the aggregation layer allows the Kubernetes apiserver to be extended with additional APIs, which are not part of the core Kubernetes APIs.

Before you begin

To check the version, enter kubectl version.

Note:

There are a few setup requirements for getting the aggregation layer working in your environment to support mutual TLS auth between the proxy and extension apiservers. Kubernetes and the kube-apiserver have multiple CAs, so make sure that the proxy is signed by the aggregation layer CA and not by something else, like the Kubernetes general CA.

Caution:

Reusing the same CA for different client types can negatively impact the cluster's ability to function. For more information, see CA Reusage and Conflicts.

Authentication Flow

Unlike Custom Resource Definitions (CRDs), the Aggregation API involves another server - your Extension apiserver - in addition to the standard Kubernetes apiserver. The Kubernetes apiserver will need to communicate with your extension apiserver, and your extension apiserver will need to communicate with the Kubernetes apiserver. In order for this communication to be secured, the Kubernetes apiserver uses x509 certificates to authenticate itself to the extension apiserver.

This section describes how the authentication and authorization flows work, and how to configure them.

The high-level flow is as follows:

Kubernetes apiserver: authenticate the requesting user and authorize their rights to the requested API path.
Kubernetes apiserver: proxy the request to the extension apiserver
Extension apiserver: authenticate the request from the Kubernetes apiserver
Extension apiserver: authorize the request from the original user
Extension apiserver: execute

The rest of this section describes these steps in detail.

The flow can be seen in the following diagram.

aggregation auth flows

The source for the above swimlanes can be found in the source of this document.

Kubernetes Apiserver Authentication and Authorization

A request to an API path that is served by an extension apiserver begins the same way as all API requests: communication to the Kubernetes apiserver. This path already has been registered with the Kubernetes apiserver by the extension apiserver.

The user communicates with the Kubernetes apiserver, requesting access to the path. The Kubernetes apiserver uses standard authentication and authorization configured with the Kubernetes apiserver to authenticate the user and authorize access to the specific path.

For an overview of authenticating to a Kubernetes cluster, see "Authenticating to a Cluster". For an overview of authorization of access to Kubernetes cluster resources, see "Authorization Overview".

Everything to this point has been standard Kubernetes API requests, authentication and authorization.

The Kubernetes apiserver now is prepared to send the request to the extension apiserver.

Kubernetes Apiserver Proxies the Request

The Kubernetes apiserver now will send, or proxy, the request to the extension apiserver that registered to handle the request. In order to do so, it needs to know several things:

How should the Kubernetes apiserver authenticate to the extension apiserver, informing the extension apiserver that the request, which comes over the network, is coming from a valid Kubernetes apiserver?
How should the Kubernetes apiserver inform the extension apiserver of the username and group for which the original request was authenticated?

In order to provide for these two, you must configure the Kubernetes apiserver using several flags.

Kubernetes Apiserver Client Authentication

The Kubernetes apiserver connects to the extension apiserver over TLS, authenticating itself using a client certificate. You must provide the following to the Kubernetes apiserver upon startup, using the provided flags:

private key file via --proxy-client-key-file
signed client certificate file via --proxy-client-cert-file
certificate of the CA that signed the client certificate file via --requestheader-client-ca-file
valid Common Name values (CNs) in the signed client certificate via --requestheader-allowed-names

The Kubernetes apiserver will use the files indicated by --proxy-client-*-file to authenticate to the extension apiserver. In order for the request to be considered valid by a compliant extension apiserver, the following conditions must be met:

The connection must be made using a client certificate that is signed by the CA whose certificate is in --requestheader-client-ca-file.
The connection must be made using a client certificate whose CN is one of those listed in --requestheader-allowed-names.

Note:

You can set this option to blank as --requestheader-allowed-names="". This will indicate to an extension apiserver that any CN is acceptable.

When started with these options, the Kubernetes apiserver will:

Use them to authenticate to the extension apiserver.
Create a configmap in the kube-system namespace called extension-apiserver-authentication, in which it will place the CA certificate and the allowed CNs. These in turn can be retrieved by extension apiservers to validate requests.

Note that the same client certificate is used by the Kubernetes apiserver to authenticate against all extension apiservers. It does not create a client certificate per extension apiserver, but rather a single one to authenticate as the Kubernetes apiserver. This same one is reused for all extension apiserver requests.

Original Request Username and Group

When the Kubernetes apiserver proxies the request to the extension apiserver, it informs the extension apiserver of the username and group with which the original request successfully authenticated. It provides these in http headers of its proxied request. You must inform the Kubernetes apiserver of the names of the headers to be used.

the header in which to store the username via --requestheader-username-headers
the header in which to store the group via --requestheader-group-headers
the prefix to append to all extra headers via --requestheader-extra-headers-prefix

These header names are also placed in the extension-apiserver-authentication configmap, so they can be retrieved and used by extension apiservers.

Extension Apiserver Authenticates the Request

The extension apiserver, upon receiving a proxied request from the Kubernetes apiserver, must validate that the request actually did come from a valid authenticating proxy, which role the Kubernetes apiserver is fulfilling. The extension apiserver validates it via:

Retrieve the following from the configmap in kube-system, as described above:
- Client CA certificate
- List of allowed names (CNs)
- Header names for username, group and extra info
Check that the TLS connection was authenticated using a client certificate which:
- Was signed by the CA whose certificate matches the retrieved CA certificate.
- Has a CN in the list of allowed CNs, unless the list is blank, in which case all CNs are allowed.
- Extract the username and group from the appropriate headers

If the above passes, then the request is a valid proxied request from a legitimate authenticating proxy, in this case the Kubernetes apiserver.

Note that it is the responsibility of the extension apiserver implementation to provide the above. Many do it by default, leveraging the k8s.io/apiserver/ package. Others may provide options to override it using command-line options.

In order to have permission to retrieve the configmap, an extension apiserver requires the appropriate role. There is a default role named extension-apiserver-authentication-reader in the kube-system namespace which can be assigned.

Extension Apiserver Authorizes the Request

The extension apiserver now can validate that the user/group retrieved from the headers are authorized to execute the given request. It does so by sending a standard SubjectAccessReview request to the Kubernetes apiserver.

In order for the extension apiserver to be authorized itself to submit the SubjectAccessReview request to the Kubernetes apiserver, it needs the correct permissions. Kubernetes includes a default ClusterRole named system:auth-delegator that has the appropriate permissions. It can be granted to the extension apiserver's service account.

Extension Apiserver Executes

If the SubjectAccessReview passes, the extension apiserver executes the request.

Enable Kubernetes Apiserver flags

Enable the aggregation layer via the following kube-apiserver flags. They may have already been taken care of by your provider.

--requestheader-client-ca-file=<path to aggregator CA cert>
--requestheader-allowed-names=front-proxy-client
--requestheader-extra-headers-prefix=X-Remote-Extra-
--requestheader-group-headers=X-Remote-Group
--requestheader-username-headers=X-Remote-User
--proxy-client-cert-file=<path to aggregator proxy cert>
--proxy-client-key-file=<path to aggregator proxy key>

CA Reusage and Conflicts

The Kubernetes apiserver has two client CA options:

--client-ca-file
--requestheader-client-ca-file

Each of these functions independently and can conflict with each other, if not used correctly.

--client-ca-file: When a request arrives to the Kubernetes apiserver, if this option is enabled, the Kubernetes apiserver checks the certificate of the request. If it is signed by one of the CA certificates in the file referenced by --client-ca-file, then the request is treated as a legitimate request, and the user is the value of the common name CN=, while the group is the organization O=. See the documentation on TLS authentication.
--requestheader-client-ca-file: When a request arrives to the Kubernetes apiserver, if this option is enabled, the Kubernetes apiserver checks the certificate of the request. If it is signed by one of the CA certificates in the file reference by --requestheader-client-ca-file, then the request is treated as a potentially legitimate request. The Kubernetes apiserver then checks if the common name CN= is one of the names in the list provided by --requestheader-allowed-names. If the name is allowed, the request is approved; if it is not, the request is not.

If both --client-ca-file and --requestheader-client-ca-file are provided, then the request first checks the --requestheader-client-ca-file CA and then the --client-ca-file. Normally, different CAs, either root CAs or intermediate CAs, are used for each of these options; regular client requests match against --client-ca-file, while aggregation requests match against --requestheader-client-ca-file. However, if both use the same CA, then client requests that normally would pass via --client-ca-file will fail, because the CA will match the CA in --requestheader-client-ca-file, but the common name CN= will not match one of the acceptable common names in --requestheader-allowed-names. This can cause your kubelets and other control plane components, as well as end-users, to be unable to authenticate to the Kubernetes apiserver.

For this reason, use different CA certs for the --client-ca-file option - to authorize control plane components and end-users - and the --requestheader-client-ca-file option - to authorize aggregation apiserver requests.

Warning:

Do not reuse a CA that is used in a different context unless you understand the risks and the mechanisms to protect the CA's usage.

If you are not running kube-proxy on a host running the API server, then you must make sure that the system is enabled with the following kube-apiserver flag:

--enable-aggregator-routing=true

Register APIService objects

You can dynamically configure what client requests are proxied to extension apiserver. The following is an example registration:


apiVersion: apiregistration.k8s.io/v1
kind: APIService
metadata:
  name: <name of the registration object>
spec:
  group: <API group name this extension apiserver hosts>
  version: <API version this extension apiserver hosts>
  groupPriorityMinimum: <priority this APIService for this group, see API documentation>
  versionPriority: <prioritizes ordering of this version within a group, see API documentation>
  service:
    namespace: <namespace of the extension apiserver service>
    name: <name of the extension apiserver service>
  caBundle: <pem encoded ca cert that signs the server cert used by the webhook>

The name of an APIService object must be a valid path segment name.

Contacting the extension apiserver

Once the Kubernetes apiserver has determined a request should be sent to an extension apiserver, it needs to know how to contact it.

The service stanza is a reference to the service for an extension apiserver. The service namespace and name are required. The port is optional and defaults to 443.

Here is an example of an extension apiserver that is configured to be called on port "1234", and to verify the TLS connection against the ServerName my-service-name.my-service-namespace.svc using a custom CA bundle.

apiVersion: apiregistration.k8s.io/v1
kind: APIService
...
spec:
  ...
  service:
    namespace: my-service-namespace
    name: my-service-name
    port: 1234
  caBundle: "Ci0tLS0tQk...<base64-encoded PEM bundle>...tLS0K"
...

What's next

Set up an extension api-server to work with the aggregation layer.
For a high level overview, see Extending the Kubernetes API with the aggregation layer.
Learn how to Extend the Kubernetes API Using Custom Resource Definitions.

11.2 - Use Custom Resources

11.2.1 - Extend the Kubernetes API with CustomResourceDefinitions

This page shows how to install a custom resource into the Kubernetes API by creating a CustomResourceDefinition.

Before you begin

Your Kubernetes server must be at or later than version 1.16. To check the version, enter kubectl version. If you are using an older version of Kubernetes that is still supported, switch to the documentation for that version to see advice that is relevant for your cluster.

Create a CustomResourceDefinition

When you create a new CustomResourceDefinition (CRD), the Kubernetes API Server creates a new RESTful resource path for each version you specify. The custom resource created from a CRD object can be either namespaced or cluster-scoped, as specified in the CRD's spec.scope field. As with existing built-in objects, deleting a namespace deletes all custom objects in that namespace. CustomResourceDefinitions themselves are non-namespaced and are available to all namespaces.

For example, if you save the following CustomResourceDefinition to resourcedefinition.yaml:

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  # name must match the spec fields below, and be in the form: <plural>.<group>
  name: crontabs.stable.example.com
spec:
  # group name to use for REST API: /apis/<group>/<version>
  group: stable.example.com
  # list of versions supported by this CustomResourceDefinition
  versions:
    - name: v1
      # Each version can be enabled/disabled by Served flag.
      served: true
      # One and only one version must be marked as the storage version.
      storage: true
      schema:
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object
              properties:
                cronSpec:
                  type: string
                image:
                  type: string
                replicas:
                  type: integer
  # either Namespaced or Cluster
  scope: Namespaced
  names:
    # plural name to be used in the URL: /apis/<group>/<version>/<plural>
    plural: crontabs
    # singular name to be used as an alias on the CLI and for display
    singular: crontab
    # kind is normally the CamelCased singular type. Your resource manifests use this.
    kind: CronTab
    # shortNames allow shorter string to match your resource on the CLI
    shortNames:
    - ct

and create it:

kubectl apply -f resourcedefinition.yaml

Then a new namespaced RESTful API endpoint is created at:

/apis/stable.example.com/v1/namespaces/*/crontabs/...

This endpoint URL can then be used to create and manage custom objects. The kind of these objects will be CronTab from the spec of the CustomResourceDefinition object you created above.

It might take a few seconds for the endpoint to be created. You can watch the Established condition of your CustomResourceDefinition to be true or watch the discovery information of the API server for your resource to show up.

Create custom objects

After the CustomResourceDefinition object has been created, you can create custom objects. Custom objects can contain custom fields. These fields can contain arbitrary JSON. In the following example, the cronSpec and image custom fields are set in a custom object of kind CronTab. The kind CronTab comes from the spec of the CustomResourceDefinition object you created above.

If you save the following YAML to my-crontab.yaml:

apiVersion: "stable.example.com/v1"
kind: CronTab
metadata:
  name: my-new-cron-object
spec:
  cronSpec: "* * * * */5"
  image: my-awesome-cron-image

and create it:

kubectl apply -f my-crontab.yaml

You can then manage your CronTab objects using kubectl. For example:

kubectl get crontab

Should print a list like this:

NAME                 AGE
my-new-cron-object   6s

Resource names are not case-sensitive when using kubectl, and you can use either the singular or plural forms defined in the CRD, as well as any short names.

You can also view the raw YAML data:

kubectl get ct -o yaml

You should see that it contains the custom cronSpec and image fields from the YAML you used to create it:

apiVersion: v1
items:
- apiVersion: stable.example.com/v1
  kind: CronTab
  metadata:
    annotations:
      kubectl.kubernetes.io/last-applied-configuration: |
        {"apiVersion":"stable.example.com/v1","kind":"CronTab","metadata":{"annotations":{},"name":"my-new-cron-object","namespace":"default"},"spec":{"cronSpec":"* * * * */5","image":"my-awesome-cron-image"}}        
    creationTimestamp: "2021-06-20T07:35:27Z"
    generation: 1
    name: my-new-cron-object
    namespace: default
    resourceVersion: "1326"
    uid: 9aab1d66-628e-41bb-a422-57b8b3b1f5a9
  spec:
    cronSpec: '* * * * */5'
    image: my-awesome-cron-image
kind: List
metadata:
  resourceVersion: ""
  selfLink: ""

Delete a CustomResourceDefinition

When you delete a CustomResourceDefinition, the server will uninstall the RESTful API endpoint and delete all custom objects stored in it.

kubectl delete -f resourcedefinition.yaml
kubectl get crontabs

Error from server (NotFound): Unable to list {"stable.example.com" "v1" "crontabs"}: the server could not
find the requested resource (get crontabs.stable.example.com)

If you later recreate the same CustomResourceDefinition, it will start out empty.

Specifying a structural schema

CustomResources store structured data in custom fields (alongside the built-in fields apiVersion, kind and metadata, which the API server validates implicitly). With OpenAPI v3.0 validation a schema can be specified, which is validated during creation and updates, compare below for details and limits of such a schema.

With apiextensions.k8s.io/v1 the definition of a structural schema is mandatory for CustomResourceDefinitions. In the beta version of CustomResourceDefinition, the structural schema was optional.

A structural schema is an OpenAPI v3.0 validation schema which:

specifies a non-empty type (via type in OpenAPI) for the root, for each specified field of an object node (via properties or additionalProperties in OpenAPI) and for each item in an array node (via items in OpenAPI), with the exception of:
- a node with x-kubernetes-int-or-string: true
- a node with x-kubernetes-preserve-unknown-fields: true
for each field in an object and each item in an array which is specified within any of allOf, anyOf, oneOf or not, the schema also specifies the field/item outside of those logical junctors (compare example 1 and 2).
does not set description, type, default, additionalProperties, nullable within an allOf, anyOf, oneOf or not, with the exception of the two pattern for x-kubernetes-int-or-string: true (see below).
if metadata is specified, then only restrictions on metadata.name and metadata.generateName are allowed.

Non-structural example 1:

allOf:
- properties:
    foo:
      ...

conflicts with rule 2. The following would be correct:

properties:
  foo:
    ...
allOf:
- properties:
    foo:
      ...

Non-structural example 2:

allOf:
- items:
    properties:
      foo:
        ...

conflicts with rule 2. The following would be correct:

items:
  properties:
    foo:
      ...
allOf:
- items:
    properties:
      foo:
        ...

Non-structural example 3:

properties:
  foo:
    pattern: "abc"
  metadata:
    type: object
    properties:
      name:
        type: string
        pattern: "^a"
      finalizers:
        type: array
        items:
          type: string
          pattern: "my-finalizer"
anyOf:
- properties:
    bar:
      type: integer
      minimum: 42
  required: ["bar"]
  description: "foo bar object"

is not a structural schema because of the following violations:

the type at the root is missing (rule 1).
the type of foo is missing (rule 1).
bar inside of anyOf is not specified outside (rule 2).
bar's type is within anyOf (rule 3).
the description is set within anyOf (rule 3).
metadata.finalizers might not be restricted (rule 4).

In contrast, the following, corresponding schema is structural:

type: object
description: "foo bar object"
properties:
  foo:
    type: string
    pattern: "abc"
  bar:
    type: integer
  metadata:
    type: object
    properties:
      name:
        type: string
        pattern: "^a"
anyOf:
- properties:
    bar:
      minimum: 42
  required: ["bar"]

Violations of the structural schema rules are reported in the NonStructural condition in the CustomResourceDefinition.

Field pruning

CustomResourceDefinitions store validated resource data in the cluster's persistence store, etcd. As with native Kubernetes resources such as ConfigMap, if you specify a field that the API server does not recognize, the unknown field is pruned (removed) before being persisted.

CRDs converted from apiextensions.k8s.io/v1beta1 to apiextensions.k8s.io/v1 might lack structural schemas, and spec.preserveUnknownFields might be true.

For legacy CustomResourceDefinition objects created as apiextensions.k8s.io/v1beta1 with spec.preserveUnknownFields set to true, the following is also true:

Pruning is not enabled.
You can store arbitrary data.

For compatibility with apiextensions.k8s.io/v1, update your custom resource definitions to:

Use a structural OpenAPI schema.
Set spec.preserveUnknownFields to false.

If you save the following YAML to my-crontab.yaml:

apiVersion: "stable.example.com/v1"
kind: CronTab
metadata:
  name: my-new-cron-object
spec:
  cronSpec: "* * * * */5"
  image: my-awesome-cron-image
  someRandomField: 42

and create it:

kubectl create --validate=false -f my-crontab.yaml -o yaml

Your output is similar to:

apiVersion: stable.example.com/v1
kind: CronTab
metadata:
  creationTimestamp: 2017-05-31T12:56:35Z
  generation: 1
  name: my-new-cron-object
  namespace: default
  resourceVersion: "285"
  uid: 9423255b-4600-11e7-af6a-28d2447dc82b
spec:
  cronSpec: '* * * * */5'
  image: my-awesome-cron-image

Notice that the field someRandomField was pruned.

This example turned off client-side validation to demonstrate the API server's behavior, by adding the --validate=false command line option. Because the OpenAPI validation schemas are also published to clients, kubectl also checks for unknown fields and rejects those objects well before they would be sent to the API server.

Controlling pruning

By default, all unspecified fields for a custom resource, across all versions, are pruned. It is possible though to opt-out of that for specific sub-trees of fields by adding x-kubernetes-preserve-unknown-fields: true in the structural OpenAPI v3 validation schema.

For example:

type: object
properties:
  json:
    x-kubernetes-preserve-unknown-fields: true

The field json can store any JSON value, without anything being pruned.

You can also partially specify the permitted JSON; for example:

type: object
properties:
  json:
    x-kubernetes-preserve-unknown-fields: true
    type: object
    description: this is arbitrary JSON

With this, only object type values are allowed.

Pruning is enabled again for each specified property (or additionalProperties):

type: object
properties:
  json:
    x-kubernetes-preserve-unknown-fields: true
    type: object
    properties:
      spec:
        type: object
        properties:
          foo:
            type: string
          bar:
            type: string

With this, the value:

json:
  spec:
    foo: abc
    bar: def
    something: x
  status:
    something: x

is pruned to:

json:
  spec:
    foo: abc
    bar: def
  status:
    something: x

This means that the something field in the specified spec object is pruned, but everything outside is not.

IntOrString

Nodes in a schema with x-kubernetes-int-or-string: true are excluded from rule 1, such that the following is structural:

type: object
properties:
  foo:
    x-kubernetes-int-or-string: true

Also those nodes are partially excluded from rule 3 in the sense that the following two patterns are allowed (exactly those, without variations in order to additional fields):

x-kubernetes-int-or-string: true
anyOf:
  - type: integer
  - type: string
...

and

x-kubernetes-int-or-string: true
allOf:
  - anyOf:
      - type: integer
      - type: string
  - ... # zero or more
...

With one of those specification, both an integer and a string validate.

In Validation Schema Publishing, x-kubernetes-int-or-string: true is unfolded to one of the two patterns shown above.

RawExtension

RawExtensions (as in runtime.RawExtension) holds complete Kubernetes objects, i.e. with apiVersion and kind fields.

It is possible to specify those embedded objects (both completely without constraints or partially specified) by setting x-kubernetes-embedded-resource: true. For example:

type: object
properties:
  foo:
    x-kubernetes-embedded-resource: true
    x-kubernetes-preserve-unknown-fields: true

Here, the field foo holds a complete object, e.g.:

foo:
  apiVersion: v1
  kind: Pod
  spec:
    ...

Because x-kubernetes-preserve-unknown-fields: true is specified alongside, nothing is pruned. The use of x-kubernetes-preserve-unknown-fields: true is optional though.

With x-kubernetes-embedded-resource: true, the apiVersion, kind and metadata are implicitly specified and validated.

Serving multiple versions of a CRD

See Custom resource definition versioning for more information about serving multiple versions of your CustomResourceDefinition and migrating your objects from one version to another.

Advanced topics

Finalizers

Finalizers allow controllers to implement asynchronous pre-delete hooks. Custom objects support finalizers similar to built-in objects.

You can add a finalizer to a custom object like this:

apiVersion: "stable.example.com/v1"
kind: CronTab
metadata:
  finalizers:
  - stable.example.com/finalizer

Identifiers of custom finalizers consist of a domain name, a forward slash and the name of the finalizer. Any controller can add a finalizer to any object's list of finalizers.

The first delete request on an object with finalizers sets a value for the metadata.deletionTimestamp field but does not delete it. Once this value is set, entries in the finalizers list can only be removed. While any finalizers remain it is also impossible to force the deletion of an object.

When the metadata.deletionTimestamp field is set, controllers watching the object execute any finalizers they handle and remove the finalizer from the list after they are done. It is the responsibility of each controller to remove its finalizer from the list.

The value of metadata.deletionGracePeriodSeconds controls the interval between polling updates.

Once the list of finalizers is empty, meaning all finalizers have been executed, the resource is deleted by Kubernetes.

Validation

Custom resources are validated via OpenAPI v3 schemas, by x-kubernetes-validations when the Validation Rules feature is enabled, and you can add additional validation using admission webhooks.

Additionally, the following restrictions are applied to the schema:

These fields cannot be set:
- definitions,
- dependencies,
- deprecated,
- discriminator,
- id,
- patternProperties,
- readOnly,
- writeOnly,
- xml,
- $ref.
The field uniqueItems cannot be set to true.
The field additionalProperties cannot be set to false.
The field additionalProperties is mutually exclusive with properties.

The x-kubernetes-validations extension can be used to validate custom resources using Common Expression Language (CEL) expressions when the Validation rules feature is enabled and the CustomResourceDefinition schema is a structural schema.

Refer to the structural schemas section for other restrictions and CustomResourceDefinition features.

The schema is defined in the CustomResourceDefinition. In the following example, the CustomResourceDefinition applies the following validations on the custom object:

spec.cronSpec must be a string and must be of the form described by the regular expression.
spec.replicas must be an integer and must have a minimum value of 1 and a maximum value of 10.

Save the CustomResourceDefinition to resourcedefinition.yaml:

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: crontabs.stable.example.com
spec:
  group: stable.example.com
  versions:
    - name: v1
      served: true
      storage: true
      schema:
        # openAPIV3Schema is the schema for validating custom objects.
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object
              properties:
                cronSpec:
                  type: string
                  pattern: '^(\d+|\*)(/\d+)?(\s+(\d+|\*)(/\d+)?){4}$'
                image:
                  type: string
                replicas:
                  type: integer
                  minimum: 1
                  maximum: 10
  scope: Namespaced
  names:
    plural: crontabs
    singular: crontab
    kind: CronTab
    shortNames:
    - ct

and create it:

kubectl apply -f resourcedefinition.yaml

A request to create a custom object of kind CronTab is rejected if there are invalid values in its fields. In the following example, the custom object contains fields with invalid values:

spec.cronSpec does not match the regular expression.
spec.replicas is greater than 10.

If you save the following YAML to my-crontab.yaml:

apiVersion: "stable.example.com/v1"
kind: CronTab
metadata:
  name: my-new-cron-object
spec:
  cronSpec: "* * * *"
  image: my-awesome-cron-image
  replicas: 15

and attempt to create it:

kubectl apply -f my-crontab.yaml

then you get an error:

The CronTab "my-new-cron-object" is invalid: []: Invalid value: map[string]interface {}{"apiVersion":"stable.example.com/v1", "kind":"CronTab", "metadata":map[string]interface {}{"name":"my-new-cron-object", "namespace":"default", "deletionTimestamp":interface {}(nil), "deletionGracePeriodSeconds":(*int64)(nil), "creationTimestamp":"2017-09-05T05:20:07Z", "uid":"e14d79e7-91f9-11e7-a598-f0761cb232d1", "clusterName":""}, "spec":map[string]interface {}{"cronSpec":"* * * *", "image":"my-awesome-cron-image", "replicas":15}}:
validation failure list:
spec.cronSpec in body should match '^(\d+|\*)(/\d+)?(\s+(\d+|\*)(/\d+)?){4}$'
spec.replicas in body should be less than or equal to 10

If the fields contain valid values, the object creation request is accepted.

Save the following YAML to my-crontab.yaml:

apiVersion: "stable.example.com/v1"
kind: CronTab
metadata:
  name: my-new-cron-object
spec:
  cronSpec: "* * * * */5"
  image: my-awesome-cron-image
  replicas: 5

And create it:

kubectl apply -f my-crontab.yaml
crontab "my-new-cron-object" created

Validation ratcheting

FEATURE STATE: Kubernetes v1.30 [beta]

If you are using a version of Kubernetes older than v1.30, you need to explicitly enable the CRDValidationRatcheting feature gate to use this behavior, which then applies to all CustomResourceDefinitions in your cluster.

Provided you enabled the feature gate, Kubernetes implements validation racheting for CustomResourceDefinitions. The API server is willing to accept updates to resources that are not valid after the update, provided that each part of the resource that failed to validate was not changed by the update operation. In other words, any invalid part of the resource that remains invalid must have already been wrong. You cannot use this mechanism to update a valid resource so that it becomes invalid.

This feature allows authors of CRDs to confidently add new validations to the OpenAPIV3 schema under certain conditions. Users can update to the new schema safely without bumping the version of the object or breaking workflows.

While most validations placed in the OpenAPIV3 schema of a CRD support ratcheting, there are a few exceptions. The following OpenAPIV3 schema validations are not supported by ratcheting under the implementation in Kubernetes 1.30 and if violated will continue to throw an error as normally:

Quantors
- allOf
- oneOf
- anyOf
- not
- any validations in a descendent of one of these fields
x-kubernetes-validations For Kubernetes 1.28, CRD validation rules are ignored by ratcheting. Starting with Alpha 2 in Kubernetes 1.29, x-kubernetes-validations are ratcheted only if they do not refer to oldSelf.

Transition Rules are never ratcheted: only errors raised by rules that do not use oldSelf will be automatically ratcheted if their values are unchanged.

To write custom ratcheting logic for CEL expressions, check out optionalOldSelf.
x-kubernetes-list-type Errors arising from changing the list type of a subschema will not be ratcheted. For example adding set onto a list with duplicates will always result in an error.
x-kubernetes-map-keys Errors arising from changing the map keys of a list schema will not be ratcheted.
required Errors arising from changing the list of required fields will not be ratcheted.
properties Adding/removing/modifying the names of properties is not ratcheted, but changes to validations in each properties' schemas and subschemas may be ratcheted if the name of the property stays the same.
additionalProperties To remove a previously specified additionalProperties validation will not be ratcheted.
metadata Errors that come from Kubernetes' built-in validation of an object's metadata are not ratcheted (such as object name, or characters in a label value). If you specify your own additional rules for the metadata of a custom resource, that additional validation will be ratcheted.

Validation rules

FEATURE STATE: Kubernetes v1.29 [stable]

Validation rules use the Common Expression Language (CEL) to validate custom resource values. Validation rules are included in CustomResourceDefinition schemas using the x-kubernetes-validations extension.

The Rule is scoped to the location of the x-kubernetes-validations extension in the schema. And self variable in the CEL expression is bound to the scoped value.

All validation rules are scoped to the current object: no cross-object or stateful validation rules are supported.

For example:

  ...
  openAPIV3Schema:
    type: object
    properties:
      spec:
        type: object
        x-kubernetes-validations:
          - rule: "self.minReplicas <= self.replicas"
            message: "replicas should be greater than or equal to minReplicas."
          - rule: "self.replicas <= self.maxReplicas"
            message: "replicas should be smaller than or equal to maxReplicas."
        properties:
          ...
          minReplicas:
            type: integer
          replicas:
            type: integer
          maxReplicas:
            type: integer
        required:
          - minReplicas
          - replicas
          - maxReplicas

will reject a request to create this custom resource:

apiVersion: "stable.example.com/v1"
kind: CronTab
metadata:
  name: my-new-cron-object
spec:
  minReplicas: 0
  replicas: 20
  maxReplicas: 10

with the response:

The CronTab "my-new-cron-object" is invalid:
* spec: Invalid value: map[string]interface {}{"maxReplicas":10, "minReplicas":0, "replicas":20}: replicas should be smaller than or equal to maxReplicas.

x-kubernetes-validations could have multiple rules. The rule under x-kubernetes-validations represents the expression which will be evaluated by CEL. The message represents the message displayed when validation fails. If message is unset, the above response would be:

The CronTab "my-new-cron-object" is invalid:
* spec: Invalid value: map[string]interface {}{"maxReplicas":10, "minReplicas":0, "replicas":20}: failed rule: self.replicas <= self.maxReplicas

Note:

You can quickly test CEL expressions in CEL Playground.

Validation rules are compiled when CRDs are created/updated. The request of CRDs create/update will fail if compilation of validation rules fail. Compilation process includes type checking as well.

The compilation failure:

no_matching_overload: this function has no overload for the types of the arguments.

For example, a rule like self == true against a field of integer type will get error:

Invalid value: apiextensions.ValidationRule{Rule:"self == true", Message:""}: compilation failed: ERROR: \<input>:1:6: found no matching overload for '_==_' applied to '(int, bool)'

no_such_field: does not contain the desired field.

For example, a rule like self.nonExistingField > 0 against a non-existing field will return the following error:

Invalid value: apiextensions.ValidationRule{Rule:"self.nonExistingField > 0", Message:""}: compilation failed: ERROR: \<input>:1:5: undefined field 'nonExistingField'

invalid argument: invalid argument to macros.

For example, a rule like has(self) will return error:

Invalid value: apiextensions.ValidationRule{Rule:"has(self)", Message:""}: compilation failed: ERROR: <input>:1:4: invalid argument to has() macro

Validation Rules Examples:

Rule	Purpose
`self.minReplicas <= self.replicas && self.replicas <= self.maxReplicas`	Validate that the three fields defining replicas are ordered appropriately
`'Available' in self.stateCounts`	Validate that an entry with the 'Available' key exists in a map
`(size(self.list1) == 0) != (size(self.list2) == 0)`	Validate that one of two lists is non-empty, but not both
`!('MY_KEY' in self.map1) \|\| self['MY_KEY'].matches('^[a-zA-Z]*$')`	Validate the value of a map for a specific key, if it is in the map
`self.envars.filter(e, e.name == 'MY_ENV').all(e, e.value.matches('^[a-zA-Z]*$')`	Validate the 'value' field of a listMap entry where key field 'name' is 'MY_ENV'
`has(self.expired) && self.created + self.ttl < self.expired`	Validate that 'expired' date is after a 'create' date plus a 'ttl' duration
`self.health.startsWith('ok')`	Validate a 'health' string field has the prefix 'ok'
`self.widgets.exists(w, w.key == 'x' && w.foo < 10)`	Validate that the 'foo' property of a listMap item with a key 'x' is less than 10
`type(self) == string ? self == '100%' : self == 1000`	Validate an int-or-string field for both the int and string cases
`self.metadata.name.startsWith(self.prefix)`	Validate that an object's name has the prefix of another field value
`self.set1.all(e, !(e in self.set2))`	Validate that two listSets are disjoint
`size(self.names) == size(self.details) && self.names.all(n, n in self.details)`	Validate the 'details' map is keyed by the items in the 'names' listSet
`size(self.clusters.filter(c, c.name == self.primary)) == 1`	Validate that the 'primary' property has one and only one occurrence in the 'clusters' listMap

Xref: Supported evaluation on CEL

If the Rule is scoped to the root of a resource, it may make field selection into any fields declared in the OpenAPIv3 schema of the CRD as well as apiVersion, kind, metadata.name and metadata.generateName. This includes selection of fields in both the spec and status in the same expression:

  ...
  openAPIV3Schema:
    type: object
    x-kubernetes-validations:
      - rule: "self.status.availableReplicas >= self.spec.minReplicas"
    properties:
        spec:
          type: object
          properties:
            minReplicas:
              type: integer
            ...
        status:
          type: object
          properties:
            availableReplicas:
              type: integer

If the Rule is scoped to an object with properties, the accessible properties of the object are field selectable via self.field and field presence can be checked via has(self.field). Null valued fields are treated as absent fields in CEL expressions.
```
  ...
  openAPIV3Schema:
    type: object
    properties:
      spec:
        type: object
        x-kubernetes-validations:
          - rule: "has(self.foo)"
        properties:
          ...
          foo:
            type: integer
```

If the Rule is scoped to an object with additionalProperties (i.e. a map) the value of the map are accessible via self[mapKey], map containment can be checked via mapKey in self and all entries of the map are accessible via CEL macros and functions such as self.all(...).

  ...
  openAPIV3Schema:
    type: object
    properties:
      spec:
        type: object
        x-kubernetes-validations:
          - rule: "self['xyz'].foo > 0"
        additionalProperties:
          ...
          type: object
          properties:
            foo:
              type: integer

If the Rule is scoped to an array, the elements of the array are accessible via self[i] and also by macros and functions.

  ...
  openAPIV3Schema:
    type: object
    properties:
      ...
      foo:
        type: array
        x-kubernetes-validations:
          - rule: "size(self) == 1"
        items:
          type: string

If the Rule is scoped to a scalar, self is bound to the scalar value.

  ...
  openAPIV3Schema:
    type: object
    properties:
      spec:
        type: object
        properties:
          ...
          foo:
            type: integer
            x-kubernetes-validations:
            - rule: "self > 0"

Examples:

type of the field rule scoped to	Rule example
root object	`self.status.actual <= self.spec.maxDesired`
map of objects	`self.components['Widget'].priority < 10`
list of integers	`self.values.all(value, value >= 0 && value < 100)`
string	`self.startsWith('kube')`

The apiVersion, kind, metadata.name and metadata.generateName are always accessible from the root of the object and from any x-kubernetes-embedded-resource annotated objects. No other metadata properties are accessible.

Unknown data preserved in custom resources via x-kubernetes-preserve-unknown-fields is not accessible in CEL expressions. This includes:

Unknown field values that are preserved by object schemas with x-kubernetes-preserve-unknown-fields.
Object properties where the property schema is of an "unknown type". An "unknown type" is recursively defined as:
- A schema with no type and x-kubernetes-preserve-unknown-fields set to true
- An array where the items schema is of an "unknown type"
- An object where the additionalProperties schema is of an "unknown type"

Only property names of the form [a-zA-Z_.-/][a-zA-Z0-9_.-/]* are accessible. Accessible property names are escaped according to the following rules when accessed in the expression:

escape sequence	property name equivalent
`__underscores__`	`__`
`__dot__`	`.`
`__dash__`	`-`
`__slash__`	`/`
`__{keyword}__`	CEL RESERVED keyword

Note: CEL RESERVED keyword needs to match the exact property name to be escaped (e.g. int in the word sprint would not be escaped).

Examples on escaping:

property name	rule with escaped property name
namespace	`self.__namespace__ > 0`
x-prop	`self.x__dash__prop > 0`
redact__d	`self.redact__underscores__d > 0`
string	`self.startsWith('kube')`

Equality on arrays with x-kubernetes-list-type of set or map ignores element order, i.e., [1, 2] == [2, 1]. Concatenation on arrays with x-kubernetes-list-type use the semantics of the list type:

set: X + Y performs a union where the array positions of all elements in X are preserved and non-intersecting elements in Y are appended, retaining their partial order.
map: X + Y performs a merge where the array positions of all keys in X are preserved but the values are overwritten by values in Y when the key sets of X and Y intersect. Elements in Y with non-intersecting keys are appended, retaining their partial order.

Here is the declarations type mapping between OpenAPIv3 and CEL type:

OpenAPIv3 type	CEL type
'object' with Properties	object / "message type"
'object' with AdditionalProperties	map
'object' with x-kubernetes-embedded-type	object / "message type", 'apiVersion', 'kind', 'metadata.name' and 'metadata.generateName' are implicitly included in schema
'object' with x-kubernetes-preserve-unknown-fields	object / "message type", unknown fields are NOT accessible in CEL expression
x-kubernetes-int-or-string	dynamic object that is either an int or a string, `type(value)` can be used to check the type
'array	list
'array' with x-kubernetes-list-type=map	list with map based Equality & unique key guarantees
'array' with x-kubernetes-list-type=set	list with set based Equality & unique entry guarantees
'boolean'	boolean
'number' (all formats)	double
'integer' (all formats)	int (64)
'null'	null_type
'string'	string
'string' with format=byte (base64 encoded)	bytes
'string' with format=date	timestamp (google.protobuf.Timestamp)
'string' with format=datetime	timestamp (google.protobuf.Timestamp)
'string' with format=duration	duration (google.protobuf.Duration)

xref: CEL types, OpenAPI types, Kubernetes Structural Schemas.

The messageExpression field

Similar to the message field, which defines the string reported for a validation rule failure, messageExpression allows you to use a CEL expression to construct the message string. This allows you to insert more descriptive information into the validation failure message. messageExpression must evaluate a string and may use the same variables that are available to the rule field. For example:

x-kubernetes-validations:
- rule: "self.x <= self.maxLimit"
  messageExpression: '"x exceeded max limit of " + string(self.maxLimit)'

Keep in mind that CEL string concatenation (+ operator) does not auto-cast to string. If you have a non-string scalar, use the string(<value>) function to cast the scalar to a string like shown in the above example.

messageExpression must evaluate to a string, and this is checked while the CRD is being written. Note that it is possible to set message and messageExpression on the same rule, and if both are present, messageExpression will be used. However, if messageExpression evaluates to an error, the string defined in message will be used instead, and the messageExpression error will be logged. This fallback will also occur if the CEL expression defined in messageExpression generates an empty string, or a string containing line breaks.

If one of the above conditions are met and no message has been set, then the default validation failure message will be used instead.

messageExpression is a CEL expression, so the restrictions listed in Resource use by validation functions apply. If evaluation halts due to resource constraints during messageExpression execution, then no further validation rules will be executed.

Setting messageExpression is optional.

The `message` field

If you want to set a static message, you can supply message rather than messageExpression. The value of message is used as an opaque error string if validation fails.

Setting message is optional.

The `reason` field

You can add a machine-readable validation failure reason within a validation, to be returned whenever a request fails this validation rule.

For example:

x-kubernetes-validations:
- rule: "self.x <= self.maxLimit"
  reason: "FieldValueInvalid"

The HTTP status code returned to the caller will match the reason of the first failed validation rule. The currently supported reasons are: "FieldValueInvalid", "FieldValueForbidden", "FieldValueRequired", "FieldValueDuplicate". If not set or unknown reasons, default to use "FieldValueInvalid".

Setting reason is optional.

The `fieldPath` field

You can specify the field path returned when the validation fails.

For example:

x-kubernetes-validations:
- rule: "self.foo.test.x <= self.maxLimit"
  fieldPath: ".foo.test.x"

In the example above, the validation checks the value of field x should be less than the value of maxLimit. If no fieldPath specified, when validation fails, the fieldPath would be default to wherever self scoped. With fieldPath specified, the returned error will have fieldPath properly refer to the location of field x.

The fieldPath value must be a relative JSON path that is scoped to the location of this x-kubernetes-validations extension in the schema. Additionally, it should refer to an existing field within the schema. For example when validation checks if a specific attribute foo under a map testMap, you could set fieldPath to ".testMap.foo" or .testMap['foo']'. If the validation requires checking for unique attributes in two lists, the fieldPath can be set to either of the lists. For example, it can be set to .testList1 or .testList2. It supports child operation to refer to an existing field currently. Refer to JSONPath support in Kubernetes for more info. The fieldPath field does not support indexing arrays numerically.

Setting fieldPath is optional.

The `optionalOldSelf` field

FEATURE STATE: Kubernetes v1.30 [beta]

If your cluster does not have CRD validation ratcheting enabled, the CustomResourceDefinition API doesn't include this field, and trying to set it may result in an error.

The optionalOldSelf field is a boolean field that alters the behavior of Transition Rules described below. Normally, a transition rule will not evaluate if oldSelf cannot be determined: during object creation or when a new value is introduced in an update.

If optionalOldSelf is set to true, then transition rules will always be evaluated and the type of oldSelf be changed to a CEL Optional type.

optionalOldSelf is useful in cases where schema authors would like a more control tool than provided by the default equality based behavior of to introduce newer, usually stricter constraints on new values, while still allowing old values to be "grandfathered" or ratcheted using the older validation.

Example Usage:

CEL	Description
`self.foo == "foo"
[oldSelf.orValue(""), self].all(x, ["OldCase1", "OldCase2"].exists(case, x == case))
oldSelf.optMap(o, o.size()).orValue(0) < 4

Validation functions

Functions available include:

CEL standard functions, defined in the list of standard definitions
CEL standard macros
CEL extended string function library
Kubernetes CEL extension library

Transition rules

A rule that contains an expression referencing the identifier oldSelf is implicitly considered a transition rule. Transition rules allow schema authors to prevent certain transitions between two otherwise valid states. For example:

type: string
enum: ["low", "medium", "high"]
x-kubernetes-validations:
- rule: "!(self == 'high' && oldSelf == 'low') && !(self == 'low' && oldSelf == 'high')"
  message: cannot transition directly between 'low' and 'high'

Unlike other rules, transition rules apply only to operations meeting the following criteria:

The operation updates an existing object. Transition rules never apply to create operations.
Both an old and a new value exist. It remains possible to check if a value has been added or removed by placing a transition rule on the parent node. Transition rules are never applied to custom resource creation. When placed on an optional field, a transition rule will not apply to update operations that set or unset the field.
The path to the schema node being validated by a transition rule must resolve to a node that is comparable between the old object and the new object. For example, list items and their descendants (spec.foo[10].bar) can't necessarily be correlated between an existing object and a later update to the same object.

Errors will be generated on CRD writes if a schema node contains a transition rule that can never be applied, e.g. "oldSelf cannot be used on the uncorrelatable portion of the schema within path".

Transition rules are only allowed on correlatable portions of a schema. A portion of the schema is correlatable if all array parent schemas are of type x-kubernetes-list-type=map; any setor atomicarray parent schemas make it impossible to unambiguously correlate a self with oldSelf.

Here are some examples for transition rules:

Transition rules examples
Use Case	Rule
Immutability	`self.foo == oldSelf.foo`
Prevent modification/removal once assigned	`oldSelf != 'bar' \|\| self == 'bar'` or `!has(oldSelf.field) \|\| has(self.field)`
Append-only set	`self.all(element, element in oldSelf)`
If previous value was X, new value can only be A or B, not Y or Z	`oldSelf != 'X' \|\| self in ['A', 'B']`
Monotonic (non-decreasing) counters	`self >= oldSelf`

Resource use by validation functions

When you create or update a CustomResourceDefinition that uses validation rules, the API server checks the likely impact of running those validation rules. If a rule is estimated to be prohibitively expensive to execute, the API server rejects the create or update operation, and returns an error message. A similar system is used at runtime that observes the actions the interpreter takes. If the interpreter executes too many instructions, execution of the rule will be halted, and an error will result. Each CustomResourceDefinition is also allowed a certain amount of resources to finish executing all of its validation rules. If the sum total of its rules are estimated at creation time to go over that limit, then a validation error will also occur.

You are unlikely to encounter issues with the resource budget for validation if you only specify rules that always take the same amount of time regardless of how large their input is. For example, a rule that asserts that self.foo == 1 does not by itself have any risk of rejection on validation resource budget groups. But if foo is a string and you define a validation rule self.foo.contains("someString"), that rule takes longer to execute depending on how long foo is. Another example would be if foo were an array, and you specified a validation rule self.foo.all(x, x > 5). The cost system always assumes the worst-case scenario if a limit on the length of foo is not given, and this will happen for anything that can be iterated over (lists, maps, etc.).

Because of this, it is considered best practice to put a limit via maxItems, maxProperties, and maxLength for anything that will be processed in a validation rule in order to prevent validation errors during cost estimation. For example, given this schema with one rule:

openAPIV3Schema:
  type: object
  properties:
    foo:
      type: array
      items:
        type: string
      x-kubernetes-validations:
        - rule: "self.all(x, x.contains('a string'))"

then the API server rejects this rule on validation budget grounds with error:

spec.validation.openAPIV3Schema.properties[spec].properties[foo].x-kubernetes-validations[0].rule: Forbidden:
CEL rule exceeded budget by more than 100x (try simplifying the rule, or adding maxItems, maxProperties, and
maxLength where arrays, maps, and strings are used)

The rejection happens because self.all implies calling contains() on every string in foo, which in turn will check the given string to see if it contains 'a string'. Without limits, this is a very expensive rule.

If you do not specify any validation limit, the estimated cost of this rule will exceed the per-rule cost limit. But if you add limits in the appropriate places, the rule will be allowed:

openAPIV3Schema:
  type: object
  properties:
    foo:
      type: array
      maxItems: 25
      items:
        type: string
        maxLength: 10
      x-kubernetes-validations:
        - rule: "self.all(x, x.contains('a string'))"

The cost estimation system takes into account how many times the rule will be executed in addition to the estimated cost of the rule itself. For instance, the following rule will have the same estimated cost as the previous example (despite the rule now being defined on the individual array items):

openAPIV3Schema:
  type: object
  properties:
    foo:
      type: array
      maxItems: 25
      items:
        type: string
        x-kubernetes-validations:
          - rule: "self.contains('a string'))"
        maxLength: 10

If a list inside of a list has a validation rule that uses self.all, that is significantly more expensive than a non-nested list with the same rule. A rule that would have been allowed on a non-nested list might need lower limits set on both nested lists in order to be allowed. For example, even without having limits set, the following rule is allowed:

openAPIV3Schema:
  type: object
  properties:
    foo:
      type: array
      items:
        type: integer
    x-kubernetes-validations:
      - rule: "self.all(x, x == 5)"

But the same rule on the following schema (with a nested array added) produces a validation error:

openAPIV3Schema:
  type: object
  properties:
    foo:
      type: array
      items:
        type: array
        items:
          type: integer
        x-kubernetes-validations:
          - rule: "self.all(x, x == 5)"

This is because each item of foo is itself an array, and each subarray in turn calls self.all. Avoid nested lists and maps if possible where validation rules are used.

Defaulting

Note:

To use defaulting, your CustomResourceDefinition must use API version apiextensions.k8s.io/v1.

Defaulting allows to specify default values in the OpenAPI v3 validation schema:

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: crontabs.stable.example.com
spec:
  group: stable.example.com
  versions:
    - name: v1
      served: true
      storage: true
      schema:
        # openAPIV3Schema is the schema for validating custom objects.
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object
              properties:
                cronSpec:
                  type: string
                  pattern: '^(\d+|\*)(/\d+)?(\s+(\d+|\*)(/\d+)?){4}$'
                  default: "5 0 * * *"
                image:
                  type: string
                replicas:
                  type: integer
                  minimum: 1
                  maximum: 10
                  default: 1
  scope: Namespaced
  names:
    plural: crontabs
    singular: crontab
    kind: CronTab
    shortNames:
    - ct

With this both cronSpec and replicas are defaulted:

apiVersion: "stable.example.com/v1"
kind: CronTab
metadata:
  name: my-new-cron-object
spec:
  image: my-awesome-cron-image

leads to

apiVersion: "stable.example.com/v1"
kind: CronTab
metadata:
  name: my-new-cron-object
spec:
  cronSpec: "5 0 * * *"
  image: my-awesome-cron-image
  replicas: 1

Defaulting happens on the object

in the request to the API server using the request version defaults,
when reading from etcd using the storage version defaults,
after mutating admission plugins with non-empty patches using the admission webhook object version defaults.

Defaults applied when reading data from etcd are not automatically written back to etcd. An update request via the API is required to persist those defaults back into etcd.

Default values must be pruned (with the exception of defaults for metadata fields) and must validate against a provided schema.

Default values for metadata fields of x-kubernetes-embedded-resources: true nodes (or parts of a default value covering metadata) are not pruned during CustomResourceDefinition creation, but through the pruning step during handling of requests.

Defaulting and Nullable

Null values for fields that either don't specify the nullable flag, or give it a false value, will be pruned before defaulting happens. If a default is present, it will be applied. When nullable is true, null values will be conserved and won't be defaulted.

For example, given the OpenAPI schema below:

type: object
properties:
  spec:
    type: object
    properties:
      foo:
        type: string
        nullable: false
        default: "default"
      bar:
        type: string
        nullable: true
      baz:
        type: string

creating an object with null values for foo and bar and baz

spec:
  foo: null
  bar: null
  baz: null

leads to

spec:
  foo: "default"
  bar: null

with foo pruned and defaulted because the field is non-nullable, bar maintaining the null value due to nullable: true, and baz pruned because the field is non-nullable and has no default.

Publish Validation Schema in OpenAPI

CustomResourceDefinition OpenAPI v3 validation schemas which are structural and enable pruning are published as OpenAPI v3 and OpenAPI v2 from Kubernetes API server. It is recommended to use the OpenAPI v3 document as it is a lossless representation of the CustomResourceDefinition OpenAPI v3 validation schema while OpenAPI v2 represents a lossy conversion.

The kubectl command-line tool consumes the published schema to perform client-side validation (kubectl create and kubectl apply), schema explanation (kubectl explain) on custom resources. The published schema can be consumed for other purposes as well, like client generation or documentation.

Compatibility with OpenAPI V2

For compatibility with OpenAPI V2, the OpenAPI v3 validation schema performs a lossy conversion to the OpenAPI v2 schema. The schema show up in definitions and paths fields in the OpenAPI v2 spec.

The following modifications are applied during the conversion to keep backwards compatibility with kubectl in previous 1.13 version. These modifications prevent kubectl from being over-strict and rejecting valid OpenAPI schemas that it doesn't understand. The conversion won't modify the validation schema defined in CRD, and therefore won't affect validation in the API server.

The following fields are removed as they aren't supported by OpenAPI v2.
- The fields allOf, anyOf, oneOf and not are removed
If nullable: true is set, we drop type, nullable, items and properties because OpenAPI v2 is not able to express nullable. To avoid kubectl to reject good objects, this is necessary.

Additional printer columns

The kubectl tool relies on server-side output formatting. Your cluster's API server decides which columns are shown by the kubectl get command. You can customize these columns for a CustomResourceDefinition. The following example adds the Spec, Replicas, and Age columns.

Save the CustomResourceDefinition to resourcedefinition.yaml:

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: crontabs.stable.example.com
spec:
  group: stable.example.com
  scope: Namespaced
  names:
    plural: crontabs
    singular: crontab
    kind: CronTab
    shortNames:
    - ct
  versions:
  - name: v1
    served: true
    storage: true
    schema:
      openAPIV3Schema:
        type: object
        properties:
          spec:
            type: object
            properties:
              cronSpec:
                type: string
              image:
                type: string
              replicas:
                type: integer
    additionalPrinterColumns:
    - name: Spec
      type: string
      description: The cron spec defining the interval a CronJob is run
      jsonPath: .spec.cronSpec
    - name: Replicas
      type: integer
      description: The number of jobs launched by the CronJob
      jsonPath: .spec.replicas
    - name: Age
      type: date
      jsonPath: .metadata.creationTimestamp

Create the CustomResourceDefinition:

kubectl apply -f resourcedefinition.yaml

Create an instance using the my-crontab.yaml from the previous section.

Invoke the server-side printing:

kubectl get crontab my-new-cron-object

Notice the NAME, SPEC, REPLICAS, and AGE columns in the output:

NAME                 SPEC        REPLICAS   AGE
my-new-cron-object   * * * * *   1          7s

Note:

The NAME column is implicit and does not need to be defined in the CustomResourceDefinition.

Priority

Each column includes a priority field. Currently, the priority differentiates between columns shown in standard view or wide view (using the -o wide flag).

Columns with priority 0 are shown in standard view.
Columns with priority greater than 0 are shown only in wide view.

Type

A column's type field can be any of the following (compare OpenAPI v3 data types):

integer – non-floating-point numbers
number – floating point numbers
string – strings
boolean – true or false
date – rendered differentially as time since this timestamp.

If the value inside a CustomResource does not match the type specified for the column, the value is omitted. Use CustomResource validation to ensure that the value types are correct.

Format

A column's format field can be any of the following:

int32
int64
float
double
byte
date
date-time
password

The column's format controls the style used when kubectl prints the value.

Field selectors

Field Selectors let clients select custom resources based on the value of one or more resource fields.

All custom resources support the metadata.name and metadata.namespace field selectors.

Fields declared in a CustomResourceDefinition may also be used with field selectors when included in the spec.versions[*].selectableFields field of the CustomResourceDefinition.

Selectable fields for custom resources

FEATURE STATE: Kubernetes v1.30 [alpha]

You need to enable the CustomResourceFieldSelectors feature gate to use this behavior, which then applies to all CustomResourceDefinitions in your cluster.

The spec.versions[*].selectableFields field of a CustomResourceDefinition may be used to declare which other fields in a custom resource may be used in field selectors. The following example adds the .spec.color and .spec.size fields as selectable fields.

Save the CustomResourceDefinition to shirt-resource-definition.yaml:

customresourcedefinition/shirt-resource-definition.yaml

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: shirts.stable.example.com
spec:
  group: stable.example.com
  scope: Namespaced
  names:
    plural: shirts
    singular: shirt
    kind: Shirt
  versions:
  - name: v1
    served: true
    storage: true
    schema:
      openAPIV3Schema:
        type: object
        properties:
          spec:
            type: object
            properties:
              color:
                type: string
              size:
                type: string
    selectableFields:
    - jsonPath: .spec.color
    - jsonPath: .spec.size
    additionalPrinterColumns:
    - jsonPath: .spec.color
      name: Color
      type: string
    - jsonPath: .spec.size
      name: Size
      type: string

Create the CustomResourceDefinition:

kubectl apply -f https://k8s.io/examples/customresourcedefinition/shirt-resource-definition.yaml

Define some Shirts by editing shirt-resources.yaml; for example:

customresourcedefinition/shirt-resources.yaml

---
apiVersion: stable.example.com/v1
kind: Shirt
metadata:
  name: example1
spec:
  color: blue
  size: S
---
apiVersion: stable.example.com/v1
kind: Shirt
metadata:
  name: example2
spec:
  color: blue
  size: M
---
apiVersion: stable.example.com/v1
kind: Shirt
metadata:
  name: example3
spec:
  color: green
  size: M

Create the custom resources:

kubectl apply -f https://k8s.io/examples/customresourcedefinition/shirt-resources.yaml

Get all the resources:

kubectl get shirts.stable.example.com

The output is:

NAME       COLOR  SIZE
example1   blue   S
example2   blue   M
example3   green  M

Fetch blue shirts (retrieve Shirts with a color of blue):

kubectl get shirts.stable.example.com --field-selector spec.color=blue

Should output:

NAME       COLOR  SIZE
example1   blue   S
example2   blue   M

Get only resources with a color of green and a size of M:

kubectl get shirts.stable.example.com --field-selector spec.color=green,spec.size=M

Should output:

NAME       COLOR  SIZE
example2   blue   M

Subresources

Custom resources support /status and /scale subresources.

The status and scale subresources can be optionally enabled by defining them in the CustomResourceDefinition.

Status subresource

When the status subresource is enabled, the /status subresource for the custom resource is exposed.

The status and the spec stanzas are represented by the .status and .spec JSONPaths respectively inside of a custom resource.
PUT requests to the /status subresource take a custom resource object and ignore changes to anything except the status stanza.
PUT requests to the /status subresource only validate the status stanza of the custom resource.
PUT/POST/PATCH requests to the custom resource ignore changes to the status stanza.
The .metadata.generation value is incremented for all changes, except for changes to .metadata or .status.
Only the following constructs are allowed at the root of the CRD OpenAPI validation schema:
- description
- example
- exclusiveMaximum
- exclusiveMinimum
- externalDocs
- format
- items
- maximum
- maxItems
- maxLength
- minimum
- minItems
- minLength
- multipleOf
- pattern
- properties
- required
- title
- type
- uniqueItems

Scale subresource

When the scale subresource is enabled, the /scale subresource for the custom resource is exposed. The autoscaling/v1.Scale object is sent as the payload for /scale.

To enable the scale subresource, the following fields are defined in the CustomResourceDefinition.

specReplicasPath defines the JSONPath inside of a custom resource that corresponds to scale.spec.replicas.
- It is a required value.
- Only JSONPaths under .spec and with the dot notation are allowed.
- If there is no value under the specReplicasPath in the custom resource, the /scale subresource will return an error on GET.
statusReplicasPath defines the JSONPath inside of a custom resource that corresponds to scale.status.replicas.
- It is a required value.
- Only JSONPaths under .status and with the dot notation are allowed.
- If there is no value under the statusReplicasPath in the custom resource, the status replica value in the /scale subresource will default to 0.
labelSelectorPath defines the JSONPath inside of a custom resource that corresponds to Scale.Status.Selector.
- It is an optional value.
- It must be set to work with HPA and VPA.
- Only JSONPaths under .status or .spec and with the dot notation are allowed.
- If there is no value under the labelSelectorPath in the custom resource, the status selector value in the /scale subresource will default to the empty string.
- The field pointed by this JSON path must be a string field (not a complex selector struct) which contains a serialized label selector in string form.

In the following example, both status and scale subresources are enabled.

Save the CustomResourceDefinition to resourcedefinition.yaml:

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: crontabs.stable.example.com
spec:
  group: stable.example.com
  versions:
    - name: v1
      served: true
      storage: true
      schema:
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object
              properties:
                cronSpec:
                  type: string
                image:
                  type: string
                replicas:
                  type: integer
            status:
              type: object
              properties:
                replicas:
                  type: integer
                labelSelector:
                  type: string
      # subresources describes the subresources for custom resources.
      subresources:
        # status enables the status subresource.
        status: {}
        # scale enables the scale subresource.
        scale:
          # specReplicasPath defines the JSONPath inside of a custom resource that corresponds to Scale.Spec.Replicas.
          specReplicasPath: .spec.replicas
          # statusReplicasPath defines the JSONPath inside of a custom resource that corresponds to Scale.Status.Replicas.
          statusReplicasPath: .status.replicas
          # labelSelectorPath defines the JSONPath inside of a custom resource that corresponds to Scale.Status.Selector.
          labelSelectorPath: .status.labelSelector
  scope: Namespaced
  names:
    plural: crontabs
    singular: crontab
    kind: CronTab
    shortNames:
    - ct

And create it:

kubectl apply -f resourcedefinition.yaml

After the CustomResourceDefinition object has been created, you can create custom objects.

If you save the following YAML to my-crontab.yaml:

apiVersion: "stable.example.com/v1"
kind: CronTab
metadata:
  name: my-new-cron-object
spec:
  cronSpec: "* * * * */5"
  image: my-awesome-cron-image
  replicas: 3

and create it:

kubectl apply -f my-crontab.yaml

Then new namespaced RESTful API endpoints are created at:

/apis/stable.example.com/v1/namespaces/*/crontabs/status

and

/apis/stable.example.com/v1/namespaces/*/crontabs/scale

A custom resource can be scaled using the kubectl scale command. For example, the following command sets .spec.replicas of the custom resource created above to 5:

kubectl scale --replicas=5 crontabs/my-new-cron-object
crontabs "my-new-cron-object" scaled

kubectl get crontabs my-new-cron-object -o jsonpath='{.spec.replicas}'
5

You can use a PodDisruptionBudget to protect custom resources that have the scale subresource enabled.

What's next

Read about custom resources.
See CustomResourceDefinition.
Serve multiple versions of a CustomResourceDefinition.

11.2.2 - Versions in CustomResourceDefinitions

This page explains how to add versioning information to CustomResourceDefinitions, to indicate the stability level of your CustomResourceDefinitions or advance your API to a new version with conversion between API representations. It also describes how to upgrade an object from one version to another.

Before you begin

You should have an initial understanding of custom resources.

Your Kubernetes server must be at or later than version v1.16. To check the version, enter kubectl version.

Overview

The CustomResourceDefinition API provides a workflow for introducing and upgrading to new versions of a CustomResourceDefinition.

When a CustomResourceDefinition is created, the first version is set in the CustomResourceDefinition spec.versions list to an appropriate stability level and a version number. For example v1beta1 would indicate that the first version is not yet stable. All custom resource objects will initially be stored at this version.

Once the CustomResourceDefinition is created, clients may begin using the v1beta1 API.

Later it might be necessary to add new version such as v1.

Adding a new version:

Pick a conversion strategy. Since custom resource objects need the ability to be served at both versions, that means they will sometimes be served in a different version than the one stored. To make this possible, the custom resource objects must sometimes be converted between the version they are stored at and the version they are served at. If the conversion involves schema changes and requires custom logic, a conversion webhook should be used. If there are no schema changes, the default None conversion strategy may be used and only the apiVersion field will be modified when serving different versions.
If using conversion webhooks, create and deploy the conversion webhook. See the Webhook conversion for more details.
Update the CustomResourceDefinition to include the new version in the spec.versions list with served:true. Also, set spec.conversion field to the selected conversion strategy. If using a conversion webhook, configure spec.conversion.webhookClientConfig field to call the webhook.

Once the new version is added, clients may incrementally migrate to the new version. It is perfectly safe for some clients to use the old version while others use the new version.

Migrate stored objects to the new version:

See the upgrade existing objects to a new stored version section.

It is safe for clients to use both the old and new version before, during and after upgrading the objects to a new stored version.

Removing an old version:

Ensure all clients are fully migrated to the new version. The kube-apiserver logs can be reviewed to help identify any clients that are still accessing via the old version.
Set served to false for the old version in the spec.versions list. If any clients are still unexpectedly using the old version they may begin reporting errors attempting to access the custom resource objects at the old version. If this occurs, switch back to using served:true on the old version, migrate the remaining clients to the new version and repeat this step.
Ensure the upgrade of existing objects to the new stored version step has been completed.
1. Verify that the storage is set to true for the new version in the spec.versions list in the CustomResourceDefinition.
2. Verify that the old version is no longer listed in the CustomResourceDefinition status.storedVersions.
Remove the old version from the CustomResourceDefinition spec.versions list.
Drop conversion support for the old version in conversion webhooks.

Specify multiple versions

The CustomResourceDefinition API versions field can be used to support multiple versions of custom resources that you have developed. Versions can have different schemas, and conversion webhooks can convert custom resources between versions. Webhook conversions should follow the Kubernetes API conventions wherever applicable. Specifically, See the API change documentation for a set of useful gotchas and suggestions.

Note:

In apiextensions.k8s.io/v1beta1, there was a version field instead of versions. The version field is deprecated and optional, but if it is not empty, it must match the first item in the versions field.

This example shows a CustomResourceDefinition with two versions. For the first example, the assumption is all versions share the same schema with no conversion between them. The comments in the YAML provide more context.

apiextensions.k8s.io/v1
apiextensions.k8s.io/v1beta1

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  # name must match the spec fields below, and be in the form: <plural>.<group>
  name: crontabs.example.com
spec:
  # group name to use for REST API: /apis/<group>/<version>
  group: example.com
  # list of versions supported by this CustomResourceDefinition
  versions:
  - name: v1beta1
    # Each version can be enabled/disabled by Served flag.
    served: true
    # One and only one version must be marked as the storage version.
    storage: true
    # A schema is required
    schema:
      openAPIV3Schema:
        type: object
        properties:
          host:
            type: string
          port:
            type: string
  - name: v1
    served: true
    storage: false
    schema:
      openAPIV3Schema:
        type: object
        properties:
          host:
            type: string
          port:
            type: string
  # The conversion section is introduced in Kubernetes 1.13+ with a default value of
  # None conversion (strategy sub-field set to None).
  conversion:
    # None conversion assumes the same schema for all versions and only sets the apiVersion
    # field of custom resources to the proper value
    strategy: None
  # either Namespaced or Cluster
  scope: Namespaced
  names:
    # plural name to be used in the URL: /apis/<group>/<version>/<plural>
    plural: crontabs
    # singular name to be used as an alias on the CLI and for display
    singular: crontab
    # kind is normally the CamelCased singular type. Your resource manifests use this.
    kind: CronTab
    # shortNames allow shorter string to match your resource on the CLI
    shortNames:
    - ct

# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
  # name must match the spec fields below, and be in the form: <plural>.<group>
  name: crontabs.example.com
spec:
  # group name to use for REST API: /apis/<group>/<version>
  group: example.com
  # list of versions supported by this CustomResourceDefinition
  versions:
  - name: v1beta1
    # Each version can be enabled/disabled by Served flag.
    served: true
    # One and only one version must be marked as the storage version.
    storage: true
  - name: v1
    served: true
    storage: false
  validation:
    openAPIV3Schema:
      type: object
      properties:
        host:
          type: string
        port:
          type: string
  # The conversion section is introduced in Kubernetes 1.13+ with a default value of
  # None conversion (strategy sub-field set to None).
  conversion:
    # None conversion assumes the same schema for all versions and only sets the apiVersion
    # field of custom resources to the proper value
    strategy: None
  # either Namespaced or Cluster
  scope: Namespaced
  names:
    # plural name to be used in the URL: /apis/<group>/<version>/<plural>
    plural: crontabs
    # singular name to be used as an alias on the CLI and for display
    singular: crontab
    # kind is normally the PascalCased singular type. Your resource manifests use this.
    kind: CronTab
    # shortNames allow shorter string to match your resource on the CLI
    shortNames:
    - ct

You can save the CustomResourceDefinition in a YAML file, then use kubectl apply to create it.

kubectl apply -f my-versioned-crontab.yaml

After creation, the API server starts to serve each enabled version at an HTTP REST endpoint. In the above example, the API versions are available at /apis/example.com/v1beta1 and /apis/example.com/v1.

Version priority

Regardless of the order in which versions are defined in a CustomResourceDefinition, the version with the highest priority is used by kubectl as the default version to access objects. The priority is determined by parsing the name field to determine the version number, the stability (GA, Beta, or Alpha), and the sequence within that stability level.

The algorithm used for sorting the versions is designed to sort versions in the same way that the Kubernetes project sorts Kubernetes versions. Versions start with a v followed by a number, an optional beta or alpha designation, and optional additional numeric versioning information. Broadly, a version string might look like v2 or v2beta1. Versions are sorted using the following algorithm:

Entries that follow Kubernetes version patterns are sorted before those that do not.
For entries that follow Kubernetes version patterns, the numeric portions of the version string is sorted largest to smallest.
If the strings beta or alpha follow the first numeric portion, they sorted in that order, after the equivalent string without the beta or alpha suffix (which is presumed to be the GA version).
If another number follows the beta, or alpha, those numbers are also sorted from largest to smallest.
Strings that don't fit the above format are sorted alphabetically and the numeric portions are not treated specially. Notice that in the example below, foo1 is sorted above foo10. This is different from the sorting of the numeric portion of entries that do follow the Kubernetes version patterns.

This might make sense if you look at the following sorted version list:

- v10
- v2
- v1
- v11beta2
- v10beta3
- v3beta1
- v12alpha1
- v11alpha2
- foo1
- foo10

For the example in Specify multiple versions, the version sort order is v1, followed by v1beta1. This causes the kubectl command to use v1 as the default version unless the provided object specifies the version.

Version deprecation

FEATURE STATE: Kubernetes v1.19 [stable]

Starting in v1.19, a CustomResourceDefinition can indicate a particular version of the resource it defines is deprecated. When API requests to a deprecated version of that resource are made, a warning message is returned in the API response as a header. The warning message for each deprecated version of the resource can be customized if desired.

A customized warning message should indicate the deprecated API group, version, and kind, and should indicate what API group, version, and kind should be used instead, if applicable.

apiextensions.k8s.io/v1
apiextensions.k8s.io/v1beta1

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
  name: crontabs.example.com
spec:
  group: example.com
  names:
    plural: crontabs
    singular: crontab
    kind: CronTab
  scope: Namespaced
  versions:
  - name: v1alpha1
    served: true
    storage: false
    # This indicates the v1alpha1 version of the custom resource is deprecated.
    # API requests to this version receive a warning header in the server response.
    deprecated: true
    # This overrides the default warning returned to API clients making v1alpha1 API requests.
    deprecationWarning: "example.com/v1alpha1 CronTab is deprecated; see http://example.com/v1alpha1-v1 for instructions to migrate to example.com/v1 CronTab"
    
    schema: ...
  - name: v1beta1
    served: true
    # This indicates the v1beta1 version of the custom resource is deprecated.
    # API requests to this version receive a warning header in the server response.
    # A default warning message is returned for this version.
    deprecated: true
    schema: ...
  - name: v1
    served: true
    storage: true
    schema: ...

# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
  name: crontabs.example.com
spec:
  group: example.com
  names:
    plural: crontabs
    singular: crontab
    kind: CronTab
  scope: Namespaced
  validation: ...
  versions:
  - name: v1alpha1
    served: true
    storage: false
    # This indicates the v1alpha1 version of the custom resource is deprecated.
    # API requests to this version receive a warning header in the server response.
    deprecated: true
    # This overrides the default warning returned to API clients making v1alpha1 API requests.
    deprecationWarning: "example.com/v1alpha1 CronTab is deprecated; see http://example.com/v1alpha1-v1 for instructions to migrate to example.com/v1 CronTab"
  - name: v1beta1
    served: true
    # This indicates the v1beta1 version of the custom resource is deprecated.
    # API requests to this version receive a warning header in the server response.
    # A default warning message is returned for this version.
    deprecated: true
  - name: v1
    served: true
    storage: true

Version removal

An older API version cannot be dropped from a CustomResourceDefinition manifest until existing stored data has been migrated to the newer API version for all clusters that served the older version of the custom resource, and the old version is removed from the status.storedVersions of the CustomResourceDefinition.

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
  name: crontabs.example.com
spec:
  group: example.com
  names:
    plural: crontabs
    singular: crontab
    kind: CronTab
  scope: Namespaced
  versions:
  - name: v1beta1
    # This indicates the v1beta1 version of the custom resource is no longer served.
    # API requests to this version receive a not found error in the server response.
    served: false
    schema: ...
  - name: v1
    served: true
    # The new served version should be set as the storage version
    storage: true
    schema: ...

Webhook conversion

FEATURE STATE: Kubernetes v1.16 [stable]

Note:

Webhook conversion is available as beta since 1.15, and as alpha since Kubernetes 1.13. The CustomResourceWebhookConversion feature must be enabled, which is the case automatically for many clusters for beta features. Please refer to the feature gate documentation for more information.

The above example has a None conversion between versions which only sets the apiVersion field on conversion and does not change the rest of the object. The API server also supports webhook conversions that call an external service in case a conversion is required. For example when:

custom resource is requested in a different version than stored version.
Watch is created in one version but the changed object is stored in another version.
custom resource PUT request is in a different version than storage version.

To cover all of these cases and to optimize conversion by the API server, the conversion requests may contain multiple objects in order to minimize the external calls. The webhook should perform these conversions independently.

Write a conversion webhook server

Please refer to the implementation of the custom resource conversion webhook server that is validated in a Kubernetes e2e test. The webhook handles the ConversionReview requests sent by the API servers, and sends back conversion results wrapped in ConversionResponse. Note that the request contains a list of custom resources that need to be converted independently without changing the order of objects. The example server is organized in a way to be reused for other conversions. Most of the common code are located in the framework file that leaves only one function to be implemented for different conversions.

Note:

The example conversion webhook server leaves the ClientAuth field empty, which defaults to NoClientCert. This means that the webhook server does not authenticate the identity of the clients, supposedly API servers. If you need mutual TLS or other ways to authenticate the clients, see how to authenticate API servers.

Permissible mutations

A conversion webhook must not mutate anything inside of metadata of the converted object other than labels and annotations. Attempted changes to name, UID and namespace are rejected and fail the request which caused the conversion. All other changes are ignored.

Deploy the conversion webhook service

Documentation for deploying the conversion webhook is the same as for the admission webhook example service. The assumption for next sections is that the conversion webhook server is deployed to a service named example-conversion-webhook-server in default namespace and serving traffic on path /crdconvert.

Note:

When the webhook server is deployed into the Kubernetes cluster as a service, it has to be exposed via a service on port 443 (The server itself can have an arbitrary port but the service object should map it to port 443). The communication between the API server and the webhook service may fail if a different port is used for the service.

Configure CustomResourceDefinition to use conversion webhooks

The None conversion example can be extended to use the conversion webhook by modifying conversion section of the spec:

apiextensions.k8s.io/v1
apiextensions.k8s.io/v1beta1

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  # name must match the spec fields below, and be in the form: <plural>.<group>
  name: crontabs.example.com
spec:
  # group name to use for REST API: /apis/<group>/<version>
  group: example.com
  # list of versions supported by this CustomResourceDefinition
  versions:
  - name: v1beta1
    # Each version can be enabled/disabled by Served flag.
    served: true
    # One and only one version must be marked as the storage version.
    storage: true
    # Each version can define its own schema when there is no top-level
    # schema is defined.
    schema:
      openAPIV3Schema:
        type: object
        properties:
          hostPort:
            type: string
  - name: v1
    served: true
    storage: false
    schema:
      openAPIV3Schema:
        type: object
        properties:
          host:
            type: string
          port:
            type: string
  conversion:
    # the Webhook strategy instructs the API server to call an external webhook for any conversion between custom resources.
    strategy: Webhook
    # webhook is required when strategy is `Webhook` and it configures the webhook endpoint to be called by API server.
    webhook:
      # conversionReviewVersions indicates what ConversionReview versions are understood/preferred by the webhook.
      # The first version in the list understood by the API server is sent to the webhook.
      # The webhook must respond with a ConversionReview object in the same version it received.
      conversionReviewVersions: ["v1","v1beta1"]
      clientConfig:
        service:
          namespace: default
          name: example-conversion-webhook-server
          path: /crdconvert
        caBundle: "Ci0tLS0tQk...<base64-encoded PEM bundle>...tLS0K"
  # either Namespaced or Cluster
  scope: Namespaced
  names:
    # plural name to be used in the URL: /apis/<group>/<version>/<plural>
    plural: crontabs
    # singular name to be used as an alias on the CLI and for display
    singular: crontab
    # kind is normally the CamelCased singular type. Your resource manifests use this.
    kind: CronTab
    # shortNames allow shorter string to match your resource on the CLI
    shortNames:
    - ct

# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
  # name must match the spec fields below, and be in the form: <plural>.<group>
  name: crontabs.example.com
spec:
  # group name to use for REST API: /apis/<group>/<version>
  group: example.com
  # prunes object fields that are not specified in OpenAPI schemas below.
  preserveUnknownFields: false
  # list of versions supported by this CustomResourceDefinition
  versions:
  - name: v1beta1
    # Each version can be enabled/disabled by Served flag.
    served: true
    # One and only one version must be marked as the storage version.
    storage: true
    # Each version can define its own schema when there is no top-level
    # schema is defined.
    schema:
      openAPIV3Schema:
        type: object
        properties:
          hostPort:
            type: string
  - name: v1
    served: true
    storage: false
    schema:
      openAPIV3Schema:
        type: object
        properties:
          host:
            type: string
          port:
            type: string
  conversion:
    # the Webhook strategy instructs the API server to call an external webhook for any conversion between custom resources.
    strategy: Webhook
    # webhookClientConfig is required when strategy is `Webhook` and it configures the webhook endpoint to be called by API server.
    webhookClientConfig:
      service:
        namespace: default
        name: example-conversion-webhook-server
        path: /crdconvert
      caBundle: "Ci0tLS0tQk...<base64-encoded PEM bundle>...tLS0K"
  # either Namespaced or Cluster
  scope: Namespaced
  names:
    # plural name to be used in the URL: /apis/<group>/<version>/<plural>
    plural: crontabs
    # singular name to be used as an alias on the CLI and for display
    singular: crontab
    # kind is normally the CamelCased singular type. Your resource manifests use this.
    kind: CronTab
    # shortNames allow shorter string to match your resource on the CLI
    shortNames:
    - ct

You can save the CustomResourceDefinition in a YAML file, then use kubectl apply to apply it.

kubectl apply -f my-versioned-crontab-with-conversion.yaml

Make sure the conversion service is up and running before applying new changes.

Contacting the webhook

Once the API server has determined a request should be sent to a conversion webhook, it needs to know how to contact the webhook. This is specified in the webhookClientConfig stanza of the webhook configuration.

Conversion webhooks can either be called via a URL or a service reference, and can optionally include a custom CA bundle to use to verify the TLS connection.

URL

url gives the location of the webhook, in standard URL form (scheme://host:port/path).

The host should not refer to a service running in the cluster; use a service reference by specifying the service field instead. The host might be resolved via external DNS in some apiservers (i.e., kube-apiserver cannot resolve in-cluster DNS as that would be a layering violation). host may also be an IP address.

Please note that using localhost or 127.0.0.1 as a host is risky unless you take great care to run this webhook on all hosts which run an apiserver which might need to make calls to this webhook. Such installations are likely to be non-portable or not readily run in a new cluster.

The scheme must be "https"; the URL must begin with "https://".

Attempting to use a user or basic auth (for example "user:password@") is not allowed. Fragments ("#...") and query parameters ("?...") are also not allowed.

Here is an example of a conversion webhook configured to call a URL (and expects the TLS certificate to be verified using system trust roots, so does not specify a caBundle):

apiextensions.k8s.io/v1
apiextensions.k8s.io/v1beta1

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
...
spec:
  ...
  conversion:
    strategy: Webhook
    webhook:
      clientConfig:
        url: "https://my-webhook.example.com:9443/my-webhook-path"
...

# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
...
spec:
  ...
  conversion:
    strategy: Webhook
    webhookClientConfig:
      url: "https://my-webhook.example.com:9443/my-webhook-path"
...

Service Reference

The service stanza inside webhookClientConfig is a reference to the service for a conversion webhook. If the webhook is running within the cluster, then you should use service instead of url. The service namespace and name are required. The port is optional and defaults to 443. The path is optional and defaults to "/".

Here is an example of a webhook that is configured to call a service on port "1234" at the subpath "/my-path", and to verify the TLS connection against the ServerName my-service-name.my-service-namespace.svc using a custom CA bundle.

apiextensions.k8s.io/v1
apiextensions.k8s.io/v1beta1

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
...
spec:
  ...
  conversion:
    strategy: Webhook
    webhook:
      clientConfig:
        service:
          namespace: my-service-namespace
          name: my-service-name
          path: /my-path
          port: 1234
        caBundle: "Ci0tLS0tQk...<base64-encoded PEM bundle>...tLS0K"
...

# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
...
spec:
  ...
  conversion:
    strategy: Webhook
    webhookClientConfig:
      service:
        namespace: my-service-namespace
        name: my-service-name
        path: /my-path
        port: 1234
      caBundle: "Ci0tLS0tQk...<base64-encoded PEM bundle>...tLS0K"
...

Webhook request and response

Request

Webhooks are sent a POST request, with Content-Type: application/json, with a ConversionReview API object in the apiextensions.k8s.io API group serialized to JSON as the body.

Webhooks can specify what versions of ConversionReview objects they accept with the conversionReviewVersions field in their CustomResourceDefinition:

apiextensions.k8s.io/v1
apiextensions.k8s.io/v1beta1

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
...
spec:
  ...
  conversion:
    strategy: Webhook
    webhook:
      conversionReviewVersions: ["v1", "v1beta1"]
      ...

conversionReviewVersions is a required field when creating apiextensions.k8s.io/v1 custom resource definitions. Webhooks are required to support at least one ConversionReview version understood by the current and previous API server.

# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
...
spec:
  ...
  conversion:
    strategy: Webhook
    conversionReviewVersions: ["v1", "v1beta1"]
    ...

If no conversionReviewVersions are specified, the default when creating apiextensions.k8s.io/v1beta1 custom resource definitions is v1beta1.

API servers send the first ConversionReview version in the conversionReviewVersions list they support. If none of the versions in the list are supported by the API server, the custom resource definition will not be allowed to be created. If an API server encounters a conversion webhook configuration that was previously created and does not support any of the ConversionReview versions the API server knows how to send, attempts to call to the webhook will fail.

This example shows the data contained in an ConversionReview object for a request to convert CronTab objects to example.com/v1:

apiextensions.k8s.io/v1
apiextensions.k8s.io/v1beta1

Response

Webhooks respond with a 200 HTTP status code, Content-Type: application and a body containing a ConversionReview object (in the same with the response stanza populated, serialized to JSON. b-size:4;-o-tab-size:4;tab-size:4;">{ "apiVersion": "apiextensions.k8s.io/v1", "kind": "ConversionReview", "request": { # Random uid uniquely identifying this conversion call "uid": "705ab4f5-6393-11e8-b7cc-42010a800002", # The API group and version the objects should be converted to "desiredAPIVersion": "example.com/v1", # The list of objects to convert. # May contain one or more objects, in one or more versions. "objects": [ { "kind": "CronTab", "apiVersion": "example.com/v1beta1", "metadata": { "creationTimestamp": "2019-09-04T14:03:02Z", "name": "local-crontab", "namespace": "default", "resourceVersion": "143", "uid": "3415a7fc-162b-4300-b5da-fd6083580d66" }, "hostPort": "localhost:1234" }, { "kind": "CronTab", "apiVersion": "example.com/v1beta1", "metadata": { "creationTimestamp": "2019-09-03T13:02:01Z", "name": "remote-crontab", "resourceVersion": "12893", "uid": "359a83ec-b575-460d-b553-d859cedde8a0" }, "hostPort": "example.com:2345" } ] } style="color:#bbb"> aria-labelledby="conversionreview-request-1"> -tab-size:4;-o-tab-size:4;tab-size:4;">{ # Deprecated in v1.16 in favor of apiextensions.k8s.io/v1 "apiVersion": "apiextensions.k8s.io/v1beta1", "kind": "ConversionReview", "request": { # Random uid uniquely identifying this conversion call "uid": "705ab4f5-6393-11e8-b7cc-42010a800002", # The API group and version the objects should be converted to "desiredAPIVersion": "example.com/v1", # The list of objects to convert. # May contain one or more objects, in one or more versions. "objects": [ { "kind": "CronTab", "apiVersion": "example.com/v1beta1", "metadata": { "creationTimestamp": "2019-09-04T14:03:02Z", "name": "local-crontab", "namespace": "default", "resourceVersion": "143", "uid": "3415a7fc-162b-4300-b5da-fd6083580d66" }, "hostPort": "localhost:1234" }, { "kind": "CronTab", "apiVersion": "example.com/v1beta1", "metadata": { "creationTimestamp": "2019-09-03T13:02:01Z", "name": "remote-crontab", "resourceVersion": "12893", "uid": "359a83ec-b575-460d-b553-d859cedde8a0" }, "hostPort": "example.com:2345" } ] } style="color:#bbb"> /json, version they were sent),


If conversion succeeds, a webhook should return a response stanza containing the following fields:

uid, copied from the request.uid sent to the webhook
result, set to {"status":"Success"}
convertedObjects, containing all of the objects from request.objects, converted to request.desiredAPIVersion

Example of a minimal successful response from a webhook:
apiextensions.k8s.io/v1
	  
		apiextensions.k8s.io/v1beta1


{
  "apiVersion": "apiextensions.k8s.io/v1",
  "kind": "ConversionReview",
  "response": {
    # must match <request.uid>
    "uid": "705ab4f5-6393-11e8-b7cc-42010a800002",
    "result": {
      "status": "Success"
    },
    # Objects must match the order of request.objects, and have apiVersion set to <request.desiredAPIVersion>.
    # kind, metadata.uid, metadata.name, and metadata.namespace fields must not be changed by the webhook.
    # metadata.labels and metadata.annotations fields may be changed by the webhook.
    # All other changes to metadata fields by the webhook are ignored.
    "convertedObjects": [
      {
        "kind": "CronTab",
        "apiVersion": "example.com/v1",
        "metadata": {
          "creationTimestamp": "2019-09-04T14:03:02Z",
          "name": "local-crontab",
          "namespace": "default",
          "resourceVersion": "143",
          "uid": "3415a7fc-162b-4300-b5da-fd6083580d66"
        },
        "host": "localhost",
        "port": "1234"
      },
      {
        "kind": "CronTab",
        "apiVersion": "example.com/v1",
        "metadata": {
          "creationTimestamp": "2019-09-03T13:02:01Z",
          "name": "remote-crontab",
          "resourceVersion": "12893",
          "uid": "359a83ec-b575-460d-b553-d859cedde8a0"
        },
        "host": "example.com",
        "port": "2345"
      }
    ]
  }
}

  

{
  # Deprecated in v1.16 in favor of apiextensions.k8s.io/v1
  "apiVersion": "apiextensions.k8s.io/v1beta1",
  "kind": "ConversionReview",
  "response": {
    # must match <request.uid>
    "uid": "705ab4f5-6393-11e8-b7cc-42010a800002",
    "result": {
      "status": "Failed"
    },
    # Objects must match the order of request.objects, and have apiVersion set to <request.desiredAPIVersion>.
    # kind, metadata.uid, metadata.name, and metadata.namespace fields must not be changed by the webhook.
    # metadata.labels and metadata.annotations fields may be changed by the webhook.
    # All other changes to metadata fields by the webhook are ignored.
    "convertedObjects": [
      {
        "kind": "CronTab",
        "apiVersion": "example.com/v1",
        "metadata": {
          "creationTimestamp": "2019-09-04T14:03:02Z",
          "name": "local-crontab",
          "namespace": "default",
          "resourceVersion": "143",
          "uid": "3415a7fc-162b-4300-b5da-fd6083580d66"
        },
        "host": "localhost",
        "port": "1234"
      },
      {
        "kind": "CronTab",
        "apiVersion": "example.com/v1",
        "metadata": {
          "creationTimestamp": "2019-09-03T13:02:01Z",
          "name": "remote-crontab",
          "resourceVersion": "12893",
          "uid": "359a83ec-b575-460d-b553-d859cedde8a0"
        },
        "host": "example.com",
        "port": "2345"
      }
    ]
  }
}


If conversion fails, a webhook should return a response stanza containing the following fields:

uid, copied from the request.uid sent to the webhook
result, set to {"status":"Failed"}

Warning:Failing conversion can disrupt read and write access to the custom resources,
including the ability to update or delete the resources. Conversion failures
should be avoided whenever possible, and should not be used to enforce validation
constraints (use validation schemas or webhook admission instead).

Example of a response from a webhook indicating a conversion request failed, with an optional message:
apiextensions.k8s.io/v1
	  
		apiextensions.k8s.io/v1beta1


{
  "apiVersion": "apiextensions.k8s.io/v1",
  "kind": "ConversionReview",
  "response": {
    "uid": "<value from request.uid>",
    "result": {
      "status": "Failed",
      "message": "hostPort could not be parsed into a separate host and port"
    }
  }
}

  

{
  # Deprecated in v1.16 in favor of apiextensions.k8s.io/v1
  "apiVersion": "apiextensions.k8s.io/v1beta1",
  "kind": "ConversionReview",
  "response": {
    "uid": "<value from request.uid>",
    "result": {
      "status": "Failed",
      "message": "hostPort could not be parsed into a separate host and port"
    }
  }
}


Writing, reading, and updating versioned CustomResourceDefinition objects
When an object is written, it is stored at the version designated as the
storage version at the time of the write. If the storage version changes,
existing objects are never converted automatically. However, newly-created
or updated objects are written at the new storage version. It is possible for an
object to have been written at a version that is no longer served.
When you read an object, you specify the version as part of the path.
You can request an object at any version that is currently served.
If you specify a version that is different from the object's stored version,
Kubernetes returns the object to you at the version you requested, but the
stored object is not changed on disk.
What happens to the object that is being returned while serving the read
request depends on what is specified in the CRD's spec.conversion:

if the default strategy value None is specified, the only modifications
to the object are changing the apiVersion string and perhaps pruning
unknown fields
(depending on the configuration). Note that this is unlikely to lead to good
results if the schemas differ between the storage and requested version.
In particular, you should not use this strategy if the same data is
represented in different fields between versions.
if webhook conversion is specified, then this
mechanism controls the conversion.

If you update an existing object, it is rewritten at the version that is
currently the storage version. This is the only way that objects can change from
one version to another.
To illustrate this, consider the following hypothetical series of events:

The storage version is v1beta1. You create an object. It is stored at version v1beta1
You add version v1 to your CustomResourceDefinition and designate it as
the storage version. Here the schemas for v1 and v1beta1 are identical,
which is typically the case when promoting an API to stable in the
Kubernetes ecosystem.
You read your object at version v1beta1, then you read the object again at
version v1. Both returned objects are identical except for the apiVersion
field.
You create a new object. It is stored at version v1. You now
have two objects, one of which is at v1beta1, and the other of which is at
v1.
You update the first object. It is now stored at version v1 since that
is the current storage version.

Previous storage versions
The API server records each version which has ever been marked as the storage
version in the status field storedVersions. Objects may have been stored
at any version that has ever been designated as a storage version. No objects
can exist in storage at a version that has never been a storage version.
Upgrade existing objects to a new stored version
When deprecating versions and dropping support, select a storage upgrade
procedure.
Option 1: Use the Storage Version Migrator

Run the storage Version migrator
Remove the old version from the CustomResourceDefinition status.storedVersions field.

Option 2: Manually upgrade the existing objects to a new stored version
The following is an example procedure to upgrade from v1beta1 to v1.

Set v1 as the storage in the CustomResourceDefinition file and apply it
using kubectl. The storedVersions is now v1beta1, v1.
Write an upgrade procedure to list all existing objects and write them with
the same content. This forces the backend to write objects in the current
storage version, which is v1.
Remove v1beta1 from the CustomResourceDefinition status.storedVersions field.


Note:
The flag --subresource is used with the kubectl get, patch, edit, and replace commands to
fetch and update the subresources, status and scale, for all the API resources that
support them. This flag is available starting from kubectl version v1.24. Previously, reading
subresources (like status) via kubectl involved using kubectl --raw, and updating
subresources using kubectl was not possible at all. Starting from v1.24, the kubectl tool
can be used to edit or patch the status subresource on a CRD object. See How to patch a Deployment using the subresource flag.
This page is part of the documentation for Kubernetes v1.30.
If you are running a different version of Kubernetes, consult the documentation for that release.
Here is an example of how to patch the status subresource for a CRD object using kubectl:
kubectl patch customresourcedefinitions <CRD_Name> --subresource='status' --type='merge' -p '{"status":{"storedVersions":["v1"]}}'





    
	
  

    
	
  
    
    
	
    


    
	11.3 - Set up an Extension API Server
    
	
Setting up an extension API server to work with the aggregation layer allows the Kubernetes apiserver to be extended with additional APIs, which are not part of the core Kubernetes APIs.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes

 
  To check the version, enter kubectl version.


You must configure the aggregation layer and enable the apiserver flags.


Set up an extension api-server to work with the aggregation layer
The following steps describe how to set up an extension-apiserver at a high level. These steps apply regardless if you're using YAML configs or using APIs. An attempt is made to specifically identify any differences between the two. For a concrete example of how they can be implemented using YAML configs, you can look at the sample-apiserver in the Kubernetes repo.
Alternatively, you can use an existing 3rd party solution, such as apiserver-builder, which should generate a skeleton and automate all of the following steps for you.

Make sure the APIService API is enabled (check --runtime-config). It should be on by default, unless it's been deliberately turned off in your cluster.
You may need to make an RBAC rule allowing you to add APIService objects, or get your cluster administrator to make one. (Since API extensions affect the entire cluster, it is not recommended to do testing/development/debug of an API extension in a live cluster.)
Create the Kubernetes namespace you want to run your extension api-service in.
Create/get a CA cert to be used to sign the server cert the extension api-server uses for HTTPS.
Create a server cert/key for the api-server to use for HTTPS. This cert should be signed by the above CA. It should also have a CN of the Kube DNS name. This is derived from the Kubernetes service and be of the form <service name>.<service name namespace>.svc
Create a Kubernetes secret with the server cert/key in your namespace.
Create a Kubernetes deployment for the extension api-server and make sure you are loading the secret as a volume. It should contain a reference to a working image of your extension api-server. The deployment should also be in your namespace.
Make sure that your extension-apiserver loads those certs from that volume and that they are used in the HTTPS handshake.
Create a Kubernetes service account in your namespace.
Create a Kubernetes cluster role for the operations you want to allow on your resources.
Create a Kubernetes cluster role binding from the service account in your namespace to the cluster role you created.
Create a Kubernetes cluster role binding from the service account in your namespace to the system:auth-delegator cluster role to delegate auth decisions to the Kubernetes core API server.
Create a Kubernetes role binding from the service account in your namespace to the extension-apiserver-authentication-reader role. This allows your extension api-server to access the extension-apiserver-authentication configmap.
Create a Kubernetes apiservice. The CA cert above should be base64 encoded, stripped of new lines and used as the spec.caBundle in the apiservice. This should not be namespaced. If using the kube-aggregator API, only pass in the PEM encoded CA bundle because the base 64 encoding is done for you.
Use kubectl to get your resource. When run, kubectl should return "No resources found.". This message
indicates that everything worked but you currently have no objects of that resource type created.

What's next

Walk through the steps to configure the API aggregation layer and enable the apiserver flags.
For a high level overview, see Extending the Kubernetes API with the aggregation layer.
Learn how to Extend the Kubernetes API using Custom Resource Definitions.






    
	
  
    
    
	
    


    
	11.4 - Configure Multiple Schedulers
    
	
Kubernetes ships with a default scheduler that is described
here.
If the default scheduler does not suit your needs you can implement your own scheduler.
Moreover, you can even run multiple schedulers simultaneously alongside the default
scheduler and instruct Kubernetes what scheduler to use for each of your pods. Let's
learn how to run multiple schedulers in Kubernetes with an example.
A detailed description of how to implement a scheduler is outside the scope of this
document. Please refer to the kube-scheduler implementation in
pkg/scheduler
in the Kubernetes source directory for a canonical example.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes

 
  To check the version, enter kubectl version.


Package the scheduler
Package your scheduler binary into a container image. For the purposes of this example,
you can use the default scheduler (kube-scheduler) as your second scheduler.
Clone the Kubernetes source code from GitHub
and build the source.
git clone https://github.com/kubernetes/kubernetes.git
cd kubernetes
make
Create a container image containing the kube-scheduler binary. Here is the Dockerfile
to build the image:
FROM busybox
ADD ./_output/local/bin/linux/amd64/kube-scheduler /usr/local/bin/kube-scheduler
Save the file as Dockerfile, build the image and push it to a registry. This example
pushes the image to
Google Container Registry (GCR).
For more details, please read the GCR
documentation. Alternatively
you can also use the docker hub. For more details
refer to the docker hub documentation.
docker build -t gcr.io/my-gcp-project/my-kube-scheduler:1.0 .     # The image name and the repository
gcloud docker -- push gcr.io/my-gcp-project/my-kube-scheduler:1.0 # used in here is just an example
Define a Kubernetes Deployment for the scheduler
Now that you have your scheduler in a container image, create a pod
configuration for it and run it in your Kubernetes cluster. But instead of creating a pod
directly in the cluster, you can use a Deployment
for this example. A Deployment manages a
Replica Set which in turn manages the pods,
thereby making the scheduler resilient to failures. Here is the deployment
config. Save it as my-scheduler.yaml:

    
    admin/sched/my-scheduler.yaml
    
    apiVersion: v1
kind: ServiceAccount
metadata:
  name: my-scheduler
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: my-scheduler-as-kube-scheduler
subjects:
- kind: ServiceAccount
  name: my-scheduler
  namespace: kube-system
roleRef:
  kind: ClusterRole
  name: system:kube-scheduler
  apiGroup: rbac.authorization.k8s.io
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: my-scheduler-as-volume-scheduler
subjects:
- kind: ServiceAccount
  name: my-scheduler
  namespace: kube-system
roleRef:
  kind: ClusterRole
  name: system:volume-scheduler
  apiGroup: rbac.authorization.k8s.io
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: my-scheduler-extension-apiserver-authentication-reader
  namespace: kube-system
roleRef:
  kind: Role
  name: extension-apiserver-authentication-reader
  apiGroup: rbac.authorization.k8s.io
subjects:
- kind: ServiceAccount
  name: my-scheduler
  namespace: kube-system
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: my-scheduler-config
  namespace: kube-system
data:
  my-scheduler-config.yaml: |
    apiVersion: kubescheduler.config.k8s.io/v1beta2
    kind: KubeSchedulerConfiguration
    profiles:
      - schedulerName: my-scheduler
    leaderElection:
      leaderElect: false    
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    component: scheduler
    tier: control-plane
  name: my-scheduler
  namespace: kube-system
spec:
  selector:
    matchLabels:
      component: scheduler
      tier: control-plane
  replicas: 1
  template:
    metadata:
      labels:
        component: scheduler
        tier: control-plane
        version: second
    spec:
      serviceAccountName: my-scheduler
      containers:
      - command:
        - /usr/local/bin/kube-scheduler
        - --config=/etc/kubernetes/my-scheduler/my-scheduler-config.yaml
        image: gcr.io/my-gcp-project/my-kube-scheduler:1.0
        livenessProbe:
          httpGet:
            path: /healthz
            port: 10259
            scheme: HTTPS
          initialDelaySeconds: 15
        name: kube-second-scheduler
        readinessProbe:
          httpGet:
            path: /healthz
            port: 10259
            scheme: HTTPS
        resources:
          requests:
            cpu: '0.1'
        securityContext:
          privileged: false
        volumeMounts:
          - name: config-volume
            mountPath: /etc/kubernetes/my-scheduler
      hostNetwork: false
      hostPID: false
      volumes:
        - name: config-volume
          configMap:
            name: my-scheduler-config


In the above manifest, you use a KubeSchedulerConfiguration
to customize the behavior of your scheduler implementation. This configuration has been passed to
the kube-scheduler during initialization with the --config option. The my-scheduler-config ConfigMap stores the configuration file. The Pod of themy-scheduler Deployment mounts the my-scheduler-config ConfigMap as a volume.
In the aforementioned Scheduler Configuration, your scheduler implementation is represented via
a KubeSchedulerProfile.

Note:
To determine if a scheduler is responsible for scheduling a specific Pod, the spec.schedulerName field in a
PodTemplate or Pod manifest must match the schedulerName field of the KubeSchedulerProfile.
All schedulers running in the cluster must have unique names.

Also, note that you create a dedicated service account my-scheduler and bind the ClusterRole
system:kube-scheduler to it so that it can acquire the same privileges as kube-scheduler.
Please see the
kube-scheduler documentation for
detailed description of other command line arguments and
Scheduler Configuration reference for
detailed description of other customizable kube-scheduler configurations.
Run the second scheduler in the cluster
In order to run your scheduler in a Kubernetes cluster, create the deployment
specified in the config above in a Kubernetes cluster:
kubectl create -f my-scheduler.yaml
Verify that the scheduler pod is running:
kubectl get pods --namespace=kube-system
NAME                                           READY     STATUS    RESTARTS   AGE
....
my-scheduler-lnf4s-4744f                       1/1       Running   0          2m
...
You should see a "Running" my-scheduler pod, in addition to the default kube-scheduler
pod in this list.
Enable leader election
To run multiple-scheduler with leader election enabled, you must do the following:
Update the following fields for the KubeSchedulerConfiguration in the my-scheduler-config ConfigMap in your YAML file:

leaderElection.leaderElect to true
leaderElection.resourceNamespace to <lock-object-namespace>
leaderElection.resourceName to <lock-object-name>


Note:The control plane creates the lock objects for you, but the namespace must already exist.
You can use the kube-system namespace.

If RBAC is enabled on your cluster, you must update the system:kube-scheduler cluster role.
Add your scheduler name to the resourceNames of the rule applied for endpoints and leases resources, as in the following example:
kubectl edit clusterrole system:kube-scheduler

    
    admin/sched/clusterrole.yaml
    
    apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  annotations:
    rbac.authorization.kubernetes.io/autoupdate: "true"
  labels:
    kubernetes.io/bootstrapping: rbac-defaults
  name: system:kube-scheduler
rules:
  - apiGroups:
      - coordination.k8s.io
    resources:
      - leases
    verbs:
      - create
  - apiGroups:
      - coordination.k8s.io
    resourceNames:
      - kube-scheduler
      - my-scheduler
    resources:
      - leases
    verbs:
      - get
      - update
  - apiGroups:
      - ""
    resourceNames:
      - kube-scheduler
      - my-scheduler
    resources:
      - endpoints
    verbs:
      - delete
      - get
      - patch
      - update


Specify schedulers for pods
Now that your second scheduler is running, create some pods, and direct them
to be scheduled by either the default scheduler or the one you deployed.
In order to schedule a given pod using a specific scheduler, specify the name of the
scheduler in that pod spec. Let's look at three examples.


Pod spec without any scheduler name

    
    admin/sched/pod1.yaml
    
    apiVersion: v1
kind: Pod
metadata:
  name: no-annotation
  labels:
    name: multischeduler-example
spec:
  containers:
  - name: pod-with-no-annotation-container
    image: registry.k8s.io/pause:2.0

When no scheduler name is supplied, the pod is automatically scheduled using the
default-scheduler.
Save this file as pod1.yaml and submit it to the Kubernetes cluster.
kubectl create -f pod1.yaml


Pod spec with default-scheduler

    
    admin/sched/pod2.yaml
    
    apiVersion: v1
kind: Pod
metadata:
  name: annotation-default-scheduler
  labels:
    name: multischeduler-example
spec:
  schedulerName: default-scheduler
  containers:
  - name: pod-with-default-annotation-container
    image: registry.k8s.io/pause:2.0


A scheduler is specified by supplying the scheduler name as a value to spec.schedulerName. In this case, we supply the name of the
default scheduler which is default-scheduler.
Save this file as pod2.yaml and submit it to the Kubernetes cluster.
kubectl create -f pod2.yaml


Pod spec with my-scheduler

    
    admin/sched/pod3.yaml
    
    apiVersion: v1
kind: Pod
metadata:
  name: annotation-second-scheduler
  labels:
    name: multischeduler-example
spec:
  schedulerName: my-scheduler
  containers:
  - name: pod-with-second-annotation-container
    image: registry.k8s.io/pause:2.0


In this case, we specify that this pod should be scheduled using the scheduler that we
deployed - my-scheduler. Note that the value of spec.schedulerName should match the name supplied for the scheduler
in the schedulerName field of the mapping KubeSchedulerProfile.
Save this file as pod3.yaml and submit it to the Kubernetes cluster.
kubectl create -f pod3.yaml
Verify that all three pods are running.
kubectl get pods



Verifying that the pods were scheduled using the desired schedulers
In order to make it easier to work through these examples, we did not verify that the
pods were actually scheduled using the desired schedulers. We can verify that by
changing the order of pod and deployment config submissions above. If we submit all the
pod configs to a Kubernetes cluster before submitting the scheduler deployment config,
we see that the pod annotation-second-scheduler remains in "Pending" state forever
while the other two pods get scheduled. Once we submit the scheduler deployment config
and our new scheduler starts running, the annotation-second-scheduler pod gets
scheduled as well.
Alternatively, you can look at the "Scheduled" entries in the event logs to
verify that the pods were scheduled by the desired schedulers.
kubectl get events
You can also use a custom scheduler configuration
or a custom container image for the cluster's main scheduler by modifying its static pod manifest
on the relevant control plane nodes.





    
	
  
    
    
	
    


    
	11.5 - Use an HTTP Proxy to Access the Kubernetes API
    
	
This page shows how to use an HTTP proxy to access the Kubernetes API.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes

 
  To check the version, enter kubectl version.

If you do not already have an application running in your cluster, start
a Hello world application by entering this command:
kubectl create deployment hello-app --image=gcr.io/google-samples/hello-app:2.0 --port=8080

Using kubectl to start a proxy server
This command starts a proxy to the Kubernetes API server:
kubectl proxy --port=8080

Exploring the Kubernetes API
When the proxy server is running, you can explore the API using curl, wget,
or a browser.
Get the API versions:
curl http://localhost:8080/api/

The output should look similar to this:
{
  "kind": "APIVersions",
  "versions": [
    "v1"
  ],
  "serverAddressByClientCIDRs": [
    {
      "clientCIDR": "0.0.0.0/0",
      "serverAddress": "10.0.2.15:8443"
    }
  ]
}

Get a list of pods:
curl http://localhost:8080/api/v1/namespaces/default/pods

The output should look similar to this:
{
  "kind": "PodList",
  "apiVersion": "v1",
  "metadata": {
    "resourceVersion": "33074"
  },
  "items": [
    {
      "metadata": {
        "name": "kubernetes-bootcamp-2321272333-ix8pt",
        "generateName": "kubernetes-bootcamp-2321272333-",
        "namespace": "default",
        "uid": "ba21457c-6b1d-11e6-85f7-1ef9f1dab92b",
        "resourceVersion": "33003",
        "creationTimestamp": "2016-08-25T23:43:30Z",
        "labels": {
          "pod-template-hash": "2321272333",
          "run": "kubernetes-bootcamp"
        },
        ...
}

What's next
Learn more about kubectl proxy.





    
	
  
    
    
	
    


    
	11.6 - Use a SOCKS5 Proxy to Access the Kubernetes API
    
	






 

  
  
    FEATURE STATE: Kubernetes v1.24 [stable]
  
  

This page shows how to use a SOCKS5 proxy to access the API of a remote Kubernetes cluster.
This is useful when the cluster you want to access does not expose its API directly on the public internet.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes

 
 
 Your Kubernetes server must be at or later than version v1.24.
  To check the version, enter kubectl version.

You need SSH client software (the ssh tool), and an SSH service running on the remote server.
You must be able to log in to the SSH service on the remote server.

Task context

Note:This example tunnels traffic using SSH, with the SSH client and server acting as a SOCKS proxy.
You can instead use any other kind of SOCKS5 proxies.

Figure 1 represents what you're going to achieve in this task.

You have a client computer, referred to as local in the steps ahead, from where you're going to create requests to talk to the Kubernetes API.
The Kubernetes server/API is hosted on a remote server.
You will use SSH client and server software to create a secure SOCKS5 tunnel between the local and
the remote server. The HTTPS traffic between the client and the Kubernetes API will flow over the SOCKS5
tunnel, which is itself tunnelled over SSH.



    
graph LR;

  subgraph local[Local client machine]
  client([client])-. local 
 traffic .->  local_ssh[Local SSH 
 SOCKS5 proxy];
  end
  local_ssh[SSH 
SOCKS5 
 proxy]-- SSH Tunnel -->sshd
  
  subgraph remote[Remote server]
  sshd[SSH 
 server]-- local traffic -->service1;
  end
  client([client])-. proxied HTTPs traffic 
 going through the proxy .->service1[Kubernetes API];

  classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000;
  classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff;
  classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5;
  class ingress,service1,service2,pod1,pod2,pod3,pod4 k8s;
  class client plain;
  class cluster cluster;





Figure 1. SOCKS5 tutorial components
Using ssh to create a SOCKS5 proxy
The following command starts a SOCKS5 proxy between your client machine and the remote SOCKS server:
# The SSH tunnel continues running in the foreground after you run this
ssh -D 1080 -q -N username@kubernetes-remote-server.example
The SOCKS5 proxy lets you connect to your cluster's API server based on the following configuration:

-D 1080: opens a SOCKS proxy on local port :1080.
-q: quiet mode. Causes most warning and diagnostic messages to be suppressed.
-N: Do not execute a remote command. Useful for just forwarding ports.
username@kubernetes-remote-server.example: the remote SSH server behind which the Kubernetes cluster
is running (eg: a bastion host).

Client configuration
To access the Kubernetes API server through the proxy you must instruct kubectl to send queries through
the SOCKS proxy we created earlier. Do this by either setting the appropriate environment variable,
or via the proxy-url attribute in the kubeconfig file. Using an environment variable:
export HTTPS_PROXY=socks5://localhost:1080
To always use this setting on a specific kubectl context, specify the proxy-url attribute in the relevant
cluster entry within the ~/.kube/config file. For example:
apiVersion: v1
clusters:
- cluster:
    certificate-authority-data: LRMEMMW2 # shortened for readability 
    server: https://<API_SERVER_IP_ADDRESS>:6443  # the "Kubernetes API" server, in other words the IP address of kubernetes-remote-server.example
    proxy-url: socks5://localhost:1080   # the "SSH SOCKS5 proxy" in the diagram above
  name: default
contexts:
- context:
    cluster: default
    user: default
  name: default
current-context: default
kind: Config
preferences: {}
users:
- name: default
  user:
    client-certificate-data: LS0tLS1CR== # shortened for readability
    client-key-data: LS0tLS1CRUdJT=      # shortened for readability
Once you have created the tunnel via the ssh command mentioned earlier, and defined either the environment variable or
the proxy-url attribute, you can interact with your cluster through that proxy. For example:
kubectl get pods
NAMESPACE     NAME                                     READY   STATUS      RESTARTS   AGE
kube-system   coredns-85cb69466-klwq8                  1/1     Running     0          5m46s

Note:

Before kubectl 1.24, most kubectl commands worked when using a socks proxy, except kubectl exec.
kubectl supports both HTTPS_PROXY and https_proxy environment variables. These are used by other
programs that support SOCKS, such as curl. Therefore in some cases it
will be better to define the environment variable on the command line:
HTTPS_PROXY=socks5://localhost:1080 kubectl get pods

When using proxy-url, the proxy is used only for the relevant kubectl context,
whereas the environment variable will affect all contexts.
The k8s API server hostname can be further protected from DNS leakage by using the socks5h protocol name
instead of the more commonly known socks5 protocol shown above. In this case, kubectl will ask the proxy server
(such as an ssh bastion) to resolve the k8s API server domain name, instead of resolving it on the system running
kubectl. Note also that with socks5h, a k8s API server URL like https://localhost:6443/api does not refer
to your local client computer. Instead, it refers to localhost as known on the proxy server (eg the ssh bastion).



Clean up
Stop the ssh port-forwarding process by pressing CTRL+C on the terminal where it is running.
Type unset https_proxy in a terminal to stop forwarding http traffic through the proxy.
Further reading

OpenSSH remote login client






    
	
  
    
    
	
    


    
	11.7 - Set up Konnectivity service
    
	
The Konnectivity service provides a TCP level proxy for the control plane to cluster
communication.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this
tutorial on a cluster with at least two nodes that are not acting as control
plane hosts. If you do not already have a cluster, you can create one by using
minikube.

Configure the Konnectivity service
The following steps require an egress configuration, for example:

    
    admin/konnectivity/egress-selector-configuration.yaml
    
    apiVersion: apiserver.k8s.io/v1beta1
kind: EgressSelectorConfiguration
egressSelections:
# Since we want to control the egress traffic to the cluster, we use the
# "cluster" as the name. Other supported values are "etcd", and "controlplane".
- name: cluster
  connection:
    # This controls the protocol between the API Server and the Konnectivity
    # server. Supported values are "GRPC" and "HTTPConnect". There is no
    # end user visible difference between the two modes. You need to set the
    # Konnectivity server to work in the same mode.
    proxyProtocol: GRPC
    transport:
      # This controls what transport the API Server uses to communicate with the
      # Konnectivity server. UDS is recommended if the Konnectivity server
      # locates on the same machine as the API Server. You need to configure the
      # Konnectivity server to listen on the same UDS socket.
      # The other supported transport is "tcp". You will need to set up TLS 
      # config to secure the TCP transport.
      uds:
        udsName: /etc/kubernetes/konnectivity-server/konnectivity-server.socket


You need to configure the API Server to use the Konnectivity service
and direct the network traffic to the cluster nodes:

Make sure that
Service Account Token Volume Projection
feature enabled in your cluster. It is enabled by default since Kubernetes v1.20.
Create an egress configuration file such as admin/konnectivity/egress-selector-configuration.yaml.
Set the --egress-selector-config-file flag of the API Server to the path of
your API Server egress configuration file.
If you use UDS connection, add volumes config to the kube-apiserver:
spec:
  containers:
    volumeMounts:
    - name: konnectivity-uds
      mountPath: /etc/kubernetes/konnectivity-server
      readOnly: false
  volumes:
  - name: konnectivity-uds
    hostPath:
      path: /etc/kubernetes/konnectivity-server
      type: DirectoryOrCreate


Generate or obtain a certificate and kubeconfig for konnectivity-server.
For example, you can use the OpenSSL command line tool to issue a X.509 certificate,
using the cluster CA certificate /etc/kubernetes/pki/ca.crt from a control-plane host.
openssl req -subj "/CN=system:konnectivity-server" -new -newkey rsa:2048 -nodes -out konnectivity.csr -keyout konnectivity.key
openssl x509 -req -in konnectivity.csr -CA /etc/kubernetes/pki/ca.crt -CAkey /etc/kubernetes/pki/ca.key -CAcreateserial -out konnectivity.crt -days 375 -sha256
SERVER=$(kubectl config view -o jsonpath='{.clusters..server}')
kubectl --kubeconfig /etc/kubernetes/konnectivity-server.conf config set-credentials system:konnectivity-server --client-certificate konnectivity.crt --client-key konnectivity.key --embed-certs=true
kubectl --kubeconfig /etc/kubernetes/konnectivity-server.conf config set-cluster kubernetes --server "$SERVER" --certificate-authority /etc/kubernetes/pki/ca.crt --embed-certs=true
kubectl --kubeconfig /etc/kubernetes/konnectivity-server.conf config set-context system:konnectivity-server@kubernetes --cluster kubernetes --user system:konnectivity-server
kubectl --kubeconfig /etc/kubernetes/konnectivity-server.conf config use-context system:konnectivity-server@kubernetes
rm -f konnectivity.crt konnectivity.key konnectivity.csr
Next, you need to deploy the Konnectivity server and agents.
kubernetes-sigs/apiserver-network-proxy
is a reference implementation.
Deploy the Konnectivity server on your control plane node. The provided
konnectivity-server.yaml manifest assumes
that the Kubernetes components are deployed as a static Pod in your cluster. If not, you can deploy the Konnectivity
server as a DaemonSet.

    
    admin/konnectivity/konnectivity-server.yaml
    
    apiVersion: v1
kind: Pod
metadata:
  name: konnectivity-server
  namespace: kube-system
spec:
  priorityClassName: system-cluster-critical
  hostNetwork: true
  containers:
  - name: konnectivity-server-container
    image: registry.k8s.io/kas-network-proxy/proxy-server:v0.0.37
    command: ["/proxy-server"]
    args: [
            "--logtostderr=true",
            # This needs to be consistent with the value set in egressSelectorConfiguration.
            "--uds-name=/etc/kubernetes/konnectivity-server/konnectivity-server.socket",
            "--delete-existing-uds-file",
            # The following two lines assume the Konnectivity server is
            # deployed on the same machine as the apiserver, and the certs and
            # key of the API Server are at the specified location.
            "--cluster-cert=/etc/kubernetes/pki/apiserver.crt",
            "--cluster-key=/etc/kubernetes/pki/apiserver.key",
            # This needs to be consistent with the value set in egressSelectorConfiguration.
            "--mode=grpc",
            "--server-port=0",
            "--agent-port=8132",
            "--admin-port=8133",
            "--health-port=8134",
            "--agent-namespace=kube-system",
            "--agent-service-account=konnectivity-agent",
            "--kubeconfig=/etc/kubernetes/konnectivity-server.conf",
            "--authentication-audience=system:konnectivity-server"
            ]
    livenessProbe:
      httpGet:
        scheme: HTTP
        host: 127.0.0.1
        port: 8134
        path: /healthz
      initialDelaySeconds: 30
      timeoutSeconds: 60
    ports:
    - name: agentport
      containerPort: 8132
      hostPort: 8132
    - name: adminport
      containerPort: 8133
      hostPort: 8133
    - name: healthport
      containerPort: 8134
      hostPort: 8134
    volumeMounts:
    - name: k8s-certs
      mountPath: /etc/kubernetes/pki
      readOnly: true
    - name: kubeconfig
      mountPath: /etc/kubernetes/konnectivity-server.conf
      readOnly: true
    - name: konnectivity-uds
      mountPath: /etc/kubernetes/konnectivity-server
      readOnly: false
  volumes:
  - name: k8s-certs
    hostPath:
      path: /etc/kubernetes/pki
  - name: kubeconfig
    hostPath:
      path: /etc/kubernetes/konnectivity-server.conf
      type: FileOrCreate
  - name: konnectivity-uds
    hostPath:
      path: /etc/kubernetes/konnectivity-server
      type: DirectoryOrCreate


Then deploy the Konnectivity agents in your cluster:

    
    admin/konnectivity/konnectivity-agent.yaml
    
    apiVersion: apps/v1
# Alternatively, you can deploy the agents as Deployments. It is not necessary
# to have an agent on each node.
kind: DaemonSet
metadata:
  labels:
    addonmanager.kubernetes.io/mode: Reconcile
    k8s-app: konnectivity-agent
  namespace: kube-system
  name: konnectivity-agent
spec:
  selector:
    matchLabels:
      k8s-app: konnectivity-agent
  template:
    metadata:
      labels:
        k8s-app: konnectivity-agent
    spec:
      priorityClassName: system-cluster-critical
      tolerations:
        - key: "CriticalAddonsOnly"
          operator: "Exists"
      containers:
        - image: us.gcr.io/k8s-artifacts-prod/kas-network-proxy/proxy-agent:v0.0.37
          name: konnectivity-agent
          command: ["/proxy-agent"]
          args: [
                  "--logtostderr=true",
                  "--ca-cert=/var/run/secrets/kubernetes.io/serviceaccount/ca.crt",
                  # Since the konnectivity server runs with hostNetwork=true,
                  # this is the IP address of the master machine.
                  "--proxy-server-host=35.225.206.7",
                  "--proxy-server-port=8132",
                  "--admin-server-port=8133",
                  "--health-server-port=8134",
                  "--service-account-token-path=/var/run/secrets/tokens/konnectivity-agent-token"
                  ]
          volumeMounts:
            - mountPath: /var/run/secrets/tokens
              name: konnectivity-agent-token
          livenessProbe:
            httpGet:
              port: 8134
              path: /healthz
            initialDelaySeconds: 15
            timeoutSeconds: 15
      serviceAccountName: konnectivity-agent
      volumes:
        - name: konnectivity-agent-token
          projected:
            sources:
              - serviceAccountToken:
                  path: konnectivity-agent-token
                  audience: system:konnectivity-server


Last, if RBAC is enabled in your cluster, create the relevant RBAC rules:

    
    admin/konnectivity/konnectivity-rbac.yaml
    
    apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: system:konnectivity-server
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: system:auth-delegator
subjects:
  - apiGroup: rbac.authorization.k8s.io
    kind: User
    name: system:konnectivity-server
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: konnectivity-agent
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile







    
	
  

    
	
  
    
    
	
    


    
	12 - TLS
    Understand how to protect traffic within your cluster using Transport Layer Security (TLS).
	




    
      
  
  
  
  

  
  

  
    
    
	
    


    
	12.1 - Configure Certificate Rotation for the Kubelet
    
	
This page shows how to enable and configure certificate rotation for the kubelet.






 

  
  
    FEATURE STATE: Kubernetes v1.19 [stable]
  
  

Before you begin

Kubernetes version 1.8.0 or later is required


Overview
The kubelet uses certificates for authenticating to the Kubernetes API.  By
default, these certificates are issued with one year expiration so that they do
not need to be renewed too frequently.
Kubernetes contains kubelet certificate
rotation,
that will automatically generate a new key and request a new certificate from
the Kubernetes API as the current certificate approaches expiration. Once the
new certificate is available, it will be used for authenticating connections to
the Kubernetes API.
Enabling client certificate rotation
The kubelet process accepts an argument --rotate-certificates that controls
if the kubelet will automatically request a new certificate as the expiration of
the certificate currently in use approaches.
The kube-controller-manager process accepts an argument
--cluster-signing-duration  (--experimental-cluster-signing-duration prior to 1.19)
that controls how long certificates will be issued for.
Understanding the certificate rotation configuration
When a kubelet starts up, if it is configured to bootstrap (using the
--bootstrap-kubeconfig flag), it will use its initial certificate to connect
to the Kubernetes API and issue a certificate signing request. You can view the
status of certificate signing requests using:
kubectl get csr
Initially a certificate signing request from the kubelet on a node will have a
status of Pending. If the certificate signing requests meets specific
criteria, it will be auto approved by the controller manager, then it will have
a status of Approved. Next, the controller manager will sign a certificate,
issued for the duration specified by the
--cluster-signing-duration parameter, and the signed certificate
will be attached to the certificate signing request.
The kubelet will retrieve the signed certificate from the Kubernetes API and
write that to disk, in the location specified by --cert-dir. Then the kubelet
will use the new certificate to connect to the Kubernetes API.
As the expiration of the signed certificate approaches, the kubelet will
automatically issue a new certificate signing request, using the Kubernetes API.
This can happen at any point between 30% and 10% of the time remaining on the
certificate. Again, the controller manager will automatically approve the certificate
request and attach a signed certificate to the certificate signing request. The
kubelet will retrieve the new signed certificate from the Kubernetes API and
write that to disk. Then it will update the connections it has to the
Kubernetes API to reconnect using the new certificate.





    
	
  
    
    
	
    


    
	12.2 - Manage TLS Certificates in a Cluster
    
	
Kubernetes provides a certificates.k8s.io API, which lets you provision TLS
certificates signed by a Certificate Authority (CA) that you control. These CA
and certificates can be used by your workloads to establish trust.
certificates.k8s.io API uses a protocol that is similar to the ACME
draft.

Note:
Certificates created using the certificates.k8s.io API are signed by a
dedicated CA. It is possible to configure your cluster to use the cluster root
CA for this purpose, but you should never rely on this. Do not assume that
these certificates will validate against the cluster root CA.

Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes


You need the cfssl tool. You can download cfssl from
https://github.com/cloudflare/cfssl/releases.
Some steps in this page use the jq tool. If you don't have jq, you can
install it via your operating system's software sources, or fetch it from
https://jqlang.github.io/jq/.

Trusting TLS in a cluster
Trusting the custom CA from an application running as a pod usually requires
some extra application configuration. You will need to add the CA certificate
bundle to the list of CA certificates that the TLS client or server trusts. For
example, you would do this with a golang TLS config by parsing the certificate
chain and adding the parsed certificates to the RootCAs field in the
tls.Config struct.

Note:
Even though the custom CA certificate may be included in the filesystem (in the
ConfigMap kube-root-ca.crt),
you should not use that certificate authority for any purpose other than to verify internal
Kubernetes endpoints. An example of an internal Kubernetes endpoint is the
Service named kubernetes in the default namespace.
If you want to use a custom certificate authority for your workloads, you should generate
that CA separately, and distribute its CA certificate using a
ConfigMap that your pods
have access to read.


Requesting a certificate
The following section demonstrates how to create a TLS certificate for a
Kubernetes service accessed through DNS.

Note:
This tutorial uses CFSSL: Cloudflare's PKI and TLS toolkit click here to know more.

Create a certificate signing request
Generate a private key and certificate signing request (or CSR) by running
the following command:
cat <<EOF | cfssl genkey - | cfssljson -bare server
{
  "hosts": [
    "my-svc.my-namespace.svc.cluster.local",
    "my-pod.my-namespace.pod.cluster.local",
    "192.0.2.24",
    "10.0.34.2"
  ],
  "CN": "my-pod.my-namespace.pod.cluster.local",
  "key": {
    "algo": "ecdsa",
    "size": 256
  }
}
EOF
Where 192.0.2.24 is the service's cluster IP,
my-svc.my-namespace.svc.cluster.local is the service's DNS name,
10.0.34.2 is the pod's IP and my-pod.my-namespace.pod.cluster.local
is the pod's DNS name. You should see the output similar to:
2022/02/01 11:45:32 [INFO] generate received request
2022/02/01 11:45:32 [INFO] received CSR
2022/02/01 11:45:32 [INFO] generating key: ecdsa-256
2022/02/01 11:45:32 [INFO] encoded CSR
This command generates two files; it generates server.csr containing the PEM
encoded PKCS#10 certification request,
and server-key.pem containing the PEM encoded key to the certificate that
is still to be created.
Create a CertificateSigningRequest object to send to the Kubernetes API
Generate a CSR manifest (in YAML), and send it to the API server. You can do that by
running the following command:
cat <<EOF | kubectl apply -f -
apiVersion: certificates.k8s.io/v1
kind: CertificateSigningRequest
metadata:
  name: my-svc.my-namespace
spec:
  request: $(cat server.csr | base64 | tr -d '\n')
  signerName: example.com/serving
  usages:
  - digital signature
  - key encipherment
  - server auth
EOF
Notice that the server.csr file created in step 1 is base64 encoded
and stashed in the .spec.request field. You are also requesting a
certificate with the "digital signature", "key encipherment", and "server
auth" key usages, signed by an example example.com/serving signer.
A specific signerName must be requested.
View documentation for supported signer names
for more information.
The CSR should now be visible from the API in a Pending state. You can see
it by running:
kubectl describe csr my-svc.my-namespace
Name:                   my-svc.my-namespace
Labels:                 <none>
Annotations:            <none>
CreationTimestamp:      Tue, 01 Feb 2022 11:49:15 -0500
Requesting User:        yourname@example.com
Signer:                 example.com/serving
Status:                 Pending
Subject:
        Common Name:    my-pod.my-namespace.pod.cluster.local
        Serial Number:
Subject Alternative Names:
        DNS Names:      my-pod.my-namespace.pod.cluster.local
                        my-svc.my-namespace.svc.cluster.local
        IP Addresses:   192.0.2.24
                        10.0.34.2
Events: <none>
Get the CertificateSigningRequest approved
Approving the certificate signing request
is either done by an automated approval process or on a one off basis by a cluster
administrator. If you're authorized to approve a certificate request, you can do that
manually using kubectl; for example:
kubectl certificate approve my-svc.my-namespace
certificatesigningrequest.certificates.k8s.io/my-svc.my-namespace approved
You should now see the following:
kubectl get csr
NAME                  AGE   SIGNERNAME            REQUESTOR              REQUESTEDDURATION   CONDITION
my-svc.my-namespace   10m   example.com/serving   yourname@example.com   <none>              Approved
This means the certificate request has been approved and is waiting for the
requested signer to sign it.
Sign the CertificateSigningRequest
Next, you'll play the part of a certificate signer, issue the certificate, and upload it to the API.
A signer would typically watch the CertificateSigningRequest API for objects with its signerName,
check that they have been approved, sign certificates for those requests,
and update the API object status with the issued certificate.
Create a Certificate Authority
You need an authority to provide the digital signature on the new certificate.
First, create a signing certificate by running the following:
cat <<EOF | cfssl gencert -initca - | cfssljson -bare ca
{
  "CN": "My Example Signer",
  "key": {
    "algo": "rsa",
    "size": 2048
  }
}
EOF
You should see output similar to:
2022/02/01 11:50:39 [INFO] generating a new CA key and certificate from CSR
2022/02/01 11:50:39 [INFO] generate received request
2022/02/01 11:50:39 [INFO] received CSR
2022/02/01 11:50:39 [INFO] generating key: rsa-2048
2022/02/01 11:50:39 [INFO] encoded CSR
2022/02/01 11:50:39 [INFO] signed certificate with serial number 263983151013686720899716354349605500797834580472
This produces a certificate authority key file (ca-key.pem) and certificate (ca.pem).
Issue a certificate

    
    tls/server-signing-config.json
    
    {
    "signing": {
        "default": {
            "usages": [
                "digital signature",
                "key encipherment",
                "server auth"
            ],
            "expiry": "876000h",
            "ca_constraint": {
                "is_ca": false
            }
        }
    }
}

Use a server-signing-config.json signing configuration and the certificate authority key file
and certificate to sign the certificate request:
kubectl get csr my-svc.my-namespace -o jsonpath='{.spec.request}' | \
  base64 --decode | \
  cfssl sign -ca ca.pem -ca-key ca-key.pem -config server-signing-config.json - | \
  cfssljson -bare ca-signed-server
You should see the output similar to:
2022/02/01 11:52:26 [INFO] signed certificate with serial number 576048928624926584381415936700914530534472870337
This produces a signed serving certificate file, ca-signed-server.pem.
Upload the signed certificate
Finally, populate the signed certificate in the API object's status:
kubectl get csr my-svc.my-namespace -o json | \
  jq '.status.certificate = "'$(base64 ca-signed-server.pem | tr -d '\n')'"' | \
  kubectl replace --raw /apis/certificates.k8s.io/v1/certificatesigningrequests/my-svc.my-namespace/status -f -

Note:
This uses the command line tool jq to populate the base64-encoded
content in the .status.certificate field.
If you do not have jq, you can also save the JSON output to a file, populate this field manually, and
upload the resulting file.

Once the CSR is approved and the signed certificate is uploaded, run:
kubectl get csr
The output is similar to:
NAME                  AGE   SIGNERNAME            REQUESTOR              REQUESTEDDURATION   CONDITION
my-svc.my-namespace   20m   example.com/serving   yourname@example.com   <none>              Approved,Issued
Download the certificate and use it
Now, as the requesting user, you can download the issued certificate
and save it to a server.crt file by running the following:
kubectl get csr my-svc.my-namespace -o jsonpath='{.status.certificate}' \
    | base64 --decode > server.crt
Now you can populate server.crt and server-key.pem in a
Secret
that you could later mount into a Pod (for example, to use with a webserver
that serves HTTPS).
kubectl create secret tls server --cert server.crt --key server-key.pem
secret/server created
Finally, you can populate ca.pem into a ConfigMap
and use it as the trust root to verify the serving certificate:
kubectl create configmap example-serving-ca --from-file ca.crt=ca.pem
configmap/example-serving-ca created
Approving CertificateSigningRequests
A Kubernetes administrator (with appropriate permissions) can manually approve
(or deny) CertificateSigningRequests by using the kubectl certificate approve and kubectl certificate deny commands. However if you intend
to make heavy usage of this API, you might consider writing an automated
certificates controller.
Caution:
The ability to approve CSRs decides who trusts whom within your environment. The
ability to approve CSRs should not be granted broadly or lightly.
You should make sure that you confidently understand both the verification requirements
that fall on the approver and the repercussions of issuing a specific certificate
before you grant the approve permission.


Whether a machine or a human using kubectl as above, the role of the approver is
to verify that the CSR satisfies two requirements:

The subject of the CSR controls the private key used to sign the CSR. This
addresses the threat of a third party masquerading as an authorized subject.
In the above example, this step would be to verify that the pod controls the
private key used to generate the CSR.
The subject of the CSR is authorized to act in the requested context. This
addresses the threat of an undesired subject joining the cluster. In the
above example, this step would be to verify that the pod is allowed to
participate in the requested service.

If and only if these two requirements are met, the approver should approve
the CSR and otherwise should deny the CSR.
For more information on certificate approval and access control, read
the Certificate Signing Requests
reference page.
Configuring your cluster to provide signing
This page assumes that a signer is set up to serve the certificates API. The
Kubernetes controller manager provides a default implementation of a signer. To
enable it, pass the --cluster-signing-cert-file and
--cluster-signing-key-file parameters to the controller manager with paths to
your Certificate Authority's keypair.





    
	
  
    
    
	
    


    
	12.3 - Manual Rotation of CA Certificates
    
	
This page shows how to manually rotate the certificate authority (CA) certificates.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes



For more information about authentication in Kubernetes, see
Authenticating.
For more information about best practices for CA certificates, see
Single root CA.


Rotate the CA certificates manually
Caution:
Make sure to back up your certificate directory along with configuration files and any other necessary files.
This approach assumes operation of the Kubernetes control plane in a HA configuration with multiple API servers.
Graceful termination of the API server is also assumed so clients can cleanly disconnect from one API server and
reconnect to another.
Configurations with a single API server will experience unavailability while the API server is being restarted.




Distribute the new CA certificates and private keys (for example: ca.crt, ca.key, front-proxy-ca.crt,
and front-proxy-ca.key) to all your control plane nodes in the Kubernetes certificates directory.


Update the --root-ca-file flag for the kube-controller-manager to include
both old and new CA, then restart the kube-controller-manager.
Any ServiceAccount created after this point will get
Secrets that include both old and new CAs.

Note:
The files specified by the kube-controller-manager flags --client-ca-file and --cluster-signing-cert-file
cannot be CA bundles. If these flags and --root-ca-file point to the same ca.crt file which is now a
bundle (includes both old and new CA) you will face an error. To workaround this problem you can copy the new CA
to a separate file and make the flags --client-ca-file and --cluster-signing-cert-file point to the copy.
Once ca.crt is no longer a bundle you can restore the problem flags to point to ca.crt and delete the copy.
Issue 1350 for kubeadm tracks an bug with the
kube-controller-manager being unable to accept a CA bundle.




Wait for the controller manager to update ca.crt in the service account Secrets to include both old and new CA certificates.
If any Pods are started before new CA is used by API servers, the new Pods get this update and will trust both
old and new CAs.


Restart all pods using in-cluster configurations (for example: kube-proxy, CoreDNS, etc) so they can use the
updated certificate authority data from Secrets that link to ServiceAccounts.

Make sure CoreDNS, kube-proxy and other Pods using in-cluster configurations are working as expected.



Append the both old and new CA to the file against --client-ca-file and --kubelet-certificate-authority
flag in the kube-apiserver configuration.


Append the both old and new CA to the file against --client-ca-file flag in the kube-scheduler configuration.


Update certificates for user accounts by replacing the content of client-certificate-data and client-key-data
respectively.
For information about creating certificates for individual user accounts, see
Configure certificates for user accounts.
Additionally, update the certificate-authority-data section in the kubeconfig files,
respectively with Base64-encoded old and new certificate authority data


Update the --root-ca-file flag for the Cloud Controller Manager to include
both old and new CA, then restart the cloud-controller-manager.

Note:
If your cluster does not have a cloud-controller-manager, you can skip this step.



Follow the steps below in a rolling fashion.


Restart any other
aggregated API servers or
webhook handlers to trust the new CA certificates.


Restart the kubelet by update the file against clientCAFile in kubelet configuration and
certificate-authority-data in kubelet.conf to use both the old and new CA on all nodes.
If your kubelet is not using client certificate rotation, update client-certificate-data and
client-key-data in kubelet.conf on all nodes along with the kubelet client certificate file
usually found in /var/lib/kubelet/pki.


Restart API servers with the certificates (apiserver.crt, apiserver-kubelet-client.crt and
front-proxy-client.crt) signed by new CA.
You can use the existing private keys or new private keys.
If you changed the private keys then update these in the Kubernetes certificates directory as well.
Since the Pods in your cluster trust both old and new CAs, there will be a momentarily disconnection
after which pods' Kubernetes clients reconnect to the new API server.
The new API server uses a certificate signed by the new CA.

Restart the kube-scheduler to use and
trust the new CAs.
Make sure control plane components logs no TLS errors.


Note:
  To generate certificates and private keys for your cluster using the `openssl` command line tool,
  see [Certificates (`openssl`)](/docs/tasks/administer-cluster/certificates/#openssl).
  You can also use [`cfssl`](/docs/tasks/administer-cluster/certificates/#cfssl).





Annotate any DaemonSets and Deployments to trigger pod replacement in a safer rolling fashion.


for namespace in $(kubectl get namespace -o jsonpath='{.items[*].metadata.name}'); do
    for name in $(kubectl get deployments -n $namespace -o jsonpath='{.items[*].metadata.name}'); do
        kubectl patch deployment -n ${namespace} ${name} -p '{"spec":{"template":{"metadata":{"annotations":{"ca-rotation": "1"}}}}}';
    done
    for name in $(kubectl get daemonset -n $namespace -o jsonpath='{.items[*].metadata.name}'); do
        kubectl patch daemonset -n ${namespace} ${name} -p '{"spec":{"template":{"metadata":{"annotations":{"ca-rotation": "1"}}}}}';
    done
done

Note:
  To limit the number of concurrent disruptions that your application experiences,
  see [configure pod disruption budget](/docs/tasks/run-application/configure-pdb/).



 Depending on how you use StatefulSets you may also need to perform similar rolling replacement.



If your cluster is using bootstrap tokens to join nodes, update the ConfigMap cluster-info in the kube-public
namespace with new CA.
base64_encoded_ca="$(base64 -w0 /etc/kubernetes/pki/ca.crt)"

kubectl get cm/cluster-info --namespace kube-public -o yaml | \
    /bin/sed "s/\(certificate-authority-data:\).*/\1 ${base64_encoded_ca}/" | \
    kubectl apply -f -


Verify the cluster functionality.


Check the logs from control plane components, along with the kubelet and the kube-proxy.
Ensure those components are not reporting any TLS errors; see
looking at the logs for more details.


Validate logs from any aggregated api servers and pods using in-cluster config.




Once the cluster functionality is successfully verified:


Update all service account tokens to include new CA certificate only.

All pods using an in-cluster kubeconfig will eventually need to be restarted to pick up the new Secret,
so that no Pods are relying on the old cluster CA.



Restart the control plane components by removing the old CA from the kubeconfig files and the files against
--client-ca-file, --root-ca-file flags resp.


On each node, restart the kubelet by removing the old CA from file against the clientCAFile flag
and from the kubelet kubeconfig file. You should carry this out as a rolling update.
If your cluster lets you make this change, you can also roll it out by replacing nodes rather than
reconfiguring them.









    
	
  

    
	
  
    
    
	
    


    
	13 - Manage Cluster Daemons
    Perform common tasks for managing a DaemonSet, such as performing a rolling update.
	




    
      
  
  
  
  

  
  

  
    
    
	
    


    
	13.1 - Perform a Rolling Update on a DaemonSet
    
	
This page shows how to perform a rolling update on a DaemonSet.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes



DaemonSet Update Strategy
DaemonSet has two update strategy types:

OnDelete: With OnDelete update strategy, after you update a DaemonSet template, new
DaemonSet pods will only be created when you manually delete old DaemonSet
pods. This is the same behavior of DaemonSet in Kubernetes version 1.5 or
before.
RollingUpdate: This is the default update strategy.

With RollingUpdate update strategy, after you update a
DaemonSet template, old DaemonSet pods will be killed, and new DaemonSet pods
will be created automatically, in a controlled fashion. At most one pod of
the DaemonSet will be running on each node during the whole update process.

Performing a Rolling Update
To enable the rolling update feature of a DaemonSet, you must set its
.spec.updateStrategy.type to RollingUpdate.
You may want to set
.spec.updateStrategy.rollingUpdate.maxUnavailable
(default to 1),
.spec.minReadySeconds
(default to 0) and
.spec.updateStrategy.rollingUpdate.maxSurge
(defaults to 0) as well.
Creating a DaemonSet with RollingUpdate update strategy
This YAML file specifies a DaemonSet with an update strategy as 'RollingUpdate'

    
    controllers/fluentd-daemonset.yaml
    
    apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: fluentd-elasticsearch
  namespace: kube-system
  labels:
    k8s-app: fluentd-logging
spec:
  selector:
    matchLabels:
      name: fluentd-elasticsearch
  updateStrategy:
    type: RollingUpdate
    rollingUpdate:
      maxUnavailable: 1
  template:
    metadata:
      labels:
        name: fluentd-elasticsearch
    spec:
      tolerations:
      # these tolerations are to have the daemonset runnable on control plane nodes
      # remove them if your control plane nodes should not run pods
      - key: node-role.kubernetes.io/control-plane
        operator: Exists
        effect: NoSchedule
      - key: node-role.kubernetes.io/master
        operator: Exists
        effect: NoSchedule
      containers:
      - name: fluentd-elasticsearch
        image: quay.io/fluentd_elasticsearch/fluentd:v2.5.2
        volumeMounts:
        - name: varlog
          mountPath: /var/log
        - name: varlibdockercontainers
          mountPath: /var/lib/docker/containers
          readOnly: true
      terminationGracePeriodSeconds: 30
      volumes:
      - name: varlog
        hostPath:
          path: /var/log
      - name: varlibdockercontainers
        hostPath:
          path: /var/lib/docker/containers


After verifying the update strategy of the DaemonSet manifest, create the DaemonSet:
kubectl create -f https://k8s.io/examples/controllers/fluentd-daemonset.yaml
Alternatively, use kubectl apply to create the same DaemonSet if you plan to
update the DaemonSet with kubectl apply.
kubectl apply -f https://k8s.io/examples/controllers/fluentd-daemonset.yaml
Checking DaemonSet RollingUpdate update strategy
Check the update strategy of your DaemonSet, and make sure it's set to
RollingUpdate:
kubectl get ds/fluentd-elasticsearch -o go-template='{{.spec.updateStrategy.type}}{{"\n"}}' -n kube-system
If you haven't created the DaemonSet in the system, check your DaemonSet
manifest with the following command instead:
kubectl apply -f https://k8s.io/examples/controllers/fluentd-daemonset.yaml --dry-run=client -o go-template='{{.spec.updateStrategy.type}}{{"\n"}}'
The output from both commands should be:
RollingUpdate
If the output isn't RollingUpdate, go back and modify the DaemonSet object or
manifest accordingly.
Updating a DaemonSet template
Any updates to a RollingUpdate DaemonSet .spec.template will trigger a rolling
update. Let's update the DaemonSet by applying a new YAML file. This can be done with several different kubectl commands.

    
    controllers/fluentd-daemonset-update.yaml
    
    apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: fluentd-elasticsearch
  namespace: kube-system
  labels:
    k8s-app: fluentd-logging
spec:
  selector:
    matchLabels:
      name: fluentd-elasticsearch
  updateStrategy:
    type: RollingUpdate
    rollingUpdate:
      maxUnavailable: 1
  template:
    metadata:
      labels:
        name: fluentd-elasticsearch
    spec:
      tolerations:
      # these tolerations are to have the daemonset runnable on control plane nodes
      # remove them if your control plane nodes should not run pods
      - key: node-role.kubernetes.io/control-plane
        operator: Exists
        effect: NoSchedule
      - key: node-role.kubernetes.io/master
        operator: Exists
        effect: NoSchedule
      containers:
      - name: fluentd-elasticsearch
        image: quay.io/fluentd_elasticsearch/fluentd:v2.5.2
        resources:
          limits:
            memory: 200Mi
          requests:
            cpu: 100m
            memory: 200Mi
        volumeMounts:
        - name: varlog
          mountPath: /var/log
        - name: varlibdockercontainers
          mountPath: /var/lib/docker/containers
          readOnly: true
      terminationGracePeriodSeconds: 30
      volumes:
      - name: varlog
        hostPath:
          path: /var/log
      - name: varlibdockercontainers
        hostPath:
          path: /var/lib/docker/containers


Declarative commands
If you update DaemonSets using
configuration files,
use kubectl apply:
kubectl apply -f https://k8s.io/examples/controllers/fluentd-daemonset-update.yaml
Imperative commands
If you update DaemonSets using
imperative commands,
use kubectl edit :
kubectl edit ds/fluentd-elasticsearch -n kube-system
Updating only the container image
If you only need to update the container image in the DaemonSet template, i.e.
.spec.template.spec.containers[*].image, use kubectl set image:
kubectl set image ds/fluentd-elasticsearch fluentd-elasticsearch=quay.io/fluentd_elasticsearch/fluentd:v2.6.0 -n kube-system
Watching the rolling update status
Finally, watch the rollout status of the latest DaemonSet rolling update:
kubectl rollout status ds/fluentd-elasticsearch -n kube-system
When the rollout is complete, the output is similar to this:
daemonset "fluentd-elasticsearch" successfully rolled out
Troubleshooting
DaemonSet rolling update is stuck
Sometimes, a DaemonSet rolling update may be stuck. Here are some possible
causes:
Some nodes run out of resources
The rollout is stuck because new DaemonSet pods can't be scheduled on at least one
node. This is possible when the node is
running out of resources.
When this happens, find the nodes that don't have the DaemonSet pods scheduled on
by comparing the output of kubectl get nodes and the output of:
kubectl get pods -l name=fluentd-elasticsearch -o wide -n kube-system
Once you've found those nodes, delete some non-DaemonSet pods from the node to
make room for new DaemonSet pods.

Note:This will cause service disruption when deleted pods are not controlled by any controllers or pods are not
replicated. This does not respect PodDisruptionBudget
either.

Broken rollout
If the recent DaemonSet template update is broken, for example, the container is
crash looping, or the container image doesn't exist (often due to a typo),
DaemonSet rollout won't progress.
To fix this, update the DaemonSet template again. New rollout won't be
blocked by previous unhealthy rollouts.
Clock skew
If .spec.minReadySeconds is specified in the DaemonSet, clock skew between
master and nodes will make DaemonSet unable to detect the right rollout
progress.
Clean up
Delete DaemonSet from a namespace :
kubectl delete ds fluentd-elasticsearch -n kube-system
What's next

See Performing a rollback on a DaemonSet
See Creating a DaemonSet to adopt existing DaemonSet pods






    
	
  
    
    
	
    


    
	13.2 - Perform a Rollback on a DaemonSet
    
	
This page shows how to perform a rollback on a DaemonSet.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes

 
 
 Your Kubernetes server must be at or later than version 1.7.
  To check the version, enter kubectl version.

You should already know how to perform a rolling update on a
DaemonSet.

Performing a rollback on a DaemonSet
Step 1: Find the DaemonSet revision you want to roll back to
You can skip this step if you only want to roll back to the last revision.
List all revisions of a DaemonSet:
kubectl rollout history daemonset <daemonset-name>
This returns a list of DaemonSet revisions:
daemonsets "<daemonset-name>"
REVISION        CHANGE-CAUSE
1               ...
2               ...
...

Change cause is copied from DaemonSet annotation kubernetes.io/change-cause
to its revisions upon creation. You may specify --record=true in kubectl
to record the command executed in the change cause annotation.

To see the details of a specific revision:
kubectl rollout history daemonset <daemonset-name> --revision=1
This returns the details of that revision:
daemonsets "<daemonset-name>" with revision #1
Pod Template:
Labels:       foo=bar
Containers:
app:
 Image:        ...
 Port:         ...
 Environment:  ...
 Mounts:       ...
Volumes:      ...
Step 2: Roll back to a specific revision
# Specify the revision number you get from Step 1 in --to-revision
kubectl rollout undo daemonset <daemonset-name> --to-revision=<revision>
If it succeeds, the command returns:
daemonset "<daemonset-name>" rolled back

Note:
If --to-revision flag is not specified, kubectl picks the most recent revision.

Step 3: Watch the progress of the DaemonSet rollback
kubectl rollout undo daemonset tells the server to start rolling back the
DaemonSet. The real rollback is done asynchronously inside the cluster
control plane.
To watch the progress of the rollback:
kubectl rollout status ds/<daemonset-name>
When the rollback is complete, the output is similar to:
daemonset "<daemonset-name>" successfully rolled out

Understanding DaemonSet revisions
In the previous kubectl rollout history step, you got a list of DaemonSet
revisions. Each revision is stored in a resource named ControllerRevision.
To see what is stored in each revision, find the DaemonSet revision raw
resources:
kubectl get controllerrevision -l <daemonset-selector-key>=<daemonset-selector-value>
This returns a list of ControllerRevisions:
NAME                               CONTROLLER                     REVISION   AGE
<daemonset-name>-<revision-hash>   DaemonSet/<daemonset-name>     1          1h
<daemonset-name>-<revision-hash>   DaemonSet/<daemonset-name>     2          1h
Each ControllerRevision stores the annotations and template of a DaemonSet
revision.
kubectl rollout undo takes a specific ControllerRevision and replaces
DaemonSet template with the template stored in the ControllerRevision.
kubectl rollout undo is equivalent to updating DaemonSet template to a
previous revision through other commands, such as kubectl edit or kubectl apply.

Note:DaemonSet revisions only roll forward. That is to say, after a
rollback completes, the revision number (.revision field) of the
ControllerRevision being rolled back to will advance. For example, if you
have revision 1 and 2 in the system, and roll back from revision 2 to revision
1, the ControllerRevision with .revision: 1 will become .revision: 3.

Troubleshooting

See troubleshooting DaemonSet rolling
update.






    
	
  
    
    
	
    


    
	13.3 - Running Pods on Only Some Nodes
    
	
This page demonstrates how can you run Pods on only some Nodes as part of a DaemonSet
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes


Running Pods on only some Nodes
Imagine that you want to run a DaemonSet, but you only need to run those daemon pods
on nodes that have local solid state (SSD) storage. For example, the Pod might provide cache service to the
node, and the cache is only useful when low-latency local storage is available.
Step 1: Add labels to your nodes
Add the label ssd=true to the nodes which have SSDs.
kubectl label nodes example-node-1 example-node-2 ssd=true
Step 2: Create the manifest
Let's create a DaemonSet which will provision the daemon pods on the SSD labeled nodes only.
Next, use a nodeSelector to ensure that the DaemonSet only runs Pods on nodes
with the ssd label set to "true".

    
    controllers/daemonset-label-selector.yaml
    
    apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: ssd-driver
  labels:
    app: nginx
spec:
  selector:
    matchLabels:
      app: ssd-driver-pod
  template:
    metadata:
      labels:
        app: ssd-driver-pod
    spec:
      nodeSelector:
        ssd: "true"
      containers:
        - name: example-container
          image: example-image

Step 3: Create the DaemonSet
Create the DaemonSet from the manifest by using kubectl create or kubectl apply
Let's label another node as ssd=true.
kubectl label nodes example-node-3 ssd=true
Labelling the node automatically triggers the control plane (specifically, the DaemonSet controller)
to run a new daemon pod on that node.
kubectl get pods -o wide
The output is similar to:
NAME                              READY     STATUS    RESTARTS   AGE    IP      NODE
<daemonset-name><some-hash-01>    1/1       Running   0          13s    .....   example-node-1
<daemonset-name><some-hash-02>    1/1       Running   0          13s    .....   example-node-2
<daemonset-name><some-hash-03>    1/1       Running   0          5s     .....   example-node-3





    
	
  

    
	
  
    
    
	
    


    
	14 - Networking
    Learn how to configure networking for your cluster.
	




    
      
  
  
  
  

  
  

  
    
    
	
    


    
	14.1 - Adding entries to Pod /etc/hosts with HostAliases
    
	
Adding entries to a Pod's /etc/hosts file provides Pod-level override of hostname resolution when DNS and other options are not applicable. You can add these custom entries with the HostAliases field in PodSpec.
Modification not using HostAliases is not suggested because the file is managed by the kubelet and can be overwritten on during Pod creation/restart.

Default hosts file content
Start an Nginx Pod which is assigned a Pod IP:
kubectl run nginx --image nginx
pod/nginx created
Examine a Pod IP:
kubectl get pods --output=wide
NAME     READY     STATUS    RESTARTS   AGE    IP           NODE
nginx    1/1       Running   0          13s    10.200.0.4   worker0
The hosts file content would look like this:
kubectl exec nginx -- cat /etc/hosts
# Kubernetes-managed hosts file.
127.0.0.1	localhost
::1	localhost ip6-localhost ip6-loopback
fe00::0	ip6-localnet
fe00::0	ip6-mcastprefix
fe00::1	ip6-allnodes
fe00::2	ip6-allrouters
10.200.0.4	nginx
By default, the hosts file only includes IPv4 and IPv6 boilerplates like
localhost and its own hostname.
Adding additional entries with hostAliases
In addition to the default boilerplate, you can add additional entries to the
hosts file.
For example: to resolve foo.local, bar.local to 127.0.0.1 and foo.remote,
bar.remote to 10.1.2.3, you can configure HostAliases for a Pod under
.spec.hostAliases:

    
    service/networking/hostaliases-pod.yaml
    
    apiVersion: v1
kind: Pod
metadata:
  name: hostaliases-pod
spec:
  restartPolicy: Never
  hostAliases:
  - ip: "127.0.0.1"
    hostnames:
    - "foo.local"
    - "bar.local"
  - ip: "10.1.2.3"
    hostnames:
    - "foo.remote"
    - "bar.remote"
  containers:
  - name: cat-hosts
    image: busybox:1.28
    command:
    - cat
    args:
    - "/etc/hosts"


You can start a Pod with that configuration by running:
kubectl apply -f https://k8s.io/examples/service/networking/hostaliases-pod.yaml
pod/hostaliases-pod created
Examine a Pod's details to see its IPv4 address and its status:
kubectl get pod --output=wide
NAME                           READY     STATUS      RESTARTS   AGE       IP              NODE
hostaliases-pod                0/1       Completed   0          6s        10.200.0.5      worker0
The hosts file content looks like this:
kubectl logs hostaliases-pod
# Kubernetes-managed hosts file.
127.0.0.1	localhost
::1	localhost ip6-localhost ip6-loopback
fe00::0	ip6-localnet
fe00::0	ip6-mcastprefix
fe00::1	ip6-allnodes
fe00::2	ip6-allrouters
10.200.0.5	hostaliases-pod

# Entries added by HostAliases.
127.0.0.1	foo.local	bar.local
10.1.2.3	foo.remote	bar.remote
with the additional entries specified at the bottom.
Why does the kubelet manage the hosts file?
The kubelet manages the
hosts file for each container of the Pod to prevent the container runtime from
modifying the file after the containers have already been started.
Historically, Kubernetes always used Docker Engine as its container runtime, and Docker Engine would
then modify the /etc/hosts file after each container had started.
Current Kubernetes can use a variety of container runtimes; even so, the kubelet manages the
hosts file within each container so that the outcome is as intended regardless of which
container runtime you use.
Caution:
Avoid making manual changes to the hosts file inside a container.
If you make manual changes to the hosts file,
those changes are lost when the container exits.







    
	
  
    
    
	
    


    
	14.2 - Extend Service IP Ranges
    
	






 


  
          
            FEATURE STATE: Kubernetes v1.27 [alpha]
          

This document shares how to extend the existing Service IP range assigned to a cluster.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:

Killercoda
Play with Kubernetes




Your Kubernetes server must be at or later than version v1.29.
 To check the version, enter kubectl version.


API
Kubernetes clusters with kube-apiservers that have enabled the MultiCIDRServiceAllocator
feature gate and the networking.k8s.io/v1alpha1 API,
will create a new ServiceCIDR object that takes the well-known name kubernetes, and that uses an IP address range
based on the value of the --service-cluster-ip-range command line argument to kube-apiserver.
kubectl get servicecidr
NAME         CIDRS          AGE
kubernetes   10.96.0.0/28   17d
The well-known kubernetes Service, that exposes the kube-apiserver endpoint to the Pods, calculates
the first IP address from the default ServiceCIDR range and uses that IP address as its
cluster IP address.
kubectl get service kubernetes
NAME         TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)   AGE
kubernetes   ClusterIP   10.96.0.1    <none>        443/TCP   17d
The default Service, in this case, uses the ClusterIP 10.96.0.1, that has the corresponding IPAddress object.
kubectl get ipaddress 10.96.0.1
NAME        PARENTREF
10.96.0.1   services/default/kubernetes
The ServiceCIDRs are protected with finalizers, to avoid leaving Service ClusterIPs orphans;
the finalizer is only removed if there is another subnet that contains the existing IPAddresses or
there are no IPAddresses belonging to the subnet.
Extend the number of available IPs for Services
There are cases that users will need to increase the number addresses available to Services, previously, increasing the Service range was a disruptive operation that could also cause data loss. With this new feature users only need to add a new ServiceCIDR to increase the number of available addresses.
Adding a new ServiceCIDR
On a cluster with a 10.96.0.0/28 range for Services, there is only 2^(32-28) - 2 = 14 IP addresses available. The kubernetes.default Service is always created; for this example, that leaves you with only 13 possible Services.
for i in $(seq 1 13); do kubectl create service clusterip "test-$i" --tcp 80 -o json | jq -r .spec.clusterIP; done
10.96.0.11
10.96.0.5
10.96.0.12
10.96.0.13
10.96.0.14
10.96.0.2
10.96.0.3
10.96.0.4
10.96.0.6
10.96.0.7
10.96.0.8
10.96.0.9
error: failed to create ClusterIP service: Internal error occurred: failed to allocate a serviceIP: range is full
You can increase the number of IP addresses available for Services, by creating a new ServiceCIDR
that extends or adds new IP address ranges.
cat <EOF | kubectl apply -f -
apiVersion: networking.k8s.io/v1alpha1
kind: ServiceCIDR
metadata:
  name: newcidr1
spec:
  cidrs:
  - 10.96.0.0/24
EOF
servicecidr.networking.k8s.io/newcidr1 created
and this will allow you to create new Services with ClusterIPs that will be picked from this new range.
for i in $(seq 13 16); do kubectl create service clusterip "test-$i" --tcp 80 -o json | jq -r .spec.clusterIP; done
10.96.0.48
10.96.0.200
10.96.0.121
10.96.0.144
Deleting a ServiceCIDR
You cannot delete a ServiceCIDR if there are IPAddresses that depend on the ServiceCIDR.
kubectl delete servicecidr newcidr1
servicecidr.networking.k8s.io "newcidr1" deleted
Kubernetes uses a finalizer on the ServiceCIDR to track this dependent relationship.
kubectl get servicecidr newcidr1 -o yaml
apiVersion: networking.k8s.io/v1alpha1
kind: ServiceCIDR
metadata:
  creationTimestamp: "2023-10-12T15:11:07Z"
  deletionGracePeriodSeconds: 0
  deletionTimestamp: "2023-10-12T15:12:45Z"
  finalizers:
  - networking.k8s.io/service-cidr-finalizer
  name: newcidr1
  resourceVersion: "1133"
  uid: 5ffd8afe-c78f-4e60-ae76-cec448a8af40
spec:
  cidrs:
  - 10.96.0.0/24
status:
  conditions:
  - lastTransitionTime: "2023-10-12T15:12:45Z"
    message: There are still IPAddresses referencing the ServiceCIDR, please remove
      them or create a new ServiceCIDR
    reason: OrphanIPAddress
    status: "False"
    type: Ready
By removing the Services containing the IP addresses that are blocking the deletion of the ServiceCIDR
for i in $(seq 13 16); do kubectl delete service "test-$i" ; done
service "test-13" deleted
service "test-14" deleted
service "test-15" deleted
service "test-16" deleted
the control plane notices the removal. The control plane then removes its finalizer,
so that the ServiceCIDR that was pending deletion will actually be removed.
kubectl get servicecidr newcidr1
Error from server (NotFound): servicecidrs.networking.k8s.io "newcidr1" not found





    
	
  
    
    
	
    


    
	14.3 - Validate IPv4/IPv6 dual-stack
    
	
This document shares how to validate IPv4/IPv6 dual-stack enabled Kubernetes clusters.
Before you begin

Provider support for dual-stack networking (Cloud provider or otherwise must be able to provide Kubernetes nodes with routable IPv4/IPv6 network interfaces)
A network plugin that supports dual-stack networking.
Dual-stack enabled cluster



Your Kubernetes server must be at or later than version v1.23.
 To check the version, enter kubectl version.


Note:
While you can validate with an earlier version, the feature is only GA and officially supported since v1.23.


Validate addressing
Validate node addressing
Each dual-stack Node should have a single IPv4 block and a single IPv6 block allocated. Validate that IPv4/IPv6 Pod address ranges are configured by running the following command. Replace the sample node name with a valid dual-stack Node from your cluster. In this example, the Node's name is k8s-linuxpool1-34450317-0:
kubectl get nodes k8s-linuxpool1-34450317-0 -o go-template --template='{{range .spec.podCIDRs}}{{printf "%s\n" .}}{{end}}'
10.244.1.0/24
2001:db8::/64
There should be one IPv4 block and one IPv6 block allocated.
Validate that the node has an IPv4 and IPv6 interface detected. Replace node name with a valid node from the cluster. In this example the node name is k8s-linuxpool1-34450317-0:
kubectl get nodes k8s-linuxpool1-34450317-0 -o go-template --template='{{range .status.addresses}}{{printf "%s: %s\n" .type .address}}{{end}}'
Hostname: k8s-linuxpool1-34450317-0
InternalIP: 10.0.0.5
InternalIP: 2001:db8:10::5
Validate Pod addressing
Validate that a Pod has an IPv4 and IPv6 address assigned. Replace the Pod name with a valid Pod in your cluster. In this example the Pod name is pod01:
kubectl get pods pod01 -o go-template --template='{{range .status.podIPs}}{{printf "%s\n" .ip}}{{end}}'
10.244.1.4
2001:db8::4
You can also validate Pod IPs using the Downward API via the status.podIPs fieldPath. The following snippet demonstrates how you can expose the Pod IPs via an environment variable called MY_POD_IPS within a container.
        env:
        - name: MY_POD_IPS
          valueFrom:
            fieldRef:
              fieldPath: status.podIPs
The following command prints the value of the MY_POD_IPS environment variable from within a container. The value is a comma separated list that corresponds to the Pod's IPv4 and IPv6 addresses.
kubectl exec -it pod01 -- set | grep MY_POD_IPS
MY_POD_IPS=10.244.1.4,2001:db8::4
The Pod's IP addresses will also be written to /etc/hosts within a container. The following command executes a cat on /etc/hosts on a dual stack Pod. From the output you can verify both the IPv4 and IPv6 IP address for the Pod.
kubectl exec -it pod01 -- cat /etc/hosts
# Kubernetes-managed hosts file.
127.0.0.1    localhost
::1    localhost ip6-localhost ip6-loopback
fe00::0    ip6-localnet
fe00::0    ip6-mcastprefix
fe00::1    ip6-allnodes
fe00::2    ip6-allrouters
10.244.1.4    pod01
2001:db8::4    pod01
Validate Services
Create the following Service that does not explicitly define .spec.ipFamilyPolicy. Kubernetes will assign a cluster IP for the Service from the first configured service-cluster-ip-range and set the .spec.ipFamilyPolicy to SingleStack.

    
    service/networking/dual-stack-default-svc.yaml
    
    apiVersion: v1
kind: Service
metadata:
  name: my-service
  labels:
    app.kubernetes.io/name: MyApp
spec:
  selector:
    app.kubernetes.io/name: MyApp
  ports:
    - protocol: TCP
      port: 80


Use kubectl to view the YAML for the Service.
kubectl get svc my-service -o yaml
The Service has .spec.ipFamilyPolicy set to SingleStack and .spec.clusterIP set to an IPv4 address from the first configured range set via --service-cluster-ip-range flag on kube-controller-manager.
apiVersion: v1
kind: Service
metadata:
  name: my-service
  namespace: default
spec:
  clusterIP: 10.0.217.164
  clusterIPs:
  - 10.0.217.164
  ipFamilies:
  - IPv4
  ipFamilyPolicy: SingleStack
  ports:
  - port: 80
    protocol: TCP
    targetPort: 9376
  selector:
    app.kubernetes.io/name: MyApp
  sessionAffinity: None
  type: ClusterIP
status:
  loadBalancer: {}
Create the following Service that explicitly defines IPv6 as the first array element in .spec.ipFamilies. Kubernetes will assign a cluster IP for the Service from the IPv6 range configured service-cluster-ip-range and set the .spec.ipFamilyPolicy to SingleStack.

    
    service/networking/dual-stack-ipfamilies-ipv6.yaml
    
    apiVersion: v1
kind: Service
metadata:
  name: my-service
  labels:
    app.kubernetes.io/name: MyApp
spec:
  ipFamilies:
  - IPv6
  selector:
    app.kubernetes.io/name: MyApp
  ports:
    - protocol: TCP
      port: 80


Use kubectl to view the YAML for the Service.
kubectl get svc my-service -o yaml
The Service has .spec.ipFamilyPolicy set to SingleStack and .spec.clusterIP set to an IPv6 address from the IPv6 range set via --service-cluster-ip-range flag on kube-controller-manager.
apiVersion: v1
kind: Service
metadata:
  labels:
    app.kubernetes.io/name: MyApp
  name: my-service
spec:
  clusterIP: 2001:db8:fd00::5118
  clusterIPs:
  - 2001:db8:fd00::5118
  ipFamilies:
  - IPv6
  ipFamilyPolicy: SingleStack
  ports:
  - port: 80
    protocol: TCP
    targetPort: 80
  selector:
    app.kubernetes.io/name: MyApp
  sessionAffinity: None
  type: ClusterIP
status:
  loadBalancer: {}
Create the following Service that explicitly defines PreferDualStack in .spec.ipFamilyPolicy. Kubernetes will assign both IPv4 and IPv6 addresses (as this cluster has dual-stack enabled) and select the .spec.ClusterIP from the list of .spec.ClusterIPs based on the address family of the first element in the .spec.ipFamilies array.

    
    service/networking/dual-stack-preferred-svc.yaml
    
    apiVersion: v1
kind: Service
metadata:
  name: my-service
  labels:
    app.kubernetes.io/name: MyApp
spec:
  ipFamilyPolicy: PreferDualStack
  selector:
    app.kubernetes.io/name: MyApp
  ports:
    - protocol: TCP
      port: 80



Note:
The kubectl get svc command will only show the primary IP in the CLUSTER-IP field.
kubectl get svc -l app.kubernetes.io/name=MyApp

NAME         TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)   AGE
my-service   ClusterIP   10.0.216.242   <none>        80/TCP    5s


Validate that the Service gets cluster IPs from the IPv4 and IPv6 address blocks using kubectl describe. You may then validate access to the service via the IPs and ports.
kubectl describe svc -l app.kubernetes.io/name=MyApp
Name:              my-service
Namespace:         default
Labels:            app.kubernetes.io/name=MyApp
Annotations:       <none>
Selector:          app.kubernetes.io/name=MyApp
Type:              ClusterIP
IP Family Policy:  PreferDualStack
IP Families:       IPv4,IPv6
IP:                10.0.216.242
IPs:               10.0.216.242,2001:db8:fd00::af55
Port:              <unset>  80/TCP
TargetPort:        9376/TCP
Endpoints:         <none>
Session Affinity:  None
Events:            <none>
Create a dual-stack load balanced Service
If the cloud provider supports the provisioning of IPv6 enabled external load balancers, create the following Service with PreferDualStack in .spec.ipFamilyPolicy, IPv6 as the first element of the .spec.ipFamilies array and the type field set to LoadBalancer.

    
    service/networking/dual-stack-prefer-ipv6-lb-svc.yaml
    
    apiVersion: v1
kind: Service
metadata:
  name: my-service
  labels:
    app.kubernetes.io/name: MyApp
spec:
  ipFamilyPolicy: PreferDualStack
  ipFamilies:
  - IPv6
  type: LoadBalancer
  selector:
    app.kubernetes.io/name: MyApp
  ports:
    - protocol: TCP
      port: 80


Check the Service:
kubectl get svc -l app.kubernetes.io/name=MyApp
Validate that the Service receives a CLUSTER-IP address from the IPv6 address block along with an EXTERNAL-IP. You may then validate access to the service via the IP and port.
NAME         TYPE           CLUSTER-IP            EXTERNAL-IP        PORT(S)        AGE
my-service   LoadBalancer   2001:db8:fd00::7ebc   2603:1030:805::5   80:30790/TCP   35s





    
	
  

    
	
  
    
    
	
    


    
	15 - Extend kubectl with plugins
    Extend kubectl by creating and installing kubectl plugins.
	
This guide demonstrates how to install and write extensions for kubectl. By thinking of core kubectl commands as essential building blocks for interacting with a Kubernetes cluster, a cluster administrator can think
of plugins as a means of utilizing these building blocks to create more complex behavior. Plugins extend kubectl with new sub-commands, allowing for new and custom features not included in the main distribution of kubectl.
Before you begin
You need to have a working kubectl binary installed.

Installing kubectl plugins
A plugin is a standalone executable file, whose name begins with kubectl-. To install a plugin, move its executable file to anywhere on your PATH.
You can also discover and install kubectl plugins available in the open source
using Krew. Krew is a plugin manager maintained by
the Kubernetes SIG CLI community.
Caution:
Kubectl plugins available via the Krew plugin index
are not audited for security. You should install and run third-party plugins at your
own risk, since they are arbitrary programs running on your machine.

Discovering plugins
kubectl provides a command kubectl plugin list that searches your PATH for valid plugin executables.
Executing this command causes a traversal of all files in your PATH. Any files that are executable, and begin with kubectl- will show up in the order in which they are present in your PATH in this command's output.
A warning will be included for any files beginning with kubectl- that are not executable.
A warning will also be included for any valid plugin files that overlap each other's name.
You can use Krew to discover and install kubectl
plugins from a community-curated
plugin index.
Limitations
It is currently not possible to create plugins that overwrite existing kubectl commands. For example, creating a plugin kubectl-version will cause that plugin to never be executed, as the existing kubectl version command will always take precedence over it. Due to this limitation, it is also not possible to use plugins to add new subcommands to existing kubectl commands. For example, adding a subcommand kubectl create foo by naming your plugin kubectl-create-foo will cause that plugin to be ignored.
kubectl plugin list shows warnings for any valid plugins that attempt to do this.
Writing kubectl plugins
You can write a plugin in any programming language or script that allows you to write command-line commands.
There is no plugin installation or pre-loading required. Plugin executables receive
the inherited environment from the kubectl binary.
A plugin determines which command path it wishes to implement based on its name.
For example, a plugin named kubectl-foo provides a command kubectl foo. You must
install the plugin executable somewhere in your PATH.
Example plugin
#!/bin/bash

# optional argument handling
if [[ "$1" == "version" ]]
then
    echo "1.0.0"
    exit 0
fi

# optional argument handling
if [[ "$1" == "config" ]]
then
    echo "$KUBECONFIG"
    exit 0
fi

echo "I am a plugin named kubectl-foo"
Using a plugin
To use a plugin, make the plugin executable:
sudo chmod +x ./kubectl-foo
and place it anywhere in your PATH:
sudo mv ./kubectl-foo /usr/local/bin
You may now invoke your plugin as a kubectl command:
kubectl foo
I am a plugin named kubectl-foo
All args and flags are passed as-is to the executable:
kubectl foo version
1.0.0
All environment variables are also passed as-is to the executable:
export KUBECONFIG=~/.kube/config
kubectl foo config
/home/<user>/.kube/config
KUBECONFIG=/etc/kube/config kubectl foo config
/etc/kube/config
Additionally, the first argument that is passed to a plugin will always be the full path to the location where it was invoked ($0 would equal /usr/local/bin/kubectl-foo in the example above).
Naming a plugin
As seen in the example above, a plugin determines the command path that it will implement based on its filename. Every sub-command in the command path that a plugin targets, is separated by a dash (-).
For example, a plugin that wishes to be invoked whenever the command kubectl foo bar baz is invoked by the user, would have the filename of kubectl-foo-bar-baz.
Flags and argument handling

Note:
The plugin mechanism does not create any custom, plugin-specific values or environment variables for a plugin process.
An older kubectl plugin mechanism provided environment variables such as KUBECTL_PLUGINS_CURRENT_NAMESPACE; that no longer happens.


kubectl plugins must parse and validate all of the arguments passed to them.
See using the command line runtime package for details of a Go library aimed at plugin authors.
Here are some additional cases where users invoke your plugin while providing additional flags and arguments. This builds upon the kubectl-foo-bar-baz plugin from the scenario above.
If you run kubectl foo bar baz arg1 --flag=value arg2, kubectl's plugin mechanism will first try to find the plugin with the longest possible name, which in this case
would be kubectl-foo-bar-baz-arg1. Upon not finding that plugin, kubectl then treats the last dash-separated value as an argument (arg1 in this case), and attempts to find the next longest possible name, kubectl-foo-bar-baz.
Upon having found a plugin with this name, kubectl then invokes that plugin, passing all args and flags after the plugin's name as arguments to the plugin process.
Example:
# create a plugin
echo -e '#!/bin/bash\n\necho "My first command-line argument was $1"' > kubectl-foo-bar-baz
sudo chmod +x ./kubectl-foo-bar-baz

# "install" your plugin by moving it to a directory in your $PATH
sudo mv ./kubectl-foo-bar-baz /usr/local/bin

# check that kubectl recognizes your plugin
kubectl plugin list
The following kubectl-compatible plugins are available:

/usr/local/bin/kubectl-foo-bar-baz
# test that calling your plugin via a "kubectl" command works
# even when additional arguments and flags are passed to your
# plugin executable by the user.
kubectl foo bar baz arg1 --meaningless-flag=true
My first command-line argument was arg1
As you can see, your plugin was found based on the kubectl command specified by a user, and all extra arguments and flags were passed as-is to the plugin executable once it was found.
Names with dashes and underscores
Although the kubectl plugin mechanism uses the dash (-) in plugin filenames to separate the sequence of sub-commands processed by the plugin, it is still possible to create a plugin
command containing dashes in its commandline invocation by using underscores (_) in its filename.
Example:
# create a plugin containing an underscore in its filename
echo -e '#!/bin/bash\n\necho "I am a plugin with a dash in my name"' > ./kubectl-foo_bar
sudo chmod +x ./kubectl-foo_bar

# move the plugin into your $PATH
sudo mv ./kubectl-foo_bar /usr/local/bin

# You can now invoke your plugin via kubectl:
kubectl foo-bar
I am a plugin with a dash in my name
Note that the introduction of underscores to a plugin filename does not prevent you from having commands such as kubectl foo_bar.
The command from the above example, can be invoked using either a dash (-) or an underscore (_):
# You can invoke your custom command with a dash
kubectl foo-bar
I am a plugin with a dash in my name
# You can also invoke your custom command with an underscore
kubectl foo_bar
I am a plugin with a dash in my name
Name conflicts and overshadowing
It is possible to have multiple plugins with the same filename in different locations throughout your PATH.
For example, given a PATH with the following value: PATH=/usr/local/bin/plugins:/usr/local/bin/moreplugins, a copy of plugin kubectl-foo could exist in /usr/local/bin/plugins and /usr/local/bin/moreplugins,
such that the output of the kubectl plugin list command is:
PATH=/usr/local/bin/plugins:/usr/local/bin/moreplugins kubectl plugin list
The following kubectl-compatible plugins are available:

/usr/local/bin/plugins/kubectl-foo
/usr/local/bin/moreplugins/kubectl-foo
  - warning: /usr/local/bin/moreplugins/kubectl-foo is overshadowed by a similarly named plugin: /usr/local/bin/plugins/kubectl-foo

error: one plugin warning was found
In the above scenario, the warning under /usr/local/bin/moreplugins/kubectl-foo tells you that this plugin will never be executed. Instead, the executable that appears first in your PATH, /usr/local/bin/plugins/kubectl-foo, will always be found and executed first by the kubectl plugin mechanism.
A way to resolve this issue is to ensure that the location of the plugin that you wish to use with kubectl always comes first in your PATH. For example, if you want to always use /usr/local/bin/moreplugins/kubectl-foo anytime that the kubectl command kubectl foo was invoked, change the value of your PATH to be /usr/local/bin/moreplugins:/usr/local/bin/plugins.
Invocation of the longest executable filename
There is another kind of overshadowing that can occur with plugin filenames. Given two plugins present in a user's PATH: kubectl-foo-bar and kubectl-foo-bar-baz, the kubectl plugin mechanism will always choose the longest possible plugin name for a given user command. Some examples below, clarify this further:
# for a given kubectl command, the plugin with the longest possible filename will always be preferred
kubectl foo bar baz
Plugin kubectl-foo-bar-baz is executed
kubectl foo bar
Plugin kubectl-foo-bar is executed
kubectl foo bar baz buz
Plugin kubectl-foo-bar-baz is executed, with "buz" as its first argument
kubectl foo bar buz
Plugin kubectl-foo-bar is executed, with "buz" as its first argument
This design choice ensures that plugin sub-commands can be implemented across multiple files, if needed, and that these sub-commands can be nested under a "parent" plugin command:
ls ./plugin_command_tree
kubectl-parent
kubectl-parent-subcommand
kubectl-parent-subcommand-subsubcommand
Checking for plugin warnings
You can use the aforementioned kubectl plugin list command to ensure that your plugin is visible by kubectl, and verify that there are no warnings preventing it from being called as a kubectl command.
kubectl plugin list
The following kubectl-compatible plugins are available:

test/fixtures/pkg/kubectl/plugins/kubectl-foo
/usr/local/bin/kubectl-foo
  - warning: /usr/local/bin/kubectl-foo is overshadowed by a similarly named plugin: test/fixtures/pkg/kubectl/plugins/kubectl-foo
plugins/kubectl-invalid
  - warning: plugins/kubectl-invalid identified as a kubectl plugin, but it is not executable

error: 2 plugin warnings were found
Using the command line runtime package
If you're writing a plugin for kubectl and you're using Go, you can make use
of the
cli-runtime utility libraries.
These libraries provide helpers for parsing or updating a user's
kubeconfig
file, for making REST-style requests to the API server, or to bind flags
associated with configuration and printing.
See the Sample CLI Plugin for
an example usage of the tools provided in the CLI Runtime repo.
Distributing kubectl plugins
If you have developed a plugin for others to use, you should consider how you
package it, distribute it and deliver updates to your users.
Krew
Krew offers a cross-platform way to package and
distribute your plugins. This way, you use a single packaging format for all
target platforms (Linux, Windows, macOS etc) and deliver updates to your users.
Krew also maintains a plugin
index so that other people can
discover your plugin and install it.
Native / platform specific package management
Alternatively, you can use traditional package managers such as, apt or yum
on Linux, Chocolatey on Windows, and Homebrew on macOS. Any package
manager will be suitable if it can place new executables placed somewhere
in the user's PATH.
As a plugin author, if you pick this option then you also have the burden
of updating your kubectl plugin's distribution package across multiple
platforms for each release.
Source code
You can publish the source code; for example, as a Git repository. If you
choose this option, someone who wants to use that plugin must fetch the code,
set up a build environment (if it needs compiling), and deploy the plugin.
If you also make compiled packages available, or use Krew, that will make
installs easier.
What's next

Check the Sample CLI Plugin repository for a
detailed example of a
plugin written in Go.
In case of any questions, feel free to reach out to the
SIG CLI team.
Read about Krew, a package manager for kubectl plugins.






    
	
  
    
    
	
    


    
	16 - Manage HugePages
    Configure and manage huge pages as a schedulable resource in a cluster.
	






 


  
          
            FEATURE STATE: Kubernetes v1.14 [stable]
          

Kubernetes supports the allocation and consumption of pre-allocated huge pages
by applications in a Pod. This page describes how users can consume huge pages.
Before you begin
Kubernetes nodes must
pre-allocate huge pages
in order for the node to report its huge page capacity.
A node can pre-allocate huge pages for multiple sizes, for instance,
the following line in /etc/default/grub allocates 2*1GiB of 1 GiB
and 512*2 MiB of 2 MiB pages:
GRUB_CMDLINE_LINUX="hugepagesz=1G hugepages=2 hugepagesz=2M hugepages=512"
The nodes will automatically discover and report all huge page resources as
schedulable resources.
When you describe the Node, you should see something similar to the following
in the following in the Capacity and Allocatable sections:
Capacity:
  cpu:                ...
  ephemeral-storage:  ...
  hugepages-1Gi:      2Gi
  hugepages-2Mi:      1Gi
  memory:             ...
  pods:               ...
Allocatable:
  cpu:                ...
  ephemeral-storage:  ...
  hugepages-1Gi:      2Gi
  hugepages-2Mi:      1Gi
  memory:             ...
  pods:               ...

Note:For dynamically allocated pages (after boot), the Kubelet needs to be restarted
for the new allocations to be refrelected.


API
Huge pages can be consumed via container level resource requirements using the
resource name hugepages-<size>, where <size> is the most compact binary
notation using integer values supported on a particular node. For example, if a
node supports 2048KiB and 1048576KiB page sizes, it will expose a schedulable
resources hugepages-2Mi and hugepages-1Gi. Unlike CPU or memory, huge pages
do not support overcommit. Note that when requesting hugepage resources, either
memory or CPU resources must be requested as well.
A pod may consume multiple huge page sizes in a single pod spec. In this case it
must use medium: HugePages-<hugepagesize> notation for all volume mounts.
apiVersion: v1
kind: Pod
metadata:
  name: huge-pages-example
spec:
  containers:
  - name: example
    image: fedora:latest
    command:
    - sleep
    - inf
    volumeMounts:
    - mountPath: /hugepages-2Mi
      name: hugepage-2mi
    - mountPath: /hugepages-1Gi
      name: hugepage-1gi
    resources:
      limits:
        hugepages-2Mi: 100Mi
        hugepages-1Gi: 2Gi
        memory: 100Mi
      requests:
        memory: 100Mi
  volumes:
  - name: hugepage-2mi
    emptyDir:
      medium: HugePages-2Mi
  - name: hugepage-1gi
    emptyDir:
      medium: HugePages-1Gi
A pod may use medium: HugePages only if it requests huge pages of one size.
apiVersion: v1
kind: Pod
metadata:
  name: huge-pages-example
spec:
  containers:
  - name: example
    image: fedora:latest
    command:
    - sleep
    - inf
    volumeMounts:
    - mountPath: /hugepages
      name: hugepage
    resources:
      limits:
        hugepages-2Mi: 100Mi
        memory: 100Mi
      requests:
        memory: 100Mi
  volumes:
  - name: hugepage
    emptyDir:
      medium: HugePages

Huge page requests must equal the limits. This is the default if limits are
specified, but requests are not.
Huge pages are isolated at a container scope, so each container has own
limit on their cgroup sandbox as requested in a container spec.
EmptyDir volumes backed by huge pages may not consume more huge page memory
than the pod request.
Applications that consume huge pages via shmget() with SHM_HUGETLB must
run with a supplemental group that matches proc/sys/vm/hugetlb_shm_group.
Huge page usage in a namespace is controllable via ResourceQuota similar
to other compute resources like cpu or memory using the hugepages-<size>
token.






    
	
  
    
    
	
    


    
	17 - Schedule GPUs
    Configure and schedule GPUs for use as a resource by nodes in a cluster.
	






 

  
  
    FEATURE STATE: Kubernetes v1.26 [stable]
  
  

Kubernetes includes stable support for managing AMD and NVIDIA GPUs
(graphical processing units) across different nodes in your cluster, using
device plugins.
This page describes how users can consume GPUs, and outlines
some of the limitations in the implementation.

Using device plugins
Kubernetes implements device plugins to let Pods access specialized hardware features such as GPUs.
Note: This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
As an administrator, you have to install GPU drivers from the corresponding
hardware vendor on the nodes and run the corresponding device plugin from the
GPU vendor. Here are some links to vendors' instructions:

AMD
Intel
NVIDIA

Once you have installed the plugin, your cluster exposes a custom schedulable resource such as amd.com/gpu or nvidia.com/gpu.
You can consume these GPUs from your containers by requesting
the custom GPU resource, the same way you request cpu or memory.
However, there are some limitations in how you specify the resource
requirements for custom devices.
GPUs are only supposed to be specified in the limits section, which means:

You can specify GPU limits without specifying requests, because
Kubernetes will use the limit as the request value by default.
You can specify GPU in both limits and requests but these two values
must be equal.
You cannot specify GPU requests without specifying limits.

Here's an example manifest for a Pod that requests a GPU:
apiVersion: v1
kind: Pod
metadata:
  name: example-vector-add
spec:
  restartPolicy: OnFailure
  containers:
    - name: example-vector-add
      image: "registry.example/example-vector-add:v42"
      resources:
        limits:
          gpu-vendor.example/example-gpu: 1 # requesting 1 GPU
Manage clusters with different types of GPUs
If different nodes in your cluster have different types of GPUs, then you
can use Node Labels and Node Selectors
to schedule pods to appropriate nodes.
For example:
# Label your nodes with the accelerator type they have.
kubectl label nodes node1 accelerator=example-gpu-x100
kubectl label nodes node2 accelerator=other-gpu-k915
That label key accelerator is just an example; you can use
a different label key if you prefer.
Automatic node labelling
As an administrator, you can automatically discover and label all your GPU enabled nodes
by deploying Kubernetes Node Feature Discovery (NFD).
NFD detects the hardware features that are available on each node in a Kubernetes cluster.
Typically, NFD is configured to advertise those features as node labels, but NFD can also add extended resources, annotations, and node taints.
NFD is compatible with all supported versions of Kubernetes.
By default NFD create the feature labels for the detected features.
Administrators can leverage NFD to also taint nodes with specific features, so that only pods that request those features can be scheduled on those nodes.
You also need a plugin for NFD that adds appropriate labels to your nodes; these might be generic
labels or they could be vendor specific. Your GPU vendor may provide a third party
plugin for NFD; check their documentation for more details.
apiVersion: v1
kind: Pod
metadata:
  name: example-vector-add
spec:
  restartPolicy: OnFailure
  # You can use Kubernetes node affinity to schedule this Pod onto a node
  # that provides the kind of GPU that its container needs in order to work
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: "gpu.gpu-vendor.example/installed-memory"
            operator: Gt # (greater than)
            values: ["40535"]
          - key: "feature.node.kubernetes.io/pci-10.present" # NFD Feature label
            values: ["true"] # (optional) only schedule on nodes with PCI device 10
  containers:
    - name: example-vector-add
      image: "registry.example/example-vector-add:v42"
      resources:
        limits:
          gpu-vendor.example/example-gpu: 1 # requesting 1 GPU
GPU vendor implementations

Intel
NVIDIA

Tasks

1 - Install Tools

kubectl

kind

minikube

kubeadm

1.1 - Install and Set Up kubectl on Linux

Before you begin

Install kubectl on Linux

Install kubectl binary with curl on Linux

Note:

Note:

Note:

Install using native package management

Note:

Note:

Note:

Note:

Install using other package management

Verify kubectl configuration

Troubleshooting the 'No Auth Provider Found' error message

Optional kubectl configurations and plugins

Enable shell autocompletion

Introduction

Install bash-completion

Enable kubectl autocompletion

Bash

Note:

Note:

Install kubectl convert plugin

Note:

What's next

1.2 - Install and Set Up kubectl on macOS

Before you begin

Install kubectl on macOS

Install kubectl binary with curl on macOS

Note:

Note:

Note:

Install with Homebrew on macOS

Install with Macports on macOS

Verify kubectl configuration

Troubleshooting the 'No Auth Provider Found' error message

Optional kubectl configurations and plugins

Enable shell autocompletion

Introduction

Warning:

Upgrade Bash

Install bash-completion

Note:

Enable kubectl autocompletion

Note:

Note:

Install kubectl convert plugin

Note:

Note:

Uninstall kubectl on macOS

Uninstall kubectl using the command-line

Uninstall kubectl using homebrew

What's next

1.3 - Install and Set Up kubectl on Windows

Before you begin

Install kubectl on Windows

Install kubectl binary with curl on Windows

Note:

Note:

Install on Windows using Chocolatey, Scoop, or winget

Note:

Verify kubectl configuration

Troubleshooting the 'No Auth Provider Found' error message

Optional kubectl configurations and plugins

Enable shell autocompletion

Install kubectl convert plugin

What's next

2 - Administer a Cluster

2.1 - Administration with kubeadm

2.1.1 - Certificate Management with kubeadm

Before you begin

Using custom certificates

External CA mode

Install `kubectl convert` plugin

Install `kubectl convert` plugin

Install `kubectl convert` plugin

Using the `cgroupfs` driver

Migrating to the `systemd` driver

Updating the `ClusterConfiguration`

Reflecting `ClusterConfiguration` changes on control plane nodes

Updating the `KubeletConfiguration`

Updating the `KubeProxyConfiguration`