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Configuration
1 - Configuration Best Practices
This document highlights and consolidates configuration best practices that are introduced throughout the user guide, Getting Started documentation, and examples.
This is a living document. If you think of something that is not on this list but might be useful to others, please don't hesitate to file an issue or submit a PR.
General Configuration Tips
When defining configurations, specify the latest stable API version.
Configuration files should be stored in version control before being pushed to the cluster. This allows you to quickly roll back a configuration change if necessary. It also aids cluster re-creation and restoration.
Write your configuration files using YAML rather than JSON. Though these formats can be used interchangeably in almost all scenarios, YAML tends to be more user-friendly.
Group related objects into a single file whenever it makes sense. One file is often easier to manage than several. See the guestbook-all-in-one.yaml file as an example of this syntax.
Note also that many
kubectl
commands can be called on a directory. For example, you can callkubectl apply
on a directory of config files.Don't specify default values unnecessarily: simple, minimal configuration will make errors less likely.
Put object descriptions in annotations, to allow better introspection.
Note:
There is a breaking change introduced in the YAML 1.2 boolean values specification with respect to YAML 1.1. This is a known issue in Kubernetes. YAML 1.2 only recognizes true and false as valid booleans, while YAML 1.1 also accepts yes, no, on, and off as booleans. However, Kubernetes uses YAML parsers that are mostly compatible with YAML 1.1, which means that using yes or no instead of true or false in a YAML manifest may cause unexpected errors or behaviors. To avoid this issue, it is recommended to always use true or false for boolean values in YAML manifests, and to quote any strings that may be confused with booleans, such as "yes" or "no".
Besides booleans, there are additional specifications changes between YAML versions. Please refer to the YAML Specification Changes documentation for a comprehensive list.
"Naked" Pods versus ReplicaSets, Deployments, and Jobs
Don't use naked Pods (that is, Pods not bound to a ReplicaSet or Deployment) if you can avoid it. Naked Pods will not be rescheduled in the event of a node failure.
A Deployment, which both creates a ReplicaSet to ensure that the desired number of Pods is always available, and specifies a strategy to replace Pods (such as RollingUpdate), is almost always preferable to creating Pods directly, except for some explicit
restartPolicy: Never
scenarios. A Job may also be appropriate.
Services
Create a Service before its corresponding backend workloads (Deployments or ReplicaSets), and before any workloads that need to access it. When Kubernetes starts a container, it provides environment variables pointing to all the Services which were running when the container was started. For example, if a Service named
foo
exists, all containers will get the following variables in their initial environment:FOO_SERVICE_HOST=<the host the Service is running on> FOO_SERVICE_PORT=<the port the Service is running on>
This does imply an ordering requirement - any
Service
that aPod
wants to access must be created before thePod
itself, or else the environment variables will not be populated. DNS does not have this restriction.An optional (though strongly recommended) cluster add-on is a DNS server. The DNS server watches the Kubernetes API for new
Services
and creates a set of DNS records for each. If DNS has been enabled throughout the cluster then allPods
should be able to do name resolution ofServices
automatically.Don't specify a
hostPort
for a Pod unless it is absolutely necessary. When you bind a Pod to ahostPort
, it limits the number of places the Pod can be scheduled, because each <hostIP
,hostPort
,protocol
> combination must be unique. If you don't specify thehostIP
andprotocol
explicitly, Kubernetes will use0.0.0.0
as the defaulthostIP
andTCP
as the defaultprotocol
.If you only need access to the port for debugging purposes, you can use the apiserver proxy or
kubectl port-forward
.If you explicitly need to expose a Pod's port on the node, consider using a NodePort Service before resorting to
hostPort
.Avoid using
hostNetwork
, for the same reasons ashostPort
.Use headless Services (which have a
ClusterIP
ofNone
) for service discovery when you don't needkube-proxy
load balancing.
Using Labels
Define and use labels that identify semantic attributes of your application or Deployment, such as
{ app.kubernetes.io/name: MyApp, tier: frontend, phase: test, deployment: v3 }
. You can use these labels to select the appropriate Pods for other resources; for example, a Service that selects alltier: frontend
Pods, or allphase: test
components ofapp.kubernetes.io/name: MyApp
. See the guestbook app for examples of this approach.A Service can be made to span multiple Deployments by omitting release-specific labels from its selector. When you need to update a running service without downtime, use a Deployment.
A desired state of an object is described by a Deployment, and if changes to that spec are applied, the deployment controller changes the actual state to the desired state at a controlled rate.
Use the Kubernetes common labels for common use cases. These standardized labels enrich the metadata in a way that allows tools, including
kubectl
and dashboard, to work in an interoperable way.You can manipulate labels for debugging. Because Kubernetes controllers (such as ReplicaSet) and Services match to Pods using selector labels, removing the relevant labels from a Pod will stop it from being considered by a controller or from being served traffic by a Service. If you remove the labels of an existing Pod, its controller will create a new Pod to take its place. This is a useful way to debug a previously "live" Pod in a "quarantine" environment. To interactively remove or add labels, use
kubectl label
.
Using kubectl
Use
kubectl apply -f <directory>
. This looks for Kubernetes configuration in all.yaml
,.yml
, and.json
files in<directory>
and passes it toapply
.Use label selectors for
get
anddelete
operations instead of specific object names. See the sections on label selectors and using labels effectively.Use
kubectl create deployment
andkubectl expose
to quickly create single-container Deployments and Services. See Use a Service to Access an Application in a Cluster for an example.
2 - ConfigMaps
A ConfigMap is an API object used to store non-confidential data in key-value pairs. Pods can consume ConfigMaps as environment variables, command-line arguments, or as configuration files in a volume.
A ConfigMap allows you to decouple environment-specific configuration from your container images, so that your applications are easily portable.
Caution:
ConfigMap does not provide secrecy or encryption. If the data you want to store are confidential, use a Secret rather than a ConfigMap, or use additional (third party) tools to keep your data private.Motivation
Use a ConfigMap for setting configuration data separately from application code.
For example, imagine that you are developing an application that you can run on your
own computer (for development) and in the cloud (to handle real traffic).
You write the code to look in an environment variable named DATABASE_HOST
.
Locally, you set that variable to localhost
. In the cloud, you set it to
refer to a Kubernetes Service
that exposes the database component to your cluster.
This lets you fetch a container image running in the cloud and
debug the exact same code locally if needed.
Note:
A ConfigMap is not designed to hold large chunks of data. The data stored in a ConfigMap cannot exceed 1 MiB. If you need to store settings that are larger than this limit, you may want to consider mounting a volume or use a separate database or file service.ConfigMap object
A ConfigMap is an API object
that lets you store configuration for other objects to use. Unlike most
Kubernetes objects that have a spec
, a ConfigMap has data
and binaryData
fields. These fields accept key-value pairs as their values. Both the data
field and the binaryData
are optional. The data
field is designed to
contain UTF-8 strings while the binaryData
field is designed to
contain binary data as base64-encoded strings.
The name of a ConfigMap must be a valid DNS subdomain name.
Each key under the data
or the binaryData
field must consist of
alphanumeric characters, -
, _
or .
. The keys stored in data
must not
overlap with the keys in the binaryData
field.
Starting from v1.19, you can add an immutable
field to a ConfigMap
definition to create an immutable ConfigMap.
ConfigMaps and Pods
You can write a Pod spec
that refers to a ConfigMap and configures the container(s)
in that Pod based on the data in the ConfigMap. The Pod and the ConfigMap must be in
the same namespace.
Here's an example ConfigMap that has some keys with single values, and other keys where the value looks like a fragment of a configuration format.
apiVersion: v1
kind: ConfigMap
metadata:
name: game-demo
data:
# property-like keys; each key maps to a simple value
player_initial_lives: "3"
ui_properties_file_name: "user-interface.properties"
# file-like keys
game.properties: |
enemy.types=aliens,monsters
player.maximum-lives=5
user-interface.properties: |
color.good=purple
color.bad=yellow
allow.textmode=true
There are four different ways that you can use a ConfigMap to configure a container inside a Pod:
- Inside a container command and args
- Environment variables for a container
- Add a file in read-only volume, for the application to read
- Write code to run inside the Pod that uses the Kubernetes API to read a ConfigMap
These different methods lend themselves to different ways of modeling the data being consumed. For the first three methods, the kubelet uses the data from the ConfigMap when it launches container(s) for a Pod.
The fourth method means you have to write code to read the ConfigMap and its data. However, because you're using the Kubernetes API directly, your application can subscribe to get updates whenever the ConfigMap changes, and react when that happens. By accessing the Kubernetes API directly, this technique also lets you access a ConfigMap in a different namespace.
Here's an example Pod that uses values from game-demo
to configure a Pod:
apiVersion: v1
kind: Pod
metadata:
name: configmap-demo-pod
spec:
containers:
- name: demo
image: alpine
command: ["sleep", "3600"]
env:
# Define the environment variable
- name: PLAYER_INITIAL_LIVES # Notice that the case is different here
# from the key name in the ConfigMap.
valueFrom:
configMapKeyRef:
name: game-demo # The ConfigMap this value comes from.
key: player_initial_lives # The key to fetch.
- name: UI_PROPERTIES_FILE_NAME
valueFrom:
configMapKeyRef:
name: game-demo
key: ui_properties_file_name
volumeMounts:
- name: config
mountPath: "/config"
readOnly: true
volumes:
# You set volumes at the Pod level, then mount them into containers inside that Pod
- name: config
configMap:
# Provide the name of the ConfigMap you want to mount.
name: game-demo
# An array of keys from the ConfigMap to create as files
items:
- key: "game.properties"
path: "game.properties"
- key: "user-interface.properties"
path: "user-interface.properties"
A ConfigMap doesn't differentiate between single line property values and multi-line file-like values. What matters is how Pods and other objects consume those values.
For this example, defining a volume and mounting it inside the demo
container as /config
creates two files,
/config/game.properties
and /config/user-interface.properties
,
even though there are four keys in the ConfigMap. This is because the Pod
definition specifies an items
array in the volumes
section.
If you omit the items
array entirely, every key in the ConfigMap becomes
a file with the same name as the key, and you get 4 files.
Using ConfigMaps
ConfigMaps can be mounted as data volumes. ConfigMaps can also be used by other parts of the system, without being directly exposed to the Pod. For example, ConfigMaps can hold data that other parts of the system should use for configuration.
The most common way to use ConfigMaps is to configure settings for containers running in a Pod in the same namespace. You can also use a ConfigMap separately.
For example, you might encounter addons or operators that adjust their behavior based on a ConfigMap.
Using ConfigMaps as files from a Pod
To consume a ConfigMap in a volume in a Pod:
- Create a ConfigMap or use an existing one. Multiple Pods can reference the same ConfigMap.
- Modify your Pod definition to add a volume under
.spec.volumes[]
. Name the volume anything, and have a.spec.volumes[].configMap.name
field set to reference your ConfigMap object. - Add a
.spec.containers[].volumeMounts[]
to each container that needs the ConfigMap. Specify.spec.containers[].volumeMounts[].readOnly = true
and.spec.containers[].volumeMounts[].mountPath
to an unused directory name where you would like the ConfigMap to appear. - Modify your image or command line so that the program looks for files in
that directory. Each key in the ConfigMap
data
map becomes the filename undermountPath
.
This is an example of a Pod that mounts a ConfigMap in a volume:
apiVersion: v1
kind: Pod
metadata:
name: mypod
spec:
containers:
- name: mypod
image: redis
volumeMounts:
- name: foo
mountPath: "/etc/foo"
readOnly: true
volumes:
- name: foo
configMap:
name: myconfigmap
Each ConfigMap you want to use needs to be referred to in .spec.volumes
.
If there are multiple containers in the Pod, then each container needs its
own volumeMounts
block, but only one .spec.volumes
is needed per ConfigMap.
Mounted ConfigMaps are updated automatically
When a ConfigMap currently consumed in a volume is updated, projected keys are eventually updated as well.
The kubelet checks whether the mounted ConfigMap is fresh on every periodic sync.
However, the kubelet uses its local cache for getting the current value of the ConfigMap.
The type of the cache is configurable using the configMapAndSecretChangeDetectionStrategy
field in
the KubeletConfiguration struct.
A ConfigMap can be either propagated by watch (default), ttl-based, or by redirecting
all requests directly to the API server.
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 the kubelet sync period + cache
propagation delay, where the cache propagation delay depends on the chosen cache type
(it equals to watch propagation delay, ttl of cache, or zero correspondingly).
ConfigMaps consumed as environment variables are not updated automatically and require a pod restart.
Note:
A container using a ConfigMap as a subPath volume mount will not receive ConfigMap updates.Using Configmaps as environment variables
To use a Configmap in an environment variable in a Pod:
- For each container in your Pod specification, add an environment variable
for each Configmap key that you want to use to the
env[].valueFrom.configMapKeyRef
field. - Modify your image and/or command line so that the program looks for values in the specified environment variables.
This is an example of defining a ConfigMap as a pod environment variable:
The following ConfigMap (myconfigmap.yaml) stores two properties: username and access_level:
apiVersion: v1
kind: ConfigMap
metadata:
name: myconfigmap
data:
username: k8s-admin
access_level: "1"
The following command will create the ConfigMap object:
kubectl apply -f myconfigmap.yaml
The following Pod consumes the content of the ConfigMap as environment variables:
apiVersion: v1
kind: Pod
metadata:
name: env-configmap
spec:
containers:
- name: app
command: ["/bin/sh", "-c", "printenv"]
image: busybox:latest
envFrom:
- configMapRef:
name: myconfigmap
The envFrom
field instructs Kubernetes to create environment variables from the sources nested within it.
The inner configMapRef
refers to a ConfigMap by its name and selects all its key-value pairs.
Add the Pod to your cluster, then retrieve its logs to see the output from the printenv command.
This should confirm that the two key-value pairs from the ConfigMap have been set as environment variables:
kubectl apply -f env-configmap.yaml
kubectl logs pod/ env-configmap
The output is similar to this:
...
username: "k8s-admin"
access_level: "1"
...
Sometimes a Pod won't require access to all the values in a ConfigMap.
For example, you could have another Pod which only uses the username value from the ConfigMap.
For this use case, you can use the env.valueFrom
syntax instead, which lets you select individual keys in
a ConfigMap. The name of the environment variable can also be different from the key within the ConfigMap.
For example:
apiVersion: v1
kind: Pod
metadata:
name: env-configmap
spec:
containers:
- name: envars-test-container
image: nginx
env:
- name: CONFIGMAP_USERNAME
valueFrom:
configMapKeyRef:
name: myconfigmap
key: username
In the Pod created from this manifest, you will see that the environment variable
CONFIGMAP_USERNAME
is set to the value of the username
value from the ConfigMap.
Other keys from the ConfigMap data are not copied into the environment.
It's important to note that the range of characters allowed for environment variable names in pods is restricted. If any keys do not meet the rules, those keys are not made available to your container, though the Pod is allowed to start.
Immutable ConfigMaps
Kubernetes v1.21 [stable]
The Kubernetes feature Immutable Secrets and ConfigMaps provides an option to set individual Secrets and ConfigMaps as immutable. For clusters that extensively use ConfigMaps (at least tens of thousands of unique ConfigMap to Pod mounts), preventing changes to their data has the following advantages:
- protects you from accidental (or unwanted) updates that could cause applications outages
- improves performance of your cluster by significantly reducing load on kube-apiserver, by closing watches for ConfigMaps marked as immutable.
You can create an immutable ConfigMap by setting the immutable
field to true
.
For example:
apiVersion: v1
kind: ConfigMap
metadata:
...
data:
...
immutable: true
Once a ConfigMap is marked as immutable, it is not possible to revert this change
nor to mutate the contents of the data
or the binaryData
field. You can
only delete and recreate the ConfigMap. Because existing Pods maintain a mount point
to the deleted ConfigMap, it is recommended to recreate these pods.
What's next
- Read about Secrets.
- Read Configure a Pod to Use a ConfigMap.
- Read about changing a ConfigMap (or any other Kubernetes object)
- Read The Twelve-Factor App to understand the motivation for separating code from configuration.
3 - Secrets
A Secret is an object that contains a small amount of sensitive data such as a password, a token, or a key. Such information might otherwise be put in a Pod specification or in a container image. Using a Secret means that you don't need to include confidential data in your application code.
Because Secrets can be created independently of the Pods that use them, there is less risk of the Secret (and its data) being exposed during the workflow of creating, viewing, and editing Pods. Kubernetes, and applications that run in your cluster, can also take additional precautions with Secrets, such as avoiding writing sensitive data to nonvolatile storage.
Secrets are similar to ConfigMaps but are specifically intended to hold confidential data.
Caution:
Kubernetes Secrets are, by default, stored unencrypted in the API server's underlying data store (etcd). Anyone with API access can retrieve or modify a Secret, and so can anyone with access to etcd. Additionally, anyone who is authorized to create a Pod in a namespace can use that access to read any Secret in that namespace; this includes indirect access such as the ability to create a Deployment.
In order to safely use Secrets, take at least the following steps:
- Enable Encryption at Rest for Secrets.
- Enable or configure RBAC rules with least-privilege access to Secrets.
- Restrict Secret access to specific containers.
- Consider using external Secret store providers.
For more guidelines to manage and improve the security of your Secrets, refer to Good practices for Kubernetes Secrets.
See Information security for Secrets for more details.
Uses for Secrets
You can use Secrets for purposes such as the following:
- Set environment variables for a container.
- Provide credentials such as SSH keys or passwords to Pods.
- Allow the kubelet to pull container images from private registries.
The Kubernetes control plane also uses Secrets; for example, bootstrap token Secrets are a mechanism to help automate node registration.
Use case: dotfiles in a secret volume
You can make your data "hidden" by defining a key that begins with a dot.
This key represents a dotfile or "hidden" file. For example, when the following Secret
is mounted into a volume, secret-volume
, the volume will contain a single file,
called .secret-file
, and the dotfile-test-container
will have this file
present at the path /etc/secret-volume/.secret-file
.
Note:
Files beginning with dot characters are hidden from the output ofls -l
;
you must use ls -la
to see them when listing directory contents.apiVersion: v1
kind: Secret
metadata:
name: dotfile-secret
data:
.secret-file: dmFsdWUtMg0KDQo=
---
apiVersion: v1
kind: Pod
metadata:
name: secret-dotfiles-pod
spec:
volumes:
- name: secret-volume
secret:
secretName: dotfile-secret
containers:
- name: dotfile-test-container
image: registry.k8s.io/busybox
command:
- ls
- "-l"
- "/etc/secret-volume"
volumeMounts:
- name: secret-volume
readOnly: true
mountPath: "/etc/secret-volume"
Use case: Secret visible to one container in a Pod
Consider a program that needs to handle HTTP requests, do some complex business logic, and then sign some messages with an HMAC. Because it has complex application logic, there might be an unnoticed remote file reading exploit in the server, which could expose the private key to an attacker.
This could be divided into two processes in two containers: a frontend container which handles user interaction and business logic, but which cannot see the private key; and a signer container that can see the private key, and responds to simple signing requests from the frontend (for example, over localhost networking).
With this partitioned approach, an attacker now has to trick the application server into doing something rather arbitrary, which may be harder than getting it to read a file.
Alternatives to Secrets
Rather than using a Secret to protect confidential data, you can pick from alternatives.
Here are some of your options:
- If your cloud-native component needs to authenticate to another application that you know is running within the same Kubernetes cluster, you can use a ServiceAccount and its tokens to identify your client.
- There are third-party tools that you can run, either within or outside your cluster, that manage sensitive data. For example, a service that Pods access over HTTPS, that reveals a Secret if the client correctly authenticates (for example, with a ServiceAccount token).
- For authentication, you can implement a custom signer for X.509 certificates, and use CertificateSigningRequests to let that custom signer issue certificates to Pods that need them.
- You can use a device plugin to expose node-local encryption hardware to a specific Pod. For example, you can schedule trusted Pods onto nodes that provide a Trusted Platform Module, configured out-of-band.
You can also combine two or more of those options, including the option to use Secret objects themselves.
For example: implement (or deploy) an operator that fetches short-lived session tokens from an external service, and then creates Secrets based on those short-lived session tokens. Pods running in your cluster can make use of the session tokens, and operator ensures they are valid. This separation means that you can run Pods that are unaware of the exact mechanisms for issuing and refreshing those session tokens.
Types of Secret
When creating a Secret, you can specify its type using the type
field of
the Secret
resource, or certain equivalent kubectl
command line flags (if available).
The Secret type is used to facilitate programmatic handling of the Secret data.
Kubernetes provides several built-in types for some common usage scenarios. These types vary in terms of the validations performed and the constraints Kubernetes imposes on them.
Built-in Type | Usage |
---|---|
Opaque | arbitrary user-defined data |
kubernetes.io/service-account-token | ServiceAccount token |
kubernetes.io/dockercfg | serialized ~/.dockercfg file |
kubernetes.io/dockerconfigjson | serialized ~/.docker/config.json file |
kubernetes.io/basic-auth | credentials for basic authentication |
kubernetes.io/ssh-auth | credentials for SSH authentication |
kubernetes.io/tls | data for a TLS client or server |
bootstrap.kubernetes.io/token | bootstrap token data |
You can define and use your own Secret type by assigning a non-empty string as the
type
value for a Secret object (an empty string is treated as an Opaque
type).
Kubernetes doesn't impose any constraints on the type name. However, if you are using one of the built-in types, you must meet all the requirements defined for that type.
If you are defining a type of Secret that's for public use, follow the convention
and structure the Secret type to have your domain name before the name, separated
by a /
. For example: cloud-hosting.example.net/cloud-api-credentials
.
Opaque Secrets
Opaque
is the default Secret type if you don't explicitly specify a type in
a Secret manifest. When you create a Secret using kubectl
, you must use the
generic
subcommand to indicate an Opaque
Secret type. For example, the
following command creates an empty Secret of type Opaque
:
kubectl create secret generic empty-secret
kubectl get secret empty-secret
The output looks like:
NAME TYPE DATA AGE
empty-secret Opaque 0 2m6s
The DATA
column shows the number of data items stored in the Secret.
In this case, 0
means you have created an empty Secret.
ServiceAccount token Secrets
A kubernetes.io/service-account-token
type of Secret is used to store a
token credential that identifies a
ServiceAccount. This
is a legacy mechanism that provides long-lived ServiceAccount credentials to
Pods.
In Kubernetes v1.22 and later, the recommended approach is to obtain a
short-lived, automatically rotating ServiceAccount token by using the
TokenRequest
API instead. You can get these short-lived tokens using the following methods:
- Call the
TokenRequest
API either directly or by using an API client likekubectl
. For example, you can use thekubectl create token
command. - Request a mounted token in a projected volume in your Pod manifest. Kubernetes creates the token and mounts it in the Pod. The token is automatically invalidated when the Pod that it's mounted in is deleted. For details, see Launch a Pod using service account token projection.
Note:
You should only create a ServiceAccount token Secret if you can't use theTokenRequest
API to obtain a token,
and the security exposure of persisting a non-expiring token credential
in a readable API object is acceptable to you. For instructions, see
Manually create a long-lived API token for a ServiceAccount.When using this Secret type, you need to ensure that the
kubernetes.io/service-account.name
annotation is set to an existing
ServiceAccount name. If you are creating both the ServiceAccount and
the Secret objects, you should create the ServiceAccount object first.
After the Secret is created, a Kubernetes controller
fills in some other fields such as the kubernetes.io/service-account.uid
annotation, and the
token
key in the data
field, which is populated with an authentication token.
The following example configuration declares a ServiceAccount token Secret:
apiVersion: v1
kind: Secret
metadata:
name: secret-sa-sample
annotations:
kubernetes.io/service-account.name: "sa-name"
type: kubernetes.io/service-account-token
data:
extra: YmFyCg==
After creating the Secret, wait for Kubernetes to populate the token
key in the data
field.
See the ServiceAccount
documentation for more information on how ServiceAccounts work.
You can also check the automountServiceAccountToken
field and the
serviceAccountName
field of the
Pod
for information on referencing ServiceAccount credentials from within Pods.
Docker config Secrets
If you are creating a Secret to store credentials for accessing a container image registry,
you must use one of the following type
values for that Secret:
kubernetes.io/dockercfg
: store a serialized~/.dockercfg
which is the legacy format for configuring Docker command line. The Secretdata
field contains a.dockercfg
key whose value is the content of a base64 encoded~/.dockercfg
file.kubernetes.io/dockerconfigjson
: store a serialized JSON that follows the same format rules as the~/.docker/config.json
file, which is a new format for~/.dockercfg
. The Secretdata
field must contain a.dockerconfigjson
key for which the value is the content of a base64 encoded~/.docker/config.json
file.
Below is an example for a kubernetes.io/dockercfg
type of Secret:
apiVersion: v1
kind: Secret
metadata:
name: secret-dockercfg
type: kubernetes.io/dockercfg
data:
.dockercfg: |
eyJhdXRocyI6eyJodHRwczovL2V4YW1wbGUvdjEvIjp7ImF1dGgiOiJvcGVuc2VzYW1lIn19fQo=
Note:
If you do not want to perform the base64 encoding, you can choose to use thestringData
field instead.When you create Docker config Secrets using a manifest, the API
server checks whether the expected key exists in the data
field, and
it verifies if the value provided can be parsed as a valid JSON. The API
server doesn't validate if the JSON actually is a Docker config file.
You can also use kubectl
to create a Secret for accessing a container
registry, such as when you don't have a Docker configuration file:
kubectl create secret docker-registry secret-tiger-docker \
--docker-email=tiger@acme.example \
--docker-username=tiger \
--docker-password=pass1234 \
--docker-server=my-registry.example:5000
This command creates a Secret of type kubernetes.io/dockerconfigjson
.
Retrieve the .data.dockerconfigjson
field from that new Secret and decode the
data:
kubectl get secret secret-tiger-docker -o jsonpath='{.data.*}' | base64 -d
The output is equivalent to the following JSON document (which is also a valid Docker configuration file):
{
"auths": {
"my-registry.example:5000": {
"username": "tiger",
"password": "pass1234",
"email": "tiger@acme.example",
"auth": "dGlnZXI6cGFzczEyMzQ="
}
}
}
Caution:
The auth
value there is base64 encoded; it is obscured but not secret.
Anyone who can read that Secret can learn the registry access bearer token.
It is suggested to use credential providers to dynamically and securely provide pull secrets on-demand.
Basic authentication Secret
The kubernetes.io/basic-auth
type is provided for storing credentials needed
for basic authentication. When using this Secret type, the data
field of the
Secret must contain one of the following two keys:
username
: the user name for authenticationpassword
: the password or token for authentication
Both values for the above two keys are base64 encoded strings. You can
alternatively provide the clear text content using the stringData
field in the
Secret manifest.
The following manifest is an example of a basic authentication Secret:
apiVersion: v1
kind: Secret
metadata:
name: secret-basic-auth
type: kubernetes.io/basic-auth
stringData:
username: admin # required field for kubernetes.io/basic-auth
password: t0p-Secret # required field for kubernetes.io/basic-auth
Note:
ThestringData
field for a Secret does not work well with server-side apply.The basic authentication Secret type is provided only for convenience.
You can create an Opaque
type for credentials used for basic authentication.
However, using the defined and public Secret type (kubernetes.io/basic-auth
) helps other
people to understand the purpose of your Secret, and sets a convention for what key names
to expect.
SSH authentication Secrets
The builtin type kubernetes.io/ssh-auth
is provided for storing data used in
SSH authentication. When using this Secret type, you will have to specify a
ssh-privatekey
key-value pair in the data
(or stringData
) field
as the SSH credential to use.
The following manifest is an example of a Secret used for SSH public/private key authentication:
apiVersion: v1
kind: Secret
metadata:
name: secret-ssh-auth
type: kubernetes.io/ssh-auth
data:
# the data is abbreviated in this example
ssh-privatekey: |
UG91cmluZzYlRW1vdGljb24lU2N1YmE=
The SSH authentication Secret type is provided only for convenience.
You can create an Opaque
type for credentials used for SSH authentication.
However, using the defined and public Secret type (kubernetes.io/ssh-auth
) helps other
people to understand the purpose of your Secret, and sets a convention for what key names
to expect.
The Kubernetes API verifies that the required keys are set for a Secret of this type.
Caution:
SSH private keys do not establish trusted communication between an SSH client and host server on their own. A secondary means of establishing trust is needed to mitigate "man in the middle" attacks, such as aknown_hosts
file added to a ConfigMap.TLS Secrets
The kubernetes.io/tls
Secret type is for storing
a certificate and its associated key that are typically used for TLS.
One common use for TLS Secrets is to configure encryption in transit for
an Ingress, but you can also use it
with other resources or directly in your workload.
When using this type of Secret, the tls.key
and the tls.crt
key must be provided
in the data
(or stringData
) field of the Secret configuration, although the API
server doesn't actually validate the values for each key.
As an alternative to using stringData
, you can use the data
field to provide
the base64 encoded certificate and private key. For details, see
Constraints on Secret names and data.
The following YAML contains an example config for a TLS Secret:
apiVersion: v1
kind: Secret
metadata:
name: secret-tls
type: kubernetes.io/tls
data:
# values are base64 encoded, which obscures them but does NOT provide
# any useful level of confidentiality
tls.crt: |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# In this example, the key data is not a real PEM-encoded private key
tls.key: |
RXhhbXBsZSBkYXRhIGZvciB0aGUgVExTIGNydCBmaWVsZA==
The TLS Secret type is provided only for convenience.
You can create an Opaque
type for credentials used for TLS authentication.
However, using the defined and public Secret type (kubernetes.io/tls
)
helps ensure the consistency of Secret format in your project. The API server
verifies if the required keys are set for a Secret of this type.
To create a TLS Secret using kubectl
, use the tls
subcommand:
kubectl create secret tls my-tls-secret \
--cert=path/to/cert/file \
--key=path/to/key/file
The public/private key pair must exist before hand. The public key certificate for --cert
must be .PEM encoded
and must match the given private key for --key
.
Bootstrap token Secrets
The bootstrap.kubernetes.io/token
Secret type is for
tokens used during the node bootstrap process. It stores tokens used to sign
well-known ConfigMaps.
A bootstrap token Secret is usually created in the kube-system
namespace and
named in the form bootstrap-token-<token-id>
where <token-id>
is a 6 character
string of the token ID.
As a Kubernetes manifest, a bootstrap token Secret might look like the following:
apiVersion: v1
kind: Secret
metadata:
name: bootstrap-token-5emitj
namespace: kube-system
type: bootstrap.kubernetes.io/token
data:
auth-extra-groups: c3lzdGVtOmJvb3RzdHJhcHBlcnM6a3ViZWFkbTpkZWZhdWx0LW5vZGUtdG9rZW4=
expiration: MjAyMC0wOS0xM1QwNDozOToxMFo=
token-id: NWVtaXRq
token-secret: a3E0Z2lodnN6emduMXAwcg==
usage-bootstrap-authentication: dHJ1ZQ==
usage-bootstrap-signing: dHJ1ZQ==
A bootstrap token Secret has the following keys specified under data
:
token-id
: A random 6 character string as the token identifier. Required.token-secret
: A random 16 character string as the actual token Secret. Required.description
: A human-readable string that describes what the token is used for. Optional.expiration
: An absolute UTC time using RFC3339 specifying when the token should be expired. Optional.usage-bootstrap-<usage>
: A boolean flag indicating additional usage for the bootstrap token.auth-extra-groups
: A comma-separated list of group names that will be authenticated as in addition to thesystem:bootstrappers
group.
You can alternatively provide the values in the stringData
field of the Secret
without base64 encoding them:
apiVersion: v1
kind: Secret
metadata:
# Note how the Secret is named
name: bootstrap-token-5emitj
# A bootstrap token Secret usually resides in the kube-system namespace
namespace: kube-system
type: bootstrap.kubernetes.io/token
stringData:
auth-extra-groups: "system:bootstrappers:kubeadm:default-node-token"
expiration: "2020-09-13T04:39:10Z"
# This token ID is used in the name
token-id: "5emitj"
token-secret: "kq4gihvszzgn1p0r"
# This token can be used for authentication
usage-bootstrap-authentication: "true"
# and it can be used for signing
usage-bootstrap-signing: "true"
Note:
ThestringData
field for a Secret does not work well with server-side apply.Working with Secrets
Creating a Secret
There are several options to create a Secret:
Constraints on Secret names and data
The name of a Secret object must be a valid DNS subdomain name.
You can specify the data
and/or the stringData
field when creating a
configuration file for a Secret. The data
and the stringData
fields are optional.
The values for all keys in the data
field have to be base64-encoded strings.
If the conversion to base64 string is not desirable, you can choose to specify
the stringData
field instead, which accepts arbitrary strings as values.
The keys of data
and stringData
must consist of alphanumeric characters,
-
, _
or .
. All key-value pairs in the stringData
field are internally
merged into the data
field. If a key appears in both the data
and the
stringData
field, the value specified in the stringData
field takes
precedence.
Size limit
Individual Secrets are limited to 1MiB in size. This is to discourage creation of very large Secrets that could exhaust the API server and kubelet memory. However, creation of many smaller Secrets could also exhaust memory. You can use a resource quota to limit the number of Secrets (or other resources) in a namespace.
Editing a Secret
You can edit an existing Secret unless it is immutable. To edit a Secret, use one of the following methods:
You can also edit the data in a Secret using the Kustomize tool. However, this
method creates a new Secret
object with the edited data.
Depending on how you created the Secret, as well as how the Secret is used in
your Pods, updates to existing Secret
objects are propagated automatically to
Pods that use the data. For more information, refer to Using Secrets as files from a Pod section.
Using a Secret
Secrets can be mounted as data volumes or exposed as environment variables to be used by a container in a Pod. Secrets can also be used by other parts of the system, without being directly exposed to the Pod. For example, Secrets can hold credentials that other parts of the system should use to interact with external systems on your behalf.
Secret volume sources are validated to ensure that the specified object reference actually points to an object of type Secret. Therefore, a Secret needs to be created before any Pods that depend on it.
If the Secret cannot be fetched (perhaps because it does not exist, or due to a temporary lack of connection to the API server) the kubelet periodically retries running that Pod. The kubelet also reports an Event for that Pod, including details of the problem fetching the Secret.
Optional Secrets
When you reference a Secret in a Pod, you can mark the Secret as optional, such as in the following example. If an optional Secret doesn't exist, Kubernetes ignores it.
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
optional: true
By default, Secrets are required. None of a Pod's containers will start until all non-optional Secrets are available.
If a Pod references a specific key in a non-optional Secret and that Secret does exist, but is missing the named key, the Pod fails during startup.
Using Secrets as files from a Pod
If you want to access data from a Secret in a Pod, one way to do that is to have Kubernetes make the value of that Secret be available as a file inside the filesystem of one or more of the Pod's containers.
For instructions, refer to Create a Pod that has access to the secret data through a Volume.
When a volume contains data from a Secret, and that Secret is updated, Kubernetes tracks this and updates the data in the volume, using an eventually-consistent approach.
Note:
A container using a Secret as a subPath volume mount does not receive automated Secret updates.The kubelet keeps a cache of the current keys and values for the Secrets that are used in
volumes for pods on that node.
You can configure the way that the kubelet detects changes from the cached values. The
configMapAndSecretChangeDetectionStrategy
field in the
kubelet configuration controls
which strategy the kubelet uses. The default strategy is Watch
.
Updates to Secrets can be either propagated by an API watch mechanism (the default), based on a cache with a defined time-to-live, or polled from the cluster API server on each kubelet synchronisation loop.
As a result, the total delay from the moment when the Secret is updated to the moment when new keys are projected to the Pod can be as long as the kubelet sync period + cache propagation delay, where the cache propagation delay depends on the chosen cache type (following the same order listed in the previous paragraph, these are: watch propagation delay, the configured cache TTL, or zero for direct polling).
Using Secrets as environment variables
To use a Secret in an environment variable in a Pod:
- For each container in your Pod specification, add an environment variable
for each Secret key that you want to use to the
env[].valueFrom.secretKeyRef
field. - Modify your image and/or command line so that the program looks for values in the specified environment variables.
For instructions, refer to Define container environment variables using Secret data.
It's important to note that the range of characters allowed for environment variable names in pods is restricted. If any keys do not meet the rules, those keys are not made available to your container, though the Pod is allowed to start.
Container image pull Secrets
If you want to fetch container images from a private repository, you need a way for the kubelet on each node to authenticate to that repository. You can configure image pull Secrets to make this possible. These Secrets are configured at the Pod level.
Using imagePullSecrets
The imagePullSecrets
field is a list of references to Secrets in the same namespace.
You can use an imagePullSecrets
to pass a Secret that contains a Docker (or other) image registry
password to the kubelet. The kubelet uses this information to pull a private image on behalf of your Pod.
See the PodSpec API
for more information about the imagePullSecrets
field.
Manually specifying an imagePullSecret
You can learn how to specify imagePullSecrets
from the
container images
documentation.
Arranging for imagePullSecrets to be automatically attached
You can manually create imagePullSecrets
, and reference these from a ServiceAccount. Any Pods
created with that ServiceAccount or created with that ServiceAccount by default, will get their
imagePullSecrets
field set to that of the service account.
See Add ImagePullSecrets to a service account
for a detailed explanation of that process.
Using Secrets with static Pods
You cannot use ConfigMaps or Secrets with static Pods.
Immutable Secrets
Kubernetes v1.21 [stable]
Kubernetes lets you mark specific Secrets (and ConfigMaps) as immutable. Preventing changes to the data of an existing Secret has the following benefits:
- protects you from accidental (or unwanted) updates that could cause applications outages
- (for clusters that extensively use Secrets - at least tens of thousands of unique Secret to Pod mounts), switching to immutable Secrets improves the performance of your cluster by significantly reducing load on kube-apiserver. The kubelet does not need to maintain a [watch] on any Secrets that are marked as immutable.
Marking a Secret as immutable
You can create an immutable Secret by setting the immutable
field to true
. For example,
apiVersion: v1
kind: Secret
metadata: ...
data: ...
immutable: true
You can also update any existing mutable Secret to make it immutable.
Note:
Once a Secret or ConfigMap is marked as immutable, it is not possible to revert this change nor to mutate the contents of thedata
field. You can only delete and recreate the Secret.
Existing Pods maintain a mount point to the deleted Secret - it is recommended to recreate
these pods.Information security for Secrets
Although ConfigMap and Secret work similarly, Kubernetes applies some additional protection for Secret objects.
Secrets often hold values that span a spectrum of importance, many of which can cause escalations within Kubernetes (e.g. service account tokens) and to external systems. Even if an individual app can reason about the power of the Secrets it expects to interact with, other apps within the same namespace can render those assumptions invalid.
A Secret is only sent to a node if a Pod on that node requires it.
For mounting Secrets into Pods, the kubelet stores a copy of the data into a tmpfs
so that the confidential data is not written to durable storage.
Once the Pod that depends on the Secret is deleted, the kubelet deletes its local copy
of the confidential data from the Secret.
There may be several containers in a Pod. By default, containers you define only have access to the default ServiceAccount and its related Secret. You must explicitly define environment variables or map a volume into a container in order to provide access to any other Secret.
There may be Secrets for several Pods on the same node. However, only the Secrets that a Pod requests are potentially visible within its containers. Therefore, one Pod does not have access to the Secrets of another Pod.
Configure least-privilege access to Secrets
To enhance the security measures around Secrets, Kubernetes provides a mechanism: you can
annotate a ServiceAccount as kubernetes.io/enforce-mountable-secrets: "true"
.
For more information, you can refer to the documentation about this annotation.
Warning:
Any containers that run withprivileged: true
on a node can access all
Secrets used on that node.What's next
- For guidelines to manage and improve the security of your Secrets, refer to Good practices for Kubernetes Secrets.
- Learn how to manage Secrets using
kubectl
- Learn how to manage Secrets using config file
- Learn how to manage Secrets using kustomize
- Read the API reference for
Secret
4 - Liveness, Readiness, and Startup Probes
Kubernetes has various types of probes:
Liveness probe
Liveness probes determine when to restart a container. For example, liveness probes could catch a deadlock when an application is running but unable to make progress.
If a container fails its liveness probe repeatedly, the kubelet restarts the container.
Liveness probes do not wait for readiness probes to succeed. If you want to wait before executing a liveness probe, you can either define initialDelaySeconds
or use a
startup probe.
Readiness probe
Readiness probes determine when a container is ready to start accepting traffic. This is useful when waiting for an application to perform time-consuming initial tasks, such as establishing network connections, loading files, and warming caches.
If the readiness probe returns a failed state, Kubernetes removes the pod from all matching service endpoints.
Readiness probes run on the container during its whole lifecycle.
Startup probe
A startup probe verifies whether the application within a container is started. 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.
If such a probe is configured, it disables liveness and readiness checks until it succeeds.
This type of probe is only executed at startup, unlike liveness and readiness probes, which are run periodically.
- Read more about the Configure Liveness, Readiness and Startup Probes.
5 - Resource Management for Pods and Containers
When you specify a Pod, you can optionally specify how much of each resource a container needs. The most common resources to specify are CPU and memory (RAM); there are others.
When you specify the resource request for containers in a Pod, the kube-scheduler uses this information to decide which node to place the Pod on. When you specify a resource limit for a container, the kubelet enforces those limits so that the running container is not allowed to use more of that resource than the limit you set. The kubelet also reserves at least the request amount of that system resource specifically for that container to use.
Requests and limits
If the node where a Pod is running has enough of a resource available, it's possible (and
allowed) for a container to use more resource than its request
for that resource specifies.
For example, if you set a memory
request of 256 MiB for a container, and that container is in
a Pod scheduled to a Node with 8GiB of memory and no other Pods, then the container can try to use
more RAM.
Limits are a different story. Both cpu
and memory
limits are applied by the kubelet (and
container runtime),
and are ultimately enforced by the kernel. On Linux nodes, the Linux kernel
enforces limits with
cgroups.
The behavior of cpu
and memory
limit enforcement is slightly different.
cpu
limits are enforced by CPU throttling. When a container approaches
its cpu
limit, the kernel will restrict access to the CPU corresponding to the
container's limit. Thus, a cpu
limit is a hard limit the kernel enforces.
Containers may not use more CPU than is specified in their cpu
limit.
memory
limits are enforced by the kernel with out of memory (OOM) kills. When
a container uses more than its memory
limit, the kernel may terminate it. However,
terminations only happen when the kernel detects memory pressure. Thus, a
container that over allocates memory may not be immediately killed. This means
memory
limits are enforced reactively. A container may use more memory than
its memory
limit, but if it does, it may get killed.
Note:
There is an alpha featureMemoryQoS
which attempts to add more preemptive
limit enforcement for memory (as opposed to reactive enforcement by the OOM
killer). However, this effort is
stalled
due to a potential livelock situation a memory hungry can cause.Note:
If you specify a limit for a resource, but do not specify any request, and no admission-time mechanism has applied a default request for that resource, then Kubernetes copies the limit you specified and uses it as the requested value for the resource.Resource types
CPU and memory are each a resource type. A resource type has a base unit. CPU represents compute processing and is specified in units of Kubernetes CPUs. Memory is specified in units of bytes. For Linux workloads, you can specify huge page resources. Huge pages are a Linux-specific feature where the node kernel allocates blocks of memory that are much larger than the default page size.
For example, on a system where the default page size is 4KiB, you could specify a limit,
hugepages-2Mi: 80Mi
. If the container tries allocating over 40 2MiB huge pages (a
total of 80 MiB), that allocation fails.
Note:
You cannot overcommithugepages-*
resources.
This is different from the memory
and cpu
resources.CPU and memory are collectively referred to as compute resources, or resources. Compute resources are measurable quantities that can be requested, allocated, and consumed. They are distinct from API resources. API resources, such as Pods and Services are objects that can be read and modified through the Kubernetes API server.
Resource requests and limits of Pod and container
For each container, you can specify resource limits and requests, including the following:
spec.containers[].resources.limits.cpu
spec.containers[].resources.limits.memory
spec.containers[].resources.limits.hugepages-<size>
spec.containers[].resources.requests.cpu
spec.containers[].resources.requests.memory
spec.containers[].resources.requests.hugepages-<size>
Although you can only specify requests and limits for individual containers, it is also useful to think about the overall resource requests and limits for a Pod. For a particular resource, a Pod resource request/limit is the sum of the resource requests/limits of that type for each container in the Pod.
Resource units in Kubernetes
CPU resource units
Limits and requests for CPU resources are measured in cpu units. In Kubernetes, 1 CPU unit is equivalent to 1 physical CPU core, or 1 virtual core, depending on whether the node is a physical host or a virtual machine running inside a physical machine.
Fractional requests are allowed. When you define a container with
spec.containers[].resources.requests.cpu
set to 0.5
, you are requesting half
as much CPU time compared to if you asked for 1.0
CPU.
For CPU resource units, the quantity expression 0.1
is equivalent to the
expression 100m
, which can be read as "one hundred millicpu". Some people say
"one hundred millicores", and this is understood to mean the same thing.
CPU resource is always specified as an absolute amount of resource, never as a relative amount. For example,
500m
CPU represents the roughly same amount of computing power whether that container
runs on a single-core, dual-core, or 48-core machine.
Note:
Kubernetes doesn't allow you to specify CPU resources with a precision finer than
1m
or 0.001
CPU. To avoid accidentally using an invalid CPU quantity, it's useful to specify CPU units using the milliCPU form
instead of the decimal form when using less than 1 CPU unit.
For example, you have a Pod that uses 5m
or 0.005
CPU and would like to decrease
its CPU resources. By using the decimal form, it's harder to spot that 0.0005
CPU
is an invalid value, while by using the milliCPU form, it's easier to spot that
0.5m
is an invalid value.
Memory resource units
Limits and requests for memory
are measured in bytes. You can express memory as
a plain integer or as a fixed-point number using one of these
quantity suffixes:
E, P, T, G, M, k. You can also use the power-of-two equivalents: Ei, Pi, Ti, Gi,
Mi, Ki. For example, the following represent roughly the same value:
128974848, 129e6, 129M, 128974848000m, 123Mi
Pay attention to the case of the suffixes. If you request 400m
of memory, this is a request
for 0.4 bytes. Someone who types that probably meant to ask for 400 mebibytes (400Mi
)
or 400 megabytes (400M
).
Container resources example
The following Pod has two containers. Both containers are defined with a request for 0.25 CPU and 64MiB (226 bytes) of memory. Each container has a limit of 0.5 CPU and 128MiB of memory. You can say the Pod has a request of 0.5 CPU and 128 MiB of memory, and a limit of 1 CPU and 256MiB of memory.
---
apiVersion: v1
kind: Pod
metadata:
name: frontend
spec:
containers:
- name: app
image: images.my-company.example/app:v4
resources:
requests:
memory: "64Mi"
cpu: "250m"
limits:
memory: "128Mi"
cpu: "500m"
- name: log-aggregator
image: images.my-company.example/log-aggregator:v6
resources:
requests:
memory: "64Mi"
cpu: "250m"
limits:
memory: "128Mi"
cpu: "500m"
How Pods with resource requests are scheduled
When you create a Pod, the Kubernetes scheduler selects a node for the Pod to run on. Each node has a maximum capacity for each of the resource types: the amount of CPU and memory it can provide for Pods. The scheduler ensures that, for each resource type, the sum of the resource requests of the scheduled containers is less than the capacity of the node. Note that although actual memory or CPU resource usage on nodes is very low, the scheduler still refuses to place a Pod on a node if the capacity check fails. This protects against a resource shortage on a node when resource usage later increases, for example, during a daily peak in request rate.
How Kubernetes applies resource requests and limits
When the kubelet starts a container as part of a Pod, the kubelet passes that container's requests and limits for memory and CPU to the container runtime.
On Linux, the container runtime typically configures kernel cgroups that apply and enforce the limits you defined.
- The CPU limit defines a hard ceiling on how much CPU time the container can use. During each scheduling interval (time slice), the Linux kernel checks to see if this limit is exceeded; if so, the kernel waits before allowing that cgroup to resume execution.
- The CPU request typically defines a weighting. If several different containers (cgroups) want to run on a contended system, workloads with larger CPU requests are allocated more CPU time than workloads with small requests.
- The memory request is mainly used during (Kubernetes) Pod scheduling. On a node that uses
cgroups v2, the container runtime might use the memory request as a hint to set
memory.min
andmemory.low
. - The memory limit defines a memory limit for that cgroup. If the container tries to allocate more memory than this limit, the Linux kernel out-of-memory subsystem activates and, typically, intervenes by stopping one of the processes in the container that tried to allocate memory. If that process is the container's PID 1, and the container is marked as restartable, Kubernetes restarts the container.
- The memory limit for the Pod or container can also apply to pages in memory backed
volumes, such as an
emptyDir
. The kubelet trackstmpfs
emptyDir volumes as container memory use, rather than as local ephemeral storage. When using memory backedemptyDir
, be sure to check the notes below.
If a container exceeds its memory request and the node that it runs on becomes short of memory overall, it is likely that the Pod the container belongs to will be evicted.
A container might or might not be allowed to exceed its CPU limit for extended periods of time. However, container runtimes don't terminate Pods or containers for excessive CPU usage.
To determine whether a container cannot be scheduled or is being killed due to resource limits, see the Troubleshooting section.
Monitoring compute & memory resource usage
The kubelet reports the resource usage of a Pod as part of the Pod
status
.
If optional tools for monitoring are available in your cluster, then Pod resource usage can be retrieved either from the Metrics API directly or from your monitoring tools.
Considerations for memory backed emptyDir
volumes
Caution:
If you do not specify asizeLimit
for an emptyDir
volume, that volume may
consume up to that pod's memory limit (Pod.spec.containers[].resources.limits.memory
).
If you do not set a memory limit, the pod has no upper bound on memory consumption,
and can consume all available memory on the node. Kubernetes schedules pods based
on resource requests (Pod.spec.containers[].resources.requests
) and will not
consider memory usage above the request when deciding if another pod can fit on
a given node. This can result in a denial of service and cause the OS to do
out-of-memory (OOM) handling. It is possible to create any number of emptyDir
s
that could potentially consume all available memory on the node, making OOM
more likely.From the perspective of memory management, there are some similarities between
when a process uses memory as a work area and when using memory-backed
emptyDir
. But when using memory as a volume, like memory-backed emptyDir
,
there are additional points below that you should be careful of:
- Files stored on a memory-backed volume are almost entirely managed by the user application. Unlike when used as a work area for a process, you can not rely on things like language-level garbage collection.
- The purpose of writing files to a volume is to save data or pass it between applications. Neither Kubernetes nor the OS may automatically delete files from a volume, so memory used by those files can not be reclaimed when the system or the pod are under memory pressure.
- A memory-backed
emptyDir
is useful because of its performance, but memory is generally much smaller in size and much higher in cost than other storage media, such as disks or SSDs. Using large amounts of memory foremptyDir
volumes may affect the normal operation of your pod or of the whole node, so should be used carefully.
If you are administering a cluster or namespace, you can also set
ResourceQuota that limits memory use;
you may also want to define a LimitRange
for additional enforcement.
If you specify a spec.containers[].resources.limits.memory
for each Pod,
then the maximum size of an emptyDir
volume will be the pod's memory limit.
As an alternative, a cluster administrator can enforce size limits for
emptyDir
volumes in new Pods using a policy mechanism such as
ValidationAdmissionPolicy.
Local ephemeral storage
Kubernetes v1.25 [stable]
Nodes have local ephemeral storage, backed by locally-attached writeable devices or, sometimes, by RAM. "Ephemeral" means that there is no long-term guarantee about durability.
Pods use ephemeral local storage for scratch space, caching, and for logs.
The kubelet can provide scratch space to Pods using local ephemeral storage to
mount emptyDir
volumes into containers.
The kubelet also uses this kind of storage to hold node-level container logs, container images, and the writable layers of running containers.
Caution:
If a node fails, the data in its ephemeral storage can be lost. Your applications cannot expect any performance SLAs (disk IOPS for example) from local ephemeral storage.Note:
To make the resource quota work on ephemeral-storage, two things need to be done:
- An admin sets the resource quota for ephemeral-storage in a namespace.
- A user needs to specify limits for the ephemeral-storage resource in the Pod spec.
If the user doesn't specify the ephemeral-storage resource limit in the Pod spec, the resource quota is not enforced on ephemeral-storage.
Kubernetes lets you track, reserve and limit the amount of ephemeral local storage a Pod can consume.
Configurations for local ephemeral storage
Kubernetes supports two ways to configure local ephemeral storage on a node:
In this configuration, you place all different kinds of ephemeral local data
(emptyDir
volumes, writeable layers, container images, logs) into one filesystem.
The most effective way to configure the kubelet means dedicating this filesystem
to Kubernetes (kubelet) data.
The kubelet also writes node-level container logs and treats these similarly to ephemeral local storage.
The kubelet writes logs to files inside its configured log directory (/var/log
by default); and has a base directory for other locally stored data
(/var/lib/kubelet
by default).
Typically, both /var/lib/kubelet
and /var/log
are on the system root filesystem,
and the kubelet is designed with that layout in mind.
Your node can have as many other filesystems, not used for Kubernetes, as you like.
You have a filesystem on the node that you're using for ephemeral data that
comes from running Pods: logs, and emptyDir
volumes. You can use this filesystem
for other data (for example: system logs not related to Kubernetes); it can even
be the root filesystem.
The kubelet also writes node-level container logs into the first filesystem, and treats these similarly to ephemeral local storage.
You also use a separate filesystem, backed by a different logical storage device. In this configuration, the directory where you tell the kubelet to place container image layers and writeable layers is on this second filesystem.
The first filesystem does not hold any image layers or writeable layers.
Your node can have as many other filesystems, not used for Kubernetes, as you like.
The kubelet can measure how much local storage it is using. It does this provided that you have set up the node using one of the supported configurations for local ephemeral storage.
If you have a different configuration, then the kubelet does not apply resource limits for ephemeral local storage.
Note:
The kubelet trackstmpfs
emptyDir volumes as container memory use, rather
than as local ephemeral storage.Note:
The kubelet will only track the root filesystem for ephemeral storage. OS layouts that mount a separate disk to/var/lib/kubelet
or /var/lib/containers
will not report ephemeral storage correctly.Setting requests and limits for local ephemeral storage
You can specify ephemeral-storage
for managing local ephemeral storage. Each
container of a Pod can specify either or both of the following:
spec.containers[].resources.limits.ephemeral-storage
spec.containers[].resources.requests.ephemeral-storage
Limits and requests for ephemeral-storage
are measured in byte quantities.
You can express storage as a plain integer or as a fixed-point number using one of these suffixes:
E, P, T, G, M, k. You can also use the power-of-two equivalents: Ei, Pi, Ti, Gi,
Mi, Ki. For example, the following quantities all represent roughly the same value:
128974848
129e6
129M
123Mi
Pay attention to the case of the suffixes. If you request 400m
of ephemeral-storage, this is a request
for 0.4 bytes. Someone who types that probably meant to ask for 400 mebibytes (400Mi
)
or 400 megabytes (400M
).
In the following example, the Pod has two containers. Each container has a request of
2GiB of local ephemeral storage. Each container has a limit of 4GiB of local ephemeral
storage. Therefore, the Pod has a request of 4GiB of local ephemeral storage, and
a limit of 8GiB of local ephemeral storage. 500Mi of that limit could be
consumed by the emptyDir
volume.
apiVersion: v1
kind: Pod
metadata:
name: frontend
spec:
containers:
- name: app
image: images.my-company.example/app:v4
resources:
requests:
ephemeral-storage: "2Gi"
limits:
ephemeral-storage: "4Gi"
volumeMounts:
- name: ephemeral
mountPath: "/tmp"
- name: log-aggregator
image: images.my-company.example/log-aggregator:v6
resources:
requests:
ephemeral-storage: "2Gi"
limits:
ephemeral-storage: "4Gi"
volumeMounts:
- name: ephemeral
mountPath: "/tmp"
volumes:
- name: ephemeral
emptyDir:
sizeLimit: 500Mi
How Pods with ephemeral-storage requests are scheduled
When you create a Pod, the Kubernetes scheduler selects a node for the Pod to run on. Each node has a maximum amount of local ephemeral storage it can provide for Pods. For more information, see Node Allocatable.
The scheduler ensures that the sum of the resource requests of the scheduled containers is less than the capacity of the node.
Ephemeral storage consumption management
If the kubelet is managing local ephemeral storage as a resource, then the kubelet measures storage use in:
emptyDir
volumes, except tmpfsemptyDir
volumes- directories holding node-level logs
- writeable container layers
If a Pod is using more ephemeral storage than you allow it to, the kubelet sets an eviction signal that triggers Pod eviction.
For container-level isolation, if a container's writable layer and log usage exceeds its storage limit, the kubelet marks the Pod for eviction.
For pod-level isolation the kubelet works out an overall Pod storage limit by
summing the limits for the containers in that Pod. In this case, if the sum of
the local ephemeral storage usage from all containers and also the Pod's emptyDir
volumes exceeds the overall Pod storage limit, then the kubelet also marks the Pod
for eviction.
Caution:
If the kubelet is not measuring local ephemeral storage, then a Pod that exceeds its local storage limit will not be evicted for breaching local storage resource limits.
However, if the filesystem space for writeable container layers, node-level logs,
or emptyDir
volumes falls low, the node
taints itself as short on local storage
and this taint triggers eviction for any Pods that don't specifically tolerate the taint.
See the supported configurations for ephemeral local storage.
The kubelet supports different ways to measure Pod storage use:
The kubelet performs regular, scheduled checks that scan each
emptyDir
volume, container log directory, and writeable container layer.
The scan measures how much space is used.
Note:
In this mode, the kubelet does not track open file descriptors for deleted files.
If you (or a container) create a file inside an emptyDir
volume,
something then opens that file, and you delete the file while it is
still open, then the inode for the deleted file stays until you close
that file but the kubelet does not categorize the space as in use.
Kubernetes v1.31 [beta]
(enabled by default: false)Project quotas are an operating-system level feature for managing
storage use on filesystems. With Kubernetes, you can enable project
quotas for monitoring storage use. Make sure that the filesystem
backing the emptyDir
volumes, on the node, provides project quota support.
For example, XFS and ext4fs offer project quotas.
Note:
Project quotas let you monitor storage use; they do not enforce limits.Kubernetes uses project IDs starting from 1048576
. The IDs in use are
registered in /etc/projects
and /etc/projid
. If project IDs in
this range are used for other purposes on the system, those project
IDs must be registered in /etc/projects
and /etc/projid
so that
Kubernetes does not use them.
Quotas are faster and more accurate than directory scanning. When a directory is assigned to a project, all files created under a directory are created in that project, and the kernel merely has to keep track of how many blocks are in use by files in that project. If a file is created and deleted, but has an open file descriptor, it continues to consume space. Quota tracking records that space accurately whereas directory scans overlook the storage used by deleted files.
To use quotas to track a pod's resource usage, the pod must be in a user namespace. Within user namespaces, the kernel restricts changes to projectIDs on the filesystem, ensuring the reliability of storage metrics calculated by quotas.
If you want to use project quotas, you should:
Enable the
LocalStorageCapacityIsolationFSQuotaMonitoring=true
feature gate using thefeatureGates
field in the kubelet configuration.Ensure the
UserNamespacesSupport
feature gate is enabled, and that the kernel, CRI implementation and OCI runtime support user namespaces.Ensure that the root filesystem (or optional runtime filesystem) has project quotas enabled. All XFS filesystems support project quotas. For ext4 filesystems, you need to enable the project quota tracking feature while the filesystem is not mounted.
# For ext4, with /dev/block-device not mounted sudo tune2fs -O project -Q prjquota /dev/block-device
Ensure that the root filesystem (or optional runtime filesystem) is mounted with project quotas enabled. For both XFS and ext4fs, the mount option is named
prjquota
.
If you don't want to use project quotas, you should:
- Disable the
LocalStorageCapacityIsolationFSQuotaMonitoring
feature gate using thefeatureGates
field in the kubelet configuration.
Extended resources
Extended resources are fully-qualified resource names outside the
kubernetes.io
domain. They allow cluster operators to advertise and users to
consume the non-Kubernetes-built-in resources.
There are two steps required to use Extended Resources. First, the cluster operator must advertise an Extended Resource. Second, users must request the Extended Resource in Pods.
Managing extended resources
Node-level extended resources
Node-level extended resources are tied to nodes.
Device plugin managed resources
See Device Plugin for how to advertise device plugin managed resources on each node.
Other resources
To advertise a new node-level extended resource, the cluster operator can
submit a PATCH
HTTP request to the API server to specify the available
quantity in the status.capacity
for a node in the cluster. After this
operation, the node's status.capacity
will include a new resource. The
status.allocatable
field is updated automatically with the new resource
asynchronously by the kubelet.
Because the scheduler uses the node's status.allocatable
value when
evaluating Pod fitness, the scheduler only takes account of the new value after
that asynchronous update. There may be a short delay between patching the
node capacity with a new resource and the time when the first Pod that requests
the resource can be scheduled on that node.
Example:
Here is an example showing how to use curl
to form an HTTP request that
advertises five "example.com/foo" resources on node k8s-node-1
whose master
is k8s-master
.
curl --header "Content-Type: application/json-patch+json" \
--request PATCH \
--data '[{"op": "add", "path": "/status/capacity/example.com~1foo", "value": "5"}]' \
http://k8s-master:8080/api/v1/nodes/k8s-node-1/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.Cluster-level extended resources
Cluster-level extended resources are not tied to nodes. They are usually managed by scheduler extenders, which handle the resource consumption and resource quota.
You can specify the extended resources that are handled by scheduler extenders in scheduler configuration
Example:
The following configuration for a scheduler policy indicates that the cluster-level extended resource "example.com/foo" is handled by the scheduler extender.
- The scheduler sends a Pod to the scheduler extender only if the Pod requests "example.com/foo".
- The
ignoredByScheduler
field specifies that the scheduler does not check the "example.com/foo" resource in itsPodFitsResources
predicate.
{
"kind": "Policy",
"apiVersion": "v1",
"extenders": [
{
"urlPrefix":"<extender-endpoint>",
"bindVerb": "bind",
"managedResources": [
{
"name": "example.com/foo",
"ignoredByScheduler": true
}
]
}
]
}
Consuming extended resources
Users can consume extended resources in Pod specs like CPU and memory. The scheduler takes care of the resource accounting so that no more than the available amount is simultaneously allocated to Pods.
The API server restricts quantities of extended resources to whole numbers.
Examples of valid quantities are 3
, 3000m
and 3Ki
. Examples of
invalid quantities are 0.5
and 1500m
(because 1500m
would result in 1.5
).
Note:
Extended resources replace Opaque Integer Resources. Users can use any domain name prefix other thankubernetes.io
which is reserved.To consume an extended resource in a Pod, include the resource name as a key
in the spec.containers[].resources.limits
map in the container spec.
Note:
Extended resources cannot be overcommitted, so request and limit must be equal if both are present in a container spec.A Pod is scheduled only if all of the resource requests are satisfied, including
CPU, memory and any extended resources. The Pod remains in the PENDING
state
as long as the resource request cannot be satisfied.
Example:
The Pod below requests 2 CPUs and 1 "example.com/foo" (an extended resource).
apiVersion: v1
kind: Pod
metadata:
name: my-pod
spec:
containers:
- name: my-container
image: myimage
resources:
requests:
cpu: 2
example.com/foo: 1
limits:
example.com/foo: 1
PID limiting
Process ID (PID) limits allow for the configuration of a kubelet to limit the number of PIDs that a given Pod can consume. See PID Limiting for information.
Troubleshooting
My Pods are pending with event message FailedScheduling
If the scheduler cannot find any node where a Pod can fit, the Pod remains
unscheduled until a place can be found. An
Event is produced
each time the scheduler fails to find a place for the Pod. You can use kubectl
to view the events for a Pod; for example:
kubectl describe pod frontend | grep -A 9999999999 Events
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedScheduling 23s default-scheduler 0/42 nodes available: insufficient cpu
In the preceding example, the Pod named "frontend" fails to be scheduled due to insufficient CPU resource on any node. Similar error messages can also suggest failure due to insufficient memory (PodExceedsFreeMemory). In general, if a Pod is pending with a message of this type, there are several things to try:
- Add more nodes to the cluster.
- Terminate unneeded Pods to make room for pending Pods.
- Check that the Pod is not larger than all the nodes. For example, if all the
nodes have a capacity of
cpu: 1
, then a Pod with a request ofcpu: 1.1
will never be scheduled. - Check for node taints. If most of your nodes are tainted, and the new Pod does not tolerate that taint, the scheduler only considers placements onto the remaining nodes that don't have that taint.
You can check node capacities and amounts allocated with the
kubectl describe nodes
command. For example:
kubectl describe nodes e2e-test-node-pool-4lw4
Name: e2e-test-node-pool-4lw4
[ ... lines removed for clarity ...]
Capacity:
cpu: 2
memory: 7679792Ki
pods: 110
Allocatable:
cpu: 1800m
memory: 7474992Ki
pods: 110
[ ... lines removed for clarity ...]
Non-terminated Pods: (5 in total)
Namespace Name CPU Requests CPU Limits Memory Requests Memory Limits
--------- ---- ------------ ---------- --------------- -------------
kube-system fluentd-gcp-v1.38-28bv1 100m (5%) 0 (0%) 200Mi (2%) 200Mi (2%)
kube-system kube-dns-3297075139-61lj3 260m (13%) 0 (0%) 100Mi (1%) 170Mi (2%)
kube-system kube-proxy-e2e-test-... 100m (5%) 0 (0%) 0 (0%) 0 (0%)
kube-system monitoring-influxdb-grafana-v4-z1m12 200m (10%) 200m (10%) 600Mi (8%) 600Mi (8%)
kube-system node-problem-detector-v0.1-fj7m3 20m (1%) 200m (10%) 20Mi (0%) 100Mi (1%)
Allocated resources:
(Total limits may be over 100 percent, i.e., overcommitted.)
CPU Requests CPU Limits Memory Requests Memory Limits
------------ ---------- --------------- -------------
680m (34%) 400m (20%) 920Mi (11%) 1070Mi (13%)
In the preceding output, you can see that if a Pod requests more than 1.120 CPUs or more than 6.23Gi of memory, that Pod will not fit on the node.
By looking at the “Pods” section, you can see which Pods are taking up space on the node.
The amount of resources available to Pods is less than the node capacity because
system daemons use a portion of the available resources. Within the Kubernetes API,
each Node has a .status.allocatable
field
(see NodeStatus
for details).
The .status.allocatable
field describes the amount of resources that are available
to Pods on that node (for example: 15 virtual CPUs and 7538 MiB of memory).
For more information on node allocatable resources in Kubernetes, see
Reserve Compute Resources for System Daemons.
You can configure resource quotas to limit the total amount of resources that a namespace can consume. Kubernetes enforces quotas for objects in particular namespace when there is a ResourceQuota in that namespace. For example, if you assign specific namespaces to different teams, you can add ResourceQuotas into those namespaces. Setting resource quotas helps to prevent one team from using so much of any resource that this over-use affects other teams.
You should also consider what access you grant to that namespace: full write access to a namespace allows someone with that access to remove any resource, including a configured ResourceQuota.
My container is terminated
Your container might get terminated because it is resource-starved. To check
whether a container is being killed because it is hitting a resource limit, call
kubectl describe pod
on the Pod of interest:
kubectl describe pod simmemleak-hra99
The output is similar to:
Name: simmemleak-hra99
Namespace: default
Image(s): saadali/simmemleak
Node: kubernetes-node-tf0f/10.240.216.66
Labels: name=simmemleak
Status: Running
Reason:
Message:
IP: 10.244.2.75
Containers:
simmemleak:
Image: saadali/simmemleak:latest
Limits:
cpu: 100m
memory: 50Mi
State: Running
Started: Tue, 07 Jul 2019 12:54:41 -0700
Last State: Terminated
Reason: OOMKilled
Exit Code: 137
Started: Fri, 07 Jul 2019 12:54:30 -0700
Finished: Fri, 07 Jul 2019 12:54:33 -0700
Ready: False
Restart Count: 5
Conditions:
Type Status
Ready False
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Scheduled 42s default-scheduler Successfully assigned simmemleak-hra99 to kubernetes-node-tf0f
Normal Pulled 41s kubelet Container image "saadali/simmemleak:latest" already present on machine
Normal Created 41s kubelet Created container simmemleak
Normal Started 40s kubelet Started container simmemleak
Normal Killing 32s kubelet Killing container with id ead3fb35-5cf5-44ed-9ae1-488115be66c6: Need to kill Pod
In the preceding example, the Restart Count: 5
indicates that the simmemleak
container in the Pod was terminated and restarted five times (so far).
The OOMKilled
reason shows that the container tried to use more memory than its limit.
Your next step might be to check the application code for a memory leak. If you find that the application is behaving how you expect, consider setting a higher memory limit (and possibly request) for that container.
What's next
- Get hands-on experience assigning Memory resources to containers and Pods.
- Get hands-on experience assigning CPU resources to containers and Pods.
- Read how the API reference defines a container and its resource requirements
- Read about project quotas in XFS
- Read more about the kube-scheduler configuration reference (v1)
- Read more about Quality of Service classes for Pods
6 - Organizing Cluster Access Using kubeconfig Files
Use kubeconfig files to organize information about clusters, users, namespaces, and
authentication mechanisms. The kubectl
command-line tool uses kubeconfig files to
find the information it needs to choose a cluster and communicate with the API server
of a cluster.
Note:
A file that is used to configure access to clusters is called a kubeconfig file. This is a generic way of referring to configuration files. It does not mean that there is a file namedkubeconfig
.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.By default, kubectl
looks for a file named config
in the $HOME/.kube
directory.
You can specify other kubeconfig files by setting the KUBECONFIG
environment
variable or by setting the
--kubeconfig
flag.
For step-by-step instructions on creating and specifying kubeconfig files, see Configure Access to Multiple Clusters.
Supporting multiple clusters, users, and authentication mechanisms
Suppose you have several clusters, and your users and components authenticate in a variety of ways. For example:
- A running kubelet might authenticate using certificates.
- A user might authenticate using tokens.
- Administrators might have sets of certificates that they provide to individual users.
With kubeconfig files, you can organize your clusters, users, and namespaces. You can also define contexts to quickly and easily switch between clusters and namespaces.
Context
A context element in a kubeconfig file is used to group access parameters
under a convenient name. Each context has three parameters: cluster, namespace, and user.
By default, the kubectl
command-line tool uses parameters from
the current context to communicate with the cluster.
To choose the current context:
kubectl config use-context
The KUBECONFIG environment variable
The KUBECONFIG
environment variable holds a list of kubeconfig files.
For Linux and Mac, the list is colon-delimited. For Windows, the list
is semicolon-delimited. The KUBECONFIG
environment variable is not
required. If the KUBECONFIG
environment variable doesn't exist,
kubectl
uses the default kubeconfig file, $HOME/.kube/config
.
If the KUBECONFIG
environment variable does exist, kubectl
uses
an effective configuration that is the result of merging the files
listed in the KUBECONFIG
environment variable.
Merging kubeconfig files
To see your configuration, enter this command:
kubectl config view
As described previously, the output might be from a single kubeconfig file, or it might be the result of merging several kubeconfig files.
Here are the rules that kubectl
uses when it merges kubeconfig files:
If the
--kubeconfig
flag is set, use only the specified file. Do not merge. Only one instance of this flag is allowed.Otherwise, if the
KUBECONFIG
environment variable is set, use it as a list of files that should be merged. Merge the files listed in theKUBECONFIG
environment variable according to these rules:- Ignore empty filenames.
- Produce errors for files with content that cannot be deserialized.
- The first file to set a particular value or map key wins.
- Never change the value or map key.
Example: Preserve the context of the first file to set
current-context
. Example: If two files specify ared-user
, use only values from the first file'sred-user
. Even if the second file has non-conflicting entries underred-user
, discard them.
For an example of setting the
KUBECONFIG
environment variable, see Setting the KUBECONFIG environment variable.Otherwise, use the default kubeconfig file,
$HOME/.kube/config
, with no merging.Determine the context to use based on the first hit in this chain:
- Use the
--context
command-line flag if it exists. - Use the
current-context
from the merged kubeconfig files.
An empty context is allowed at this point.
- Use the
Determine the cluster and user. At this point, there might or might not be a context. Determine the cluster and user based on the first hit in this chain, which is run twice: once for user and once for cluster:
- Use a command-line flag if it exists:
--user
or--cluster
. - If the context is non-empty, take the user or cluster from the context.
The user and cluster can be empty at this point.
- Use a command-line flag if it exists:
Determine the actual cluster information to use. At this point, there might or might not be cluster information. Build each piece of the cluster information based on this chain; the first hit wins:
- Use command line flags if they exist:
--server
,--certificate-authority
,--insecure-skip-tls-verify
. - If any cluster information attributes exist from the merged kubeconfig files, use them.
- If there is no server location, fail.
- Use command line flags if they exist:
Determine the actual user information to use. Build user information using the same rules as cluster information, except allow only one authentication technique per user:
- Use command line flags if they exist:
--client-certificate
,--client-key
,--username
,--password
,--token
. - Use the
user
fields from the merged kubeconfig files. - If there are two conflicting techniques, fail.
- Use command line flags if they exist:
For any information still missing, use default values and potentially prompt for authentication information.
File references
File and path references in a kubeconfig file are relative to the location of the kubeconfig file.
File references on the command line are relative to the current working directory.
In $HOME/.kube/config
, relative paths are stored relatively, and absolute paths
are stored absolutely.
Proxy
You can configure kubectl
to use a proxy per cluster using proxy-url
in your kubeconfig file, like this:
apiVersion: v1
kind: Config
clusters:
- cluster:
proxy-url: http://proxy.example.org:3128
server: https://k8s.example.org/k8s/clusters/c-xxyyzz
name: development
users:
- name: developer
contexts:
- context:
name: development
What's next
7 - Resource Management for Windows nodes
This page outlines the differences in how resources are managed between Linux and Windows.
On Linux nodes, cgroups are used as a pod boundary for resource control. Containers are created within that boundary for network, process and file system isolation. The Linux cgroup APIs can be used to gather CPU, I/O, and memory use statistics.
In contrast, Windows uses a job object per container with a system namespace filter to contain all processes in a container and provide logical isolation from the host. (Job objects are a Windows process isolation mechanism and are different from what Kubernetes refers to as a Job).
There is no way to run a Windows container without the namespace filtering in place. This means that system privileges cannot be asserted in the context of the host, and thus privileged containers are not available on Windows. Containers cannot assume an identity from the host because the Security Account Manager (SAM) is separate.
Memory management
Windows does not have an out-of-memory process killer as Linux does. Windows always treats all user-mode memory allocations as virtual, and pagefiles are mandatory.
Windows nodes do not overcommit memory for processes. The net effect is that Windows won't reach out of memory conditions the same way Linux does, and processes page to disk instead of being subject to out of memory (OOM) termination. If memory is over-provisioned and all physical memory is exhausted, then paging can slow down performance.
CPU management
Windows can limit the amount of CPU time allocated for different processes but cannot guarantee a minimum amount of CPU time.
On Windows, the kubelet supports a command-line flag to set the
scheduling priority of the
kubelet process: --windows-priorityclass
. This flag allows the kubelet process to get
more CPU time slices when compared to other processes running on the Windows host.
More information on the allowable values and their meaning is available at
Windows Priority Classes.
To ensure that running Pods do not starve the kubelet of CPU cycles, set this flag to ABOVE_NORMAL_PRIORITY_CLASS
or above.
Resource reservation
To account for memory and CPU used by the operating system, the container runtime, and by
Kubernetes host processes such as the kubelet, you can (and should) reserve
memory and CPU resources with the --kube-reserved
and/or --system-reserved
kubelet flags.
On Windows these values are only used to calculate the node's
allocatable resources.
Caution:
As you deploy workloads, set resource memory and CPU limits on containers.
This also subtracts from NodeAllocatable
and helps the cluster-wide scheduler in determining which pods to place on which nodes.
Scheduling pods without limits may over-provision the Windows nodes and in extreme cases can cause the nodes to become unhealthy.
On Windows, a good practice is to reserve at least 2GiB of memory.
To determine how much CPU to reserve, identify the maximum pod density for each node and monitor the CPU usage of the system services running there, then choose a value that meets your workload needs.