This the multi-page printable view of this section. Click here to print.
Configuration
- 1: Configuration Best Practices
- 2: ConfigMaps
- 3: Secrets
- 4: Managing Resources for Containers
- 5: Organizing Cluster Access Using kubeconfig Files
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.
"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: 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: 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.
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.
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 byte sequences 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.
spec
of a static Pod cannot refer to a ConfigMap
or any other API objects.
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.
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.
This feature is controlled by the ImmutableEphemeralVolumes
feature gate.
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 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 confidential data to nonvolatile storage.
Secrets are similar to ConfigMaps but are specifically intended to hold confidential data.
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 that restrict reading data in Secrets (including via indirect means).
- Where appropriate, also use mechanisms such as RBAC to limit which principals are allowed to create new Secrets or replace existing ones.
Overview of Secrets
To use a Secret, a Pod needs to reference the Secret. A Secret can be used with a Pod in three ways:
- As files in a volume mounted on one or more of its containers.
- As container environment variable.
- By the kubelet when pulling images for the Pod.
The Kubernetes control plane also uses Secrets; for example, bootstrap token Secrets are a mechanism to help automate node registration.
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.
Types of Secret
When creating a Secret, you can specify its type using the type
field of
a Secret resource, or certain equivalent kubectl
command line flags (if available).
The type
of a Secret is used to facilitate programmatic handling of different
kinds of confidential data.
Kubernetes provides several builtin types for some common usage scenarios. These types vary in terms of the validations performed and the constraints Kubernetes imposes on them.
Builtin Type | Usage |
---|---|
Opaque |
arbitrary user-defined data |
kubernetes.io/service-account-token |
service account 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 builtin types, you must meet all the requirements defined
for that type.
Opaque secrets
Opaque
is the default Secret type if omitted from a Secret configuration file.
When you create a Secret using kubectl
, you will 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 we have created an empty Secret.
Service account token Secrets
A kubernetes.io/service-account-token
type of Secret is used to store a
token that identifies a service account. When using this Secret type, you need
to ensure that the kubernetes.io/service-account.name
annotation is set to an
existing service account name. 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 set to actual token content.
The following example configuration declares a service account 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:
# You can include additional key value pairs as you do with Opaque Secrets
extra: YmFyCg==
When creating a Pod
, Kubernetes automatically creates a service account Secret
and automatically modifies your Pod to use this Secret. The service account token
Secret contains credentials for accessing the API.
The automatic creation and use of API credentials can be disabled or overridden if desired. However, if all you need to do is securely access the API server, this is the recommended workflow.
See the ServiceAccount
documentation for more information on how service accounts work.
You can also check the automountServiceAccountToken
field and the
serviceAccountName
field of the
Pod
for information on referencing service account from Pods.
Docker config Secrets
You can use one of the following type
values to create a Secret to
store the credentials for accessing a Docker registry for images.
kubernetes.io/dockercfg
kubernetes.io/dockerconfigjson
The kubernetes.io/dockercfg
type is reserved to store a serialized
~/.dockercfg
which is the legacy format for configuring Docker command line.
When using this Secret type, you have to ensure the Secret data
field
contains a .dockercfg
key whose value is content of a ~/.dockercfg
file
encoded in the base64 format.
The kubernetes.io/dockerconfigjson
type is designed for storing a serialized
JSON that follows the same format rules as the ~/.docker/config.json
file
which is a new format for ~/.dockercfg
.
When using this Secret type, the data
field of the Secret object must
contain a .dockerconfigjson
key, in which the content for the
~/.docker/config.json
file is provided as a base64 encoded string.
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: |
"<base64 encoded ~/.dockercfg file>"
stringData
field instead.
When you create these types of Secrets using a manifest, the API
server checks whether the expected key does 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.
When you do not have a Docker config file, or you want to use kubectl
to create a Docker registry Secret, you can do:
kubectl create secret docker-registry secret-tiger-docker \
--docker-username=tiger \
--docker-password=pass113 \
--docker-email=tiger@acme.com \
--docker-server=my-registry.example:5000
This command creates a Secret of type kubernetes.io/dockerconfigjson
.
If you dump the .dockerconfigjson
content from the data
field, you will
get the following JSON content which is a valid Docker configuration created
on the fly:
{
"apiVersion": "v1",
"data": {
".dockerconfigjson": "eyJhdXRocyI6eyJteS1yZWdpc3RyeTo1MDAwIjp7InVzZXJuYW1lIjoidGlnZXIiLCJwYXNzd29yZCI6InBhc3MxMTMiLCJlbWFpbCI6InRpZ2VyQGFjbWUuY29tIiwiYXV0aCI6ImRHbG5aWEk2Y0dGemN6RXhNdz09In19fQ=="
},
"kind": "Secret",
"metadata": {
"creationTimestamp": "2021-07-01T07:30:59Z",
"name": "secret-tiger-docker",
"namespace": "default",
"resourceVersion": "566718",
"uid": "e15c1d7b-9071-4100-8681-f3a7a2ce89ca"
},
"type": "kubernetes.io/dockerconfigjson"
}
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 authentication;password
: the password or token for authentication.
Both values for the above two keys are base64 encoded strings. You can, of
course, provide the clear text content using the stringData
for Secret
creation.
The following YAML is an example config for a basic authentication Secret:
apiVersion: v1
kind: Secret
metadata:
name: secret-basic-auth
type: kubernetes.io/basic-auth
stringData:
username: admin
password: t0p-Secret
The basic authentication Secret type is provided only for user's convenience.
You can create an Opaque
for credentials used for basic authentication.
However, using the builtin Secret type helps unify the formats of your credentials
and the API server does verify if the required keys are provided in a Secret
configuration.
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 YAML is an example config for a SSH authentication Secret:
apiVersion: v1
kind: Secret
metadata:
name: secret-ssh-auth
type: kubernetes.io/ssh-auth
data:
# the data is abbreviated in this example
ssh-privatekey: |
MIIEpQIBAAKCAQEAulqb/Y ...
The SSH authentication Secret type is provided only for user's convenience.
You can create an Opaque
for credentials used for SSH authentication.
However, using the builtin Secret type helps unify the formats of your credentials
and the API server does verify if the required keys are provided in a Secret
configuration.
known_hosts
file added to a
ConfigMap.
TLS secrets
Kubernetes provides a builtin Secret type kubernetes.io/tls
for storing
a certificate and its associated key that are typically used for TLS . This
data is primarily used with TLS termination of the Ingress resource, but may
be used with other resources or directly by a 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.
The following YAML contains an example config for a TLS Secret:
apiVersion: v1
kind: Secret
metadata:
name: secret-tls
type: kubernetes.io/tls
data:
# the data is abbreviated in this example
tls.crt: |
MIIC2DCCAcCgAwIBAgIBATANBgkqh ...
tls.key: |
MIIEpgIBAAKCAQEA7yn3bRHQ5FHMQ ...
The TLS Secret type is provided for user's convenience. You can create an Opaque
for credentials used for TLS server and/or client. However, using the builtin Secret
type helps ensure the consistency of Secret format in your project; the API server
does verify if the required keys are provided in a Secret configuration.
When creating a TLS Secret using kubectl
, you can use the tls
subcommand
as shown in the following example:
kubectl create secret tls my-tls-secret \
--cert=path/to/cert/file \
--key=path/to/key/file
The public/private key pair must exist beforehand. The public key certificate
for --cert
must be .PEM encoded (Base64-encoded DER format), and match the
given private key for --key
.
The private key must be in what is commonly called PEM private key format,
unencrypted. In both cases, the initial and the last lines from PEM (for
example, --------BEGIN CERTIFICATE-----
and -------END CERTIFICATE----
for
a certificate) are not included.
Bootstrap token Secrets
A bootstrap token Secret can be created by explicitly specifying the Secret
type
to bootstrap.kubernetes.io/token
. This type of Secret is designed 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 type 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.
The above YAML may look confusing because the values are all in base64 encoded strings. In fact, you can create an identical Secret using the following YAML:
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"
Creating a Secret
There are several options to create a Secret:
Editing a Secret
An existing Secret may be edited with the following command:
kubectl edit secrets mysecret
This will open the default configured editor and allow for updating the base64 encoded Secret values in the data
field:
# 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 will be
# reopened with the relevant failures.
#
apiVersion: v1
data:
username: YWRtaW4=
password: MWYyZDFlMmU2N2Rm
kind: Secret
metadata:
annotations:
kubectl.kubernetes.io/last-applied-configuration: { ... }
creationTimestamp: 2016-01-22T18:41:56Z
name: mysecret
namespace: default
resourceVersion: "164619"
uid: cfee02d6-c137-11e5-8d73-42010af00002
type: Opaque
Using Secrets
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.
Using Secrets as files from a Pod
To consume a Secret in a volume in a Pod:
- Create a secret or use an existing one. Multiple Pods can reference the same secret.
- Modify your Pod definition to add a volume under
.spec.volumes[]
. Name the volume anything, and have a.spec.volumes[].secret.secretName
field equal to the name of the Secret object. - Add a
.spec.containers[].volumeMounts[]
to each container that needs the secret. Specify.spec.containers[].volumeMounts[].readOnly = true
and.spec.containers[].volumeMounts[].mountPath
to an unused directory name where you would like the secrets to appear. - Modify your image or command line so that the program looks for files in that directory. Each key in the secret
data
map becomes the filename undermountPath
.
This is an example of a Pod that mounts a Secret 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
secret:
secretName: mysecret
Each Secret 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 Secret.
You can package many files into one secret, or use many secrets, whichever is convenient.
Projection of Secret keys to specific paths
You can also control the paths within the volume where Secret keys are projected.
You can 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
What will happen:
username
secret is stored under/etc/foo/my-group/my-username
file instead of/etc/foo/username
.password
secret is not projected.
If .spec.volumes[].secret.items
is used, 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.
Secret files permissions
You can set the 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 mode for the entire Secret volume and 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
Then, the secret will be mounted on /etc/foo
and all the files created by the
secret volume mount will have permission 0400
.
Note that the JSON spec doesn't support octal notation, so use the value 256 for 0400 permissions. If you use YAML instead of JSON for the Pod, you can use octal notation to specify permissions in a more natural way.
Note if you kubectl exec
into the Pod, you need to follow the symlink to find
the expected file mode. For example,
Check the secrets file mode on the pod.
kubectl exec mypod -it sh
cd /etc/foo
ls -l
The output is similar to this:
total 0
lrwxrwxrwx 1 root root 15 May 18 00:18 password -> ..data/password
lrwxrwxrwx 1 root root 15 May 18 00:18 username -> ..data/username
Follow the symlink to find the correct file mode.
cd /etc/foo/..data
ls -l
The output is similar to this:
total 8
-r-------- 1 root root 12 May 18 00:18 password
-r-------- 1 root root 5 May 18 00:18 username
You can also use mapping, as in the previous example, and specify different permissions for different files 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
items:
- key: username
path: my-group/my-username
mode: 0777
In this case, the file resulting in /etc/foo/my-group/my-username
will have
permission value of 0777
. If you use JSON, owing to JSON limitations, you
must specify the mode in decimal notation, 511
.
Note that this permission value might be displayed in decimal notation if you read it later.
Consuming Secret values from volumes
Inside the container that mounts a secret volume, the secret keys appear as files and the secret values are base64 decoded and stored inside these files. This is the result of commands executed inside the container from the example above:
ls /etc/foo/
The output is similar to:
username
password
cat /etc/foo/username
The output is similar to:
admin
cat /etc/foo/password
The output is similar to:
1f2d1e2e67df
The program in a container is responsible for reading the secrets from the files.
Mounted Secrets are updated automatically
When a secret currently consumed in a volume is updated, projected keys are eventually updated as well.
The kubelet checks whether the mounted secret is fresh on every periodic sync.
However, the kubelet uses its local cache for getting the current value of the Secret.
The type of the cache is configurable using the ConfigMapAndSecretChangeDetectionStrategy
field in
the KubeletConfiguration struct.
A Secret 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 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
(it equals to watch propagation delay, ttl of cache, or zero correspondingly).
Using Secrets as environment variables
To use a secret in an environment variable in a Pod:
- Create a secret or use an existing one. Multiple Pods can reference the same secret.
- Modify your Pod definition in each container that you wish to consume the value of a secret key to add an environment variable for each secret key you wish to consume. The environment variable that consumes the secret key should populate the secret's name and key in
env[].valueFrom.secretKeyRef
. - Modify your image and/or command line so that the program looks for values in the specified environment variables.
This is an example of a Pod that uses secrets from environment variables:
apiVersion: v1
kind: Pod
metadata:
name: secret-env-pod
spec:
containers:
- name: mycontainer
image: redis
env:
- name: SECRET_USERNAME
valueFrom:
secretKeyRef:
name: mysecret
key: username
- name: SECRET_PASSWORD
valueFrom:
secretKeyRef:
name: mysecret
key: password
restartPolicy: Never
Consuming Secret Values from environment variables
Inside a container that consumes a secret in the environment variables, the secret keys appear as normal environment variables containing the base64 decoded values of the secret data. This is the result of commands executed inside the container from the example above:
echo $SECRET_USERNAME
The output is similar to:
admin
echo $SECRET_PASSWORD
The output is similar to:
1f2d1e2e67df
Environment variables are not updated after a secret update
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.
Immutable Secrets
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 Secrets (at least tens of thousands of unique Secret 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 secrets marked as immutable.
This feature is controlled by the ImmutableEphemeralVolumes
feature gate,
which is enabled by default since v1.19. You can create an immutable
Secret by setting the immutable
field to true
. For example,
apiVersion: v1
kind: Secret
metadata:
...
data:
...
immutable: true
data
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.
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 it 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.
Details
Restrictions
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.
Secret resources reside in a namespace. Secrets can only be referenced by Pods in that same namespace.
Individual secrets are limited to 1MiB in size. This is to discourage creation of very large secrets which would exhaust the API server and kubelet memory. However, creation of many smaller secrets could also exhaust memory. More comprehensive limits on memory usage due to secrets is a planned feature.
The kubelet only supports the use of secrets for Pods where the secrets
are obtained from the API server.
This includes any Pods created using kubectl
, or indirectly via a replication
controller. It does not include Pods created as a result of the kubelet
--manifest-url
flag, its --config
flag, or its REST API (these are
not common ways to create Pods).
The spec
of a static Pod cannot refer to a Secret
or any other API objects.
Secrets must be created before they are consumed in Pods as environment variables unless they are marked as optional. References to secrets that do not exist will prevent the Pod from starting.
References (secretKeyRef
field) to keys that do not exist in a named Secret
will prevent the Pod from starting.
Secrets used to populate environment variables by the envFrom
field that have keys
that are considered invalid environment variable names will have those keys
skipped. The Pod will be allowed to start. There will be an event whose
reason is InvalidVariableNames
and the message will contain the list of
invalid keys that were skipped. The example shows a pod which refers to the
default/mysecret that contains 2 invalid keys: 1badkey
and 2alsobad
.
kubectl get events
The output is similar to:
LASTSEEN FIRSTSEEN COUNT NAME KIND SUBOBJECT TYPE REASON
0s 0s 1 dapi-test-pod Pod Warning InvalidEnvironmentVariableNames kubelet, 127.0.0.1 Keys [1badkey, 2alsobad] from the EnvFrom secret default/mysecret were skipped since they are considered invalid environment variable names.
Secret and Pod lifetime interaction
When a Pod is created by calling the Kubernetes API, there is no check if a referenced secret exists. Once a Pod is scheduled, the kubelet will try to fetch the secret value. If the secret cannot be fetched because it does not exist or because of a temporary lack of connection to the API server, the kubelet will periodically retry. It will report an event about the Pod explaining the reason it is not started yet. Once the secret is fetched, the kubelet will create and mount a volume containing it. None of the Pod's containers will start until all the Pod's volumes are mounted.
Use cases
Use-Case: As container environment variables
Create a secret
apiVersion: v1
kind: Secret
metadata:
name: mysecret
type: Opaque
data:
USER_NAME: YWRtaW4=
PASSWORD: MWYyZDFlMmU2N2Rm
Create the Secret:
kubectl apply -f mysecret.yaml
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.
apiVersion: v1
kind: Pod
metadata:
name: secret-test-pod
spec:
containers:
- name: test-container
image: k8s.gcr.io/busybox
command: [ "/bin/sh", "-c", "env" ]
envFrom:
- secretRef:
name: mysecret
restartPolicy: Never
Use-Case: Pod with ssh keys
Create a secret containing some ssh keys:
kubectl create secret generic ssh-key-secret --from-file=ssh-privatekey=/path/to/.ssh/id_rsa --from-file=ssh-publickey=/path/to/.ssh/id_rsa.pub
The output is similar to:
secret "ssh-key-secret" created
You can also create a kustomization.yaml
with a secretGenerator
field containing ssh keys.
Now you can create a Pod which references the secret with the ssh key and consumes it in a volume:
apiVersion: v1
kind: Pod
metadata:
name: secret-test-pod
labels:
name: secret-test
spec:
volumes:
- name: secret-volume
secret:
secretName: ssh-key-secret
containers:
- name: ssh-test-container
image: mySshImage
volumeMounts:
- name: secret-volume
readOnly: true
mountPath: "/etc/secret-volume"
When the container's command runs, the pieces of the key will be available in:
/etc/secret-volume/ssh-publickey
/etc/secret-volume/ssh-privatekey
The container is then free to use the secret data to establish an ssh connection.
Use-Case: Pods with prod / test credentials
This example illustrates a Pod which consumes a secret containing production credentials and another Pod which consumes a secret with test environment credentials.
You can create a kustomization.yaml
with a secretGenerator
field or run
kubectl create secret
.
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
You can also 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
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$zDsb=
, you should execute the command this way:
kubectl create secret generic dev-db-secret --from-literal=username=devuser --from-literal=password='S!B\*d$zDsb='
You do not need to escape special characters in passwords from files (--from-file
).
Now make the Pods:
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
Add the pods to the same kustomization.yaml:
cat <<EOF >> kustomization.yaml
resources:
- pod.yaml
EOF
Apply all those objects on the API server by running:
kubectl apply -k .
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
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.
You could further simplify the base Pod specification by using two service accounts:
prod-user
with theprod-db-secret
test-user
with thetest-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
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
:
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: k8s.gcr.io/busybox
command:
- ls
- "-l"
- "/etc/secret-volume"
volumeMounts:
- name: secret-volume
readOnly: true
mountPath: "/etc/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
.
ls -l
;
you must use ls -la
to see them when listing directory contents.
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.
Best practices
Clients that use the Secret API
When deploying applications that interact with the Secret API, you should limit access using authorization policies such as RBAC.
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.
For these reasons watch
and list
requests for secrets within a namespace are
extremely powerful capabilities and should be avoided, since listing secrets allows
the clients to inspect the values of all secrets that are in that namespace. The ability to
watch
and list
all secrets in a cluster should be reserved for only the most
privileged, system-level components.
Applications that need to access the Secret API should perform get
requests on
the secrets they need. This lets administrators restrict access to all secrets
while white-listing access to individual instances that
the app needs.
For improved performance over a looping get
, clients can design resources that
reference a secret then watch
the resource, re-requesting the secret when the
reference changes. Additionally, a "bulk watch" API
to let clients watch
individual resources has also been proposed, and will likely
be available in future releases of Kubernetes.
Security properties
Protections
Because secrets can be created independently of the Pods that use them, there is less risk of the secret being exposed during the workflow of creating, viewing, and editing Pods. The system can also take additional precautions with Secrets, such as avoiding writing them to disk where possible.
A secret is only sent to a node if a Pod on that node requires it.
The kubelet stores the secret into a tmpfs
so that the secret is not written
to disk storage. Once the Pod that depends on the secret is deleted, the kubelet
will delete its local copy of the secret data as well.
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.
There may be several containers in a Pod. However, each container in a Pod has
to request the secret volume in its volumeMounts
for it to be visible within
the container. This can be used to construct useful security partitions at the
Pod level.
On most Kubernetes distributions, communication between users and the API server, and from the API server to the kubelets, is protected by SSL/TLS. Secrets are protected when transmitted over these channels.
Kubernetes v1.13 [beta]
You can enable encryption at rest for secret data, so that the secrets are not stored in the clear into etcd.
Risks
- In the API server, secret data is stored in etcd;
therefore:
- Administrators should enable encryption at rest for cluster data (requires v1.13 or later).
- Administrators should limit access to etcd to admin users.
- Administrators may want to wipe/shred disks used by etcd when no longer in use.
- If running etcd in a cluster, administrators should make sure to use SSL/TLS for etcd peer-to-peer communication.
- If you configure the secret through a manifest (JSON or YAML) file which has the secret data encoded as base64, sharing this file or checking it in to a source repository means the secret is compromised. Base64 encoding is not an encryption method and is considered the same as plain text.
- Applications still need to protect the value of secret after reading it from the volume, such as not accidentally logging it or transmitting it to an untrusted party.
- A user who can create a Pod that uses a secret can also see the value of that secret. Even if the API server policy does not allow that user to read the Secret, the user could run a Pod which exposes the secret.
What's next
- Learn how to manage Secret using
kubectl
- Learn how to manage Secret using config file
- Learn how to manage Secret using kustomize
- Read the API reference for
Secret
4 - Managing Resources for 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 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.
However, a container is not allowed to use more than its resource limit
.
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.
If you set a memory
limit of 4GiB for that Container, the kubelet (and
container runtime) enforce the limit.
The runtime prevents the container from using more than the configured resource limit. For example:
when a process in the container tries to consume more than the allowed amount of memory,
the system kernel terminates the process that attempted the allocation, with an out of memory
(OOM) error.
Limits can be implemented either reactively (the system intervenes once it sees a violation) or by enforcement (the system prevents the container from ever exceeding the limit). Different runtimes can have different ways to implement the same restrictions.
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. If you're using Kubernetes v1.14 or newer, 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.
hugepages-*
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
Each Container of a Pod can specify one or more of 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 requests and limits can only be specified on individual Containers, it is convenient to talk about Pod resource requests and limits. A Pod resource request/limit for a particular resource type is the sum of the resource requests/limits of that type for each Container in the Pod.
Resource units in Kubernetes
Meaning of CPU
Limits and requests for CPU resources are measured in cpu units. One cpu, in Kubernetes, is equivalent to 1 vCPU/Core for cloud providers and 1 hyperthread on bare-metal Intel processors.
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 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. A
request with a decimal point, like 0.1
, is converted to 100m
by the API, and
precision finer than 1m
is not allowed. For this reason, the form 100m
might
be preferred.
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.
Meaning of memory
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 suffixes:
E, P, T, G, M, k, m (millis). 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
Here's an example. The following Pod has two Containers. Each Container has a request of 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 Pods with resource limits are run
When the kubelet starts a Container of a Pod, it passes the CPU and memory limits to the container runtime.
When using Docker:
-
The
spec.containers[].resources.requests.cpu
is converted to its core value, which is potentially fractional, and multiplied by 1024. The greater of this number or 2 is used as the value of the--cpu-shares
flag in thedocker run
command. -
The
spec.containers[].resources.limits.cpu
is converted to its millicore value and multiplied by 100. The resulting value is the total amount of CPU time in microseconds that a container can use every 100ms. A container cannot use more than its share of CPU time during this interval.Note: The default quota period is 100ms. The minimum resolution of CPU quota is 1ms. -
The
spec.containers[].resources.limits.memory
is converted to an integer, and used as the value of the--memory
flag in thedocker run
command.
If a Container exceeds its memory limit, it might be terminated. If it is restartable, the kubelet will restart it, as with any other type of runtime failure.
If a Container exceeds its memory request, it is likely that its Pod will be evicted whenever the node runs out of memory.
A Container might or might not be allowed to exceed its CPU limit for extended periods of time. However, it will not be killed 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 resource usage of a Pod is reported 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.
Local ephemeral storage
Kubernetes v1.10 [beta]
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.
As a beta feature, 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:
- the
LocalStorageCapacityIsolation
feature gate is enabled (the feature is on by default), and - 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.
tmpfs
emptyDir volumes as container memory use, rather
than as local ephemeral storage.
Setting requests and limits for local ephemeral storage
You can use ephemeral-storage for managing local ephemeral storage. Each Container of a Pod can specify one or more of the following:
spec.containers[].resources.limits.ephemeral-storage
spec.containers[].resources.requests.ephemeral-storage
Limits and requests for ephemeral-storage
are measured in bytes. 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 represent roughly the same value:
128974848, 129e6, 129M, 123Mi
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.
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: {}
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.
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.
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 v1.15 [alpha]
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.
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.
If you want to use project quotas, you should:
-
Enable the
LocalStorageCapacityIsolationFSQuotaMonitoring=true
feature gate using thefeatureGates
field in the kubelet configuration or the--feature-gates
command line flag. -
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
.
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. Note that because the scheduler uses the node
status.allocatable
value when evaluating Pod fitness, there may be a short
delay between patching the node capacity with a new resource and the first Pod
that requests the resource to 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
~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 policy 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
.
kubernetes.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.
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, like this:
kubectl describe pod frontend | grep -A 3 Events
Events:
FirstSeen LastSeen Count From Subobject PathReason Message
36s 5s 6 {scheduler } FailedScheduling Failed for reason PodExceedsFreeCPU and possibly others
In the preceding example, the Pod named "frontend" fails to be scheduled due to insufficient CPU resource on the 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.
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 1120m CPUs or 6.23Gi of memory, it 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. The allocatable
field
NodeStatus
gives the amount of resources that are available to Pods. For more information, see
Node Allocatable Resources.
The resource quota feature can be configured to limit the total amount of resources that can be consumed. If used in conjunction with namespaces, it can prevent one team from hogging all the resources.
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
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
Replication Controllers: simmemleak (1/1 replicas created)
Containers:
simmemleak:
Image: saadali/simmemleak
Limits:
cpu: 100m
memory: 50Mi
State: Running
Started: Tue, 07 Jul 2015 12:54:41 -0700
Last Termination State: Terminated
Exit Code: 1
Started: Fri, 07 Jul 2015 12:54:30 -0700
Finished: Fri, 07 Jul 2015 12:54:33 -0700
Ready: False
Restart Count: 5
Conditions:
Type Status
Ready False
Events:
FirstSeen LastSeen Count From SubobjectPath Reason Message
Tue, 07 Jul 2015 12:53:51 -0700 Tue, 07 Jul 2015 12:53:51 -0700 1 {scheduler } scheduled Successfully assigned simmemleak-hra99 to kubernetes-node-tf0f
Tue, 07 Jul 2015 12:53:51 -0700 Tue, 07 Jul 2015 12:53:51 -0700 1 {kubelet kubernetes-node-tf0f} implicitly required container POD pulled Pod container image "k8s.gcr.io/pause:0.8.0" already present on machine
Tue, 07 Jul 2015 12:53:51 -0700 Tue, 07 Jul 2015 12:53:51 -0700 1 {kubelet kubernetes-node-tf0f} implicitly required container POD created Created with docker id 6a41280f516d
Tue, 07 Jul 2015 12:53:51 -0700 Tue, 07 Jul 2015 12:53:51 -0700 1 {kubelet kubernetes-node-tf0f} implicitly required container POD started Started with docker id 6a41280f516d
Tue, 07 Jul 2015 12:53:51 -0700 Tue, 07 Jul 2015 12:53:51 -0700 1 {kubelet kubernetes-node-tf0f} spec.containers{simmemleak} created Created with docker id 87348f12526a
In the preceding example, the Restart Count: 5
indicates that the simmemleak
Container in the Pod was terminated and restarted five times.
You can call kubectl get pod
with the -o go-template=...
option to fetch the status
of previously terminated Containers:
kubectl get pod -o go-template='{{range.status.containerStatuses}}{{"Container Name: "}}{{.name}}{{"\r\nLastState: "}}{{.lastState}}{{end}}' simmemleak-hra99
Container Name: simmemleak
LastState: map[terminated:map[exitCode:137 reason:OOM Killed startedAt:2015-07-07T20:58:43Z finishedAt:2015-07-07T20:58:43Z containerID:docker://0e4095bba1feccdfe7ef9fb6ebffe972b4b14285d5acdec6f0d3ae8a22fad8b2]]
You can see that the Container was terminated because of reason:OOM Killed
, where OOM
stands for Out Of Memory.
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.
- For more details about the difference between requests and limits, see Resource QoS.
- Read the Container API reference
- Read the ResourceRequirements API reference
- Read about project quotas in XFS
- Read more about the kube-scheduler Policy reference (v1)
5 - 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.
kubeconfig
.
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.