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Steve Perry
2017-02-01 14:21:38 -08:00
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docs/api-reference/v1/definitions.html
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@@ -4176,7 +4176,7 @@ The resulting set of endpoints can be viewed as:<br>
</tr>
<tr>
<td class="tableblock halign-left valign-top"><p class="tableblock">nodeSelector</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">NodeSelector is a selector which must be true for the pod to fit on a node. Selector which must match a node&#8217;s labels for the pod to be scheduled on that node. More info: <a href="http://kubernetes.io/docs/user-guide/node-selection/README">http://kubernetes.io/docs/user-guide/node-selection/README</a></p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">NodeSelector is a selector which must be true for the pod to fit on a node. Selector which must match a node&#8217;s labels for the pod to be scheduled on that node. More info: <a href="http://kubernetes.io/docs/user-guide/node-selection">http://kubernetes.io/docs/user-guide/node-selection</a></p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">false</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">object</p></td>
<td class="tableblock halign-left valign-top"></td>
@@ -8267,4 +8267,4 @@ Last updated 2016-11-17 06:26:10 UTC
</div>
</div>
</body>
</html>
</html>
docs/concepts/tools/kubectl/object-management-overview.md
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@@ -59,12 +59,6 @@ Disadvantages compared to object configuration:
- Commands do not provide a source of records except for what is live.
- Commands do not provide a template for creating new objects.
{% comment %}
If we use Markdown comments instead of HTML comments, they won't appear in the built HTML files.
For a tutorial on how to use Imperative Commands for app management, see:
[App Management Using Comands](/docs/tutorials/kubectl/app-management-using-commands/)
{% endcomment %}
## Imperative object configuration
When using imperative object configuration, a user operates on object
@@ -124,11 +118,6 @@ Disadvantages compared to declarative object configuration:
- Imperative object configuration works best on files, not directories.
- Updates to live objects must be reflected in configuration files, or they will be lost during the next replacement.
{% comment %}
For a tutorial on how to use Yaml Config for app management, see:
[App Management Yaml Config](/docs/tutorials/kubectl/app-management-using-yaml-config/)
{% endcomment %}
## Declarative object configuration
When using declarative object configuration, a user operates on object
@@ -170,20 +159,16 @@ Disadvantages compared to imperative object configuration:
- Declarative object configuration is harder to debug and understand results when they are unexpected.
- Partial updates using diffs create complex merge and patch operations.
{% comment %}
For a tutorial on how to use Yaml Config with multiple writers, see:
[App Management Yaml Config](/docs/tutorials/kubectl/app-management-using-yaml-config-multiple-writers/)
{% endcomment %}
{% endcapture %}
{% capture whatsnext %}
- [Managing Kubernetes Objects Using Imperative Commands](/docs/concepts/tools/kubectl/object-management-using-imperative-commands/)
- [Managing Kubernetes Objects Using Object Configuration (Imperative)](/docs/concepts/tools/kubectl/object-management-using-imperative-config/)
- [Managing Kubernetes Objects Using Object Configuration (Declarative)](/docs/concepts/tools/kubectl/object-management-using-declarative-config/)
- [Kubectl Command Reference](/docs/user-guide/kubectl/v1.5/)
- [Kubernetes Object Schema Reference](/docs/resources-reference/v1.5/)
{% comment %}
- [App Management Using Yaml Config](/docs/tutorials/kubectl/declarative-app-management-using-yaml-config/)
- [App Management Using Yaml Config With Multiple Writers](/docs/tutorials/kubectl/declarative-app-management-using-yaml-config-multiple-writers/)
{% endcomment %}
{% endcapture %}
docs/concepts/tools/kubectl/object-management-using-declarative-config.md
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@@ -0,0 +1,958 @@
---
title: Declarative Management of Kubernetes Objects Using Configuration Files
---
{% capture overview %}
Kubernetes objects can be created, updated, and deleted by storing multiple
object configuration files in a directory and using `kubectl apply` to
recursively create and update those objects as needed. This method
retains writes made to live objects without merging the changes
back into the object configuration files.
{% endcapture %}
{% capture body %}
## Trade-offs
The `kubectl` tool supports three kinds of object management:
* Imperative commands
* Imperative object configuration
* Declarative object configuration
See [Kubernetes Object Management](/docs/concepts/tools/kubectl/object-management-overview/)
for a discussion of the advantages and disadvantage of each kind of object management.
## Before you begin
Declarative object configuration requires a firm understanding of
the Kubernetes object definitions and configuration. Read and complete
the following documents if you have not already:
- [Managing Kubernetes Objects Using Imperative Commands](/docs/concepts/tools/kubectl/object-management-using-imperative-commands/)
- [Imperative Management of Kubernetes Objects Using Configuration Files](/docs/concepts/tools/kubectl/object-management-using-imperative-config/)
Following are definitions for terms used in this document:
- *object configuration file / configuration file*: A file that defines the
configuration for a Kubernetes object. This topic shows how to pass configuration
files to `kubectl apply`. Configuration files are typically stored in source control, such as Git.
- *live object configuration / live configuration*: The live configuration
values of an object, as observed by the Kubernetes cluster. These are kept in the Kubernetes
cluster storage, typically etcd.
- *declarative configuration writer / declarative writer*: A person or software component
that makes updates to a live object. The live writers refered to in this topic make changes
to object configuration files and run `kubectl apply` to write the changes.
## How to create objects
Use `kubectl apply` to create all objects, except those that already exist,
defined by configuration files in a specified directory:
```shell
kubectl apply -f <directory>/
```
This sets the `kubectl.kubernetes.io/last-applied-configuration: '{...}'`
annotation on each object. The annotation contains the contents of the object
configuration file that was used to create the object.
**Note**: Add the `-R` flag to recursively process directories.
Here's an example of an object configuration file:
{% include code.html language="yaml" file="simple_deployment.yaml" ghlink="/docs/concepts/tools/simple_deployment.yaml" %}
Create the object using `kubectl apply`:
```shell
kubectl apply -f http://k8s.io/docs/concepts/tools/kubectl/simple_deployment.yaml
```
Print the live configuration using `kubectl get`:
```shell
kubectl get -f http://k8s.io/docs/concepts/tools/kubectl/simple_deployment.yaml -o yaml
```
The output shows that the `kubectl.kubernetes.io/last-applied-configuration` annotation
was written to the live configuration, and it matches the configuration file:
```shell
kind: Deployment
metadata:
annotations:
# ...
# This is the json representation of simple_deployment.yaml
# It was written by kubectl apply when the object was created
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"extensions/v1beta1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"minReadySeconds":5,"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.7.9","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}
# ...
spec:
# ...
minReadySeconds: 5
template:
metadata:
# ...
labels:
app: nginx
spec:
containers:
- image: nginx:1.7.9
# ...
name: nginx
ports:
- containerPort: 80
# ...
# ...
# ...
# ...
```
## How to update objects
You can also use `kubectl apply` to update all objects defined in a directory, even
if those objects already exist. This approach accomplishes the following:
1. Sets fields that appear in the configuration file in the live configuration.
2. Clears fields removed from the configuration file in the live configuration.
```shell
kubectl apply -f <directory>/
```
**Note**: Add the `-R` flag to recursively process directories.
Here's an example configuration file:
{% include code.html language="yaml" file="simple_deployment.yaml" ghlink="/docs/concepts/tools/simple_deployment.yaml" %}
Create the object using `kubectl apply`:
```shell
kubectl apply -f http://k8s.io/docs/concepts/tools/kubectl/simple_deployment.yaml
```
**Note:** For purposes of illustration, the preceding command refers to a single
configuration file instead of a directory.
Print the live configuration using `kubectl get`:
```shell
kubectl get -f http://k8s.io/docs/concepts/tools/kubectl/simple_deployment.yaml -o yaml
```
The output shows that the `kubectl.kubernetes.io/last-applied-configuration` annotation
was written to the live configuration, and it matches the configuration file:
```shell
kind: Deployment
metadata:
annotations:
# ...
# This is the json representation of simple_deployment.yaml
# It was written by kubectl apply when the object was created
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"extensions/v1beta1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"minReadySeconds":5,"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.7.9","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}
# ...
spec:
# ...
minReadySeconds: 5
template:
metadata:
# ...
labels:
app: nginx
spec:
containers:
- image: nginx:1.7.9
# ...
name: nginx
ports:
- containerPort: 80
# ...
# ...
# ...
# ...
```
Directly update the `replicas` field in the live configuration by using `kubectl scale`.
This does not use `kubectl apply`:
```shell
kubectl scale deployment/nginx-deployment --replicas 2
```
Print the live configuration using `kubectl get`:
```shell
kubectl get -f http://k8s.io/docs/concepts/tools/kubectl/simple_deployment.yaml -o yaml
```
The output shows that the `replicas` field has been set to 2, and the `last-applied-configuration`
annotation does not contain a `replicas` field:
```
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
annotations:
# ...
# note that the annotation does not contain replicas
# because it was not updated through apply
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"extensions/v1beta1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"minReadySeconds":5,"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.7.9","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}
# ...
spec:
replicas: 2 # written by scale
# ...
minReadySeconds: 5
template:
metadata:
# ...
labels:
app: nginx
spec:
containers:
- image: nginx:1.7.9
# ...
name: nginx
ports:
- containerPort: 80
# ...
```
Update the `simple_deployment.yaml` configuration file to change the image from
`nginx:1.7.9` to `nginx:1.11.9`, and delete the `minReadySeconds` field:
{% include code.html language="yaml" file="update_deployment.yaml" ghlink="/docs/concepts/tools/update_deployment.yaml" %}
Apply the changes made to the configuration file:
```shell
kubectl apply -f http://k8s.io/docs/concepts/tools/kubectl/updated_deployment.yaml
```
Print the live configuration using `kubectl get`:
```
kubectl get -f http://k8s.io/docs/concepts/tools/kubectl/simple_deployment.yaml -o yaml
```
The output shows the following changes to the live configuration:
- The `replicas` field retains the value of 2 set by `kubectl scale`.
This is possible because it is omitted from the configuration file.
- The `image` field has been updated to `nginx:1.11.9` from `nginx:1.7.9`.
- The `last-applied-configuration` annotation has been updated with the new image.
- The `minReadySeconds` field has been cleared.
- The `last-applied-configuration` annotation no longer contains the `minReadySeconds` field.
```shell
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
annotations:
# ...
# The annotation contains the updated image to nginx 1.11.9,
# but does not contain the updated replicas to 2
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"extensions/v1beta1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.11.9","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}
# ...
spec:
replicas: 2 # Set by `kubectl scale`. Ignored by `kubectl apply`.
# minReadySeconds cleared by `kubectl apply`
# ...
template:
metadata:
# ...
labels:
app: nginx
spec:
containers:
- image: nginx:1.11.9 # Set by `kubectl apply`
# ...
name: nginx
ports:
- containerPort: 80
# ...
# ...
# ...
# ...
```
**Warning**: Mixing `kubectl apply` with the imperative object configuration commands
`create` and `replace` is not supported. This is because `create`
and `replace` do not retain the `kubectl.kubernetes.io/last-applied-configuration`
that `kubectl apply` uses to compute updates.
**Warning**: As of Kubernetes 1.5, the `kubectl edit` command is
incompatible with `kubectl apply`, and the two should not be
used together.
## How to delete objects
There are two approaches to delete objects managed by `kubectl apply`.
### Recommended: `delete -f <filename>`
Manually deleting objects using the imperative command is the recommended
approach, as it is more explicit about what is being deleted, and less likely
to result in the user deleting something unintentionally:
```shell
delete -f <filename>
```
### Alternative: `kubectl apply -f <directory/> --prune -l your=label`
Only use this if you know what you are doing.
**Warning:** `kubectl apply --prune` is in alpha, and backwards incompatible
changes might be introduced in subsequent releases.
**Warning**: You must be careful when using this command, so that you
do not delete objects unintentionally.
As an alternative to `kubectl delete`, you can use `kubectl apply` to identify objects to be deleted after their
configuration files have been removed from the directory. Apply with `--prune`
queries the API server for all objects matching a set of labels, and attempts
to match the returned live object configurations against the object
configuration files. If an object matches the query, and it does not have a
configuration file in the directory, and it does not have a `last-applied-configuration` annotation,
it is deleted.
{% comment %}
TODO(pwittrock): We need to change the behavior to prevent the user from running apply on subdirectories unintentionally.
{% endcomment %}
```shell
kubectl apply -f <directory/> --prune -l <labels>
```
**Important:** Apply with prune should only be run against the root directory
containing the object configuration files. Running against sub-directories
can cause objects to be unintentionally deleted if they are returned
by the label selector query specified with `-l <labels>` and
do not appear in the subdirectory.
## How to view an object
You can use `kubectl get` with `-o yaml` to view the configuration of a live object:
```shell
kubectl get -f <filename|url> -o yaml
```
## How apply calculates differences and merges changes
**Definition:** A *patch* is an update operation that is scoped to specific
fields of an object instead of the entire object.
This enables updating only a specific set of fields on an object without
reading the object first.
When `kubectl apply` updates the live configuration for an object,
it does so by sending a patch request to the API server. The
patch defines updates scoped to specific fields of the live object
configuration. The `kubectl apply` command calculates this patch request
using the configuration file, the live configuration, and the
`last-applied-configuration` annotation stored in the live configuration.
### Merge patch calculation
The `kubectl apply` command writes the contents of the configuration file to the
`kubectl.kubernetes.io/last-applied-configuration` annotation. This
is used to identify fields that have been removed from the configuration
file and need to be cleared from the live configuration. Here are the steps used
to caluculate which fields should be deleted or set:
1. Calculate the fields to delete. Thes are the fields present in `last-applied-configuration` and missing from the configuration file.
2. Calculate the fields to add or set. These are the fields present in the configuration file whose values don't match the live configuration.
Here's an example. Suppose this is the configuration file for a Deployment object:
{% include code.html language="yaml" file="update_deployment.yaml" ghlink="/docs/concepts/tools/update_deployment.yaml" %}
Also, suppose this is the live configuration for the same Deployment object:
```shell
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
annotations:
# ...
# note that the annotation does not contain replicas
# because it was not updated through apply
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"extensions/v1beta1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"minReadySeconds":5,"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.7.9","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}
# ...
spec:
replicas: 2 # written by scale
# ...
minReadySeconds: 5
template:
metadata:
# ...
labels:
app: nginx
spec:
containers:
- image: nginx:1.7.9
# ...
name: nginx
ports:
- containerPort: 80
# ...
```
Here are the merge calculations that would be performed by `kubectl apply`:
1. Calculate the fields to delete by reading values from
`last-applied-configuration` and comparing them to values in the
configuration file. In this example, `minReadySeconds` appears in the
`last-applied-configuration` annotation, but does not appear in the configuration file.
**Action:** Clear `minReadySeconds` from the live configuration.
2. Calculate the fields to set by reading values from the configuration
file and comparing them to values in the live configuration. In this example,
the value of `image` in the configuration file does not match
the value in the live configuration. **Action:** Set the value of `image` in the live configuration.
3. Set the `last-applied-configuration` annotation to match the value
of the configuration file.
4. Merge the results from 1, 2, 3 into a single patch request to the API server.
Here is the live configuration that is the result of the merge:
```shell
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
annotations:
# ...
# The annotation contains the updated image to nginx 1.11.9,
# but does not contain the updated replicas to 2
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"extensions/v1beta1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.11.9","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}
# ...
spec:
replicas: 2 # Set by `kubectl scale`. Ignored by `kubectl apply`.
# minReadySeconds cleared by `kubectl apply`
# ...
template:
metadata:
# ...
labels:
app: nginx
spec:
containers:
- image: nginx:1.11.9 # Set by `kubectl apply`
# ...
name: nginx
ports:
- containerPort: 80
# ...
# ...
# ...
# ...
```
{% comment %}
TODO(1.6): For 1.6, add the following bullet point to 1.
- clear fields explicitly set to null in the local object configuration file regardless of whether they appear in the last-applied-configuration
{% endcomment %}
### How different types of fields are merged
How a particular field in a configuration file is merged with
with the live configuration depends on the
type of the field. There are several types of fields:
- *primitive*: A field of type string, integer, or boolean.
For example, `image` and `replicas` are primitive fields. **Action:** Replace.
- *map*, also called *object*: A field of type map or a complex type that contains subfields. For example `labels`
and `annotations` are maps; `spec` and `metadata` are complex types. **Action:** Merge elements or subfields.
- *list*: A field containing a list of items that can be either primitive types, maps, or complex types.
For example, `containers`, `ports`, and `args` are lists. **Action:** Varies.
When `kubectl apply` updates a map or list field, it typically does
not replace the entire field, but instead updates the individual subelements.
For instance, when merging the `spec` on a Deployment, the entire `spec` is
not replaced. Instead the subfields of `spec`, such as `replicas`, are compared
and merged.
### Merging changes to primitive fields
Primative fieldss are replaced or cleared.
**Note:** '-' is used for "not applicable" because the value is not used.
| Field in object configuration file | Field in live object configuration | Field in last-applied-configuration | Action |
|-------------------------------------|------------------------------------|-------------------------------------|-------------------------------------------|
| Yes | Yes | - | Set live to configuration file value. |
| Yes | No | - | Set live to local configuration. |
| No | - | Yes | Clear from live configuration. |
| No | - | No | Do nothing. Keep live value. |
### Merging changes to map or complex fields
Fields that represent maps or complex-types are merged by comparing each of the sub fields or elements of of the map / complex-type:
**Note:** '-' is used for "not applicable" because the value is not used.
| Key in object configuration file | Key in live object configuration | Field in last-applied-configuration | Action |
|-------------------------------------|------------------------------------|-------------------------------------|----------------------------------|
| Yes | Yes | - | Compare sub fields values. |
| Yes | No | - | Set live to local configuration. |
| No | - | Yes | Delete from live configuration. |
| No | - | No | Do nothing. Keep live value. |
### Merging changes for fields of type list
Merging changes to a list uses one of three strategies:
* Replace the list.
* Merge individual elements in a list of complex elements.
* Merge a list of primitive elements.
The choice of strategy is made on a per-field basis.
#### Replace the list
Treat the list the same as a primitive field. Replace or delete the
entire list. This preserves ordering.
**Example:** Use `kubectl apply` to update the `args` field of a Container in a Pod. This sets
the value of `args` in the live configuration to the value in the configuration file.
Any `args` elements that had previously been added to the live configuration are lost.
The order of the `args` elements defined in the configuration file is
retained in the live configuration.
```yaml
# last-applied-configuration value
args: ["a, b"]
# configuration file value
args: ["a", "c"]
# live configuration
args: ["a", "b", "d"]
# result after merge
args: ["a", "c"]
```
**Explanation:** The merge used the configuration file value as the new list value.
#### Merge individual elements of a list of complex elements:
Treat the list as a map, and treat a specific field of each element as a key.
Add, delete, or update individual elements. This does not preserve ordering.
This merge strategy uses a special tag on each field called a `patchMergeKey`. The
`patchMergeKey` is defined for each field in the Kubernetes source code:
[types.go](https://github.com/kubernetes/kubernetes/blob/master/pkg/api/v1/types.go#L2119)
When merging a list of complex elements, the field specified as the `patchMergeKey` for a given element
is used like a map key for that element.
**Example:** Use `kubectl apply` to update the `containers` field of a PodSpec.
This merges the list as though `containers` was a map where each element is keyed
by `name`.
```yaml
# last-applied-configuration value
containers:
- name: nginx
image: nginx:1.10
- name: nginx-helper-a # key: nginx-helper-a; will be deleted in result
image: helper:1.3
- name: nginx-helper-b # key: nginx-helper-b; will be retained
image: helper:1.3
# configuration file value
containers:
- name: nginx
image: nginx:1.11
- name: nginx-helper-b
image: helper:1.3
- name: nginx-helper-c # key: nginx-helper-c; will be added in result
image: helper:1.3
# live configuration
containers:
- name: nginx
image: nginx:1.10
- name: nginx-helper-a
image: helper:1.3
- name: nginx-helper-b
image: helper:1.3
args: ["run"] # Field will be retained
- name: nginx-helper-d # key: nginx-helper-d; will be retained
image: helper:1.3
# result after merge
containers:
- name: nginx
image: nginx:1.10
# Element nginx-helper-a was deleted
- name: nginx-helper-b
image: helper:1.3
args: ["run"] # Field was retained
- name: nginx-helper-c # Element was added
image: helper:1.3
- name: nginx-helper-d # Element was ignored
image: helper:1.3
```
**Explanation:**
- The container named "nginx-helper-a" was deleted because no container
named "nginx-helper-a" appeared in the configuration file.
- The container named "nginx-helper-b" retained the changes to `args`
in the live configuration. `kubectl apply` was able to identify
that "nginx-helper-b" in the live configuration was the same
"nginx-helper-b" as in the configuration file, even though their fields
had different values (no `args` in the configuration file). This is
because the `patchMergeKey` field value (name) was identical in both.
- The container named "nginx-helper-c" was added because no container
with that name appeared in the live configuration, but one with
that name appeared in the configuration file.
- The container named "nginx-helper-d" was retained because
no element with that name appeared in the last-applied-configuration.
#### Merge a list of primitive elements
As of Kubernetes 1.5, merging lists of primitive elements is not supported.
**Note:** Which of the above strategies is chosen for a given field is controlled by
the `patchStrategy` tag in [types.go](https://github.com/kubernetes/kubernetes/blob/master/pkg/api/v1/types.go#L2119)
If no `patchStrategy` is specified for a field of type list, then
the list is replaced.
{% comment %}
TODO(pwittrock): Uncomment this for 1.6
- Treat the list as a set of primitives. Replace or delete individual
elements. Does not preserve ordering. Does not preserve duplicates.
**Example:** Using apply to update the `finalizers` field of ObjectMeta
keeps elements added to the live configuration. Ordering of finalizers
is lost.
{% endcomment %}
## Default field values
The API server sets certain fields to default values in the live configuration if they are
not specified when the object is created.
Here's a configuration file for a Deployment. The file does not specify `strategy` or `selector`:
{% include code.html language="yaml" file="simple_deployment.yaml" ghlink="/docs/concepts/tools/simple_deployment.yaml" %}
Create the object using `kubectl apply`:
```shell
kubectl apply -f http://k8s.io/docs/concepts/tools/kubectl/simple_deployment.yaml
```
Print the live configuration using `kubectl get`:
```shell
kubectl get -f http://k8s.io/docs/concepts/tools/kubectl/simple_deployment.yaml -o yaml
```
The output shows that the API server set several fields to default values in the live
configuration. These fields were not specified in the configuration file.
```shell
apiVersion: extensions/v1beta1
kind: Deployment
# ...
spec:
minReadySeconds: 5
replicas: 1 # defaulted by apiserver
selector:
matchLabels: # defaulted by apiserver - derived from template.metadata.labels
app: nginx
strategy:
rollingUpdate: # defaulted by apiserver - derived from strategy.type
maxSurge: 1
maxUnavailable: 1
type: RollingUpdate # defaulted apiserver
template:
metadata:
creationTimestamp: null
labels:
app: nginx
spec:
containers:
- image: nginx:1.7.9
imagePullPolicy: IfNotPresent # defaulted by apiserver
name: nginx
ports:
- containerPort: 80
protocol: TCP # defaulted by apiserver
resources: {} # defaulted by apiserver
terminationMessagePath: /dev/termination-log # defaulted by apiserver
dnsPolicy: ClusterFirst # defaulted by apiserver
restartPolicy: Always # defaulted by apiserver
securityContext: {} # defaulted by apiserver
terminationGracePeriodSeconds: 30 # defaulted by apiserver
# ...
```
**Note:** Some of the fields' default values have been derived from
the values of other fields that were specified in the configuration file,
such as the `selector` field.
In a patch request, defaulted fields are not re-defaulted unless they are explicitly cleared
as part of a patch request. This can cause unexpected behavior for
fields that are defaulted based
on the values of other fields. When the other fields are later changed,
the values defaulted from them will not be updated unless they are
explicitly cleared.
For this reason, it is recommended that certain fields defaulted
by the server are explicitly defined in the configuration file, even
if the desired values match the server defaults. This makes it
easier to recognize conflicting values that will not be re-defaulted
by the server.
**Example:**
```yaml
# last-applied-configuration
spec:
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.7.9
ports:
- containerPort: 80
# configuration file
spec:
strategy:
type: Recreate # updated value
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.7.9
ports:
- containerPort: 80
# live configuration
spec:
strategy:
type: RollingUpdate # defaulted value
rollingUpdate: # defaulted value derived from type
maxSurge : 1
maxUnavailable: 1
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.7.9
ports:
- containerPort: 80
# result after merge - ERROR!
spec:
strategy:
type: Recreate # updated value: incompatible with rollingUpdate
rollingUpdate: # defaulted value: incompatible with "type: Recreate"
maxSurge : 1
maxUnavailable: 1
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.7.9
ports:
- containerPort: 80
```
**Explanation:**
1. The user creates a Deployment without defining `strategy.type`.
2. The server defaults `strategy.type` to `RollingUpdate` and defaults the
`strategy.rollingUpdate` values.
3. The user changes `strategy.type` to `Recreate`. The `strategy.rollingUpdate`
values remain at their defaulted values, though the server expects them to be cleared.
If the `strategy.rollingUpdate` values had been defined initially in the configuration file,
it would have been more clear that they needed to be deleted.
4. Apply fails because `strategy.rollingUpdate` is not cleared. The `strategy.rollingupdate`
field cannot be defined with a `strategy.type` of `Recreate`.
Recommendation: These fields should be explicitly defined in the object configuration file:
- Selectors and PodTemplate labels on workloads, such as Deployment, StatefulSet, Job, DaemonSet,
ReplicaSet, and ReplicationController
- Deployment rollout strategy
### How to clear server-defaulted fields or fields set by other writers
As of Kubernetes 1.5, fields that do not appear in the configuration file cannot be
cleared by a merge operation. Here are some workarounds:
Option 1: Remove the field by directly modifying the live object.
**Note:** As of Kubernetes 1.5, `kubectl edit` does not work with `kubectl apply`.
Using these together will cause unexpected behavior.
Option 2: Remove the field through the configuration file.
1. Add the field to the configuration file to match the live object.
1. Apply the configuration file; this updates the annotation to include the field.
1. Delete the field from the configuration file.
1. Apply the configuration file; this deletes the field from the live object and annotation.
{% comment %}
TODO(1.6): Update this with the following for 1.6
Fields that do not appear in the configuration file can be cleared by
setting their values to `null` and then applying the configuration file.
For fields defaulted by the server, this triggers re-defaulting
the values.
{% endcomment %}
## How to change ownership of a field between the configuration file and direct imperative writers
These are the only methods you should use to change an individual object field:
- Use `kubectl apply`.
- Write directly to the live configuration without modifying the configuration file:
for example, use `kubectl scale`.
### Changing the owner from a direct imperative writer to a configuration file
Add the field to the configuration file. For the field, discontinue direct updates to
the live configuration that do not go through `kubectl apply`.
### Changing the owner from a configuration file to a direct imperative writer
As of Kubernetes 1.5, changing ownership of a field from a configuration file to
an imperative writer requires manual steps:
- Remove the field from the configuration file.
- Remove the field from the `kubectl.kubernetes.io/last-applied-configuration` annotation on the live object.
## Changing management methods
Kubernetes objects should be managed using only one method at a time.
Switching from one method to another is possible, but is a manual process.
**Exception:** It is OK to use imperative deletion with declarative management.
{% comment %}
TODO(pwittrock): We need to make using imperative commands with
declarative object configuration work so that it doesn't write the
fields to the annotation, and instead. Then add this bullet point.
- using imperative commands with declarative configuration to manage where each manages different fields.
{% endcomment %}
### Migrating from imperative command management to declarative object configuration
Migrating from imperative command management to declarative object
configuration involves several manual steps:
1. Export the live object to a local configuration file:
kubectl get <kind>/<name> -o yaml --export > <kind>_<name>.yaml
1. Manually remove the `status` field from the configuration file.
**Note:** This step is optional, as `kubectl apply` does not update the status field
even if it is present in the configuration file.
1. Set the `kubectl.kubernetes.io/last-applied-configuration` annotation on the object:
kubectl replace --save-config -f <kind>_<name>.yaml
1. Change processes to use `kubectl apply` for managing the object exclusively.
{% comment %}
TODO(pwittrock): Why doesn't export remove the status field? Seems like it should.
{% endcomment %}
### Migrating from imperative object configuration to declarative object configuration
1. Set the `kubectl.kubernetes.io/last-applied-configuration` annotation on the object:
kubectl replace --save-config -f <kind>_<name>.yaml
1. Change processes to use `kubectl apply` for managing the object exclusively.
## Defining controller selectors and PodTemplate labels
**Warning**: Updating selectors on controllers is strongly discouraged.
The recommended approach is to define a single, immutable PodTemplate label
used only by the controller selector with no other semantic meaning.
**Example:**
```yaml
selector:
matchLabels:
controller-selector: "v1beta1/deployment/nginx"
template:
metadata:
labels:
controller-selector: "v1beta1/deployment/nginx"
```
## Support for ThirdPartyResources
As of Kubernetes 1.5, ThirdPartyResources are not supported by `kubectl apply`.
The recommended approach for ThirdPartyResources is to use [imperative object configuration](/docs/concepts/tools/kubectl/object-management-using-imperative-config/).
{% endcapture %}
{% capture whatsnext %}
- [Managing Kubernetes Objects Using Imperative Commands](/docs/concepts/tools/kubectl/object-management-using-imperative-commands/)
- [Imperative Management of Kubernetes Objects Using Configuration Files](/docs/concepts/tools/kubectl/object-management-using-imperative-config/)
- [Kubectl Command Reference](/docs/user-guide/kubectl/v1.5/)
- [Kubernetes Object Schema Reference](/docs/resources-reference/v1.5/)
{% endcapture %}
{% include templates/concept.md %}
docs/concepts/tools/kubectl/object-management-using-imperative-commands.md
+159
View File
@@ -0,0 +1,159 @@
---
title: Managing Kubernetes Objects Using Imperative Commands
---
{% capture overview %}
Kubernetes objects can quickly be created, updated, and deleted directly using
imperative commands built into the `kubectl` command-line tool. This document
explains how those commands are organized and how to use them to manage live objects.
{% endcapture %}
{% capture body %}
## Trade-offs
The `kubectl` tool supports three kinds of object management:
* Imperative commands
* Imperative object configuration
* Declarative object configuration
See [Kubernetes Object Management](/docs/concepts/tools/kubectl/object-management-overview/)
for a discussion of the advantages and disadvantage of each kind of object management.
## How to create objects
The `kubectl` tool supports verb-driven commands for creating some of the most common
object types. The commands are named to be recognizable to users unfamiliar with
the Kubernetes object types.
- `run`: Create a new Deployment object to run Containers in one or more Pods.
- `expose`: Create a new Service object to load balance traffic across Pods.
- `autoscale`: Create a new Autoscaler object to automatically horizontally scale a controller, such as a Deployment.
The `kubectl` tool also supports creation commands driven by object type.
These commands support more object types and are more explicit about
their intent, but require users to know the type of objects they intend
to create.
- `create <objecttype> [<subtype>] <instancename>`
Some objects types have subtypes that you can specify in the `create` command.
For example, the Service object has several subtypes including ClusterIP,
LoadBalancer, and NodePort. Here's an example that creates a Service with
subtype NodePort:
```shell
kubectl create service nodeport <myservicename>
```
In the preceding example, the `create service nodeport` command is called
a subcommand of the `create service` command.
You can use the `-h` flag to find the arguments and flags supported by
a subcommand:
```shell
kubectl create service nodeport -h
```
## How to update objects
The `kubectl` command supports verb-driven commands for some common update operations.
These commands are named to enable users unfamiliar with Kubernetes
objects to perform updates without knowing the specific fields
that must be set:
- `scale`: Horizontally scale a controller to add or remove Pods by updating the replica count of the controller.
- `annotate`: Add or remove an annotation from an object.
- `label`: Add or remove a label from an object.
The `kubectl` command also supports update commands driven by an aspect of the object.
Setting this aspect may set different fields for different object types:
- `set` <field>: Set an aspect of an object.
**Note**: In Kubernetes version 1.5, not every verb-driven command has an
associated field-driven command.
The `kubectl` tool supports these additional ways to update a live object directly,
however they require a better understanding of the Kubernetes object schema.
- `edit`: Directly edit the raw configuration of a live object by opening its configuration in an editor.
- `patch`: Directly modify specific fields of a live object by using a patch string.
For more details on patch strings, see the patch section in
[API Conventions](https://github.com/kubernetes/community/blob/master/contributors/devel/api-conventions.md#patch-operations).
## How to delete objects
You can use the `delete` command to delete an object from a cluster:
- `delete <type>/<name>`
**Note**: You can use `kubectl delete` for both imperative commands and imperative object
configuration. The difference is in the arguments passed to the command. To use
`kubectl delete` as an imperative command, pass the object to be deleted as
an argument. Here's an example that passes a Deployment object named nginx:
```shell
kubectl delete deployment/nginx
```
## How to view an object
{% comment %}
TODO(pwittrock): Uncomment this when implemented.
You can use `kubectl view` to print specific fields of an object.
- `view`: Prints the value of a specific field of an object.
{% endcomment %}
There are several commands for printing information about an object:
- `get`: Prints basic information about matching objects. Use `get -h` to see a list of options.
- `describe`: Prints aggregated detailed information about matching objects.
- `logs`: Prints the stdout and stderr for a container running in a Pod.
## Using `set` commands to modify objects before creation
There are some object fields that don't have a flag you can use
in a `create` command. In some of those cases, you can use a combination of
`set` and `create` to specify a value for the field before object
creation. This is done by piping the output of the `create` command to the
`set` command, and then back to the `create` command. Here's an example:
```sh
kubectl create service clusterip <myservicename> -o yaml --dry-run | kubectl set selector --local -f - 'environment=qa' -o yaml | kubectl create -f -
```
1. The `create service -o yaml --dry-run` command creates the configuration for the Service, but prints it to stdout as YAML instead of sending it to the Kubernetes API server.
1. The `set --local -f - -o yaml` command reads the configuration from stdin, and writes the updated configuration to stdout as YAML.
1. The `kubectl create -f -` command creates the object using the configuration provided via stdin.
## Using `--edit` to modify objects before creation
You can use `kubectl create --edit` to make arbitrary changes to an object
before it is created. Here's an example:
```sh
kubectl create service clusterip my-svc -o yaml --dry-run > /tmp/srv.yaml
kubectl create --edit -f /tmp/srv.yaml
```
1. The `create service` command creates the configuration for the Service and saves it to `/tmp/srv.yaml`.
1. The `create --edit` command opens the configuration file for editing before it creates the object.
{% endcapture %}
{% capture whatsnext %}
- [Managing Kubernetes Objects Using Object Configuration (Imperative)](/docs/concepts/tools/kubectl/object-management-using-imperative-config/)
- [Managing Kubernetes Objects Using Object Configuration (Declarative)](/docs/concepts/tools/kubectl/object-management-using-declarative-config/)
- [Kubectl Command Reference](/docs/user-guide/kubectl/v1.5/)
- [Kubernetes Object Schema Reference](/docs/resources-reference/v1.5/)
{% endcapture %}
{% include templates/concept.md %}
docs/concepts/tools/kubectl/object-management-using-imperative-config.md
+129
View File
@@ -0,0 +1,129 @@
---
title: Imperative Management of Kubernetes Objects Using Configuration Files
---
{% capture overview %}
Kubernetes objects can be created, updated, and deleted by using the `kubectl`
command-line tool along with an object configuration file written in YAML or JSON.
This document explains how to define and manage objects using configuration files.
{% endcapture %}
{% capture body %}
## Trade-offs
The `kubectl` tool supports three kinds of object management:
* Imperative commands
* Imperative object configuration
* Declarative object configuration
See [Kubernetes Object Management](/docs/concepts/tools/kubectl/object-management-overview/)
for a discussion of the advantages and disadvantage of each kind of object management.
## How to create objects
You can use `kubectl create -f` to create an object from a configuration file.
Refer to the [kubernetes object schema reference](/docs/resources-reference/v1.5/)
for details.
- `create -f <filename|url>`
## How to update objects
You can use `kubectl replace -f` to update a live object according to a
configuration file.
- `replace -f <filename|url>`
## How to delete objects
You can use `kubectl delete -f` to delete an object that is described in a
configuration file.
- `delete -f <filename|url>`
## How to view an object
You can use `kubectl get -f` to view information about an object that is
described in a configuration file.
- `get -f <filename|url> -o yaml`
The `-o yaml` flag specifies that the full object configuration is printed.
Use `get -h` to see a list of options.
## Limitations
The `create`, `replace`, and `delete` commands work well when each object's
configuration is fully defined and recorded in its configuration
file. However when a live object is updated, and the updates are not merged
into its configuration file, the updates will be lost the next time a `replace`
is executed. This is can happen if a controller, such as
a HorizontalPodAutoscaler, makes updates directly to a live object. Here's
an example:
1. You create an object from a configuration file.
1. Another source updates the object by changing some field.
1. You replace the object from the configuration file. Changes made by
the other source in step 2 are lost.
If you need to support multiple writers to the same object, you can use
`kubectl apply` to manage the object.
## Creating and editing an object from a URL without saving the configuration
Suppose you have the URL of an object configuration file. You can use
`kubectl create --edit` to make changes to the configuration before the
object is created. This is particularly useful for tutorials and tasks
that point to a configuration file that could be modified by the reader.
```sh
kubectl create -f <url> --edit
```
## Migrating from imperative commands to imperative object configuration
Migrating from imperative commands to imperative object configuration involves
several manual steps.
1. Export the live object to a local object configuration file:
kubectl get <kind>/<name> -o yaml --export > <kind>_<name>.yaml
1. Manually remove the status field from the object configuration file.
1. For subsequent object management, use `replace` exclusively.
kubectl replace -f <kind>_<name>.yaml
## Defining controller selectors and PodTemplate labels
**Warning**: Updating selectors on controllers is strongly discouraged.
The recommended approach is to define a single, immutable PodTemplate label
used only by the controller selector with no other semantic meaning.
Example label:
```yaml
selector:
matchLabels:
controller-selector: "v1beta1/deployment/nginx"
template:
metadata:
labels:
controller-selector: "v1beta1/deployment/nginx"
```
{% endcapture %}
{% capture whatsnext %}
- [Managing Kubernetes Objects Using Imperative Commands](/docs/concepts/tools/kubectl/object-management-using-imperative-commands/)
- [Managing Kubernetes Objects Using Object Configuration (Declarative)](/docs/concepts/tools/kubectl/object-management-using-declarative-config/)
- [Kubectl Command Reference](/docs/user-guide/kubectl/v1.5/)
- [Kubernetes Object Schema Reference](/docs/resources-reference/v1.5/)
{% endcapture %}
{% include templates/concept.md %}
docs/concepts/tools/kubectl/simple_deployment.yaml
+16
View File
@@ -0,0 +1,16 @@
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: nginx-deployment
spec:
minReadySeconds: 5
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.7.9
ports:
- containerPort: 80
docs/concepts/tools/kubectl/update_deployment.yaml
+15
View File
@@ -0,0 +1,15 @@
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: nginx-deployment
spec:
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.11.9 # update the image
ports:
- containerPort: 80
docs/getting-started-guides/binary_release.md
+1 -1
View File
@@ -33,7 +33,7 @@ cd kubernetes
make release
```
For more details on the release process see the [`build-tools/`](http://releases.k8s.io/{{page.githubbranch}}/build-tools/) directory
For more details on the release process see the [`build`](http://releases.k8s.io/{{page.githubbranch}}/build/) directory
### Download Kubernetes and automatically set up a default cluster
docs/getting-started-guides/kubectl.md
-111
View File
@@ -1,111 +0,0 @@
---
title: Installing kubectl
---
<style>
li>.highlighter-rouge {position:relative; top:3px;}
</style>
## Overview
kubectl is the command line tool you use to interact with Kubernetes clusters.
You should use a version of kubectl that is at least as new as your server.
`kubectl version` will print the server and client versions. Using the same version of kubectl
as your server naturally works; using a newer kubectl than your server also works; but if you use
an older kubectl with a newer server you may see odd validation errors .
## Download a release
Download kubectl from the [official Kubernetes releases](https://console.cloud.google.com/storage/browser/kubernetes-release/release/):
On MacOS:
```shell
wget https://storage.googleapis.com/kubernetes-release/release/v1.4.4/bin/darwin/amd64/kubectl
chmod +x kubectl
mv kubectl /usr/local/bin/kubectl
```
On Linux:
```shell
wget https://storage.googleapis.com/kubernetes-release/release/v1.4.4/bin/linux/amd64/kubectl
chmod +x kubectl
mv kubectl /usr/local/bin/kubectl
```
You may need to `sudo` the `mv`; you can put it anywhere in your `PATH` - some people prefer to install to `~/bin`.
## Alternatives
### Download as part of the Google Cloud SDK
kubectl can be installed as part of the Google Cloud SDK:
First install the [Google Cloud SDK](https://cloud.google.com/sdk/).
After Google Cloud SDK installs, run the following command to install `kubectl`:
```shell
gcloud components install kubectl
```
Do check that the version is sufficiently up-to-date using `kubectl version`.
### Install with brew
If you are on MacOS and using brew, you can install with:
```shell
brew install kubectl
```
The homebrew project is independent from Kubernetes, so do check that the version is
sufficiently up-to-date using `kubectl version`.
# Enabling shell autocompletion
kubectl includes autocompletion support, which can save a lot of typing!
The completion script itself is generated by kubectl, so you typically just need to invoke it from your profile.
Common examples are provided here, but for more details please consult `kubectl completion -h`
## On Linux, using bash
To add it to your current shell: `source <(kubectl completion bash)`
To add kubectl autocompletion to your profile (so it is automatically loaded in future shells):
```shell
echo "source <(kubectl completion bash)" >> ~/.bashrc
```
## On MacOS, using bash
On MacOS, you will need to install the bash-completion support first:
```shell
brew install bash-completion
```
To add it to your current shell:
```shell
source $(brew --prefix)/etc/bash_completion
source <(kubectl completion bash)
```
To add kubectl autocompletion to your profile (so it is automatically loaded in future shells):
```shell
echo "source $(brew --prefix)/etc/bash_completion" >> ~/.bash_profile
echo "source <(kubectl completion bash)" >> ~/.bash_profile
```
Please note that this only appears to work currently if you install using `brew install kubectl`,
and not if you downloaded kubectl directly.
docs/getting-started-guides/ubuntu/index.md
+1
View File
@@ -43,6 +43,7 @@ These are more in-depth guides for users choosing to run Kubernetes in productio
- [Storage](/docs/getting-started-guides/ubuntu/storage)
- [Troubleshooting](/docs/getting-started-guides/ubuntu/troubleshooting)
- [Decommissioning](/docs/getting-started-guides/ubuntu/decommissioning)
- [Operational Considerations](/docs/getting-started-guides/ubuntu/operational-considerations)
- [Glossary](/docs/getting-started-guides/ubuntu/glossary)
## Developer Guides
docs/getting-started-guides/ubuntu/logging.md
+40
View File
@@ -18,3 +18,43 @@ The `juju debug-log` will show all of the consolidated logs of all the Juju agen
See the [Juju documentation](https://jujucharms.com/docs/stable/troubleshooting-logs) for more information.
## Managing log verbosity
Log verbosity in Juju is set at the model level. You can adjust it at any time:
```
juju add-model k8s-development --config logging-config='<root>=DEBUG;unit=DEBUG'
```
and later
```
juju config-model k8s-production --config logging-config='<root>=ERROR;unit=ERROR'
```
In addition, the jujud daemon is started in debug mode by default on all controllers. To remove that behavior edit ```/var/lib/juju/init/jujud-machine-0/exec-start.sh``` on the controller node and comment the ```--debug``` section.
It then contains:
```
#!/usr/bin/env bash
# Set up logging.
touch '/var/log/juju/machine-0.log'
chown syslog:syslog '/var/log/juju/machine-0.log'
chmod 0600 '/var/log/juju/machine-0.log'
exec >> '/var/log/juju/machine-0.log'
exec 2>&1
# Run the script.
'/var/lib/juju/tools/machine-0/jujud' machine --data-dir '/var/lib/juju' --machine-id 0 # --debug
```
Then restart the service with:
```
sudo systemctl restart jujud-machine-0.service
```
See the [official documentation](https://jujucharms.com/docs/stable/models-config) for more information about logging and other model settings in Juju.
docs/getting-started-guides/ubuntu/operational-considerations.md
+160
View File
@@ -0,0 +1,160 @@
---
title: Operational Considerations
---
{% capture overview %}
This page gives recommendations and hints for people managing long lived clusters
{% endcapture %}
{% capture prerequisites %}
This page assumes you understand the basics of Juju and Kubernetes.
{% endcapture %}
{% capture steps %}
## Managing Juju
### Sizing your controller node
The Juju Controller:
* requires about 2 to 2.5GB RAM to operate.
* uses a MongoDB database as a storage backend for the configuration and state of the cluster. This database can grow significantly, and can also be the biggest consumer of CPU cycles on the instance
* aggregates and stores the log data of all services and units. Therefore, significant storage is needed for long lived models. If your intention is to keep the cluster running, make sure to provision at least 64GB for the logs.
To bootstrap a controller with constraints run the following command:
```
juju bootstrap --contraints "mem=8GB cpu-cores=4 root-disk=128G"
```
Juju will select the cheapest instance type matching your constraints on your target cloud. You can also use the ```instance-type``` constraint in conjunction with ```root-disk``` for strict control. For more information about the constraints available, refer to the [official documentation](https://jujucharms.com/docs/stable/reference-constraints)
Additional information about logging can be found in the [logging section](/docs/getting-started-guides/ubuntu/logging)
### SSHing into the Controller Node
By default, Juju will create a pair of SSH keys that it will use to automate the connection to units. They are stored on the client node in ```~/.local/share/juju/ssh/```
After deployment, Juju Controller is a "silent unit" that acts as a proxy between the client and the deployed applications. Nevertheless it can be useful to SSH into it.
First you need to understand your environment, especially if you run several Juju models and controllers. Run
```
juju list-models --all
$ juju models --all
Controller: k8s
Model Cloud/Region Status Machines Cores Access Last connection
admin/controller lxd/localhost available 1 - admin just now
admin/default lxd/localhost available 0 - admin 2017-01-23
admin/whale* lxd/localhost available 6 - admin 3 minutes ago
```
The first line ```Controller: k8s``` refers to how you bootstrapped.
Then you will see 2, 3 or more models listed below.
* admin/controller is the default model that hosts all controller units of juju
* admin/default is created by default as the primary model to host the user application, such as the Kubernetes cluster
* admin/whale is an additional model created if you use conjure-up as an overlay on top of Juju.
Now to ssh into a controller node, you first ask Juju to switch context, then ssh as you would with a normal unit:
```
juju switch controller
```
At this stage, you can query the controller model as well:
```
juju status
Model Controller Cloud/Region Version
controller k8s lxd/localhost 2.0.2
App Version Status Scale Charm Store Rev OS Notes
Unit Workload Agent Machine Public address Ports Message
Machine State DNS Inst id Series AZ
0 started 10.191.22.15 juju-2a5ed8-0 xenial
```
Note that if you had bootstrapped in HA mode, you would see several machines listed.
Now ssh-ing into the controller follows the same semantic as classic Juju commands:
```
$ juju ssh 0
Welcome to Ubuntu 16.04.1 LTS (GNU/Linux 4.8.0-34-generic x86_64)
* Documentation: https://help.ubuntu.com
* Management: https://landscape.canonical.com
* Support: https://ubuntu.com/advantage
Get cloud support with Ubuntu Advantage Cloud Guest:
http://www.ubuntu.com/business/services/cloud
0 packages can be updated.
0 updates are security updates.
Last login: Tue Jan 24 16:38:13 2017 from 10.191.22.1
ubuntu@juju-2a5ed8-0:~$
```
When you are done and want to come back to your initial model, exit the controller and
Then if you need to switch back to your cluster and ssh into the units, run
```
juju switch default
```
## Managing your Kubernetes cluster
### Running privileged containers
By default juju-deployed clusters do not support running privileged containers. If you need them, you have to edit ```/etc/default/kube-apiserver``` on the master nodes, and ```/etc/default/kubelet``` on your worker nodes.
On Kubernetes Core or on small deployment, run the following commands from the Juju client:
#### Manually
1. Update the Master
```
juju ssh kubernetes-master/0 "sudo sed -i 's/KUBE_API_ARGS=\"/KUBE_API_ARGS=\"--allow-privileged\ /' /etc/default/kube-apiserver && sudo systemctl restart kube-apiserver.service"
```
2. Update the Worker(s)
```
juju ssh kubernetes-worker/0 "sudo sed -i 's/KUBELET_ARGS=\"/KUBELET_ARGS=\"--allow-privileged\ /' /etc/default/kubelet && sudo systemctl restart kubelet.service"
```
#### Programmatically
If the deployment is larger the following commands will run on all units successively:
1. Update all Masters
```
juju show-status kubernetes-master --format json | \
jq --raw-output '.applications."kubernetes-master".units | keys[]' | \
xargs -I UNIT juju ssh UNIT "sudo sed -i 's/KUBE_API_ARGS=\"/KUBE_API_ARGS=\"--allow-privileged\ /' /etc/default/kube-apiserver && sudo systemctl restart kube-apiserver.service"
```
2. Update all workers
```
juju show-status kubernetes-worker --format json | \
jq --raw-output '.applications."kubernetes-worker".units | keys[]' | \
xargs -I UNIT juju ssh UNIT "sudo sed -i 's/KUBELET_ARGS=\"/KUBELET_ARGS=\"--allow-privileged\ /' /etc/default/kubelet && sudo systemctl restart kubelet.service"
```
{% endcapture %}
{% include templates/task.md %}
docs/getting-started-guides/ubuntu/troubleshooting.md
+96
View File
@@ -105,6 +105,102 @@ charm unit data, etc. Additional application-specific information may be
included as well.
## Common Problems
### Load Balancer interfering with Helm
This section assumes you have a working deployment of Kubernetes via Juju using a Load Balancer for the API, and that you are using Helm to deploy charts.
To deploy Helm you will have run:
```
helm init
$HELM_HOME has been configured at /home/ubuntu/.helm
Tiller (the helm server side component) has been installed into your Kubernetes Cluster.
Happy Helming!
```
Then when using helm you may see one of the following errors:
* Helm doesn't get the version from the Tiller server
```
helm version
Client: &version.Version{SemVer:"v2.1.3", GitCommit:"5cbc48fb305ca4bf68c26eb8d2a7eb363227e973", GitTreeState:"clean"}
Error: cannot connect to Tiller
```
* Helm cannot install your chart
```
helm install <chart> --debug
Error: forwarding ports: error upgrading connection: Upgrade request required
```
This is caused by the API load balancer not forwarding ports in the context of the helm client-server relationship. To deploy using helm, you will need to follow these steps:
1. Expose the Kubernetes Master service
```
juju expose kubernetes-master
```
2. Identify the public IP address of one of your masters
```
juju status kubernetes-master
Model Controller Cloud/Region Version
production k8s-admin aws/us-east-1 2.0.0
App Version Status Scale Charm Store Rev OS Notes
flannel 0.6.1 active 1 flannel jujucharms 7 ubuntu
kubernetes-master 1.5.1 active 1 kubernetes-master jujucharms 10 ubuntu exposed
Unit Workload Agent Machine Public address Ports Message
kubernetes-master/0* active idle 5 54.210.100.102 6443/tcp Kubernetes master running.
flannel/0 active idle 54.210.100.102 Flannel subnet 10.1.50.1/24
Machine State DNS Inst id Series AZ
5 started 54.210.100.102 i-002b7150639eb183b xenial us-east-1a
Relation Provides Consumes Type
certificates easyrsa kubernetes-master regular
etcd etcd flannel regular
etcd etcd kubernetes-master regular
cni flannel kubernetes-master regular
loadbalancer kubeapi-load-balancer kubernetes-master regular
cni kubernetes-master flannel subordinate
cluster-dns kubernetes-master kubernetes-worker regular
cni kubernetes-worker flannel subordinate
```
In this context the public IP address is 54.210.100.102.
If you want to access this data programmatically you can use the JSON output:
```
juju show-status kubernetes-master --format json | jq --raw-output '.applications."kubernetes-master".units | keys[]'
54.210.100.102
```
3. Update the kubeconfig file
Identify the kubeconfig file or section used for this cluster, and edit the server configuration.
By default, it will look like ```https://54.213.123.123:443```. Replace it with the Kubernetes Master endpoint ```https://54.210.100.102:6443``` and save.
Note that the default port used by CDK for the Kubernetes Master API is 6443 while the port exposed by the load balancer is 443.
4. Start helming again!
```
helm install <chart> --debug
Created tunnel using local port: '36749'
SERVER: "localhost:36749"
CHART PATH: /home/ubuntu/.helm/<chart>
NAME: <chart>
...
...
```
## etcd
docs/hellonode.md
-433
View File
@@ -1,433 +0,0 @@
---
assignees:
- dchen1107
- pwittrock
title: Hello World on Google Container Engine
---
* TOC
{:toc}
## Introduction
The goal of this codelab is for you to turn a simple Hello World node.js app into a replicated application running on Kubernetes. We will show you how to take code that you have developed on your machine, turn it into a Docker container image, and then run that image on [Google Container Engine](https://cloud.google.com/container-engine/).
Here's a diagram of the various parts in play in this codelab to help you understand how pieces fit with one another. Use this as a reference as we progress through the codelab; it should all make sense by the time we get to the end.
![image](/images/hellonode/image_1.png)
Kubernetes is an open source project which can run on many different environments, from laptops to high-availability multi-node clusters, from public clouds to on-premise deployments, from virtual machines to bare metal. Using a managed environment such as Google Container Engine (a Google-hosted version of Kubernetes) will allow you to focus more on experiencing Kubernetes rather than setting up the underlying infrastructure.
## Setup and Requirements
If you don't already have a Google Account (Gmail or Google Apps), you must [create one](https://accounts.google.com/SignUp). Then, sign-in to Google Cloud Platform console ([console.cloud.google.com](http://console.cloud.google.com)) and create a new project:
![image](/images/hellonode/image_2.png)
![image](/images/hellonode/image_3.png)
Remember the project ID; it will be referred to later in this codelab as `$PROJECT_ID`.
Make sure you have a Linux terminal available, you will use it to control your cluster via command line. You can use [Google Cloud Shell](https://console.cloud.google.com?cloudshell=true), it has the software this codelab uses pre-installed so that you can skip most of the environment configuration steps below.
It may be helpful to store your project ID into a variable as many commands below use it:
```shell
export PROJECT_ID="your-project-id"
```
Next, [enable billing](https://console.cloud.google.com/billing) in the Cloud Console in order to use Google Cloud resources and [enable the Container Engine API](https://console.cloud.google.com/project/_/kubernetes/list).
New users of Google Cloud Platform receive a [$300 free trial](https://console.cloud.google.com/billing/freetrial?hl=en). Running through this codelab shouldn't cost you more than a few dollars of that trial. Google Container Engine pricing is documented [here](https://cloud.google.com/container-engine/pricing).
Next, make sure you [download Node.js](https://nodejs.org/en/download/). You can skip this and the steps for installing Docker and Cloud SDK if you're using Cloud Shell.
Then install [Docker](https://docs.docker.com/engine/installation/), and [Google Cloud SDK](https://cloud.google.com/sdk/).
Finally, after Google Cloud SDK installs, run the following command to install [`kubectl`](http://kubernetes.io/docs/user-guide/kubectl-overview/):
```shell
gcloud components install kubectl
```
You're all set up with an environment that can build container images, run Node apps, run Kubernetes clusters locally, and deploy Kubernetes clusters to Google Container Engine. Let's begin!
## Create your Node.js application
The first step is to write the application. Save this code in a folder called "`hellonode/`" with the filename `server.js`:
#### server.js
```javascript
const http = require('http');
const handleRequest = (request, response) => {
console.log('Received request for URL: ' + request.url);
response.writeHead(200);
response.end('Hello World!');
};
const www = http.createServer(handleRequest);
www.listen(8080);
```
Now run this simple command:
```shell
node server.js
```
You should be able to see your "Hello World!" message at http://localhost:8080/. If using Cloud Shell, use [Web Preview](https://cloud.google.com/shell/docs/using-web-preview) to view the URL.
Stop the running node server by pressing Ctrl-C.
Now let's package this application in a Docker container.
## Create a Docker container image
Next, create a file, also within `hellonode/` named `Dockerfile`. A Dockerfile describes the image that you want to build. Docker container images can extend from other existing images so for this image, we'll extend from an existing Node image.
#### Dockerfile
```conf
FROM node:4.5
EXPOSE 8080
COPY server.js .
CMD node server.js
```
This "recipe" for the Docker image will start from the official Node.js LTS image found on the Docker registry, expose port 8080, copy our `server.js` file to the image and start the Node server.
Now build an image of your container by running `docker build`, tagging the image with the Google Container Registry repo for your `$PROJECT_ID`:
```shell
docker build -t gcr.io/$PROJECT_ID/hello-node:v1 .
```
Now there is a trusted source for getting an image of your containerized app.
Let's try your image out with Docker:
```shell
docker run -d -p 8080:8080 --name hello_tutorial gcr.io/$PROJECT_ID/hello-node:v1
```
Visit your app in the browser, or use `curl` or `wget` if you'd like :
```shell
curl http://localhost:8080
```
You should see `Hello World!`
**Note:** *If you receive a `Connection refused` message from Docker for Mac, ensure you are using the latest version of Docker (1.12 or later). Alternatively, if you are using Docker Toolbox on OSX, make sure you are using the VM's IP and not localhost:*
```shell
curl "http://$(docker-machine ip YOUR-VM-MACHINE-NAME):8080"
```
Let's now stop the container. You can list the docker containers with:
```shell
docker ps
```
You should see something like this:
```shell
CONTAINER ID IMAGE COMMAND NAMES
c5b6d4b9f36d gcr.io/$PROJECT_ID/hello-node:v1 "/bin/sh -c 'node ser" hello_tutorial
```
Now stop the running container with
```
docker stop hello_tutorial
```
Now that the image works as intended and is all tagged with your `$PROJECT_ID`, we can push it to the [Google Container Registry](https://cloud.google.com/tools/container-registry/), a private repository for your Docker images accessible from every Google Cloud project (but also from outside Google Cloud Platform) :
```shell
gcloud docker -- push gcr.io/$PROJECT_ID/hello-node:v1
```
If all goes well, you should be able to see the container image listed in the console: *Compute > Container Engine > Container Registry*. We now have a project-wide Docker image available which Kubernetes can access and orchestrate.
If you see an error message like the following: __denied: Unable to create the repository, please check that you have access to do so.__ ensure that you are pushing the image to Container Registry with the correct user credentials, use `gcloud auth list` and then `gcloud config set account example@gmail.com`.
![image](/images/hellonode/image_10.png)
**Note:** *Docker for Windows, Version 1.12 or 1.12.1, does not yet support this procedure. Instead, it replies with the message 'denied: Unable to access the repository; please check that you have permission to access it'. A bugfix is available at http://stackoverflow.com/questions/39277986/unable-to-push-to-google-container-registry-unable-to-access-the-repository?answertab=votes#tab-top.*
## Create your Kubernetes Cluster
A cluster consists of a Master API server and a set of worker VMs called Nodes.
First, choose a [Google Cloud Project zone](https://cloud.google.com/compute/docs/regions-zones/regions-zones) to run
your service. For this tutorial, we will be using **us-central1-a**. This is
configured on the command line via:
```
gcloud config set compute/zone us-central1-a
```
Now, create a cluster via the `gcloud` command line tool:
```shell
gcloud container clusters create hello-world
```
Alternatively, you can create a cluster via the [Google Cloud Console](https://console.cloud.google.com): *Compute > Container Engine > Container Clusters > New container cluster*. Set the name to **hello-world**, leaving all other options default.
You should get a Kubernetes cluster with three nodes, ready to receive your container image! (this may take a couple of minutes)
![image](/images/hellonode/image_11.png)
It's now time to deploy your own containerized application to the Kubernetes cluster!
```shell
gcloud container clusters get-credentials hello-world
```
**The rest of this document requires both the Kubernetes client and server version to be 1.3. Run `kubectl version` to see your current versions.** For 1.2 see [this document](https://github.com/kubernetes/kubernetes.github.io/blob/release-1.2/docs/hellonode.md).
## Create your pod
A Kubernetes **[pod](/docs/user-guide/pods/)** is a group of containers, tied together for the purposes of administration and networking. It can contain a single container or multiple.
Create a Pod with the `kubectl run` command:
```shell
kubectl run hello-node --image=gcr.io/$PROJECT_ID/hello-node:v1 --port=8080
```
As shown in the output, the `kubectl run` created a **[Deployment](/docs/user-guide/deployments/)** object. Deployments are the recommended way for managing creation and scaling of pods. In this example, a new deployment manages a single pod replica running the *hello-node:v1* image.
To view the Deployment we just created run:
```shell
kubectl get deployments
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
hello-node 1 1 1 1 3m
```
To view the Pod created by the deployment run:
```shell
kubectl get pods
NAME READY STATUS RESTARTS AGE
hello-node-714049816-ztzrb 1/1 Running 0 6m
```
To view the stdout / stderr from a Pod run (probably empty currently):
```shell
kubectl logs <POD-NAME>
```
To view metadata about the cluster run:
```shell
kubectl cluster-info
```
To view cluster events run:
```shell
kubectl get events
```
To view the kubectl configuration run:
```shell
kubectl config view
```
Full documentation for kubectl commands is available **[here](/docs/user-guide/kubectl-overview/)**:
At this point you should have our container running under the control of Kubernetes but we still have to make it accessible to the outside world.
## Allow external traffic
By default, the pod is only accessible by its internal IP within the Kubernetes cluster. In order to make the `hello-node` container accessible from outside the Kubernetes virtual network, you have to expose the Pod as a Kubernetes **[Service](/docs/user-guide/services/)**.
From our Development machine we can expose the pod to the public internet using the `kubectl expose` command combined with the `--type="LoadBalancer"` flag. The flag is needed for the creation of an externally accessible ip:
```shell
kubectl expose deployment hello-node --type="LoadBalancer"
```
**If this fails, make sure your client and server are both version 1.3. See the [Create your cluster](#create-your-cluster) section for details.**
The flag used in this command specifies that we'll be using the load-balancer provided by the underlying infrastructure (in this case the [Compute Engine load balancer](https://cloud.google.com/compute/docs/load-balancing/)). Note that we expose the deployment, and not the pod directly. This will cause the resulting service to load balance traffic across all pods managed by the deployment (in this case only 1 pod, but we will add more replicas later).
The Kubernetes master creates the load balancer and related Compute Engine forwarding rules, target pools, and firewall rules to make the service fully accessible from outside of Google Cloud Platform.
To find the ip addresses associated with the service run:
```shell
kubectl get services hello-node
NAME CLUSTER_IP EXTERNAL_IP PORT(S) AGE
hello-node 10.3.246.12 8080/TCP 23s
```
The `EXTERNAL_IP` may take several minutes to become available and visible. If the `EXTERNAL_IP` is missing, wait a few minutes and try again.
```shell
kubectl get services hello-node
NAME CLUSTER_IP EXTERNAL_IP PORT(S) AGE
hello-node 10.3.246.12 23.251.159.72 8080/TCP 2m
```
Note there are 2 IP addresses listed, both serving port 8080. `CLUSTER_IP` is only visible inside your cloud virtual network. `EXTERNAL_IP` is externally accessible. In this example, the external IP address is 23.251.159.72.
You should now be able to reach the service by pointing your browser to this address: http://EXTERNAL_IP**:8080** or running `curl http://EXTERNAL_IP:8080`.
![image](/images/hellonode/image_12.png)
Assuming you've sent requests to your new webservice via the browser or curl,
you should now be able to see some logs by running:
```shell
kubectl logs <POD-NAME>
```
## Scale up your website
One of the powerful features offered by Kubernetes is how easy it is to scale your application. Suppose you suddenly need more capacity for your application; you can simply tell the deployment to manage a new number of replicas for your pod:
```shell
kubectl scale deployment hello-node --replicas=4
```
You now have four replicas of your application, each running independently on the cluster with the load balancer you created earlier and serving traffic to all of them.
```shell
kubectl get deployment
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
hello-node 4 4 4 3 40m
```
```shell
kubectl get pods
NAME READY STATUS RESTARTS AGE
hello-node-714049816-g4azy 1/1 Running 0 1m
hello-node-714049816-rk0u6 1/1 Running 0 1m
hello-node-714049816-sh812 1/1 Running 0 1m
hello-node-714049816-ztzrb 1/1 Running 0 41m
```
Note the **declarative approach** here - rather than starting or stopping new instances you declare how many instances you want to be running. Kubernetes reconciliation loops simply make sure the reality matches what you requested and take action if needed.
Here's a diagram summarizing the state of our Kubernetes cluster:
![image](/images/hellonode/image_13.png)
## Roll out an upgrade to your website
As always, the application you deployed to production requires bug fixes or additional features. Kubernetes is here to help you deploy a new version to production without impacting your users.
First, let's modify the application. On the development machine, edit server.js and update the response message:
```javascript
response.end('Hello Kubernetes World!');
```
We can now build and publish a new container image to the registry with an incremented tag:
```shell
docker build -t gcr.io/$PROJECT_ID/hello-node:v2 .
gcloud docker -- push gcr.io/$PROJECT_ID/hello-node:v2
```
Building and pushing this updated image should be much quicker as we take full advantage of the Docker cache.
We're now ready for Kubernetes to smoothly update our deployment to the new version of the application. In order to change
the image label for our running container, we will need to edit the existing *hello-node deployment* and change the image from
`gcr.io/$PROJECT_ID/hello-node:v1` to `gcr.io/$PROJECT_ID/hello-node:v2`. To do this, we will use the `kubectl set image` command.
```shell
kubectl set image deployment/hello-node hello-node=gcr.io/$PROJECT_ID/hello-node:v2
```
This updates the deployment with the new image, causing new pods to be created with the new image and old pods to be deleted.
```
kubectl get deployments
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
hello-node 4 5 4 3 1h
```
While this is happening, the users of the services should not see any interruption. After a little while they will start accessing the new version of your application. You can find more details in the [deployment documentation](/docs/user-guide/deployments/).
Hopefully with these deployment, scaling and update features you'll agree that once you've setup your environment (your GKE/Kubernetes cluster here), Kubernetes is here to help you focus on the application rather than the infrastructure.
## Observe the Kubernetes Web UI (optional)
Kubernetes comes with a graphical web user interface that is enabled by default with your clusters.
This user interface allows you to get started quickly and enables some of the functionality found in the CLI as a more approachable and discoverable way of interacting with the system.
Enjoy the Kubernetes graphical dashboard and use it for deploying containerized applications, as well as for monitoring and managing your clusters!
![image](/images/docs/ui-dashboard-workloadview.png)
Learn more about the web interface by taking the [Dashboard tour](/docs/user-guide/ui/).
## Cleaning it Up
That's it for the demo! So you don't leave this all running and incur charges, let's learn how to tear things down.
Delete the Deployment (which also deletes the running pods) and Service (which also deletes your external load balancer):
```shell
kubectl delete service,deployment hello-node
```
Delete your cluster:
```shell
gcloud container clusters delete hello-world
```
You should see:
```
The following clusters will be deleted.
- [hello-world] in [us-central1-a]
Do you want to continue (Y/n)?
Deleting cluster hello-world...done.
Deleted [https://container.googleapis.com/v1/projects/<$PROJECT_ID>/zones/us-central1-a/clusters/hello-world].
```
This deletes the Google Compute Engine instances that are running the cluster.
Finally delete the Docker registry storage bucket hosting your image(s) by using
`gsutil`, which should have been installed during the gcloud installation
process. For more information on gsutil, see [the gsutil documentation](https://cloud.google.com/storage/docs/gsutil)
To list the images we created earlier in the tutorial:
```shell
gsutil ls
```
You should see:
```shell
gs://artifacts.<$PROJECT_ID>.appspot.com/
```
And then to remove the all the images under this path, run:
```shell
gsutil rm -r gs://artifacts.$PROJECT_ID.appspot.com/
```
You can also delete the entire Google Cloud project but note that you must first disable billing on the project. Additionally, deleting a project will only happen after the current billing cycle ends.
docs/tasks/configure-pod-container/communicate-containers-same-pod.md
+1 -1
View File
@@ -137,7 +137,7 @@ the shared Volume is lost.
[composite containers for modular architecture](http://www.slideshare.net/Docker/slideshare-burns).
* See
[Configuring a Pod to Use a Volume for Storage](http://localhost:4000/docs/tasks/configure-pod-container/configure-volume-storage/).
[Configuring a Pod to Use a Volume for Storage](/docs/tasks/configure-pod-container/configure-volume-storage/).
* See [Volume](/docs/api-reference/v1/definitions/#_v1_volume).
docs/tutorials/stateless-application/hello-minikube.md
+4 -1
View File
@@ -1,6 +1,9 @@
---
title: Hello Minikube
redirect_from:
- "/docs/hellonode/"
- "/docs/hellonode.html"
---
{% capture overview %}
@@ -88,7 +91,7 @@ If a proxy server is required, use the following method to start Minikube cluste
minikube start --vm-driver=xhyve --docker-env HTTP_PROXY=http://your-http-proxy-host:your-http-proxy-port --docker-env HTTPS_PROXY=http(s)://your-https-proxy-host:your-https-proxy-port
```
The `--vm-driver=xyhve` flag specifies that you are using Docker for Mac. The
The `--vm-driver=xhyve` flag specifies that you are using Docker for Mac. The
default VM driver is VirtualBox.
Now set the Minikube context. The context is what determines which cluster
docs/user-guide/debugging-services.md
+2 -2
View File
@@ -566,11 +566,11 @@ This mostly happens when `kube-proxy` is running in `iptables` mode and Pods
are connected with bridge network. The `Kubelet` exposes a `hairpin-mode`
[flag](http://kubernetes.io/docs/admin/kubelet/) that allows endpoints of a Service to loadbalance back to themselves
if they try to access their own Service VIP. The `hairpin-mode` flag must either be
set to `haripin-veth` or `promiscuous-bridge`.
set to `hairpin-veth` or `promiscuous-bridge`.
The common steps to trouble shoot this are as follows:
* Confirm `hairpin-mode` is set to `haripin-veth` or `promiscuous-bridge`.
* Confirm `hairpin-mode` is set to `hairpin-veth` or `promiscuous-bridge`.
You should see something like the below. `hairpin-mode` is set to
`promiscuous-bridge` in the following example.
docs/user-guide/persistent-volumes/index.md
+1 -1
View File
@@ -543,7 +543,7 @@ and need persistent storage, we recommend that you use the following pattern:
- Do include PersistentVolumeClaim objects in your bundle of config (alongside Deployments, ConfigMaps, etc).
- Do not include PersistentVolume objects in the config, since the user instantiating the config may not have
permission to create PersistentVolumes.
- Give the user the option of providing a storage class name when instantating the template.
- Give the user the option of providing a storage class name when instantiating the template.
- If the user provides a storage class name, and the cluster is version 1.4 or newer, put that value into the `volume.beta.kubernetes.io/storage-class` annotation of the PVC.
This will cause the PVC to match the right storage class if the cluster has StorageClasses enabled by the admin.
- If the user does not provide a storage class name or the cluster is version 1.3, then instead put a `volume.alpha.kubernetes.io/storage-class: default` annotation on the PVC.
docs/user-guide/prereqs.md
+84 -1
View File
@@ -3,9 +3,24 @@ assignees:
- bgrant0607
- mikedanese
title: Installing and Setting up kubectl
redirect_from:
- "/docs/getting-started-guides/kubectl/"
- "/docs/getting-started-guides/kubectl.html"
---
To deploy and manage applications on Kubernetes, you'll use the Kubernetes command-line tool, [kubectl](/docs/user-guide/kubectl/). It lets you inspect your cluster resources, create, delete, and update components, and much more. You will use it to look at your new cluster and bring up example apps.
To deploy and manage applications on Kubernetes, you'll use the
Kubernetes command-line tool, [kubectl](/docs/user-guide/kubectl/). It
lets you inspect your cluster resources, create, delete, and update
components, and much more. You will use it to look at your new cluster
and bring up example apps.
You should use a version of kubectl that is at least as new as your
server. `kubectl version` will print the server and client versions.
Using the same version of kubectl as your server naturally works;
using a newer kubectl than your server also works; but if you use an
older kubectl with a newer server you may see odd validation errors.
Here are a few methods to install kubectl.
## Install kubectl Binary Via curl
@@ -65,6 +80,31 @@ export PATH=<path/to/kubernetes-directory>/platforms/darwin/amd64:$PATH
export PATH=<path/to/kubernetes-directory>/platforms/linux/amd64:$PATH
```
## Download as part of the Google Cloud SDK
kubectl can be installed as part of the Google Cloud SDK:
First install the [Google Cloud SDK](https://cloud.google.com/sdk/).
After Google Cloud SDK installs, run the following command to install `kubectl`:
```shell
gcloud components install kubectl
```
Do check that the version is sufficiently up-to-date using `kubectl version`.
## Install with brew
If you are on MacOS and using brew, you can install with:
```shell
brew install kubectl
```
The homebrew project is independent from kubernetes, so do check that the version is
sufficiently up-to-date using `kubectl version`.
## Configuring kubectl
In order for kubectl to find and access the Kubernetes cluster, it needs a [kubeconfig file](/docs/user-guide/kubeconfig-file), which is created automatically when creating a cluster using kube-up.sh (see the [getting started guides](/docs/getting-started-guides/) for more about creating clusters). If you need access to a cluster you didn't create, see the [Sharing Cluster Access document](/docs/user-guide/sharing-clusters).
@@ -80,6 +120,49 @@ $ kubectl cluster-info
If you see a url response, you are ready to go.
## Enabling shell autocompletion
kubectl includes autocompletion support, which can save a lot of typing!
The completion script itself is generated by kubectl, so you typically just need to invoke it from your profile.
Common examples are provided here, but for more details please consult `kubectl completion -h`
### On Linux, using bash
To add it to your current shell: `source <(kubectl completion bash)`
To add kubectl autocompletion to your profile (so it is automatically loaded in future shells):
```shell
echo "source <(kubectl completion bash)" >> ~/.bashrc
```
### On MacOS, using bash
On MacOS, you will need to install the bash-completion support first:
```shell
brew install bash-completion
```
To add it to your current shell:
```shell
source $(brew --prefix)/etc/bash_completion
source <(kubectl completion bash)
```
To add kubectl autocompletion to your profile (so it is automatically loaded in future shells):
```shell
echo "source $(brew --prefix)/etc/bash_completion" >> ~/.bash_profile
echo "source <(kubectl completion bash)" >> ~/.bash_profile
```
Please note that this only appears to work currently if you install using `brew install kubectl`,
and not if you downloaded kubectl directly.
## What's next?
[Learn how to launch and expose your application.](/docs/user-guide/quick-start)
docs/user-guide/secrets/index.md
+1 -1
View File
@@ -763,7 +763,7 @@ make that key begin with a dot. For example, when the following secret is mount
{
"name": "dotfile-test-container",
"image": "gcr.io/google_containers/busybox",
"command": "ls -l /etc/secret-volume",
"command": [ "ls", "-l", "/etc/secret-volume" ],
"volumeMounts": [
{
"name": "secret-volume",