restful api访问k8s集群，增删改 查信息，做界面二次开发。

需要预先创建访问权限的配置。

下面罗列部分api

curl -u admin:admin "https://localhost:6443/api/v1" -k

curl -u admin:admin "https://localhost:6443/api/v1/pods" -k

curl -u admin:admin "https://localhost:6443/api/v1/namespaces/{namespace}/pods" -k

curl -u admin:admin "https://localhost:6443/api/v1/namespaces/default/pods" -k

获取节点信息

curl -u admin:admin "https://localhost:6443/api/v1/nodes/{nodename}" -k

curl -u admin:admin "https://localhost:6443/api/v1/nodes/tensorflow1" -k

...

"status": {

"capacity": {

"cpu": "4",

"memory": "7970316Ki",

"pods": "110"

},

"allocatable": {

"cpu": "4",

"memory": "7867916Ki",

"pods": "110"

},

...

获取namespace信息

curl -u admin:admin "https://localhost:6443/api/v1/namespaces/{namespace}" -k

curl -u admin:admin "https://localhost:6443/api/v1/namespaces/default" -k

获得quota信息

curl -u admin:admin "https://localhost:6443/api/v1/namespaces/{namespace}/resourcequotas/" -k

curl -u admin:admin "https://localhost:6443/api/v1/namespaces/default/resourcequotas/" -k

实践

k8s_master_ip:192.168.1.138

username 不同用户不同

password 不同用户不同

namespace 不同用户不同

查看容器

curl -u {username}:{password} "https://{k8s_master_ip}:6443/api/v1/namespaces/{namespace}/pods/" -k

curl -u admin:admin "https://192.168.1.138:6443/api/v1/namespaces/default/pods/" -k

看起来像是把所有的pod都拿出来了，包括活的和死的。

看了一下信息很多不过没有资源使用信息。

"phase": "Running"

这个是正在运行的pod

"phase": "Failed"

"reason":"Evicted"

这种是删除了的，状态是failed 原因是被驱逐

增加continue参数取出正在运行的容器

curl -u {username}:{password} "https://{k8s_master_ip}:6443/api/v1/namespaces/{namespace}/pods?continue" -k

curl -u admin:admin "https://192.168.1.138:6443/api/v1/namespaces/default/pods?continue" -k

查看replicationcontroller

curl -u admin:admin "https://192.168.1.138:6443/api/v1/namespaces/user1/replicationcontrollers/" -k

查看service

curl -u admin:admin "https://192.168.1.138:6443/api/v1/namespaces/user1/services/" -k

查看资源总览resourcequotas

curl -u {username}:{password} "https://{k8s_master_ip}:6443/api/v1/namespaces/{namespace}/resourcequotas/" -k

[root@tensorflow1 info]# curl -u admin:admin "https://localhost:6443/api/v1/namespaces/default/resourcequotas/" -k

...

"status": {

"hard": {

"limits.cpu": "2",

"limits.memory": "6Gi",

"pods": "20",

"requests.cpu": "1",

"requests.memory": "1Gi"

},

"used": {

"limits.cpu": "400m",

"limits.memory": "1Gi",

"pods": "2",

"requests.cpu": "200m",

"requests.memory": "512Mi"

}

}

...

hard是限额   used是当前申请的限额

limits 和 requests 的区别是 limits是上限，不能突破，但不保证能给。 requests是下限，保证能给。 举例说明：一个容器 requests.memory 512Mi，limits.memory 1Gi。宿主机内存使用量高时，一定会留512Mi内存给这个容器，不一定能拿到1Gi内存。宿主机内存使用量低时，容器不能突破1Gi内存。

Gi 和 G 的区别是 Gi是1024进制，G是1000进制，M Mi也是同理。就像一个U盘8G但实际能使用的是7.45G(其实这里单位就是Gi)

pods是指容器，单位个

cpu单位 m指千分之一，200m即0.2个cpu。这是绝对值，不是相对值。比如0.1CPU不管是在单核或者多核机器上都是一样的，都严格等于0.1CPU core

实时数据

下载 metrics-server 压缩包文件

下载 googlecontainer/metrics-server-amd64:v0.2.0

cd metrics-server-0.2.1/deploy

修改 metrics-server-deployment.yaml 文件 image 和 imagePullPolicy: IfNotPresent

kubectl create -f .

获取节点信息

curl -u {username}:{password} "https://{k8s_master_ip}:6443/apis/metrics.k8s.io/v1beta1/nodes" -k

curl -u admin:admin "https://192.168.1.138:6443/apis/metrics.k8s.io/v1beta1/nodes" -k

{

"kind": "NodeMetricsList",

"apiVersion": "metrics.k8s.io/v1beta1",

"metadata": {

"selfLink": "/apis/metrics.k8s.io/v1beta1/nodes"

},

"items": [

...

{

"metadata": {

"name": "tensorflow1",

"selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/tensorflow1",

"creationTimestamp": "2018-04-09T08:44:17Z"

},

"timestamp": "2018-04-09T08:44:00Z",

"window": "1m0s",

"usage": {

"cpu": "265m",

"memory": "3448228Ki"

}

}

...

]

}

获取pod信息

curl -u {username}:{password} "https://{k8s_master_ip}:6443/apis/metrics.k8s.io/v1beta1/namespaces/{namespace}/pods" -k

curl -u admin:admin "https://192.168.1.138:6443/apis/metrics.k8s.io/v1beta1/namespaces/default/pods" -k

{

"kind": "PodMetricsList",

"apiVersion": "metrics.k8s.io/v1beta1",

"metadata": {

"selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/default/pods"

},

"items": [

...

{

"metadata": {

"name": "tensorflow-worker-rc-998wf",

"namespace": "default",

"selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/default/pods/tensorflow-worker-rc-998wf",

"creationTimestamp": "2018-04-09T08:52:38Z"

},

"timestamp": "2018-04-09T08:52:00Z",

"window": "1m0s",

"containers": [

{

"name": "worker",

"usage": {

"cpu": "0",

"memory": "39964Ki"

}

}

]

}

...

]

}

获取namespace信息

没找到url，就把上面获取pod的使用量全加起来就是namespace的使用量了

Metrics API 文档

网上找不到文档 只能从 kubectl top 命令帮助里找

[root@tensorflow1 ~]# kubectl top

Display Resource (CPU/Memory/Storage) usage.

The top command allows you to see the resource consumption for nodes or pods.

This command requires Heapster to be correctly configured and working on the server.

Available Commands:

node Display Resource (CPU/Memory/Storage) usage of nodes

pod Display Resource (CPU/Memory/Storage) usage of pods

Usage:

kubectl top [options]

Use "kubectl --help" for more information about a given command.

Use "kubectl options" for a list of global command-line options (applies to all commands).

[root@tensorflow1 ~]# kubectl top pod --help

Display Resource (CPU/Memory/Storage) usage of pods.

The 'top pod' command allows you to see the resource consumption of pods.

Due to the metrics pipeline delay, they may be unavailable for a few minutes since pod creation.

Aliases:

pod, pods, po

Examples:

# Show metrics for all pods in the default namespace

kubectl top pod

# Show metrics for all pods in the given namespace

kubectl top pod --namespace=NAMESPACE

# Show metrics for a given pod and its containers

kubectl top pod POD_NAME --containers

# Show metrics for the pods defined by label name=myLabel

kubectl top pod -l name=myLabel

Options:

--all-namespaces=false: If present, list the requested object(s) across all namespaces. Namespace in current

context is ignored even if specified with --namespace.

--containers=false: If present, print usage of containers within a pod.

--heapster-namespace='kube-system': Namespace Heapster service is located in

--heapster-port='': Port name in service to use

--heapster-scheme='http': Scheme (http or https) to connect to Heapster as

--heapster-service='heapster': Name of Heapster service

-l, --selector='': Selector (label query) to filter on, supports '=', '==', and '!='.(e.g. -l key1=value1,key2=value2)

Usage:

kubectl top pod [NAME | -l label] [options]

Use "kubectl options" for a list of global command-line options (applies to all commands).

获取heapster url

[root@tensorflow1 influxdb]kubectl cluster-info

Kubernetes master is running at https://192.168.1.138:6443

Heapster is running at https://192.168.1.138:6443/api/v1/namespaces/kube-system/services/heapster/proxy

KubeDNS is running at https://192.168.1.138:6443/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy

monitoring-grafana is running at https://192.168.1.138:6443/api/v1/namespaces/kube-system/services/monitoring-grafana/proxy

monitoring-influxdb is running at https://192.168.1.138:6443/api/v1/namespaces/kube-system/services/monitoring-influxdb/proxy

curl -u admin:admin "https://192.168.1.138:6443/api/v1/namespaces/kube-system/services/heapster/proxy/api/v1/model/namespaces/" -k

curl -u admin:admin "https://192.168.1.138:6443/api/v1/namespaces/kube-system/services/heapster/proxy/api/v1/model/namespaces/default/metrics" -k

[

"memory/request",

"memory/limit",

"cpu/usage_rate",

"memory/usage",

"cpu/request",

"cpu/limit"

]

[root@tensorflow1 influxdb]# curl -u admin:admin "https://192.168.1.138:6443/api/v1/namespaces/kube-system/services/heapster/proxy/api/v1/model/namespaces/default/metrics/memory/usage" -k

{

"metrics": [

...

{

"timestamp": "2018-04-09T07:45:00Z",

"value": 81121280

},

{

"timestamp": "2018-04-09T07:46:00Z",

"value": 81121280

}

...

],

"latestTimestamp": "2018-04-09T07:46:00Z"

}