k8s api文档 调用heapster metrics
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。
官网api文档
https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.9/
版本更新到v1.10以后 上面这个链接就找不到了 要把v1.9改成v1.10才能访问。
https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/
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
1.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 # > pods.txt 输出到 \\192.168.1.138\hadoop\info\pods.json了
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
2.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
官方文档
https://kubernetes.io/docs/tasks/debug-application-cluster/core-metrics-pipeline/
https://github.com/kubernetes/metrics
https://github.com/kubernetes-incubator/metrics-server
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
弃用的数据获取
参考 https://jimmysong.io/posts/using-heapster-to-get-object-metrics/
官方api文档 https://github.com/kubernetes/heapster/blob/master/docs/model.md 弃用了
弃用的api取值 https://blog.csdn.net/mofiu/article/details/77126848
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" }
本文转自CSDN-k8s api文档 调用heapster metrics
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k8s namespace限制调研
1.创建namespace gpu 2.增加限制 [root@tensorflow1 gpu-namespace]# cat compute-resources.yaml apiVersion: v1 kind: ResourceQuota metadata: name: compute-resources namespace: gpu spec: hard: pods: "5" requests.cpu: "1" requests.memory: 1Gi limits.cpu: "2" limits.memory: 2Gi [root@tensorflow1 gpu-namespace]# kubectl describe namespace gpu Name: gpu Labels: <none> Annotations: <none> Status: Active Resource Quotas Name: compute-resources Resource Used Hard -------- --- --- limits.cpu 0 2 limits...
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k8s RBAC 多租户权限控制实现
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