监控架构概述

kubernetes监控指标大体可以分为两类：核心监控指标和自定义指标，核心监控指标是kubernetes内置稳定可靠监控指标，早期由heapster完成，现由metric-server实现；自定义指标用于实现核心指标的扩展，能够提供更丰富的指标支持，如应用状态指标，自定义指标需要通过Aggregator和k8s api集成，当前主流通过promethues实现。

监控指标用途：

kubectl top 查看node和pod的cpu+内存使用情况
kubernetes-dashbaord 控制台查看节点和pod资源监控
Horizontal Pod Autoscaler 水平横向动态扩展
Scheduler 调度器调度选择条件

metric-server架构和安装

metric-server简介

Metrics Server is a cluster-wide aggregator of resource usage data. Resource metrics are used by components like kubectl top and the Horizontal Pod Autoscaler to scale workloads. To autoscale based upon a custom metric, you need to use the Prometheus Adapter Metric-server是一个集群级别的资源指标收集器，用于收集资源指标数据

提供基础资源如CPU、内存监控接口查询；
接口通过 Kubernetes aggregator注册到kube-apiserver中；
对外通过Metric API暴露给外部访问；
自定义指标使用需要借助Prometheus实现。

The Metrics API

/node 获取所有节点的指标，指标名称为NodeMetrics
/node/<node_name> 特定节点指标
/namespaces/{namespace}/pods 获取命名空间下的所有pod指标
/namespaces/{namespace}/pods/{pod} 特定pod的指标，指标名称为PodMetrics

未来将能够支持指标聚合，如max最大值，min最小值，95th峰值，以及自定义时间窗口，如1h，1d，1w等。

metric-server架构

监控架构分两部分内容：核心监控(图白色部分)和自定义监控（图蓝色部分）

1、核心监控实现

通过kubelet收集资源估算+使用估算
metric-server负责数据收集，不负责数据存储
metric-server对外暴露Metric API接口
核心监控指标客用户HPA，kubectl top，scheduler和dashboard

2、自定义监控实现

自定义监控指标包括监控指标和服务指标
需要在每个node上部署一个agent上报至集群监控agent，如prometheus
集群监控agent收集数据后需要将监控指标+服务指标通过API adaptor转换为apiserver能够处理的接口
HPA通过自定义指标实现更丰富的弹性扩展能力，需要通过HPA adaptor API做次转换。

metric-server部署

1、获取metric-server安装文件，当前具有两个版本：1.7和1.8+，kubernetes1.7版本安装1.7的metric-server版本，kubernetes 1.8后版本安装metric server 1.8+版本，现在最新的版本推荐使用0.3.7 版本

https://github.com/kubernetes-sigs/metrics-server/

2、部署metric-server，部署0.3.7版本

wget https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.3.7/components.yaml

核心的配置文件是metrics-server-deployment.yaml，metric-server以Deployment的方式部署在集群中，镜像k8s.gcr.io/metrics-server-amd64:v0.3.7需要提前下载好，其对应的安装文件内容如下：

# 修改后的metrics-server文件

cat metrics-server-0.3.7.yaml

---

apiVersion: rbac.authorization.k8s.io/v1

kind: ClusterRole

metadata:

  name: system:aggregated-metrics-reader

  labels:

    rbac.authorization.k8s.io/aggregate-to-view: "true"

    rbac.authorization.k8s.io/aggregate-to-edit: "true"

    rbac.authorization.k8s.io/aggregate-to-admin: "true"

rules:

- apiGroups: ["metrics.k8s.io"]

  resources: ["pods", "nodes"]

  verbs: ["get", "list", "watch"]

---

apiVersion: rbac.authorization.k8s.io/v1

kind: ClusterRoleBinding

metadata:

  name: metrics-server:system:auth-delegator

roleRef:

  apiGroup: rbac.authorization.k8s.io

  kind: ClusterRole

  name: system:auth-delegator

subjects:

- kind: ServiceAccount

  name: metrics-server

  namespace: kube-system

---

apiVersion: rbac.authorization.k8s.io/v1

kind: RoleBinding

metadata:

  name: metrics-server-auth-reader

  namespace: kube-system

roleRef:

  apiGroup: rbac.authorization.k8s.io

  kind: Role

  name: extension-apiserver-authentication-reader

subjects:

- kind: ServiceAccount

  name: metrics-server

  namespace: kube-system

---

apiVersion: apiregistration.k8s.io/v1beta1

kind: APIService

metadata:

  name: v1beta1.metrics.k8s.io

spec:

  service:

    name: metrics-server

    namespace: kube-system

  group: metrics.k8s.io

  version: v1beta1

  insecureSkipTLSVerify: true

  groupPriorityMinimum: 100

  versionPriority: 100

---

apiVersion: v1

kind: ServiceAccount

metadata:

  name: metrics-server

  namespace: kube-system

---

apiVersion: apps/v1

kind: Deployment

metadata:

  name: metrics-server

  namespace: kube-system

  labels:

    k8s-app: metrics-server

spec:

  selector:

    matchLabels:

      k8s-app: metrics-server

  replicas: 1

  strategy:

    rollingUpdate:

      maxSurge: 25%

      maxUnavailable: 25%

  template:

    metadata:

      name: metrics-server

      labels:

        k8s-app: metrics-server

    spec:

      serviceAccountName: metrics-server

      volumes:

      # mount in tmp so we can safely use from-scratch images and/or read-only containers

      - name: tmp-dir

        emptyDir: {}

      containers:

      - name: metrics-server

        image: k8s.gcr.io/metrics-server/metrics-server:v0.3.7

        imagePullPolicy: IfNotPresent

        args:

          - --cert-dir=/tmp

#          - --v=6

          - --metric-resolution=30s

          - --secure-port=4443

          - --kubelet-insecure-tls

          - --kubelet-preferred-address-types=InternalIP,Hostname,InternalDNS,ExternalDNS,ExternalIP

        ports:

        - name: main-port

          containerPort: 4443

          protocol: TCP

       # add livenessProbe

        livenessProbe:

          httpGet:

            path: /healthz

            port: 4443

            scheme: HTTPS

          initialDelaySeconds: 30

          periodSeconds: 30

          successThreshold: 1

          failureThreshold: 3

        readinessProbe:

          httpGet:

            path: /healthz

            port: 4443

            scheme: HTTPS

          initialDelaySeconds: 30

          periodSeconds: 30

          successThreshold: 1

          failureThreshold: 3

        securityContext:

          readOnlyRootFilesystem: true

          runAsNonRoot: true

          runAsUser: 1000

        volumeMounts:

        - name: tmp-dir

          mountPath: /tmp

      nodeSelector:

        kubernetes.io/os: linux

        kubernetes.io/arch: "amd64"

---

apiVersion: v1

kind: Service

metadata:

  name: metrics-server

  namespace: kube-system

  labels:

    kubernetes.io/name: "Metrics-server"

    kubernetes.io/cluster-service: "true"

spec:

  selector:

    k8s-app: metrics-server

  ports:

  - port: 443

    protocol: TCP

    targetPort: main-port

---

apiVersion: rbac.authorization.k8s.io/v1

kind: ClusterRole

metadata:

  name: system:metrics-server

rules:

- apiGroups:

  - ""

  resources:

  - pods

  - nodes

  - nodes/stats

  - namespaces

  - configmaps

  verbs:

  - get

  - list

  - watch

---

apiVersion: rbac.authorization.k8s.io/v1

kind: ClusterRoleBinding

metadata:

  name: system:metrics-server

roleRef:

  apiGroup: rbac.authorization.k8s.io

  kind: ClusterRole

  name: system:metrics-server

subjects:

- kind: ServiceAccount

  name: metrics-server

  namespace: kube-system

3、检查metric-server部署的情况,查看metric-server的Pod已部署成功

[root@localhost metrisc-server]# kubectl get deployments metrics-server -n kube-system

NAME             READY   UP-TO-DATE   AVAILABLE   AGE

metrics-server   0/1     1            0           10s

[root@localhost metrisc-server]# kubectl get pods -n kube-system -l k8s-app=metrics-server

NAME                              READY   STATUS    RESTARTS   AGE

metrics-server-74c4d67d6f-g6rgq   1/1     Running   0          12m

metric-server api测试

1、安装完metric-server后会增加一个metrics.k8s.io/v1beta1的API组，该API组通过Aggregator接入apiserver中

[root@localhost metrisc-server]# kubectl get apiservices.apiregistration.k8s.io |grep metrics-server

v1beta1.metrics.k8s.io                 kube-system/metrics-server   True        13m

2、使用命令行查看kubectl top node的监控信息,可以看到CPU和内存的利用率

[root@localhost metrisc-server]# kubectl top nodes

NAME                 CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%

test-control-plane   48m          4%     1152Mi          38%

3、查看pod监控信息,可以看到pod中CPU和内存的使用情况

kubectl top po

NAME                                    CPU(cores)   MEMORY(bytes)

haproxy-ingress-demo-855c859585-4fj7k   0m           2Mi

haproxy-ingress-demo-855c859585-nf8gl   0m           2Mi

haproxy-ingress-demo-855c859585-nkkv7   0m           2Mi

haproxy-ingress-demo2-744696b97-6hrx5   0m           2Mi

haproxy-ingress-demo2-744696b97-g9n5k   0m           2Mi

haproxy-ingress-demo2-744696b97-r8czj   0m           2Mi

hit-counter-app-77c47576d4-2xhwn        3m           45Mi

4、除了用命令行连接metricc-server获取监控资源，还可以通过API方式链接方式获取，可用API有

如下测试API接口的使用：

a、创建一个kube proxy代理，用于链接apiserver，默认将监听在127的8001端口

[root@node-1 ~]# kubectl proxy

Starting to serve on 127.0.0.1:8001

b、查看node列表的监控数据，可以获取到所有node的资源监控数据，usage中包含cpu和memory

[root@node-1 ~]# curl http://127.0.0.1:8001/apis/metrics.k8s.io/v1beta1/nodes

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current

                                 Dload  Upload   Total   Spent    Left  Speed

100  1167  100  1167    0     0   393k      0 --:--:-- --:--:-- --:--:--  569k

{

  "kind": "NodeMetricsList",

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

  "metadata": {

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

  },

  "items": [

    {

      "metadata": {

        "name": "node-3",

        "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/node-3",

        "creationTimestamp": "2019-12-30T14:23:00Z"

      },

      "timestamp": "2019-12-30T14:22:07Z",

      "window": "30s",

      "usage": {

        "cpu": "32868032n",

        "memory": "1027108Ki"

      }

    },

    {

      "metadata": {

        "name": "node-1",

        "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/node-1",

        "creationTimestamp": "2019-12-30T14:23:00Z"

      },

      "timestamp": "2019-12-30T14:22:07Z",

      "window": "30s",

      "usage": {

        "cpu": "108639556n",

        "memory": "4305356Ki"

      }

    },

    {

      "metadata": {

        "name": "node-2",

        "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/node-2",

        "creationTimestamp": "2019-12-30T14:23:00Z"

      },

      "timestamp": "2019-12-30T14:22:12Z",

      "window": "30s",

      "usage": {

        "cpu": "47607386n",

        "memory": "1119960Ki"

      }

    }

  ]

}

c、指定某个具体的node访问到具体node的资源监控数据

[root@node-1 ~]# curl http://127.0.0.1:8001/apis/metrics.k8s.io/v1beta1/nodes/node-2

{

  "kind": "NodeMetrics",

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

  "metadata": {

    "name": "node-2",

    "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/node-2",

    "creationTimestamp": "2019-12-30T14:24:39Z"

  },

  "timestamp": "2019-12-30T14:24:12Z",

  "window": "30s",

  "usage": {

    "cpu": "43027609n",

    "memory": "1120168Ki"

  }

}

d、查看所有pod的列表信息

curl http://127.0.0.1:8001/apis/metrics.k8s.io/v1beta1/pods

e、查看某个具体pod的监控数据

[root@node-1 ~]# curl http://127.0.0.1:8001/apis/metrics.k8s.io/v1beta1/namespaces/default/pods/haproxy-ingress-demo-5d487d4fc-sr8tm

{

  "kind": "PodMetrics",

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

  "metadata": {

    "name": "haproxy-ingress-demo-5d487d4fc-sr8tm",

    "namespace": "default",

    "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/default/pods/haproxy-ingress-demo-5d487d4fc-sr8tm",

    "creationTimestamp": "2019-12-30T14:36:30Z"

  },

  "timestamp": "2019-12-30T14:36:13Z",

  "window": "30s",

  "containers": [

    {

      "name": "haproxy-ingress-demo",

      "usage": {

        "cpu": "0",

        "memory": "1428Ki"

      }

    }

  ]

}

5、当然也可以通过kubectl -raw的方式访问接口,如调用node-3的数据

[root@node-1 ~]# kubectl get --raw /apis/metrics.k8s.io/v1beta1/nodes/node-3 | jq .

{

  "kind": "NodeMetrics",

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

  "metadata": {

    "name": "node-3",

    "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/node-3",

    "creationTimestamp": "2019-12-30T14:44:46Z"

  },

  "timestamp": "2019-12-30T14:44:09Z",

  "window": "30s",

  "usage": {

    "cpu": "35650151n",

    "memory": "1026820Ki"

  }

}

其他近似的接口有：

kubectl get --raw /apis/metrics.k8s.io/v1beta1/nodes 获取所有node的数据

kubectl get --raw /apis/metrics.k8s.io/v1beta1/nodes/<node_name> 获取特定node数据

kubectl get --raw /apis/metrics.k8s.io/v1beta1/pods 获取所有pod的数据

kubectl get --raw /apis/metrics.k8s.io/v1beta1/namespaces/default/pods/haproxy-ingress-demo-5d487d4fc-sr8tm 获取某个特定pod的数据

HPA水平横向动态扩展

HPA概述

The Horizontal Pod Autoscaler automatically scales the number of pods in a replication controller, deployment, replica set or stateful set based on observed CPU utilization (or, with custom metrics support, on some other application-provided metrics). Note that Horizontal Pod Autoscaling does not apply to objects that can’t be scaled, for example, DaemonSets.

HPA即Horizontal Pod Autoscaler,Pod水平横向动态扩展，即根据应用分配资源使用情况，动态增加或者减少Pod副本数量，以实现集群资源的扩容，其实现机制为：

HPA需要依赖于监控组件，调用监控数据实现动态伸缩，如调用Metrics API接口
HPA是二级的副本控制器，建立在Deployments，ReplicaSet，StatefulSets等副本控制器基础之上
HPA根据获取资源指标不同支持两个版本：v1和v2alpha1
HPA V1获取核心资源指标，如CPU和内存利用率，通过调用Metric-server API接口实现
HPA V2获取自定义监控指标，通过Prometheus获取监控数据实现
HPA根据资源API周期性调整副本数，检测周期horizontal-pod-autoscaler-sync-period定义的值，默认15s

HPA实现

如下开始延时HPA功能的实现，先创建一个Deployment副本控制器，然后再通过HPA定义资源度量策略，当CPU利用率超过requests分配的80%时即扩容。

1、创建Deployment副本控制器

[root@node-1 ~]# cat nginx-test.yaml

apiVersion: apps/v1

kind: Deployment

metadata:

  labels:

    app: hpa-demo

  name: hpa-demo

spec:

  replicas: 3

  selector:

    matchLabels:

      app: hpa-demo

  template:

    metadata:

      labels:

        app: hpa-demo

    spec:

      containers:

      - image: nginx:1.7.9

        name: nginx

         resources:

          requests:

            cpu: 10m

            memory: 80Mi

---

apiVersion: v1

kind: Service

metadata:

  name: hpa-demo

  namespace: default

spec:

  clusterIP: 10.109.197.67

  ports:

  - port: 80

    protocol: TCP

    targetPort: 80

  selector:

    app: hpa-demo

  type: ClusterIP

[root@node-1 ~]# kubectl get deployments hpa-demo -o yaml

apiVersion: extensions/v1beta1

kind: Deployment

metadata:

  annotations:

    deployment.kubernetes.io/revision: "1"

  creationTimestamp: "2019-12-31T01:43:24Z"

  generation: 1

  labels:

    run: hpa-demo

  name: hpa-demo

  namespace: default

  resourceVersion: "14451208"

  selfLink: /apis/extensions/v1beta1/namespaces/default/deployments/hpa-demo

  uid: 3b0f29e8-8606-4e52-8f5b-6c960d396136

spec:

  progressDeadlineSeconds: 600

  replicas: 1

  revisionHistoryLimit: 10

  selector:

    matchLabels:

      run: hpa-demo

  strategy:

    rollingUpdate:

      maxSurge: 25%

      maxUnavailable: 25%

    type: RollingUpdate

  template:

    metadata:

      creationTimestamp: null

      labels:

        run: hpa-demo

    spec:

      containers:

      - image: nginx:1.7.9

        imagePullPolicy: IfNotPresent

        name: hpa-demo

        ports:

        - containerPort: 80

          protocol: TCP

        resources:

          requests:

            cpu: 200m

            memory: 64Mi

        terminationMessagePath: /dev/termination-log

        terminationMessagePolicy: File

      dnsPolicy: ClusterFirst

      restartPolicy: Always

      schedulerName: default-scheduler

      securityContext: {}

      terminationGracePeriodSeconds: 30

status:

  availableReplicas: 1

  conditions:

  - lastTransitionTime: "2019-12-31T01:43:25Z"

    lastUpdateTime: "2019-12-31T01:43:25Z"

    message: Deployment has minimum availability.

    reason: MinimumReplicasAvailable

    status: "True"

    type: Available

  - lastTransitionTime: "2019-12-31T01:43:24Z"

    lastUpdateTime: "2019-12-31T01:43:25Z"

    message: ReplicaSet "hpa-demo-755bdd875c" has successfully progressed.

    reason: NewReplicaSetAvailable

    status: "True"

    type: Progressing

  observedGeneration: 1

  readyReplicas: 1

  replicas: 1

  updatedReplicas: 1

2、创建HPA控制器，基于CPU实现横向扩展,策略为至少2个Pod，最大5个，targetCPUUtilizationPercentage表示CPU实际使用率占requests百分比

vim hpa-demo.yaml

apiVersion: autoscaling/v1

kind: HorizontalPodAutoscaler

metadata:

  name: hpa-demo

spec:

  maxReplicas: 5

  minReplicas: 2

  scaleTargetRef:

    apiVersion: apps/v1

    kind: Deployment

    name: hpa-demo

  targetCPUUtilizationPercentage: 80

3、应用HPA规则并查看详情，由于策略需确保最小2个副本，Deployment默认不是2个副本，因此需要扩容，在详情日志中看到副本扩展至2个

[root@node-1 ~]# kubectl apply -f hpa-demo.yaml

horizontalpodautoscaler.autoscaling/hpa-demo created

#查看HPA列表

[root@node-1 ~]# kubectl get horizontalpodautoscalers.autoscaling

NAME       REFERENCE             TARGETS         MINPODS   MAXPODS   REPLICAS   AGE

hpa-demo   Deployment/hpa-demo   <unknown>/80%   2         5         0          7s

#查看HPA详情

[root@node-1 ~]# kubectl describe horizontalpodautoscalers.autoscaling hpa-demo

Name:                                                  hpa-demo

Namespace:                                             default

Labels:                                                <none>

Annotations:                                           kubectl.kubernetes.io/last-applied-configuration:

                                                         {"apiVersion":"autoscaling/v1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"hpa-demo","namespace":"default"},"spe...

CreationTimestamp:                                     Tue, 31 Dec 2019 09:52:51 +0800

Reference:                                             Deployment/hpa-demo

Metrics:                                               ( current / target )

  resource cpu on pods  (as a percentage of request):  <unknown> / 80%

Min replicas:                                          2

Max replicas:                                          5

Deployment pods:                                       1 current / 2 desired

Conditions:

  Type         Status  Reason            Message

  ----         ------  ------            -------

  AbleToScale  True    SucceededRescale  the HPA controller was able to update the target scale to 2

Events:

  Type    Reason             Age   From                       Message

  ----    ------             ----  ----                       -------

  Normal  SuccessfulRescale  1s    horizontal-pod-autoscaler  New size: 2; reason: Current number of replicas below Spec.MinReplicas #副本扩容至2个，根据MinReplica的策略

4、查看Deployment列表校验确认扩容情况，已达到HPA基础最小化策略

[root@node-1 ~]# kubectl get deployments hpa-demo  --show-labels

NAME       READY   UP-TO-DATE   AVAILABLE   AGE   LABELS

hpa-demo   2/2     2            2           94m   run=hpa-demo

[root@node-1 ~]# kubectl get pods -l run=hpa-demo

NAME                        READY   STATUS    RESTARTS   AGE

hpa-demo-5fcd9c757d-7q4td   1/1     Running   0          5m10s

hpa-demo-5fcd9c757d-cq6k6   1/1     Running   0          10m

5、假如业务增长期间，CPU利用率增高，会自动横向增加Pod来实现，下面开始通过CPU压测来演示Deployment的扩展

[root@node-1 ~]# kubectl exec -it hpa-demo-5fcd9c757d-cq6k6  /bin/bash

root@hpa-demo-5fcd9c757d-cq6k6:/#  dd if=/dev/zero of=/dev/null 

再次查看HPA的日志，提示已扩容，原因是cpu resource utilization (percentage of request) above target，即CPU资源利用率超过requests设置的百分比

[root@node-1 ~]# kubectl describe horizontalpodautoscalers.autoscaling hpa-demo

Name:                                                  hpa-demo

Namespace:                                             default

Labels:                                                <none>

Annotations:                                           kubectl.kubernetes.io/last-applied-configuration:

                                                         {"apiVersion":"autoscaling/v1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"hpa-demo","namespace":"default"},"spe...

CreationTimestamp:                                     Tue, 31 Dec 2019 09:52:51 +0800

Reference:                                             Deployment/hpa-demo

Metrics:                                               ( current / target )

  resource cpu on pods  (as a percentage of request):  99% (199m) / 80%

Min replicas:                                          2

Max replicas:                                          5

Deployment pods:                                       5 current / 5 desired

Conditions:

  Type            Status  Reason            Message

  ----            ------  ------            -------

  AbleToScale     True    ReadyForNewScale  recommended size matches current size

  ScalingActive   True    ValidMetricFound  the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)

  ScalingLimited  True    TooManyReplicas   the desired replica count is more than the maximum replica count

Events:

  Type     Reason                   Age                   From                       Message

  ----     ------                   ----                  ----                       -------

  Normal   SuccessfulRescale        8m2s                  horizontal-pod-autoscaler  New size: 4; reason: cpu resource utilization (percentage of request) above target

查看副本的个数，确认扩容情况，已成功扩容至5个

[root@node-1 ~]# kubectl get pods

NAME                                   READY   STATUS    RESTARTS   AGE

hpa-demo-5fcd9c757d-7q4td              1/1     Running   0          16m

hpa-demo-5fcd9c757d-cq6k6              1/1     Running   0          21m

hpa-demo-5fcd9c757d-jmb6w              1/1     Running   0          16m

hpa-demo-5fcd9c757d-lpxk8              1/1     Running   0          16m

hpa-demo-5fcd9c757d-zs6cg              1/1     Running   0          21m

6、停止CPU压测业务，HPA会自定缩减Pod的副本个数，直至满足条件

[root@node-1 ~]# kubectl describe horizontalpodautoscalers.autoscaling hpa-demo

Name:                                                  hpa-demo

Namespace:                                             default

Labels:                                                <none>

Annotations:                                           kubectl.kubernetes.io/last-applied-configuration:

                                                         {"apiVersion":"autoscaling/v1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"hpa-demo","namespace":"default"},"spe...

CreationTimestamp:                                     Tue, 31 Dec 2019 09:52:51 +0800

Reference:                                             Deployment/hpa-demo

Metrics:                                               ( current / target )

  resource cpu on pods  (as a percentage of request):  0% (0) / 80%

Min replicas:                                          2

Max replicas:                                          5

Deployment pods:                                       2 current / 2 desired

Conditions:

  Type            Status  Reason            Message

  ----            ------  ------            -------

  AbleToScale     True    ReadyForNewScale  recommended size matches current size

  ScalingActive   True    ValidMetricFound  the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)

  ScalingLimited  True    TooFewReplicas    the desired replica count is increasing faster than the maximum scale rate

Events:

  Type     Reason                   Age                   From                       Message

  ----     ------                   ----                  ----                       -------

  Normal   SuccessfulRescale        18m                   horizontal-pod-autoscaler  New size: 4; reason: cpu resource utilization (percentage of request) above target

  Normal   SuccessfulRescale        113s                  horizontal-pod-autoscaler  New size: 2; reason: All metrics below target   #缩减至2个pod副本

确认副本的个数，已缩减至最小数量2个

[root@node-1 ~]# kubectl get pods -l run=hpa-demo

NAME                        READY   STATUS    RESTARTS   AGE

hpa-demo-5fcd9c757d-cq6k6   1/1     Running   0          24m

hpa-demo-5fcd9c757d-zs6cg   1/1     Running   0          24m

通过上面的例子可以知道，HPA可以基于metric-server提供的API监控数据实现水平动态弹性扩展的需求，从而可以根据业务CPU使用情况，动态水平横向扩展，保障业务的可用性。当前HPA V1扩展使用指标只能基于CPU分配使用率进行扩展，功能相对有限，更丰富的功能需要由HPA V2版来实现，其由不同的API来实现：

metrics.k8s.io 资源指标API，通过metric-server提供，提供node和pod的cpu，内存资源查询；
custom.metrics.k8s.io 自定义指标，通过adapter和kube-apiserver集成，如promethues；
external.metrics.k8s.io 外部指标，和自定义指标类似，需要通过adapter和k8s集成。

参考文献

资源指标说明：https://kubernetes.io/docs/tasks/debug-application-cluster/resource-metrics-pipeline/

部署官方说明：(https://github.com/kubernetes-sigs/metrics-server)

参考文档：https://cloud.tencent.com/developer/article/1579216

巴特西

Kubernetes使用metric-server让HPA弹性伸缩运行