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Kubernetes HPA

作者:互联网

HPA控制器介绍

当系统资源过高的时候,我们可以使用如下命令来实现 Pod 的扩缩容功能

$ kubectl -n luffy scale deployment myblog --replicas=2

但是这个过程是手动操作的。在实际项目中,我们需要做到是的是一个自动化感知并自动扩容的操作。Kubernetes 也为提供了这样的一个资源对象:Horizontal Pod Autoscaling(Pod 水平自动伸缩),简称HPA
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基本原理:HPA 通过监控分析控制器控制的所有 Pod 的负载变化情况来确定是否需要调整 Pod 的副本数量

HPA的实现有两个版本:

如何获取Pod的监控数据?

思考:为什么之前用 heapster ,现在废弃了项目,改用 metric-server ?

heapster时代,apiserver 会直接将metric请求通过apiserver proxy 的方式转发给集群内的 hepaster 服务,采用这种 proxy 方式是有问题的:

于是官方从 1.8 版本开始逐步废弃 heapster,并提出了上边 Metric api 的概念,而 metrics-server 就是这种概念下官方的一种实现,用于从 kubelet获取指标,替换掉之前的 heapster。

Metrics Server 可以通过标准的 Kubernetes API 把监控数据暴露出来,比如获取某一Pod的监控数据:

https://192.168.136.10:6443/apis/metrics.k8s.io/v1beta1/namespaces/<namespace-name>/pods/<pod-name>

# https://192.168.136.10:6443/api/v1/namespaces/luffy/pods?limit=500

目前的采集流程:

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Metric Server

官方介绍

...
Metric server collects metrics from the Summary API, exposed by Kubelet on each node.

Metrics Server registered in the main API server through Kubernetes aggregator, which was introduced in Kubernetes 1.7
...
安装

官方代码仓库地址:https://github.com/kubernetes-sigs/metrics-server

Depending on your cluster setup, you may also need to change flags passed to the Metrics Server container. Most useful flags:

$ wget https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.3.6/components.yaml

修改args参数:

...
 84       containers:
 85       - name: metrics-server
 86         image: registry.aliyuncs.com/google_containers/metrics-server-amd64:v0.3.6
 87         imagePullPolicy: IfNotPresent
 88         args:
 89           - --cert-dir=/tmp
 90           - --secure-port=4443
 91           - --kubelet-insecure-tls
 92           - --kubelet-preferred-address-types=InternalIP
...

执行安装:

$ kubectl create -f components.yaml

$ kubectl -n kube-system get pods

$ kubectl top nodes
kubelet的指标采集

无论是 heapster还是 metric-server,都只是数据的中转和聚合,两者都是调用的 kubelet 的 api 接口获取的数据,而 kubelet 代码中实际采集指标的是 cadvisor 模块,你可以在 node 节点访问 10250 端口获取监控数据:

调用示例:

$ curl -k  -H "Authorization: Bearer eyJhbGciOiJSUzI1NiIsImtpZCI6InhXcmtaSG5ZODF1TVJ6dUcycnRLT2c4U3ZncVdoVjlLaVRxNG1wZ0pqVmcifQ.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uYW1lc3BhY2UiOiJrdWJlcm5ldGVzLWRhc2hib2FyZCIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VjcmV0Lm5hbWUiOiJhZG1pbi10b2tlbi1xNXBueiIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VydmljZS1hY2NvdW50Lm5hbWUiOiJhZG1pbiIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VydmljZS1hY2NvdW50LnVpZCI6ImViZDg2ODZjLWZkYzAtNDRlZC04NmZlLTY5ZmE0ZTE1YjBmMCIsInN1YiI6InN5c3RlbTpzZXJ2aWNlYWNjb3VudDprdWJlcm5ldGVzLWRhc2hib2FyZDphZG1pbiJ9.iEIVMWg2mHPD88GQ2i4uc_60K4o17e39tN0VI_Q_s3TrRS8hmpi0pkEaN88igEKZm95Qf1qcN9J5W5eqOmcK2SN83Dd9dyGAGxuNAdEwi0i73weFHHsjDqokl9_4RGbHT5lRY46BbIGADIphcTeVbCggI6T_V9zBbtl8dcmsd-lD_6c6uC2INtPyIfz1FplynkjEVLapp_45aXZ9IMy76ljNSA8Uc061Uys6PD3IXsUD5JJfdm7lAt0F7rn9SdX1q10F2lIHYCMcCcfEpLr4Vkymxb4IU4RCR8BsMOPIO_yfRVeYZkG4gU2C47KwxpLsJRrTUcUXJktSEPdeYYXf9w" https://localhost:10250/metrics

kubelet虽然提供了 metric 接口,但实际监控逻辑由内置的cAdvisor模块负责,早期的时候,cadvisor是单独的组件,从k8s 1.12开始,cadvisor 监听的端口在k8s中被删除,所有监控数据统一由Kubelet的API提供。

cadvisor获取指标时实际调用的是 runc/libcontainer库,而libcontainer是对 cgroup文件 的封装,即 cadvsior也只是个转发者,它的数据来自于cgroup文件。

cgroup文件中的值是监控数据的最终来源,如

Metrics数据流:
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思考:

Metrics Server是独立的一个服务,只能服务内部实现自己的api,是如何做到通过标准的kubernetes 的API格式暴露出去的?
kube-aggregator

kube-aggregator聚合器及Metric-Server的实现

kube-aggregator是对 apiserver 的api的一种拓展机制,它允许开发人员编写一个自己的服务,并把这个服务注册到k8s的api里面,即扩展 API 。
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定义一个APIService对象:

apiVersion: apiregistration.k8s.io/v1
kind: APIService
metadata:
  name: v1beta1.luffy.k8s.io
spec:
  group: luffy.k8s.io
  groupPriorityMinimum: 100
  insecureSkipTLSVerify: true
  service:
    name: service-A       # 必须https访问
    namespace: luffy
    port: 443   
  version: v1beta1
  versionPriority: 100

k8s会自动帮我们代理如下url的请求:

proxyPath := "/apis/" + apiService.Spec.Group + "/" + apiService.Spec.Version

即:https://192.168.136.10:6443/apis/luffy.k8s.io/v1beta1/xxxx转到我们的service-A服务中,service-A中只需要实现 https://service-A/luffy.k8s.io/v1beta1/xxxx 即可。

看下metric-server的实现:

$ kubectl get apiservice 
NAME                       SERVICE                      AVAILABLE                      
v1beta1.metrics.k8s.io   kube-system/metrics-server		True

$ kubectl get apiservice v1beta1.metrics.k8s.io -oyaml
...
spec:
  group: metrics.k8s.io
  groupPriorityMinimum: 100
  insecureSkipTLSVerify: true
  service:
    name: metrics-server
    namespace: kube-system
    port: 443
  version: v1beta1
  versionPriority: 100
...

$ kubectl -n kube-system get svc metrics-server
NAME             TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)   AGE
metrics-server   ClusterIP   10.110.111.146   <none>        443/TCP   11h

$ curl -k  -H "Authorization: Bearer xxxx" https://10.110.111.146
{
  "paths": [
    "/apis",
    "/apis/metrics.k8s.io",
    "/apis/metrics.k8s.io/v1beta1",
    "/healthz",
    "/healthz/healthz",
    "/healthz/log",
    "/healthz/ping",
    "/healthz/poststarthook/generic-apiserver-start-informers",
    "/metrics",
    "/openapi/v2",
    "/version"
  ]

# https://192.168.136.10:6443/apis/metrics.k8s.io/v1beta1/namespaces/<namespace-name>/pods/<pod-name>
# 
$ curl -k  -H "Authorization: Bearer xxxx" https://10.110.111.146/apis/metrics.k8s.io/v1beta1/namespaces/luffy/pods/myblog-5d9ff54d4b-4rftt

$ curl -k  -H "Authorization: Bearer xxxx" https://192.168.136.10:6443/apis/metrics.k8s.io/v1beta1/namespaces/luffy/pods/myblog-5d9ff54d4b-4rftt

HPA实践
基于CPU的动态伸缩

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创建hpa对象:

# 方式一
$ cat hpa-myblog.yaml
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: hpa-myblog-cpu
  namespace: luffy
spec:
  maxReplicas: 3
  minReplicas: 1
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myblog
  targetCPUUtilizationPercentage: 10

# 方式二
$ kubectl -n luffy autoscale deployment myblog --cpu-percent=10 --min=1 --max=3

Deployment对象必须配置requests的参数,不然无法获取监控数据,也无法通过HPA进行动态伸缩
验证:

$ yum -y install httpd-tools
$ kubectl -n luffy get svc myblog
myblog   ClusterIP   10.104.245.225   <none>        80/TCP    6d18h

# 为了更快看到效果,先调整副本数为1
$ kubectl -n luffy scale deploy myblog --replicas=1

# 模拟1000个用户并发访问页面10万次
$ ab -n 100000 -c 1000 http://10.104.245.225/blog/index/

$ kubectl get hpa
$ kubectl -n luffy get pods

压力降下来后,会有默认5分钟的scaledown的时间,可以通过controller-manager的如下参数设置:

--horizontal-pod-autoscaler-downscale-stabilization

The value for this option is a duration that specifies how long the autoscaler has to wait before another downscale operation can be performed after the current one has completed. The default value is 5 minutes (5m0s).

是一个逐步的过程,当前的缩放完成后,下次缩放的时间间隔,比如从3个副本降低到1个副本,中间大概会等待2*5min = 10分钟

基于内存的动态伸缩

创建hpa对象

$ cat hpa-demo-mem.yaml
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: hpa-demo-mem
  namespace: luffy
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: hpa-demo-mem
  minReplicas: 1
  maxReplicas: 3
  metrics:
  - type: Resource
    resource:
      name: memory
      targetAverageUtilization: 30

加压演示脚本:

$ cat increase-mem-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: increase-mem-config
  namespace: luffy
data:
  increase-mem.sh: |
    #!/bin/bash  
    mkdir /tmp/memory  
    mount -t tmpfs -o size=40M tmpfs /tmp/memory  
    dd if=/dev/zero of=/tmp/memory/block  
    sleep 60 
    rm /tmp/memory/block  
    umount /tmp/memory  
    rmdir /tmp/memory

测试deployment:

$ cat hpa-demo-mem-deploy.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: hpa-demo-mem
  namespace: luffy
spec:
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      volumes:
      - name: increase-mem-script
        configMap:
          name: increase-mem-config
      containers:
      - name: nginx
        image: nginx:alpine
        ports:
        - containerPort: 80
        volumeMounts:
        - name: increase-mem-script
          mountPath: /etc/script
        resources:
          requests:
            memory: 50Mi
            cpu: 50m
        securityContext:
          privileged: true

测试:

$ kubectl create -f increase-mem-config.yaml
$ kubectl create -f hpa-demo-mem.yaml
$ kubectl create -f hpa-demo-mem-deploy.yaml

$ kubectl -n luffy exec -ti hpa-demo-mem-7fc75bf5c8-xx424 sh
#/ sh /etc/script/increase-mem.sh


# 观察hpa及pod
$ kubectl -n luffy get hpa
$ kubectl -n luffy get po
基于自定义指标的动态伸缩

除了基于 CPU 和内存来进行自动扩缩容之外,我们还可以根据自定义的监控指标来进行。这个我们就需要使用 Prometheus Adapter,Prometheus 用于监控应用的负载和集群本身的各种指标,Prometheus Adapter 可以帮我们使用 Prometheus 收集的指标并使用它们来制定扩展策略,这些指标都是通过 APIServer 暴露的,而且 HPA 资源对象也可以很轻易的直接使用。

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架构图:
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标签:name,Kubernetes,mem,luffy,metrics,memory,HPA,k8s
来源: https://www.cnblogs.com/99jianshao/p/15024919.html