这篇文章给大家分享的是有关docker中资源指标API及自定义指标API的示例分析的内容。小编觉得挺实用的，因此分享给大家做个参考，一起跟随小编过来看看吧。
以前是用heapster来收集资源指标才能看，现在heapster要废弃了。
从k8s v1.8开始后，引入了新的功能，即把资源指标引入api。
资源指标：metrics-server
自定义指标： prometheus,k8s-prometheus-adapter
因此，新一代架构：
1） 核心指标流水线：由kubelet、metrics-server以及由API server提供的api组成；cpu累计利用率、内存实时利用率、pod的资源占用率及容器的磁盘占用率
2） 监控流水线：用于从系统收集各种指标数据并提供终端用户、存储系统以及HPA，他们包含核心指标以及许多非核心指标。非核心指标不能被k8s所解析。
metrics-server是个api server，仅仅收集cpu利用率、内存利用率等。
[root@master ~]# kubectl api-versionsadmissionregistration.k8s.io/v1beta1apiextensions.k8s.io/v1beta1apiregistration.k8s.io/v1apiregistration.k8s.io/v1beta1apps/v1apps/v1beta1apps/v1beta2authentication.k8s.io/v1authentication.k8s.io/v1beta1authorization.k8s.io/v1资源指标（metrics）
访问https://github.com/kubernetes/kubernetes/tree/master/cluster/addons/metrics-server
把文件下载到本地目录,，注意，一定要到和自己k8s集群版本一致目录里面下载，比如我的k8s 是v1.11.2。否则安装后metrics的pod运行不起来。
[root@master metrics-server]# cd kubernetes-1.11.2/cluster/addons/metrics-server[root@master metrics-server]# lsauth-delegator.yaml metrics-apiservice.yaml metrics-server-service.yamlauth-reader.yaml metrics-server-deployment.yaml resource-reader.yaml注意：需要修改的地方：
metrics-server-deployment.yaml# - --source=kubernetes.summary_api:''- --source=kubernetes.summary_api:https://kubernetes.default?kubeletHttps=true&kubeletPort=10250&insecure=true resource-reader.yaml resources: - pods - nodes - namespaces - nodes/stats #新加[root@master metrics-server]# kubectl apply -f ./clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator createdrolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader createdapiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io createdserviceaccount/metrics-server createdconfigmap/metrics-server-config createddeployment.extensions/metrics-server-v0.3.1 createdservice/metrics-server createdclusterrole.rbac.authorization.k8s.io/system:metrics-server createdclusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created[root@master metrics-server]# kubectl get pods -n kube-system -o wideNAME READY STATUS RESTARTS AGE IP NODEmetrics-server-v0.2.1-fd596d746-c7x6q 2/2 Running 0 1m 10.244.2.49 node2[root@master metrics-server]# kubectl api-versionsmetrics.k8s.io/v1beta1 看到api-version里面有metrics了。
[root@master ~]# kubectl proxy --port=8080Starting to serve on 127.0.0.1:8080[root@master ~]# curl http://localhost:8080/apis/metrics.k8s.io/v1beta1{ "kind": "APIResourceList", "apiVersion": "v1", "groupVersion": "metrics.k8s.io/v1beta1", "resources": [ { "name": "nodes", "singularName": "", "namespaced": false, "kind": "NodeMetrics", "verbs": [ "get", "list" ] }, { "name": "pods", "singularName": "", "namespaced": true, "kind": "PodMetrics", "verbs": [ "get", "list" ] } ][root@master metrics-server]# curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/pods{ "kind": "PodMetricsList", "apiVersion": "metrics.k8s.io/v1beta1", "metadata": { "selfLink": "/apis/metrics.k8s.io/v1beta1/pods" }, "items": [ { "metadata": { "name": "pod1", "namespace": "dev", "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/dev/pods/pod1", "creationTimestamp": "2018-10-15T09:26:57Z" }, "timestamp": "2018-10-15T09:26:00Z", "window": "1m0s", "containers": [ { "name": "myapp", "usage": { "cpu": "0", "memory": "2940Ki" } } ] }, { "metadata": { "name": "rook-ceph-osd-0-b9b94dc6c-ffs8z", "namespace": "rook-ceph", "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/rook-ceph/pods/rook-ceph-osd-0-b9b94dc6c-ffs8z", "creationTimestamp": "2018-10-15T09:26:57Z" }, "timestamp": "2018-10-15T09:26:00Z", "window": "1m0s", "containers": [ {[root@master metrics-server]# curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/nodes{ "kind": "NodeMetricsList", "apiVersion": "metrics.k8s.io/v1beta1", "metadata": { "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes" }, "items": [ { "metadata": { "name": "node2", "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/node2", "creationTimestamp": "2018-10-15T09:27:26Z" }, "timestamp": "2018-10-15T09:27:00Z", "window": "1m0s", "usage": { "cpu": "90m", "memory": "1172044Ki" } }, { "metadata": { "name": "master", "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/master", "creationTimestamp": "2018-10-15T09:27:26Z" }, "timestamp": "2018-10-15T09:27:00Z", "window": "1m0s", "usage": { "cpu": "186m", "memory": "1582972Ki" } }, { "metadata": { "name": "node1", "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/node1", "creationTimestamp": "2018-10-15T09:27:26Z" }, "timestamp": "2018-10-15T09:27:00Z", "window": "1m0s", "usage": { "cpu": "68m", "memory": "1079332Ki" } } ]}[root@master metrics-server]# 看到iterms里面有数据了，说明可以采集各节点和pod里面的资源使用情况了。注意，如果你看不到就多等一会，如果等了很长的时间,iterm里面还是空，那么就看看metrics容器里面的日志是不是有报错。查看日志的方法为：
[root@master metrics-server]#kubectl get pods -n kube-systemNAME READY STATUS RESTARTS AGEmetrics-server-v0.2.1-84678c956-jdtr5 2/2 Running 0 14m[root@master metrics-server]# kubectl logs metrics-server-v0.2.1-84678c956-jdtr5 -c metrics-server -n kube-system-8r6lzI1015 09:26:57.117323 1 reststorage.go:93] No metrics for pod rook-ceph/rook-ceph-osd-prepare-node1-8r6lzI1015 09:26:57.117336 1 reststorage.go:140] No metrics for container rook-ceph-osd in pod rook-ceph/rook-ceph-osd-prepare-node2-vnr97I1015 09:26:57.117347 1 reststorage.go:93] No metrics for pod rook-ceph/rook-ceph-osd-prepare-node2-vnr97 这样，kubectl top命令就能使用了：
[root@master ~]# kubectl top nodesNAME CPU(cores) CPU% MEMORY(bytes) MEMORY% master 131m 3% 1716Mi 46% node1 68m 1% 1169Mi 31% node2 96m 2% 1236Mi 33%[root@master manifests]# kubectl top pods NAME CPU(cores) MEMORY(bytes) myapp-deploy-69b47bc96d-dfpvp 0m 2Mi myapp-deploy-69b47bc96d-g9kkz 0m 2Mi[root@master manifests]# kubectl top pods -n kube-systemNAME CPU(cores) MEMORY(bytes) canal-4h3ww 11m 49Mi canal-6tdxn 11m 49Mi canal-z2tp4 11m 43Mi coredns-78fcdf6894-2l2cf 1m 9Mi coredns-78fcdf6894-dkkfq 1m 10Mi etcd-master 14m 242Mi kube-apiserver-master 26m 527Mi kube-controller-manager-master 20m 68Mi kube-flannel-ds-amd64-6zqzr 2m 15Mi kube-flannel-ds-amd64-7qtcl 2m 17Mi kube-flannel-ds-amd64-kpctn 2m 18Mi kube-proxy-9snbs 2m 16Mi kube-proxy-psmxj 2m 18Mi kube-proxy-tc8g6 2m 17Mi kube-scheduler-master 6m 16Mi kubernetes-dashboard-767dc7d4d-4mq9z 0m 12Mi metrics-server-v0.2.1-84678c956-jdtr5 0m 29Mi自定义指标（prometheus)
大家看到，我们的metrics已经可以正常工作了。不过，metrics只能监控cpu和内存，对于其他指标如用户自定义的监控指标，metrics就无法监控到了。这时就需要另外一个组件叫prometheus。
prometheus的部署非常麻烦。
node_exporter是agent;
PromQL相当于sql语句来查询数据;
k8s-prometheus-adapter:prometheus是不能直接解析k8s的指标的，需要借助k8s-prometheus-adapter转换成api
kube-state-metrics是用来整合数据的。
下面开始部署。
访问https://github.com/ikubernetes/k8s-prom
[root@master pro]# git clone https://github.com/iKubernetes/k8s-prom.git 先创建一个叫prom的名称空间：
[root@master k8s-prom]# kubectl apply -f namespace.yaml namespace/prom created 部署node_exporter：
[root@master k8s-prom]# cd node_exporter/[root@master node_exporter]# lsnode-exporter-ds.yaml node-exporter-svc.yaml[root@master node_exporter]# kubectl apply -f .daemonset.apps/prometheus-node-exporter createdservice/prometheus-node-exporter created[root@master node_exporter]# kubectl get pods -n promNAME READY STATUS RESTARTS AGEprometheus-node-exporter-dmmjj 1/1 Running 0 7mprometheus-node-exporter-ghz2l 1/1 Running 0 7mprometheus-node-exporter-zt2lw 1/1 Running 0 7m 部署prometheus:
[root@master k8s-prom]# cd prometheus/[root@master prometheus]# lsprometheus-cfg.yaml prometheus-deploy.yaml prometheus-rbac.yaml prometheus-svc.yaml[root@master prometheus]# kubectl apply -f .configmap/prometheus-config createddeployment.apps/prometheus-server createdclusterrole.rbac.authorization.k8s.io/prometheus createdserviceaccount/prometheus createdclusterrolebinding.rbac.authorization.k8s.io/prometheus createdservice/prometheus created 看prom名称空间中的所有资源：
[root@master prometheus]# kubectl get all -n promNAME READY STATUS RESTARTS AGEpod/prometheus-node-exporter-dmmjj 1/1 Running 0 10mpod/prometheus-node-exporter-ghz2l 1/1 Running 0 10mpod/prometheus-node-exporter-zt2lw 1/1 Running 0 10mpod/prometheus-server-65f5d59585-6l8m8 1/1 Running 0 55sNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEservice/prometheus NodePort 10.111.127.64 <none> 9090:30090/TCP 56sservice/prometheus-node-exporter ClusterIP None <none> 9100/TCP 10mNAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGEdaemonset.apps/prometheus-node-exporter 3 3 3 3 3 <none> 10mNAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGEdeployment.apps/prometheus-server 1 1 1 1 56sNAME DESIRED CURRENT READY AGEreplicaset.apps/prometheus-server-65f5d59585 1 1 1 56s 上面我们看到通过NodePorts的方式，可以通过宿主机的30090端口，来访问prometheus容器里面的应用。
最好挂载个pvc的存储，要不这些监控数据过一会就没了。
部署kube-state-metrics，用来整合数据：
[root@master k8s-prom]# cd kube-state-metrics/[root@master kube-state-metrics]# lskube-state-metrics-deploy.yaml kube-state-metrics-rbac.yaml kube-state-metrics-svc.yaml[root@master kube-state-metrics]# kubectl apply -f .deployment.apps/kube-state-metrics createdserviceaccount/kube-state-metrics createdclusterrole.rbac.authorization.k8s.io/kube-state-metrics createdclusterrolebinding.rbac.authorization.k8s.io/kube-state-metrics createdservice/kube-state-metrics created[root@master kube-state-metrics]# kubectl get all -n promNAME READY STATUS RESTARTS AGEpod/kube-state-metrics-58dffdf67d-v9klh 1/1 Running 0 14mNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEservice/kube-state-metrics ClusterIP 10.111.41.139 <none> 8080/TCP 14m部署k8s-prometheus-adapter,这个需要自制证书：
[root@master k8s-prometheus-adapter]# cd /etc/kubernetes/pki/[root@master pki]# (umask 077; openssl genrsa -out serving.key 2048)Generating RSA private key, 2048 bit long modulus...........................................................................................+++...............+++e is 65537 (0x10001) 证书请求：
[root@master pki]# openssl req -new -key serving.key -out serving.csr -subj "/CN=serving" 开始签证：
[root@master pki]# openssl x509 -req -in serving.csr -CA ./ca.crt -CAkey ./ca.key -CAcreateserial -out serving.crt -days 3650Signature oksubject=/CN=servingGetting CA Private Key 创建加密的配置文件：
[root@master pki]# kubectl create secret generic cm-adapter-serving-certs --from-file=serving.crt=./serving.crt --from-file=serving.key=./serving.key -n promsecret/cm-adapter-serving-certs created注：cm-adapter-serving-certs是custom-metrics-apiserver-deployment.yaml文件里面的名字。
[root@master pki]# kubectl get secrets -n promNAME TYPE DATA AGEcm-adapter-serving-certs Opaque 2 51sdefault-token-knsbg kubernetes.io/service-account-token 3 4hkube-state-metrics-token-sccdf kubernetes.io/service-account-token 3 3hprometheus-token-nqzbz kubernetes.io/service-account-token 3 3h部署k8s-prometheus-adapter：
[root@master k8s-prom]# cd k8s-prometheus-adapter/[root@master k8s-prometheus-adapter]# lscustom-metrics-apiserver-auth-delegator-cluster-role-binding.yaml custom-metrics-apiserver-service.yamlcustom-metrics-apiserver-auth-reader-role-binding.yaml custom-metrics-apiservice.yamlcustom-metrics-apiserver-deployment.yaml custom-metrics-cluster-role.yamlcustom-metrics-apiserver-resource-reader-cluster-role-binding.yaml custom-metrics-resource-reader-cluster-role.yamlcustom-metrics-apiserver-service-account.yaml hpa-custom-metrics-cluster-role-binding.yaml由于k8s v1.11.2和k8s-prometheus-adapter最新版不兼容，解决办法就是访问https://github.com/DirectXMan12/k8s-prometheus-adapter/tree/master/deploy/manifests下载最新版的custom-metrics-apiserver-deployment.yaml文件，并把里面的namespace的名字改成prom；同时还要下载custom-metrics-config-map.yaml文件到本地来，并把里面的namespace的名字改成prom。
[root@master k8s-prometheus-adapter]# kubectl apply -f .clusterrolebinding.rbac.authorization.k8s.io/custom-metrics:system:auth-delegator createdrolebinding.rbac.authorization.k8s.io/custom-metrics-auth-reader createddeployment.apps/custom-metrics-apiserver createdclusterrolebinding.rbac.authorization.k8s.io/custom-metrics-resource-reader createdserviceaccount/custom-metrics-apiserver createdservice/custom-metrics-apiserver createdapiservice.apiregistration.k8s.io/v1beta1.custom.metrics.k8s.io createdclusterrole.rbac.authorization.k8s.io/custom-metrics-server-resources createdclusterrole.rbac.authorization.k8s.io/custom-metrics-resource-reader createdclusterrolebinding.rbac.authorization.k8s.io/hpa-controller-custom-metrics created[root@master k8s-prometheus-adapter]# kubectl get all -n promNAME READY STATUS RESTARTS AGEpod/custom-metrics-apiserver-65f545496-64lsz 1/1 Running 0 6mpod/kube-state-metrics-58dffdf67d-v9klh 1/1 Running 0 4hpod/prometheus-node-exporter-dmmjj 1/1 Running 0 4hpod/prometheus-node-exporter-ghz2l 1/1 Running 0 4hpod/prometheus-node-exporter-zt2lw 1/1 Running 0 4hpod/prometheus-server-65f5d59585-6l8m8 1/1 Running 0 4hNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEservice/custom-metrics-apiserver ClusterIP 10.103.87.246 <none> 443/TCP 36mservice/kube-state-metrics ClusterIP 10.111.41.139 <none> 8080/TCP 4hservice/prometheus NodePort 10.111.127.64 <none> 9090:30090/TCP 4hservice/prometheus-node-exporter ClusterIP None <none> 9100/TCP 4hNAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGEdaemonset.apps/prometheus-node-exporter 3 3 3 3 3 <none> 4hNAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGEdeployment.apps/custom-metrics-apiserver 1 1 1 1 36mdeployment.apps/kube-state-metrics 1 1 1 1 4hdeployment.apps/prometheus-server 1 1 1 1 4hNAME DESIRED CURRENT READY AGEreplicaset.apps/custom-metrics-apiserver-5f6b4d857d 0 0 0 36mreplicaset.apps/custom-metrics-apiserver-65f545496 1 1 1 6mreplicaset.apps/custom-metrics-apiserver-86ccf774d5 0 0 0 17mreplicaset.apps/kube-state-metrics-58dffdf67d 1 1 1 4hreplicaset.apps/prometheus-server-65f5d59585 1 1 1 4h 最终看到prom名称空间里面的所有资源都是running状态了。
[root@master k8s-prometheus-adapter]# kubectl api-versionscustom.metrics.k8s.io/v1beta1可以看到custom.metrics.k8s.io/v1beta1这个api了。
开个代理：
[root@master k8s-prometheus-adapter]# kubectl proxy --port=8080可以看到指标数据了：
[root@master pki]# curl http://localhost:8080/apis/custom.metrics.k8s.io/v1beta1/ { "name": "pods/ceph_rocksdb_submit_transaction_sync", "singularName": "", "namespaced": true, "kind": "MetricValueList", "verbs": [ "get" ] }, { "name": "jobs.batch/kube_deployment_created", "singularName": "", "namespaced": true, "kind": "MetricValueList", "verbs": [ "get" ] }, { "name": "jobs.batch/kube_pod_owner", "singularName": "", "namespaced": true, "kind": "MetricValueList", "verbs": [ "get" ] },下面我们就可以愉快的创建HPA了（水平Pod自动伸缩）。
另外，prometheus还可以和grafana整合。如下步骤。
先下载文件grafana.yaml，访问https://github.com/kubernetes/heapster/blob/master/deploy/kube-config/influxdb/grafana.yaml
[root@master pro]# wget修改grafana.yaml文件内容：
把namespace: kube-system改成prom，有两处； 把env里面的下面两个注释掉： - name: INFLUXDB_HOST value: monitoring-influxdb 在最有一行加个type: NodePort ports: - port: 80 targetPort: 3000 selector: k8s-app: grafana type: NodePort[root@master pro]# kubectl apply -f grafana.yaml deployment.extensions/monitoring-grafana createdservice/monitoring-grafana created[root@master pro]# kubectl get pods -n promNAME READY STATUS RESTARTS AGEmonitoring-grafana-ffb4d59bd-gdbsk 1/1 Running 0 5s 看到grafana这个pod运行起来了。
[root@master pro]# kubectl get svc -n promNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEmonitoring-grafana NodePort 10.106.164.205 <none> 80:32659/TCP 19m我们可以访问宿主机ip：http://172.16.1.100:32659
然后，就能从界面上看到相应的数据了。
登录下面的网站下载个grafana监控k8s-prometheus的模板：
然后再grafana的界面中导入上面下载的模板：
导入模板之后，就能看到监控数据了：
HPA（水平pod自动扩展）
当pod压力大了，会根据负载自动扩展Pod个数以均匀压力。
目前，HPA只支持两个版本，v1版本只支持核心指标的定义（只能根据cpu利用率的指标进行pod的扩展）；
[root@master pro]# kubectl explain hpa.spec.scaleTargetRefscaleTargetRef：表示基于什么指标来计算pod伸缩的标准[root@master pro]# kubectl api-versions |grep autoautoscaling/v1autoscaling/v2beta1 上面看到分别支持hpav1和hpav2。
下面我们用命令行的方式重新创建一个带有资源限制的pod myapp：
[root@master ~]# kubectl run myapp --image=ikubernetes/myapp:v1 --replicas=1 --requests='cpu=50m,memory=256Mi' --limits='cpu=50m,memory=256Mi' --labels='app=myapp' --expose --port=80service/myapp createddeployment.apps/myapp created[root@master ~]# kubectl get podsNAME READY STATUS RESTARTS AGEmyapp-6985749785-fcvwn 1/1 Running 0 58s 下面我们让myapp 这个pod能自动水平扩展，用kubectl autoscale，其实就是指明HPA控制器的。
[root@master ~]# kubectl autoscale deployment myapp --min=1 --max=8 --cpu-percent=60horizontalpodautoscaler.autoscaling/myapp autoscaled --min：表示最小扩展pod的个数
--max：表示最多扩展pod的个数
--cpu-percent：cpu利用率
[root@master ~]# kubectl get hpaNAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGEmyapp Deployment/myapp 0%/60% 1 8 1 4m[root@master ~]# kubectl get svcNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEmyapp ClusterIP 10.105.235.197 <none> 80/TCP 19下面我们把service改成NodePort的方式：
[root@master ~]# kubectl patch svc myapp -p '{"spec":{"type": "NodePort"}}'service/myapp patched[root@master ~]# kubectl get svcNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEmyapp NodePort 10.105.235.197 <none> 80:31990/TCP 22m[root@master ~]# yum install httpd-tools #主要是为了安装ab压测工具[root@master ~]# kubectl get pods -o wideNAME READY STATUS RESTARTS AGE IP NODEmyapp-6985749785-fcvwn 1/1 Running 0 25m 10.244.2.84 node2 开始用ab工具压测
[root@master ~]# ab -c 1000 -n 5000000 http://172.16.1.100:31990/index.htmlThis is ApacheBench, Version 2.3 <$Revision: 1430300 $>Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/Licensed to The Apache Software Foundation, http://www.apache.org/Benchmarking 172.16.1.100 (be patient) 多等一会，会看到pods的cpu利用率为98%，需要扩展为2个pod了：
[root@master ~]# kubectl describe hparesource cpu on pods (as a percentage of request): 98% (49m) / 60%Deployment pods: 1 current / 2 desired[root@master ~]# kubectl top podsNAME CPU(cores) MEMORY(bytes) myapp-6985749785-fcvwn 49m （我们设置的总cpu是50m） 3Mi[root@master ~]# kubectl get pods -o wideNAME READY STATUS RESTARTS AGE IP NODEmyapp-6985749785-fcvwn 1/1 Running 0 32m 10.244.2.84 node2myapp-6985749785-sr4qv 1/1 Running 0 2m 10.244.1.105 node1 上面我们看到已经自动扩展为2个pod了，再等一会，随着cpu压力的上升，还会看到自动扩展为4个或更多的pod：
[root@master ~]# kubectl get pods -o wideNAME READY STATUS RESTARTS AGE IP NODEmyapp-6985749785-2mjrd 1/1 Running 0 1m 10.244.1.107 node1myapp-6985749785-bgz6p 1/1 Running 0 1m 10.244.1.108 node1myapp-6985749785-fcvwn 1/1 Running 0 35m 10.244.2.84 node2myapp-6985749785-sr4qv 1/1 Running 0 5m 10.244.1.105 node1等压测一停止，pod个数还会收缩为正常个数的。
上面我们用的是hpav1来做的水平pod自动扩展的功能，我们前面也说过，hpa v1版本只能根据cpu利用率括水平自动扩展pod。
下面我们介绍一下hpa v2的功能，它可以根据自定义指标利用率来水平扩展pod。
在使用hpa v2版本前，我们先把前面创建的hpa v1版本删除了，以免和我们测试的hpa v2版本冲突：
[root@master hpa]# kubectl delete hpa myapphorizontalpodautoscaler.autoscaling "myapp" deleted 好了，下面我们创建一个hpa v2：
[root@master hpa]# cat hpa-v2-demo.yaml apiVersion: autoscaling/v2beta1 #从这可以看出是hpa v2版本kind: HorizontalPodAutoscalermetadata: name: myapp-hpa-v2spec: scaleTargetRef: #根据什么指标来做评估压力 apiVersion: apps/v1 #对谁来做自动扩展 kind: Deployment name: myapp minReplicas: 1 #最少副本数量 maxReplicas: 10 metrics: #表示依据哪些指标来进行评估 - type: Resource #表示基于资源进行评估 resource: name: cpu targetAverageUtilization: 55 #表示pod cpu使用率超过55%，就自动水平扩展pod个数 - type: Resource resource: name: memory #我们知道hpa v1版本只能根据cpu来进行评估，而到了我们的hpa v2版本就可以根据内存来进行评估了 targetAverageValue: 50Mi #表示pod内存使用超过50M，就自动水平扩展pod个数[root@master hpa]# kubectl apply -f hpa-v2-demo.yaml horizontalpodautoscaler.autoscaling/myapp-hpa-v2 created[root@master hpa]# kubectl get hpaNAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGEmyapp-hpa-v2 Deployment/myapp 3723264/50Mi, 0%/55% 1 10 1 37s 我们看到现在只有一个pod
[root@master hpa]# kubectl get pods -o wideNAME READY STATUS RESTARTS AGE IP NODEmyapp-6985749785-fcvwn 1/1 Running 0 57m 10.244.2.84 node2 开始压测：
[root@master ~]# ab -c 100 -n 5000000 http://172.16.1.100:31990/index.html 看hpa v2的检测情况：
[root@master hpa]# kubectl describe hpaMetrics: ( current / target ) resource memory on pods: 3756032 / 50Mi resource cpu on pods (as a percentage of request): 82% (41m) / 55%Min replicas: 1Max replicas: 10Deployment pods: 1 current / 2 desired[root@master hpa]# kubectl get pods -o wideNAME READY STATUS RESTARTS AGE IP NODEmyapp-6985749785-8frq4 1/1 Running 0 1m 10.244.1.109 node1myapp-6985749785-fcvwn 1/1 Running 0 1h 10.244.2.84 node2 看到自动扩展出了2个Pod。等压测一停止，pod个数还会收缩为正常个数的。
将来我们不光可以用hpa v2，根据cpu和内存使用率进行伸缩Pod个数，还可以根据http并发量等。
比如下面的：
[root@master hpa]# cat hpa-v2-custom.yaml apiVersion: autoscaling/v2beta1 #从这可以看出是hpa v2版本kind: HorizontalPodAutoscalermetadata: name: myapp-hpa-v2spec: scaleTargetRef: #根据什么指标来做评估压力 apiVersion: apps/v1 #对谁来做自动扩展 kind: Deployment name: myapp minReplicas: 1 #最少副本数量 maxReplicas: 10 metrics: #表示依据哪些指标来进行评估 - type: Pods #表示基于资源进行评估 pods: metricName: http_requests#自定义的资源指标 targetAverageValue: 800m #m表示个数,表示并发数800感谢各位的阅读！关于“docker中资源指标API及自定义指标API的示例分析”这篇文章就分享到这里了，希望以上内容可以对大家有一定的帮助，让大家可以学到更多知识，如果觉得文章不错，可以把它分享出去让更多的人看到吧！
