docker筆記35-資源指標API及自定義指標API
以前是用heapster來收集資源指標才能看，現在heapster要廢棄了。
從k8s v1.8開始後，引入了新的功能，即把資源指標引入api。
資源指標：metrics-server
自定義指標： prometheus,k8s-prometheus-adapter
因此，新一代架構：
1） 核心指標流水線：由kubelet、metrics-server以及由API server提供的api組成；cpu累計利用率、記憶體實時利用率、pod的資源佔用率及容器的磁碟佔用率
2） 監控流水線：用於從系統收集各種指標資料並提供終端使用者、儲存系統以及HPA，他們包含核心指標以及許多非核心指標。非核心指標不能被k8s所解析。
metrics-server是個api server，僅僅收集cpu利用率、記憶體利用率等。
[root@master ~]# kubectl api-versions admissionregistration.k8s.io/v1beta1 apiextensions.k8s.io/v1beta1 apiregistration.k8s.io/v1 apiregistration.k8s.io/v1beta1 apps/v1 apps/v1beta1 apps/v1beta2 authentication.k8s.io/v1 authentication.k8s.io/v1beta1 authorization.k8s.io/v1
資源指標（metrics）
訪問
把檔案下載到本地目錄,，注意， 一定要到和自己k8s叢集版本一致目錄裡面下載 ，比如我的k8s 是v1.11.2。否則安裝後metrics的pod執行不起來。
[root@master metrics-server]# cd kubernetes-1.11.2/cluster/addons/metrics-server
[root@master metrics-server]# ls auth-delegator.yaml metrics-apiservice.yaml metrics-server-service.yaml auth-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 created rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created apiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io created serviceaccount/metrics-server created configmap/metrics-server-config created deployment.extensions/metrics-server-v0.3.1 created service/metrics-server created clusterrole.rbac.authorization.k8s.io/system:metrics-server created clusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created
[root@master metrics-server]# kubectl get pods -n kube-system -o wide NAME READY STATUS RESTARTS AGE IP NODE metrics-server-v0.2.1-fd596d746-c7x6q 2/2 Running 0 1m 10.244.2.49 node2
[root@master metrics-server]# kubectl api-versions metrics.k8s.io/v1beta1
看到api-version裡面有metrics了。
[root@master ~]# kubectl proxy --port=8080 Starting to serve on 127.0.0.1:8080
[root@master ~]# curl
{
"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 /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 /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裡面有資料了，說明可以採集各節點和pod裡面的資源使用情況了。注意，如果你看不到就多等一會，如果等了很長的時間,iterm裡面還是空，那麼就看看metrics容器裡面的日誌是不是有報錯。檢視日誌的方法為：
[root@master metrics-server]#kubectl get pods -n kube-system NAME READY STATUS RESTARTS AGE metrics-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 -8r6lz I1015 09:26:57.117323 1 reststorage.go:93] No metrics for pod rook-ceph/rook-ceph-osd-prepare-node1-8r6lz I1015 09:26:57.117336 1 reststorage.go:140] No metrics for container rook-ceph-osd in pod rook-ceph/rook-ceph-osd-prepare-node2-vnr97 I1015 09:26:57.117347 1 reststorage.go:93] No metrics for pod rook-ceph/rook-ceph-osd-prepare-node2-vnr97
這樣，kubectl top命令就能使用了：
[root@master ~]# kubectl top nodes NAME 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-system NAME CPU(cores) MEMORY(bytes) canal-4h2ww 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
自定義指標（prometheus)
大家看到，我們的metrics已經可以正常工作了。不過，metrics只能監控cpu和記憶體，對於其他指標如使用者自定義的監控指標，metrics就無法監控到了。這時就需要另外一個元件叫prometheus。
prometheus的部署非常麻煩。
node_exporter是agent;
PromQL相當於sql語句來查詢資料;
k8s-prometheus-adapter:prometheus是不能直接解析k8s的指標的，需要藉助k8s-prometheus-adapter轉換成api
kube-state-metrics是用來整合資料的。
下面開始部署。
訪問
[root@master pro]# git clone
先建立一個叫prom的名稱空間：
[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]# ls node-exporter-ds.yaml node-exporter-svc.yaml [root@master node_exporter]# kubectl apply -f . daemonset.apps/prometheus-node-exporter created service/prometheus-node-exporter created
[root@master node_exporter]# kubectl get pods -n prom NAME READY STATUS RESTARTS AGE prometheus-node-exporter-dmmjj 1/1 Running 0 7m prometheus-node-exporter-ghz2l 1/1 Running 0 7m prometheus-node-exporter-zt2lw 1/1 Running 0 7m
部署prometheus:
[root@master k8s-prom]# cd prometheus/ [root@master prometheus]# ls prometheus-cfg.yaml prometheus-deploy.yaml prometheus-rbac.yaml prometheus-svc.yaml [root@master prometheus]# kubectl apply -f . configmap/prometheus-config created deployment.apps/prometheus-server created clusterrole.rbac.authorization.k8s.io/prometheus created serviceaccount/prometheus created clusterrolebinding.rbac.authorization.k8s.io/prometheus created service/prometheus created
看prom名稱空間中的所有資源：
[root@master prometheus]# kubectl get all -n prom NAME READY STATUS RESTARTS AGE pod/prometheus-node-exporter-dmmjj 1/1 Running 0 10m pod/prometheus-node-exporter-ghz2l 1/1 Running 0 10m pod/prometheus-node-exporter-zt2lw 1/1 Running 0 10m pod/prometheus-server-65f5d59585-6l8m8 1/1 Running 0 55s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/prometheus NodePort 10.111.127.64 <none> 9090:30090/TCP 56s service/prometheus-node-exporter ClusterIP None <none> 9100/TCP 10m NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE daemonset.apps/prometheus-node-exporter 3 3 3 3 3 <none> 10m NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deployment.apps/prometheus-server 1 1 1 1 56s NAME DESIRED CURRENT READY AGE replicaset.apps/prometheus-server-65f5d59585 1 1 1 56s
上面我們看到透過NodePorts的方式，可以透過宿主機的30090埠，來訪問prometheus容器裡面的應用。
最好掛載個pvc的儲存，要不這些監控資料過一會就沒了。
部署kube-state-metrics，用來整合資料：
[root@master k8s-prom]# cd kube-state-metrics/ [root@master kube-state-metrics]# ls kube-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 created serviceaccount/kube-state-metrics created clusterrole.rbac.authorization.k8s.io/kube-state-metrics created clusterrolebinding.rbac.authorization.k8s.io/kube-state-metrics created service/kube-state-metrics created
[root@master kube-state-metrics]# kubectl get all -n prom NAME READY STATUS RESTARTS AGE pod/kube-state-metrics-58dffdf67d-v9klh 1/1 Running 0 14m NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/kube-state-metrics ClusterIP 10.111.41.139 <none> 8080/TCP 14m
部署k8s-prometheus-adapter,這個需要自制證書：
[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)
證書請求：
[root@master pki]# openssl req -new -key serving.key -out serving.csr -subj "/CN=serving"
開始簽證：
[root@master pki]# openssl x509 -req -in serving.csr -CA ./ca.crt -CAkey ./ca.key -CAcreateserial -out serving.crt -days 3650 Signature ok subject=/CN=serving Getting CA Private Key
建立加密的配置檔案：
[root@master pki]# kubectl create secret generic cm-adapter-serving-certs --from-file=serving.crt=./serving.crt --from-file=serving.key=./serving.key -n prom secret/cm-adapter-serving-certs created
注：cm-adapter-serving-certs是custom-metrics-apiserver-deployment.yaml檔案裡面的名字。
[root@master pki]# kubectl get secrets -n prom NAME TYPE DATA AGE cm-adapter-serving-certs Opaque 2 51s default-token-knsbg kubernetes.io/service-account-token 3 4h kube-state-metrics-token-sccdf kubernetes.io/service-account-token 3 3h prometheus-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]# ls custom-metrics-apiserver-auth-delegator-cluster-role-binding.yaml custom-metrics-apiserver-service.yaml custom-metrics-apiserver-auth-reader-role-binding.yaml custom-metrics-apiservice.yaml custom-metrics-apiserver-deployment.yaml custom-metrics-cluster-role.yaml custom-metrics-apiserver-resource-reader-cluster-role-binding.yaml custom-metrics-resource-reader-cluster-role.yaml custom-metrics-apiserver-service-account.yaml hpa-custom-metrics-cluster-role-binding.yaml
由於k8s v1.11.2和k8s-prometheus-adapter最新版不相容，解決辦法就是訪問下載最新版的custom-metrics-apiserver-deployment.yaml檔案，並把裡面的namespace的名字改成prom；同時還要下載custom-metrics-config-map.yaml檔案到本地來，並把裡面的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 created rolebinding.rbac.authorization.k8s.io/custom-metrics-auth-reader created deployment.apps/custom-metrics-apiserver created clusterrolebinding.rbac.authorization.k8s.io/custom-metrics-resource-reader created serviceaccount/custom-metrics-apiserver created service/custom-metrics-apiserver created apiservice.apiregistration.k8s.io/v1beta1.custom.metrics.k8s.io created clusterrole.rbac.authorization.k8s.io/custom-metrics-server-resources created clusterrole.rbac.authorization.k8s.io/custom-metrics-resource-reader created clusterrolebinding.rbac.authorization.k8s.io/hpa-controller-custom-metrics created
[root@master k8s-prometheus-adapter]# kubectl get all -n prom NAME READY STATUS RESTARTS AGE pod/custom-metrics-apiserver-65f545496-64lsz 1/1 Running 0 6m pod/kube-state-metrics-58dffdf67d-v9klh 1/1 Running 0 4h pod/prometheus-node-exporter-dmmjj 1/1 Running 0 4h pod/prometheus-node-exporter-ghz2l 1/1 Running 0 4h pod/prometheus-node-exporter-zt2lw 1/1 Running 0 4h pod/prometheus-server-65f5d59585-6l8m8 1/1 Running 0 4h NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/custom-metrics-apiserver ClusterIP 10.103.87.246 <none> 443/TCP 36m service/kube-state-metrics ClusterIP 10.111.41.139 <none> 8080/TCP 4h service/prometheus NodePort 10.111.127.64 <none> 9090:30090/TCP 4h service/prometheus-node-exporter ClusterIP None <none> 9100/TCP 4h NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE daemonset.apps/prometheus-node-exporter 3 3 3 3 3 <none> 4h NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deployment.apps/custom-metrics-apiserver 1 1 1 1 36m deployment.apps/kube-state-metrics 1 1 1 1 4h deployment.apps/prometheus-server 1 1 1 1 4h NAME DESIRED CURRENT READY AGE replicaset.apps/custom-metrics-apiserver-5f6b4d857d 0 0 0 36m replicaset.apps/custom-metrics-apiserver-65f545496 1 1 1 6m replicaset.apps/custom-metrics-apiserver-86ccf774d5 0 0 0 17m replicaset.apps/kube-state-metrics-58dffdf67d 1 1 1 4h replicaset.apps/prometheus-server-65f5d59585 1 1 1 4h
最終看到prom名稱空間裡面的所有資源都是running狀態了。
[root@master k8s-prometheus-adapter]# kubectl api-versions custom.metrics.k8s.io/v1beta1
可以看到custom.metrics.k8s.io/v1beta1這個api了。
開個代理：
[root@master k8s-prometheus-adapter]# kubectl proxy --port=8080
可以看到指標資料了：
[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"
]
},
下面我們就可以愉快的建立HPA了（水平Pod自動伸縮）。
另外，prometheus還可以和grafana整合。如下步驟。
先下載檔案grafana.yaml，訪問
[root@master pro]# wget
修改grafana.yaml檔案內容：
把namespace: kube-system改成prom，有兩處； 把env裡面的下面兩個註釋掉： - name: INFLUXDB_HOST value: monitoring-influxdb 在最有一行加個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 created service/monitoring-grafana created
[root@master pro]# kubectl get pods -n prom NAME READY STATUS RESTARTS AGE monitoring-grafana-ffb4d59bd-gdbsk 1/1 Running 0 5s
看到grafana這個pod執行起來了。
[root@master pro]# kubectl get svc -n prom NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE monitoring-grafana NodePort 10.106.164.205 <none> 80:32659/TCP 19m
我們可以訪問宿主機ip：
然後，就能從介面上看到相應的資料了。
登入下面的網站下載個grafana監控k8s-prometheus的模板：
然後再grafana的介面中匯入上面下載的模板：
匯入模板之後，就能看到監控資料了：
HPA（水平pod自動擴充套件）
當pod壓力大了，會根據負載自動擴充套件Pod個數以均勻壓力。
目前，HPA只支援兩個版本，v1版本只支援核心指標的定義（只能根據cpu利用率的指標進行pod的擴充套件）；
[root@master pro]# kubectl explain hpa.spec.scaleTargetRef scaleTargetRef：表示基於什麼指標來計算pod伸縮的標準
[root@master pro]# kubectl api-versions |grep auto autoscaling/v1 autoscaling/v2beta1
上面看到分別支援hpav1和hpav2。
下面我們用命令列的方式重新建立一個帶有資源限制的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=80 service/myapp created deployment.apps/myapp created
[root@master ~]# kubectl get pods NAME READY STATUS RESTARTS AGE myapp-6985749785-fcvwn 1/1 Running 0 58s
下面我們讓myapp 這個pod能自動水平擴充套件，用kubectl autoscale，其實就是指明HPA控制器的。
[root@master ~]# kubectl autoscale deployment myapp --min=1 --max=8 --cpu-percent=60 horizontalpodautoscaler.autoscaling/myapp autoscaled
--min：表示最小擴充套件pod的個數
--max：表示最多擴充套件pod的個數
--cpu-percent：cpu利用率
[root@master ~]# kubectl get hpa NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE myapp Deployment/myapp 0%/60% 1 8 1 4m
[root@master ~]# kubectl get svc NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE myapp ClusterIP 10.105.235.197 <none> 80/TCP 19
下面我們把service改成NodePort的方式：
[root@master ~]# kubectl patch svc myapp -p '{"spec":{"type": "NodePort"}}'
service/myapp patched
[root@master ~]# kubectl get svc NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE myapp NodePort 10.105.235.197 <none> 80:31990/TCP 22m
[root@master ~]# yum install httpd-tools #主要是為了安裝ab壓測工具
[root@master ~]# kubectl get pods -o wide NAME READY STATUS RESTARTS AGE IP NODE myapp-6985749785-fcvwn 1/1 Running 0 25m 10.244.2.84 node2
開始用ab工具壓測
[root@master ~]# ab -c 1000 -n 5000000 This is ApacheBench, Version 2.3 <$Revision: 1430300 $> Copyright 1996 Adam Twiss, Zeus Technology Ltd, Licensed to The Apache Software Foundation, Benchmarking 172.16.1.100 (be patient)
多等一會，會看到pods的cpu利用率為98%，需要擴充套件為2個pod了：
[root@master ~]# kubectl describe hpa resource cpu on pods (as a percentage of request): 98% (49m) / 60% Deployment pods: 1 current / 2 desired
[root@master ~]# kubectl top pods NAME CPU(cores) MEMORY(bytes) myapp-6985749785-fcvwn 49m （我們設定的總cpu是50m） 3Mi
[root@master ~]# kubectl get pods -o wide NAME READY STATUS RESTARTS AGE IP NODE myapp-6985749785-fcvwn 1/1 Running 0 32m 10.244.2.84 node2 myapp-6985749785-sr4qv 1/1 Running 0 2m 10.244.1.105 node1
上面我們看到已經自動擴充套件為2個pod了，再等一會，隨著cpu壓力的上升，還會看到自動擴充套件為4個或更多的pod：
[root@master ~]# kubectl get pods -o wide NAME READY STATUS RESTARTS AGE IP NODE myapp-6985749785-2mjrd 1/1 Running 0 1m 10.244.1.107 node1 myapp-6985749785-bgz6p 1/1 Running 0 1m 10.244.1.108 node1 myapp-6985749785-fcvwn 1/1 Running 0 35m 10.244.2.84 node2 myapp-6985749785-sr4qv 1/1 Running 0 5m 10.244.1.105 node1
等壓測一停止，pod個數還會收縮為正常個數的。
上面我們用的是hpav1來做的水平pod自動擴充套件的功能，我們前面也說過，hpa v1版本只能根據cpu利用率括水平自動擴充套件pod。
下面我們介紹一下hpa v2的功能，它可以根據自定義指標利用率來水平擴充套件pod。
在使用hpa v2版本前，我們先把前面建立的hpa v1版本刪除了，以免和我們測試的hpa v2版本衝突：
[root@master hpa]# kubectl delete hpa myapp horizontalpodautoscaler.autoscaling "myapp" deleted
好了，下面我們建立一個hpa v2：
[root@master hpa]# cat hpa-v2-demo.yaml apiVersion: autoscaling/v2beta1 #從這可以看出是hpa v2版本 kind: HorizontalPodAutoscaler metadata: name: myapp-hpa-v2 spec: scaleTargetRef: #根據什麼指標來做評估壓力 apiVersion: apps/v1 #對誰來做自動擴充套件 kind: Deployment name: myapp minReplicas: 1 #最少副本數量 maxReplicas: 10 metrics: #表示依據哪些指標來進行評估 - type: Resource #表示基於資源進行評估 resource: name: cpu targetAverageUtilization: 55 #表示pod cpu使用率超過55%，就自動水平擴充套件pod個數 - type: Resource resource: name: memory #我們知道hpa v1版本只能根據cpu來進行評估，而到了我們的hpa v2版本就可以根據記憶體來進行評估了 targetAverageValue: 50Mi #表示pod記憶體使用超過50M，就自動水平擴充套件pod個數
[root@master hpa]# kubectl apply -f hpa-v2-demo.yaml horizontalpodautoscaler.autoscaling/myapp-hpa-v2 created
[root@master hpa]# kubectl get hpa NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE myapp-hpa-v2 Deployment/myapp 3723264/50Mi, 0%/55% 1 10 1 37s
我們看到現在只有一個pod
[root@master hpa]# kubectl get pods -o wide NAME READY STATUS RESTARTS AGE IP NODE myapp-6985749785-fcvwn 1/1 Running 0 57m 10.244.2.84 node2
開始壓測：
[root@master ~]# ab -c 100 -n 5000000
看hpa v2的檢測情況：
[root@master hpa]# kubectl describe hpa Metrics: ( current / target ) resource memory on pods: 3756032 / 50Mi resource cpu on pods (as a percentage of request): 82% (41m) / 55% Min replicas: 1 Max replicas: 10 Deployment pods: 1 current / 2 desired
[root@master hpa]# kubectl get pods -o wide NAME READY STATUS RESTARTS AGE IP NODE myapp-6985749785-8frq4 1/1 Running 0 1m 10.244.1.109 node1 myapp-6985749785-fcvwn 1/1 Running 0 1h 10.244.2.84 node2
看到自動擴充套件出了2個Pod。等壓測一停止，pod個數還會收縮為正常個數的。
將來我們不光可以用hpa v2，根據cpu和記憶體使用率進行伸縮Pod個數，還可以根據http併發量等。
比如下面的：
[root@master hpa]# cat hpa-v2-custom.yaml apiVersion: autoscaling/v2beta1 #從這可以看出是hpa v2版本 kind: HorizontalPodAutoscaler metadata: name: myapp-hpa-v2 spec: scaleTargetRef: #根據什麼指標來做評估壓力 apiVersion: apps/v1 #對誰來做自動擴充套件 kind: Deployment name: myapp minReplicas: 1 #最少副本數量 maxReplicas: 10 metrics: #表示依據哪些指標來進行評估 - type: Pods #表示基於資源進行評估 pods: metricName: http_requests#自定義的資源指標 targetAverageValue: 800m #m表示個數,表示併發數800
關於併發數的hpa，具體映象可以參考，我這裡就不演示了，因為我是初學，這些內容基本夠用，以後生產需要再繼續深造。
來自 “ ITPUB部落格 ” ，連結：http://blog.itpub.net/28916011/viewspace-2216340/，如需轉載，請註明出處，否則將追究法律責任。
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