Kebernetes 學習總結(14) K8s Horizontal Pod Autoscaling

1、概述
HPA全稱Horizontal Pod Autoscaling，即pod的水平自動擴展。HPA的操做對象是RC、RS或Deployment對應的Pod，根據觀察到的CPU實際使用量與用戶的指望值進行比對，作出是否須要增減實例數量的決策。
它根據Pod當前系統的負載來自動水平擴容，若是系統負載超過預約值，就開始增長Pod的個數，若是低於某個值，就自動減小Pod的個數。目前K8S的HPA只能根據CPU和內存去度量系統的負載，並且目前還依賴heapster去收集CPU的使用狀況。
HPA經過按期（按期輪詢的時間經過–horizontal-pod-autoscaler-sync-period選項來設置，默認的時間爲30秒）經過Status.PodSelector來查詢pods的狀態，得到pod的CPU使用率。而後，經過現有pods的CPU使用率的平均值（計算方式是最近的pod使用量除以設定的每一個Pod的CPU使用率限額，例最近一分鐘從heapster中得到的平均值除以Pod的CPU limits值）跟目標使用率進行比較，而且在擴容時，還要遵循預先設定的副本數限制：MinReplicas <= Replicas <= MaxReplicas。
計算擴容後Pod的個數：sum(最近一分鐘內某個Pod的CPU使用率/量的平均值)/CPU使用上限的整數+1
考慮到自動擴展的決策可能須要一段時間纔會生效，甚至在短期內會引入一些噪聲。例如當pod所須要的CPU負荷過大，從而運行一個新的pod進行分流，在建立過程當中，系統的CPU使用量可能會有一個攀升的過程。因此，在每一次做出決策後的一段時間內，將再也不進行擴展決策。對於ScaleUp而言，這個時間段爲3分鐘，Scaledown爲5分鐘。
HPA容許必定範圍內的CPU使用量的不穩定，只有avg(CurrentPodsConsumption) / Target小於90%或者大於110%時纔會觸發擴容或縮容，避免頻繁擴容、縮容形成顛簸。
爲了簡便，選用了相對比率（90%的CPU資源）而不是0.6個CPU core來描述擴容、縮容條件。若是選擇使用絕對度量，用戶須要保證目標（限額）要比請求使用的低，不然，過載的Pod未必可以消耗那麼多，從而自動擴容永遠不會被觸發：假設設置CPU爲1個核，那麼這個pod只能使用1個核，可能Pod在過載的狀況下也不能徹底利用這個核，因此擴容不會發生。在修改申請資源時，還有同時調整擴容的條件，好比將1個core變爲1.2core，那麼擴容條件應該同步改成1.2core，真是太麻煩了，與自動擴容的目標相悖。
例1：

[root@docker79 ~]# kubectl get pods
No resources found.
[root@docker79 ~]# 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@docker79 ~]# kubectl get pods
NAME                     READY     STATUS    RESTARTS   AGE
myapp-6985749785-l4c87   1/1       Running   0          15s
[root@docker79 ~]# kubectl get svc
NAME         TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE
kubernetes   ClusterIP   10.96.0.1       <none>        443/TCP   20d
myapp        ClusterIP   10.100.44.181   <none>        80/TCP    20s
[root@docker79 ~]# kubectl autoscale deployment myapp --min=1 --max=4 --cpu-percent=40
horizontalpodautoscaler.autoscaling/myapp autoscaled
[root@docker79 ~]# kubectl get hpa
NAME      REFERENCE          TARGETS         MINPODS   MAXPODS   REPLICAS   AGE
myapp     Deployment/myapp   <unknown>/40%   1         4         0          6s
[root@docker79 ~]#
[root@docker79 ~]# kubectl patch svc myapp -p '{"spec":{"type":"NodePort"}}'
service/myapp patched
[root@docker79 ~]# kubectl get svc
NAME         TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)        AGE
kubernetes   ClusterIP   10.96.0.1       <none>        443/TCP        20d
myapp        NodePort    10.100.44.181   <none>        80:30193/TCP   4m
[root@docker79 ~]#
[root@docker79 ~]# kubectl get hpa
NAME      REFERENCE          TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
myapp     Deployment/myapp   0%/40%    1         4         1          6m
[root@docker79 ~]#

以上建立hap，並定義了CPU使用率達到40%時進行擴展，下面開啓 ab壓測，而後使其自動擴展

[root@docker79 ~]# ab -c 100 -n 50000 http://docker78:30193/index.html
This 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 docker78 (be patient)
Completed 5000 requests
Completed 10000 requests
Completed 15000 requests
Completed 20000 requests
......

[root@docker79 ~]# kubectl describe hpa
Name:                                                  myapp
Namespace:                                             default
Labels:                                                <none>
Annotations:                                           <none>
CreationTimestamp:                                     Mon, 17 Sep 2018 16:40:26 +0800
Reference:                                             Deployment/myapp
Metrics:                                               ( current / target )
  resource cpu on pods  (as a percentage of request):  102% (51m) / 40%
Min replicas:                                          1
Max replicas:                                          4
Deployment pods:                                       1 current / 3 desired
Conditions:
  Type            Status  Reason              Message
  ----            ------  ------              -------
  AbleToScale     True    SucceededRescale    the HPA controller was able to update the target scale to 3
  ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
  ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
Events:
  Type    Reason             Age   From                       Message
  ----    ------             ----  ----                       -------
  Normal  SuccessfulRescale  14s   horizontal-pod-autoscaler  New size: 3; reason: cpu resource utilization (percentage of request) above target
[root@docker79 ~]#
[root@docker79 ~]# kubectl get pods
NAME                     READY     STATUS    RESTARTS   AGE
myapp-6985749785-fvxbz   1/1       Running   0          1m
myapp-6985749785-l4c87   1/1       Running   0          14m
myapp-6985749785-xdmnw   1/1       Running   0          1m
[root@docker79 ~]#

例2，接上以manifests yaml的格式定義HorizontalPodAutoscaler ，並同時定義CPU、Memory資源的閥值，以便pod擴展：

[root@docker79 ~]# cd manifests/
[root@docker79 manifests]# mkdir hpa
[root@docker79 manifests]# cd hpa
[root@docker79 hpa]# vim hpa-demo-v2.yaml
[root@docker79 hpa]# cat hpa-demo-v2.yaml
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa-v2
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 1
  maxReplicas: 5
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 55
  - type: Resource
    resource:
      name: memory
      targetAverageValue: 50Mi
[root@docker79 hpa]#
[root@docker79 hpa]# kubectl delete hpa myapp
horizontalpodautoscaler.autoscaling "myapp" deleted
[root@docker79 hpa]#
[root@docker79 hpa]# kubectl apply -f hpa-demo-v2.yaml
horizontalpodautoscaler.autoscaling/myapp-hpa-v2 created
[root@docker79 hpa]# kubectl get hpa
NAME           REFERENCE          TARGETS                         MINPODS   MAXPODS   REPLICAS   AGE
myapp-hpa-v2   Deployment/myapp   <unknown>/50Mi, <unknown>/55%   1         5         0          6s
[root@docker79 hpa]#

再次開啓壓測

[root@docker79 ~]# ab -c 100 -n 30000 http://docker77:30193/index.html
This 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 docker77 (be patient)
Completed 3000 requests
Completed 6000 requests
......
[root@docker79 hpa]# kubectl describe hpa
Name:                                                  myapp-hpa-v2
Namespace:                                             default
Labels:                                                <none>
Annotations:                                           kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"autoscaling/v2beta1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"myapp-hpa-v2","namespace":"default"},"spec":{...
CreationTimestamp:                                     Mon, 17 Sep 2018 17:30:53 +0800
Reference:                                             Deployment/myapp
Metrics:                                               ( current / target )
  resource memory on pods:                             3442688 / 50Mi
  resource cpu on pods  (as a percentage of request):  68% (34m) / 55%
Min replicas:                                          1
Max replicas:                                          5
Deployment pods:                                       2 current / 3 desired
Conditions:
  Type            Status  Reason              Message
  ----            ------  ------              -------
  AbleToScale     True    SucceededRescale    the HPA controller was able to update the target scale to 3
  ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
  ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
Events:
  Type    Reason             Age   From                       Message
  ----    ------             ----  ----                       -------
  Normal  SuccessfulRescale  5m    horizontal-pod-autoscaler  New size: 2; reason: cpu resource utilization (percentage of request) above target
  Normal  SuccessfulRescale  9s    horizontal-pod-autoscaler  New size: 3; reason: cpu resource utilization (percentage of request) above target
[root@docker79 hpa]#

仔細觀察hpa 的信息，發現已經擴展了兩個new pods。
例3：
pods 能夠自定義一些資源指標，並將其輸出成Restful風格的API，並支持讓prometheus 讀取、認識這些自定義的指標值 ，而後hpa 就能夠根據這些指標值 進行擴展或收縮。以下所示：

[root@docker79 hpa]# cat hpa-demo-custom.yaml
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa-v2
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 1
  maxReplicas: 5
  metrics:
  - type: Pods
    pods:
      metricName: http_requests
      targetAverageValue: 800m
[root@docker79 hpa]#

因爲hpa 我使用還不是特別熟練，因此簡單總結這麼多，後續慢慢補充。你們勿噴。