HPA 控制器¶
在前面的学习中我们使用用一个 kubectl scale 命令可以来实现 Pod 的扩缩容功能，但是这个毕竟是完全手动操作的，要应对线上的各种复杂情况，我们需要能够做到自动化去感知业务，来自动进行扩缩容。为此，Kubernetes 也为我们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，HPA 通过监控分析一些控制器控制的所有 Pod 的负载变化情况来确定是否需要调整 Pod 的副本数量，这是 HPA 最基本的原理：
我们可以简单的通过 kubectl autoscale 命令来创建一个 HPA 资源对象，HPA Controller默认30s轮询一次（可通过 kube-controller-manager 的--horizontal-pod-autoscaler-sync-period 参数进行设置），查询指定的资源中的 Pod 资源使用率，并且与创建时设定的值和指标做对比，从而实现自动伸缩的功能。
Metrics Server¶
在 HPA 的第一个版本中，我们需要 Heapster 提供 CPU 和内存指标，在 HPA v2 过后就需要安装 Metrcis Server 了，Metrics Server 可以通过标准的 Kubernetes API 把监控数据暴露出来，有了 Metrics Server 之后，我们就完全可以通过标准的 Kubernetes API 来访问我们想要获取的监控数据了：
https://10.96.0.1/apis/metrics.k8s.io/v1beta1/namespaces/<namespace-name>/pods/<pod-name>
比如当我们访问上面的 API 的时候，我们就可以获取到该 Pod 的资源数据，这些数据其实是来自于 kubelet 的 Summary API 采集而来的。不过需要说明的是我们这里可以通过标准的 API 来获取资源监控数据，并不是因为 Metrics Server 就是 APIServer 的一部分，而是通过 Kubernetes 提供的 Aggregator 汇聚插件来实现的，是独立于 APIServer 之外运行的。
聚合 API¶
Aggregator 允许开发人员编写一个自己的服务，把这个服务注册到 Kubernetes 的 APIServer 里面去，这样我们就可以像原生的 APIServer 提供的 API 使用自己的 API 了，我们把自己的服务运行在 Kubernetes 集群里面，然后 Kubernetes 的 Aggregator 通过 Service 名称就可以转发到我们自己写的 Service 里面去了。这样这个聚合层就带来了很多好处：
- 增加了 API 的扩展性，开发人员可以编写自己的 API 服务来暴露他们想要的 API。
- 丰富了 API，核心 kubernetes 团队阻止了很多新的 API 提案，通过允许开发人员将他们的 API 作为单独的服务公开，这样就无须社区繁杂的审查了。
- 开发分阶段实验性 API，新的 API 可以在单独的聚合服务中开发，当它稳定之后，在合并会 APIServer 就很容易了。
- 确保新 API 遵循 Kubernetes 约定，如果没有这里提出的机制，社区成员可能会被迫推出自己的东西，这样很可能造成社区成员和社区约定不一致。
安装¶
所以现在我们要使用 HPA，就需要在集群中安装 Metrics Server 服务，要安装 Metrics Server 就需要开启 Aggregator，因为 Metrics Server 就是通过该代理进行扩展的，不过我们集群是通过 Kubeadm 搭建的，默认已经开启了，如果是二进制方式安装的集群，需要单独配置 kube-apsierver 添加如下所示的参数：
--requestheader-client-ca-file=<path to aggregator CA cert>
--requestheader-allowed-names=aggregator
--requestheader-extra-headers-prefix=X-Remote-Extra-
--requestheader-group-headers=X-Remote-Group
--requestheader-username-headers=X-Remote-User
--proxy-client-cert-file=<path to aggregator proxy cert>
--proxy-client-key-file=<path to aggregator proxy key>
如果 kube-proxy 没有和 APIServer 运行在同一台主机上，那么需要确保启用了如下 kube-apsierver 的参数：
--enable-aggregator-routing=true
对于这些证书的生成方式，我们可以查看官方文档：https://github.com/kubernetes-sigs/apiserver-builder-alpha/blob/master/docs/concepts/auth.md。
Aggregator 聚合层启动完成后，就可以来安装 Metrics Server 了，我们可以获取该仓库的官方安装资源清单：
# 官方仓库地址：https://github.com/kubernetes-sigs/metrics-server
$ wget https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.4.0/components.yaml
在部署之前，修改 components.yaml 的镜像地址为：
hostNetwork: true # 使用hostNetwork模式
containers:
- name: metrics-server
image: cnych/metrics-server:v0.4.0
等部署完成后，可以查看 Pod 日志是否正常：
$ kubectl apply -f components.yaml
$ kubectl get pods -n kube-system -l k8s-app=metrics-server
NAME READY STATUS RESTARTS AGE
metrics-server-84944fdf6b-4ktfk 1/1 Running 0 33s
$ kubectl logs -f metrics-server-84944fdf6b-4ktfk -n kube-system
......
E1107 09:55:23.061340 1 server.go:132] unable to fully scrape metrics: [unable to fully scrape metrics from node node1: unable to fetch metrics from node node1: Get "https://10.151.30.22:10250/stats/summary?only_cpu_and_memory=true": x509: cannot validate certificate for 10.151.30.22 because it doesn't contain any IP SANs, unable to fully scrape metrics from node master1: unable to fetch metrics from node master1: Get "https://10.151.30.11:10250/stats/summary?only_cpu_and_memory=true": x509: cannot validate certificate for 10.151.30.11 because it doesn't contain any IP SANs, unable to fully scrape metrics from node node2: unable to fetch metrics from node node2: Get "https://10.151.30.23:10250/stats/summary?only_cpu_and_memory=true": x509: cannot validate certificate for 10.151.30.23 because it doesn't contain any IP SANs]
I1107 09:55:23.588099 1 secure_serving.go:197] Serving securely on [::]:4443
......
因为部署集群的时候，CA 证书并没有把各个节点的 IP 签上去，所以这里 Metrics Server 通过 IP 去请求时，提示签的证书没有对应的 IP（错误：x509: cannot validate certificate for 10.151.30.22 because it doesn’t contain any IP SANs），我们可以添加一个--kubelet-insecure-tls参数跳过证书校验：
args:
- --cert-dir=/tmp
- --secure-port=4443
- --kubelet-insecure-tls
- --kubelet-preferred-address-types=InternalIP
然后再重新安装即可成功！可以通过如下命令来验证：
$ kubectl apply -f components.yaml
$ kubectl get pods -n kube-system -l k8s-app=metrics-server
NAME READY STATUS RESTARTS AGE
metrics-server-58fc6858f9-64pp2 1/1 Running 0 14s
$ kubectl logs -f metrics-server-58fc6858f9-64pp2 -n kube-system
I1119 09:10:44.249092 1 serving.go:312] Generated self-signed cert (/tmp/apiserver.crt, /tmp/apiserver.key)
I1119 09:10:45.264076 1 secure_serving.go:116] Serving securely on [::]:4443
......
$ kubectl get apiservice | grep metrics
v1beta1.metrics.k8s.io kube-system/metrics-server True 38m
$ kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes"
{"kind":"NodeMetricsList","apiVersion":"metrics.k8s.io/v1beta1","metadata":{"selfLink":"/apis/metrics.k8s.io/v1beta1/nodes"},"items":[{"metadata":{"name":"master1","selfLink":"/apis/metrics.k8s.io/v1beta1/nodes/master1","creationTimestamp":"2020-11-07T10:01:00Z"},"timestamp":"2020-11-07T10:00:10Z","window":"30s","usage":{"cpu":"745212560n","memory":"2062508Ki"}},{"metadata":{"name":"node1","selfLink":"/apis/metrics.k8s.io/v1beta1/nodes/node1","creationTimestamp":"2020-11-07T10:01:00Z"},"timestamp":"2020-11-07T10:00:09Z","window":"30s","usage":{"cpu":"710212745n","memory":"3995052Ki"}},{"metadata":{"name":"node2","selfLink":"/apis/metrics.k8s.io/v1beta1/nodes/node2","creationTimestamp":"2020-11-07T10:01:00Z"},"timestamp":"2020-11-07T10:00:14Z","window":"30s","usage":{"cpu":"744952415n","memory":"4846984Ki"}}]}
$ kubectl top nodes
NAME CPU(cores) CPU% MEMORY(bytes) MEMORY%
master1 733m 36% 2012Mi 54%
node1 791m 19% 3886Mi 50%
node2 794m 19% 4743Mi 61%
现在我们可以通过 kubectl top 命令来获取到资源数据了，证明 Metrics Server 已经安装成功了。
HPA¶
现在我们用 Deployment 来创建一个 Nginx Pod，然后利用 HPA 来进行自动扩缩容。资源清单如下所示：（hpa-demo.yaml）
apiVersion: apps/v1
kind: Deployment
metadata:
name: hpa-demo
spec:
selector:
matchLabels:
app: nginx
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx
ports:
- containerPort: 80
然后直接创建 Deployment：
$ kubectl apply -f hpa-demo.yaml
deployment.apps/hpa-demo created
$ kubectl get pods -l app=nginx
NAME READY STATUS RESTARTS AGE
hpa-demo-85ff79dd56-pz8th 1/1 Running 0 21s
现在我们来创建一个 HPA 资源对象，可以使用kubectl autoscale命令来创建：
$ kubectl autoscale deployment hpa-demo --cpu-percent=10 --min=1 --max=10
horizontalpodautoscaler.autoscaling/hpa-demo autoscaled
$ kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
hpa-demo Deployment/hpa-demo <unknown>/10% 1 10 0 6s
此命令创建了一个关联资源 hpa-demo 的 HPA，最小的 Pod 副本数为1，最大为10。HPA 会根据设定的 cpu 使用率（10%）动态的增加或者减少 Pod 数量。
当然我们依然还是可以通过创建 YAML 文件的形式来创建 HPA 资源对象。如果我们不知道怎么编写的话，可以查看上面命令行创建的HPA的YAML文件：
$ kubectl get hpa hpa-demo -o yaml
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
annotations:
autoscaling.alpha.kubernetes.io/conditions: '[{"type":"AbleToScale","status":"True","lastTransitionTime":"2020-11-07T10:02:51Z","reason":"SucceededGetScale","message":"the
HPA controller was able to get the target''s current scale"},{"type":"ScalingActive","status":"False","lastTransitionTime":"2020-11-07T10:02:51Z","reason":"FailedGetResourceMetric","message":"the
HPA was unable to compute the replica count: missing request for cpu"}]'
creationTimestamp: "2020-11-07T10:02:35Z"
managedFields:
- apiVersion: autoscaling/v1
fieldsType: FieldsV1
fieldsV1:
f:spec:
f:maxReplicas: {}
f:minReplicas: {}
f:scaleTargetRef:
f:apiVersion: {}
f:kind: {}
f:name: {}
f:targetCPUUtilizationPercentage: {}
manager: kubectl
operation: Update
time: "2020-11-07T10:02:35Z"
- apiVersion: autoscaling/v1
fieldsType: FieldsV1
fieldsV1:
f:metadata:
f:annotations:
.: {}
f:autoscaling.alpha.kubernetes.io/conditions: {}
f:status:
f:currentReplicas: {}
manager: kube-controller-manager
operation: Update
time: "2020-11-07T10:02:51Z"
name: hpa-demo
namespace: default
resourceVersion: "4219341"
selfLink: /apis/autoscaling/v1/namespaces/default/horizontalpodautoscalers/hpa-demo
uid: 030bc938-5dda-4b95-b335-78e12a39a093
spec:
maxReplicas: 10
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: hpa-demo
targetCPUUtilizationPercentage: 10
status:
currentReplicas: 1
desiredReplicas: 0
然后我们可以根据上面的 YAML 文件就可以自己来创建一个基于 YAML 的 HPA 描述文件了。但是我们发现上面信息里面出现了一些 Fail 信息，我们来查看下这个 HPA 对象的信息：
$ kubectl describe hpa hpa-demo
Name: hpa-demo
Namespace: default
Labels: <none>
Annotations: <none>
CreationTimestamp: Sat, 07 Nov 2020 18:02:35 +0800
Reference: Deployment/hpa-demo
Metrics: ( current / target )
resource cpu on pods (as a percentage of request): <unknown> / 10%
Min replicas: 1
Max replicas: 10
Deployment pods: 1 current / 0 desired
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale True SucceededGetScale the HPA controller was able to get the target's current scale
ScalingActive False FailedGetResourceMetric the HPA was unable to compute the replica count: missing request for cpu
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedGetResourceMetric 8s (x4 over 54s) horizontal-pod-autoscaler missing request for cpu
Warning FailedComputeMetricsReplicas 8s (x4 over 54s) horizontal-pod-autoscaler invalid metrics (1 invalid out of 1), first error is: failed to get cpu utilization: missing request for cpu
我们可以看到上面的事件信息里面出现了 failed to get cpu utilization: missing request for cpu 这样的错误信息。这是因为我们上面创建的 Pod 对象没有添加 request 资源声明，这样导致 HPA 读取不到 CPU 指标信息，所以如果要想让 HPA 生效，对应的 Pod 资源必须添加 requests 资源声明，更新我们的资源清单文件：
apiVersion: apps/v1
kind: Deployment
metadata:
name: hpa-demo
spec:
selector:
matchLabels:
app: nginx
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx
ports:
- containerPort: 80
resources:
requests:
memory: 50Mi
cpu: 50m
然后重新更新 Deployment，重新创建 HPA 对象：
$ kubectl apply -f hpa.yaml
deployment.apps/hpa-demo configured
$ kubectl get pods -o wide -l app=nginx
NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES
hpa-demo-6b4467b546-th974 1/1 Running 0 31s 10.244.1.113 node1 <none> <none>
$ kubectl delete hpa hpa-demo
horizontalpodautoscaler.autoscaling "hpa-demo" deleted
$ kubectl autoscale deployment hpa-demo --cpu-percent=10 --min=1 --max=10
horizontalpodautoscaler.autoscaling/hpa-demo autoscaled
$ kubectl describe hpa hpa-demo
Name: hpa-demo
Namespace: default
Labels: <none>
Annotations: <none>
CreationTimestamp: Sat, 07 Nov 2020 18:05:22 +0800
Reference: Deployment/hpa-demo
Metrics: ( current / target )
resource cpu on pods (as a percentage of request): 0% (0) / 10%
Min replicas: 1
Max replicas: 10
Deployment pods: 1 current / 1 desired
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale True ScaleDownStabilized recent recommendations were higher than current one, applying the highest recent recommendation
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: <none>
$ kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
hpa-demo Deployment/hpa-demo 0%/10% 1 10 1 36s
现在可以看到 HPA 资源对象已经正常了，现在我们来增大负载进行测试，我们来创建一个 busybox 的 Pod，并且循环访问上面创建的 Pod：
$ kubectl run -it --image busybox test-hpa --restart=Never --rm /bin/sh
If you don't see a command prompt, try pressing enter.
/ # while true; do wget -q -O- http://10.244.1.113; done
下图可以看到，HPA 已经开始工作：
$ kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
hpa-demo Deployment/hpa-demo 320%/10% 1 10 1 2m7s
$ kubectl get pods -l app=nginx --watch
NAME READY STATUS RESTARTS AGE
hpa-demo-69968bb59f-8hjnn 1/1 Running 0 22s
hpa-demo-69968bb59f-9ss9f 1/1 Running 0 22s
hpa-demo-69968bb59f-bllsd 1/1 Running 0 22s
hpa-demo-69968bb59f-lnh8k 1/1 Running 0 37s
hpa-demo-69968bb59f-r8zfh 1/1 Running 0 22s
hpa-demo-69968bb59f-twtdp 1/1 Running 0 6m43s
hpa-demo-69968bb59f-w792g 1/1 Running 0 37s
hpa-demo-69968bb59f-zlxkp 1/1 Running 0 37s
hpa-demo-69968bb59f-znp6q 0/1 ContainerCreating 0 6s
hpa-demo-69968bb59f-ztnvx 1/1 Running 0 6s
我们可以看到已经自动拉起了很多新的 Pod，最后定格在了我们上面设置的 10 个 Pod，同时查看资源 hpa-demo 的副本数量，副本数量已经从原来的1变成了10个：
$ kubectl get deployment hpa-demo
NAME READY UP-TO-DATE AVAILABLE AGE
hpa-demo 10/10 10 10 17m
查看 HPA 资源的对象了解工作过程：
$ kubectl describe hpa hpa-demo
Name: hpa-demo
Namespace: default
Labels: <none>
Annotations: <none>
CreationTimestamp: Sat, 07 Nov 2020 18:05:22 +0800
Reference: Deployment/hpa-demo
Metrics: ( current / target )
resource cpu on pods (as a percentage of request): 320% (160m) / 10%
Min replicas: 1
Max replicas: 10
Deployment pods: 8 current / 10 desired
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale True SucceededRescale the HPA controller was able to update the target scale to 10
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 46s horizontal-pod-autoscaler New size: 4; reason: cpu resource utilization (percentage of request) above target
Normal SuccessfulRescale 30s horizontal-pod-autoscaler New size: 8; reason: cpu resource utilization (percentage of request) above target
Normal SuccessfulRescale 14s horizontal-pod-autoscaler New size: 10; reason: cpu resource utilization (percentage of request) above target
同样的这个时候我们来关掉 busybox 来减少负载，然后等待一段时间观察下 HPA 和 Deployment 对象：
$ kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
hpa-demo Deployment/hpa-demo 0%/10% 1 10 1 14m
$ kubectl get deployment hpa-demo
NAME READY UP-TO-DATE AVAILABLE AGE
hpa-demo 1/1 1 1 24m
缩放间隙
从 Kubernetes v1.12 版本开始我们可以通过设置 kube-controller-manager 组件的--horizontal-pod-autoscaler-downscale-stabilization 参数来设置一个持续时间，用于指定在当前操作完成后，HPA 必须等待多长时间才能执行另一次缩放操作。默认为5分钟，也就是默认需要等待5分钟后才会开始自动缩放。
可以看到副本数量已经由 10 变为 1，当前我们只是演示了 CPU 使用率这一个指标，在后面的课程中我们还会学习到根据自定义的监控指标来自动对 Pod 进行扩缩容。
内存¶
要使用基于内存或者自定义指标进行扩缩容（现在的版本都必须依赖 metrics-server 这个项目）。现在我们再用 Deployment 来创建一个 Nginx Pod，然后利用 HPA 来进行自动扩缩容。资源清单如下所示：（hpa-mem-demo.yaml）
apiVersion: apps/v1
kind: Deployment
metadata:
name: hpa-mem-demo
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
ports:
- containerPort: 80
volumeMounts:
- name: increase-mem-script
mountPath: /etc/script
resources:
requests:
memory: 50Mi
cpu: 50m
securityContext:
privileged: true
这里和前面普通的应用有一些区别，我们将一个名为 increase-mem-config 的 ConfigMap 资源对象挂载到了容器中，该配置文件是用于后面增加容器内存占用的脚本，配置文件如下所示：（increase-mem-cm.yaml）
apiVersion: v1
kind: ConfigMap
metadata:
name: increase-mem-config
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
由于这里增加内存的脚本需要使用到 mount 命令，这需要声明为特权模式，所以我们添加了 securityContext.privileged=true 这个配置。现在我们直接创建上面的资源对象即可：
$ kubectl apply -f increase-mem-cm.yaml
$ kubectl apply -f hpa-mem-demo.yaml
$ kubectl get pods -l app=nginx
NAME READY STATUS RESTARTS AGE
hpa-mem-demo-66944b79bf-tqrn9 1/1 Running 0 35s
然后需要创建一个基于内存的 HPA 资源对象：（hpa-mem.yaml）
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: nginx-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: hpa-mem-demo
minReplicas: 1
maxReplicas: 5
metrics:
- type: Resource
resource:
name: memory
targetAverageUtilization: 60
要注意这里使用的 apiVersion 是 autoscaling/v2beta1，然后 metrics 属性里面指定的是内存的配置，直接创建上面的资源对象即可：
$ kubectl apply -f hpa-mem.yaml
horizontalpodautoscaler.autoscaling/nginx-hpa created
$ kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
nginx-hpa Deployment/hpa-mem-demo 2%/60% 1 5 1 12s
到这里证明 HPA 资源对象已经部署成功了，接下来我们对应用进行压测，将内存压上去，直接执行上面我们挂载到容器中的 increase-mem.sh 脚本即可：
$ kubectl exec -it hpa-mem-demo-66944b79bf-tqrn9 /bin/bash
root@hpa-mem-demo-66944b79bf-tqrn9:/# ls /etc/script/
increase-mem.sh
root@hpa-mem-demo-66944b79bf-tqrn9:/# source /etc/script/increase-mem.sh
dd: writing to '/tmp/memory/block': No space left on device
81921+0 records in
81920+0 records out
41943040 bytes (42 MB, 40 MiB) copied, 0.584029 s, 71.8 MB/s
然后打开另外一个终端观察 HPA 资源对象的变化情况：
$ kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
nginx-hpa Deployment/hpa-mem-demo 83%/60% 1 5 1 5m3s
$ kubectl describe hpa nginx-hpa
Name: nginx-hpa
Namespace: default
Labels: <none>
Annotations: kubectl.kubernetes.io/last-applied-configuration:
{"apiVersion":"autoscaling/v2beta1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"nginx-hpa","namespace":"default"...
CreationTimestamp: Tue, 07 Apr 2020 13:13:59 +0800
Reference: Deployment/hpa-mem-demo
Metrics: ( current / target )
resource memory on pods (as a percentage of request): 3% (1740800) / 60%
Min replicas: 1
Max replicas: 5
Deployment pods: 2 current / 2 desired
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale True ScaleDownStabilized recent recommendations were higher than current one, applying the highest recent recommendation
ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from memory resource utilization (percentage of request)
ScalingLimited False DesiredWithinRange the desired count is within the acceptable range
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedGetResourceMetric 5m26s (x3 over 5m58s) horizontal-pod-autoscaler unable to get metrics for resource memory: no metrics returned from resource metrics API
Warning FailedComputeMetricsReplicas 5m26s (x3 over 5m58s) horizontal-pod-autoscaler invalid metrics (1 invalid out of 1), first error is: failed to get memory utilization: unable to get metrics for resource memory: no metrics returned from resource metrics API
Normal SuccessfulRescale 77s horizontal-pod-autoscaler New size: 2; reason: memory resource utilization (percentage of request) above target
$ kubectl top pod hpa-mem-demo-66944b79bf-tqrn9
NAME CPU(cores) MEMORY(bytes)
hpa-mem-demo-66944b79bf-tqrn9 0m 41Mi
可以看到内存使用已经超过了我们设定的 60% 这个阈值了，HPA 资源对象也已经触发了自动扩容，变成了两个副本了：
$ kubectl get pods -l app=nginx
NAME READY STATUS RESTARTS AGE
hpa-mem-demo-66944b79bf-8m4d9 1/1 Running 0 2m51s
hpa-mem-demo-66944b79bf-tqrn9 1/1 Running 0 8m11s
当内存释放掉后，controller-manager 默认5分钟过后会进行缩放，到这里就完成了基于内存的 HPA 操作。