wen Kubernetes使用Keda进行弹性伸缩，更合理利用资源
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1 简介
Kubernetes自带的HPA是只支持CPU/MEM的，很多时候我们并不根据这两项指标来进行伸缩资源。比如消费者不断处理MQ的消息，我们希望MQ如果堆积过多，就启动更多的消费者来处理任务。而Keda给了我们很多选择。
KEDA 是 Kubernetes 基于事件驱动的自动伸缩工具，通过 KEDA 我们可以根据需要处理的事件数量来驱动 Kubernetes 中任何容器的扩展。KEDA 可以直接部署到任何 Kubernetes 集群中和标准的组件一起工作。
Keda所支持的事件源非常丰富，本文我们以RabbitMQ为例进行演示。
2 安装Keda
安装的方法很多，我们直接通过yaml文件来安装，这样还可以修改镜像地址等。先从（ https://github.com/kedacore/keda/releases/download/v2.2.0/keda-2.2.0.yaml ）下载yaml文件，然后执行：
$ kubectl apply -f ~/Downloads/keda-2.2.0.yaml
namespace/keda created
customresourcedefinition.apiextensions.k8s.io/clustertriggerauthentications.keda.sh created
customresourcedefinition.apiextensions.k8s.io/scaledjobs.keda.sh created
customresourcedefinition.apiextensions.k8s.io/scaledobjects.keda.sh created
customresourcedefinition.apiextensions.k8s.io/triggerauthentications.keda.sh created
serviceaccount/keda-operator created
clusterrole.rbac.authorization.k8s.io/keda-external-metrics-reader created
clusterrole.rbac.authorization.k8s.io/keda-operator created
rolebinding.rbac.authorization.k8s.io/keda-auth-reader created
clusterrolebinding.rbac.authorization.k8s.io/keda-hpa-controller-external-metrics created
clusterrolebinding.rbac.authorization.k8s.io/keda-operator created
clusterrolebinding.rbac.authorization.k8s.io/keda:system:auth-delegator created
service/keda-metrics-apiserver created
deployment.apps/keda-metrics-apiserver created
deployment.apps/keda-operator created
apiservice.apiregistration.k8s.io/v1beta1.external.metrics.k8s.io created
检查一下是否都已经启动完成：
$ kubectl get all -n keda
NAME READY STATUS RESTARTS AGE
pod/keda-metrics-apiserver-55dc9f9498-smc2d 1/1 Running 0 2m41s
pod/keda-operator-59dcf989d6-pxcbb 1/1 Running 0 2m41s
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/keda-metrics-apiserver ClusterIP 10.104.255.44 <none> 443/TCP,80/TCP 2m41s
NAME READY UP-TO-DATE AVAILABLE AGE
deployment.apps/keda-metrics-apiserver 1/1 1 1 2m42s
deployment.apps/keda-operator 1/1 1 1 2m42s
NAME DESIRED CURRENT READY AGE
replicaset.apps/keda-metrics-apiserver-55dc9f9498 1 1 1 2m42s
replicaset.apps/keda-operator-59dcf989d6 1 1 1 2m42s
也可以看到镜像多了：
$ docker images | grep keda
ghcr.io/kedacore/keda-metrics-apiserver 2.2.0 a43d40453368 6 weeks ago 95.3MB
ghcr.io/kedacore/keda 2.2.0 42b88f042914 6 weeks ago 83MB
如果要卸载请执行：
$ kubectl delete -f ~/Downloads/keda-2.2.0.yaml
3 安装RabbitMQ
为了快速安装，也方便日后删除，我们通过Helm来安装RabbitMQ。
查看可用的chart：
$ helm search repo rabbit
执行安装：
$ helm install azure-rabbitmq azure/rabbitmq
检查一下：
$ helm list
NAME NAMESPACE REVISION UPDATED STATUS CHART APP VERSION
azure-ingress default 1 2021-02-14 01:21:07.212107 +0800 CST deployed nginx-ingress-1.41.3 v0.34.1
azure-rabbitmq default 1 2021-05-05 11:29:06.979437 +0800 CST deployed rabbitmq-6.18.2 3.8.2
用户名为user，密码获取如下：
$ echo "Password : $(kubectl get secret --namespace default azure-rabbitmq -o jsonpath="{.data.rabbitmq-password}" | base64 --decode)"
Password : YNsEayx8w2
4 测试
部署消费者，注意这里有个MQ连接信息和加密，要根据自己情况修改。
$ kubectl apply -f src/main/kubernetes/deploy-consumer.yaml
secret/rabbitmq-consumer-secret created
deployment.apps/rabbitmq-consumer created
scaledobject.keda.sh/rabbitmq-consumer created
triggerauthentication.keda.sh/rabbitmq-consumer-trigger created
查看deployment，发现是没有Pod创建，因为还不需要处理，MQ现在的队列为0。
$ kubectl get deployments
NAME READY UP-TO-DATE AVAILABLE AGE
azure-ingress-nginx-ingress-controller 1/1 1 1 80d
azure-ingress-nginx-ingress-default-backend 1/1 1 1 80d
rabbitmq-consumer 0/0 0 0 131m
部署生产者，往MQ发送消息：
$ kubectl apply -f src/main/kubernetes/deploy-publisher-job.yaml
job.batch/rabbitmq-publish created
可以看到，慢慢消费者就起来了，并且创建了越来越多的Pod来处理MQ：
$ kubectl get deployments rabbitmq-consumer
NAME READY UP-TO-DATE AVAILABLE AGE
rabbitmq-consumer 1/1 1 1 167m
$ kubectl get deployments rabbitmq-consumer
NAME READY UP-TO-DATE AVAILABLE AGE
rabbitmq-consumer 3/4 4 3 168m
$ kubectl get deployments rabbitmq-consumer
NAME READY UP-TO-DATE AVAILABLE AGE
rabbitmq-consumer 4/8 8 4 168m
$ kubectl get deployments rabbitmq-consumer
NAME READY UP-TO-DATE AVAILABLE AGE
rabbitmq-consumer 6/8 8 6 169m
$ kubectl get deployments rabbitmq-consumer
NAME READY UP-TO-DATE AVAILABLE AGE
rabbitmq-consumer 0/0 0 0 171m
查看Deployment的Event也可以看到结果：
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal ScalingReplicaSet 5m55s (x2 over 172m) deployment-controller Scaled up replica set rabbitmq-consumer-7b477f78b4 to 1
Normal ScalingReplicaSet 5m6s deployment-controller Scaled up replica set rabbitmq-consumer-7b477f78b4 to 4
Normal ScalingReplicaSet 4m6s deployment-controller Scaled up replica set rabbitmq-consumer-7b477f78b4 to 8
Normal ScalingReplicaSet 3m5s deployment-controller Scaled up replica set rabbitmq-consumer-7b477f78b4 to 16
Normal ScalingReplicaSet 3m3s (x2 over 172m) deployment-controller Scaled down replica set rabbitmq-consumer-7b477f78b4 to 0
处理完成后，又会回到0了。
总结
代码请查看：https://github.com/LarryDpk/pkslow-samples
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