Flagger|Flagger on ASM——基于Mixerless Telemetry实现渐进式灰度发布系列 2 应用级扩缩容 FlaggeronASM——基于MixerlessTele

简介：应用级扩缩容是相对于运维级而言的。像监控CPU/内存的利用率就属于应用无关的纯运维指标，针对这种指标进行扩缩容的HPA配置就是运维级扩缩容。而像请求数量、请求延迟、P99分布等指标就属于应用相关的，或者叫业务感知的监控指标。本篇将介绍3种应用级监控指标在HPA中的配置，以实现应用级自动扩缩容。

应用级扩缩容是相对于运维级而言的。像监控CPU/内存的利用率就属于应用无关的纯运维指标，针对这种指标进行扩缩容的HPA配置就是运维级扩缩容。而像请求数量、请求延迟、P99分布等指标就属于应用相关的，或者叫业务感知的监控指标。
本篇将介绍3种应用级监控指标在HPA中的配置，以实现应用级自动扩缩容。
Setup HPA 1 部署metrics-adapter
执行如下命令部署kube-metrics-adapter(完整脚本参见：demo\_hpa.sh)。：

helm --kubeconfig "$USER_CONFIG" -n kube-system install asm-custom-metrics \ $KUBE_METRICS_ADAPTER_SRC/deploy/charts/kube-metrics-adapter \ --set prometheus.url=http://prometheus.istio-system.svc:9090

执行如下命令验证部署情况：

#验证POD kubectl --kubeconfig "$USER_CONFIG" get po -n kube-system | grep metrics-adapterasm-custom-metrics-kube-metrics-adapter-6fb4949988-ht8pv1/1Running030s#验证CRD kubectl --kubeconfig "$USER_CONFIG" api-versions | grep "autoscaling/v2beta"autoscaling/v2beta1 autoscaling/v2beta2#验证CRD kubectl --kubeconfig "$USER_CONFIG" get --raw "/apis/external.metrics.k8s.io/v1beta1" | jq .{ "kind": "APIResourceList", "apiVersion": "v1", "groupVersion": "external.metrics.k8s.io/v1beta1", "resources": [] }

2 部署loadtester
执行如下命令部署flagger loadtester：

kubectl --kubeconfig "$USER_CONFIG" apply -f $FLAAGER_SRC/kustomize/tester/deployment.yaml -n test kubectl --kubeconfig "$USER_CONFIG" apply -f $FLAAGER_SRC/kustomize/tester/service.yaml -n test

3 部署HPA
3.1 根据应用请求数量扩缩容首先我们创建一个感知应用请求数量(istio_requests_total)的HorizontalPodAutoscaler配置：

apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler metadata: name: podinfo-total namespace: test annotations: metric-config.external.prometheus-query.prometheus/processed-requests-per-second: | sum(rate(istio_requests_total{destination_workload_namespace="test",reporter="destination"}[1m])) spec: maxReplicas: 5 minReplicas: 1 scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: podinfo metrics: - type: External external: metric: name: prometheus-query selector: matchLabels: query-name: processed-requests-per-second target: type: AverageValue averageValue: "10"

执行如下命令部署这个HPA配置：

kubectl --kubeconfig "$USER_CONFIG" apply -f resources_hpa/requests_total_hpa.yaml

执行如下命令校验：

kubectl --kubeconfig "$USER_CONFIG" get --raw "/apis/external.metrics.k8s.io/v1beta1" | jq .

结果如下：

{ "kind": "APIResourceList", "apiVersion": "v1", "groupVersion": "external.metrics.k8s.io/v1beta1", "resources": [ { "name": "prometheus-query", "singularName": "", "namespaced": true, "kind": "ExternalMetricValueList", "verbs": [ "get" ] } ] }

类似地，我们可以使用其他维度的应用级监控指标配置HPA。举例如下，不再冗述。
3.2 根据平均延迟扩缩容

apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler metadata: name: podinfo-latency-avg namespace: test annotations: metric-config.external.prometheus-query.prometheus/latency-average: | sum(rate(istio_request_duration_milliseconds_sum{destination_workload_namespace="test",reporter="destination"}[1m])) /sum(rate(istio_request_duration_milliseconds_count{destination_workload_namespace="test",reporter="destination"}[1m])) spec: maxReplicas: 5 minReplicas: 1 scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: podinfo metrics: - type: External external: metric: name: prometheus-query selector: matchLabels: query-name: latency-average target: type: AverageValue averageValue: "0.005"

3.3 根据P95分布扩缩容

apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler metadata: name: podinfo-p95 namespace: test annotations: metric-config.external.prometheus-query.prometheus/p95-latency: | histogram_quantile(0.95,sum(irate(istio_request_duration_milliseconds_bucket{destination_workload_namespace="test",destination_canonical_service="podinfo"}[5m]))by (le)) spec: maxReplicas: 5 minReplicas: 1 scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: podinfo metrics: - type: External external: metric: name: prometheus-query selector: matchLabels: query-name: p95-latency target: type: AverageValue averageValue: "4"

验证HPA 1 生成负载
执行如下命令产生实验流量，以验证HPA配置自动扩容生效。

alias k="kubectl --kubeconfig $USER_CONFIG" loadtester=$(k -n test get pod -l "app=flagger-loadtester" -o jsonpath='{.items..metadata.name}') k -n test exec -it ${loadtester} -c loadtester -- hey -z 5m -c 2 -q 10 http://podinfo:9898

这里运行了一个持续5分钟、QPS=10、并发数为2的请求。
hey命令详细参考如下：

Usage: hey [options...] Options: -nNumber of requests to run. Default is 200. -cNumber of workers to run concurrently. Total number of requests cannot be smaller than the concurrency level. Default is 50. -qRate limit, in queries per second (QPS) per worker. Default is no rate limit. -zDuration of application to send requests. When duration is reached, application stops and exits. If duration is specified, n is ignored. Examples: -z 10s -z 3m. -oOutput type. If none provided, a summary is printed. "csv" is the only supported alternative. Dumps the response metrics in comma-separated values format.-mHTTP method, one of GET, POST, PUT, DELETE, HEAD, OPTIONS. -HCustom HTTP header. You can specify as many as needed by repeating the flag. For example, -H "Accept: text/html" -H "Content-Type: application/xml" . -tTimeout for each request in seconds. Default is 20, use 0 for infinite. -AHTTP Accept header. -dHTTP request body. -DHTTP request body from file. For example, /home/user/file.txt or ./file.txt. -TContent-type, defaults to "text/html". -aBasic authentication, username:password. -xHTTP Proxy address as host:port. -h2 Enable HTTP/2.-host HTTP Host header.-disable-compressionDisable compression. -disable-keepaliveDisable keep-alive, prevents re-use of TCP connections between different HTTP requests. -disable-redirectsDisable following of HTTP redirects -cpusNumber of used cpu cores. (default for current machine is 4 cores)

2 自动扩容
执行如下命令观察扩容情况：

watch kubectl --kubeconfig $USER_CONFIG -n test get hpa/podinfo-total

结果如下：

Every 2.0s: kubectl --kubeconfig /Users/han/shop_config/ack_zjk -n test get hpa/podinfoEast6C16G: Tue Jan 26 18:01:30 2021NAMEREFERENCETARGETSMINPODSMAXPODSREPLICASAGE podinfoDeployment/podinfo10056m/10 (avg)1524m45s

另外两个HPA类似，命令如下：

kubectl --kubeconfig $USER_CONFIG -n test get hpawatch kubectl --kubeconfig $USER_CONFIG -n test get hpa/podinfo-latency-avg watch kubectl --kubeconfig $USER_CONFIG -n test get hpa/podinfo-p95

3 监控指标
同时，我们可以实时在Prometheus中查看相关的应用级监控指标的实时数据。示意如下：
【Flagger|Flagger on ASM——基于Mixerless Telemetry实现渐进式灰度发布系列 2 应用级扩缩容】

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