关于xml: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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简介：利用级扩缩容是绝对于运维级而言的。像监控 CPU/ 内存的利用率就属于利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。而像申请数量、申请提早、P99 散布等指标就属于利用相干的，或者叫业务感知的监控指标。本篇将介绍 3 种利用级监控指标在 HPA 中的配置，以实现利用级主动扩缩容。

利用级扩缩容是绝对于运维级而言的。像监控 CPU/ 内存的利用率就属于利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。而像申请数量、申请提早、P99 散布等指标就属于利用相干的，或者叫业务感知的监控指标。

本篇将介绍 3 种利用级监控指标在 HPA 中的配置，以实现利用级主动扩缩容。

执行如下命令部署 kube-metrics-adapter(残缺脚本参见：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状况：

# 验证 POD
kubectl --kubeconfig "$USER_CONFIG" get po -n kube-system | grep metrics-adapter

asm-custom-metrics-kube-metrics-adapter-6fb4949988-ht8pv   1/1     Running     0          30s

#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": []}

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

首先咱们创立一个感知 利用申请数量 (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 .

后果如下：

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

相似地，咱们能够应用其余维度的利用级监控指标配置 HPA。举例如下，不再冗述。

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"

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"

执行如下命令产生试验流量，以验证 HPA 配置主动扩容失效。

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

这里运行了一个继续 5 分钟、QPS=10、并发数为 2 的申请。

hey 命令具体参考如下：

Usage: hey [options...] <url>

Options:
  -n  Number of requests to run. Default is 200.
  -c  Number of workers to run concurrently. Total number of requests cannot
      be smaller than the concurrency level. Default is 50.
  -q  Rate limit, in queries per second (QPS) per worker. Default is no rate limit.
  -z  Duration 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.
  -o  Output type. If none provided, a summary is printed.
      "csv" is the only supported alternative. Dumps the response
      metrics in comma-separated values format.

  -m  HTTP method, one of GET, POST, PUT, DELETE, HEAD, OPTIONS.
  -H  Custom HTTP header. You can specify as many as needed by repeating the flag.
      For example, -H "Accept: text/html" -H "Content-Type: application/xml" .
  -t  Timeout for each request in seconds. Default is 20, use 0 for infinite.
  -A  HTTP Accept header.
  -d  HTTP request body.
  -D  HTTP request body from file. For example, /home/user/file.txt or ./file.txt.
  -T  Content-type, defaults to "text/html".
  -a  Basic authentication, username:password.
  -x  HTTP Proxy address as host:port.
  -h2 Enable HTTP/2.

  -host HTTP Host header.

  -disable-compression  Disable compression.
  -disable-keepalive    Disable keep-alive, prevents re-use of TCP
                        connections between different HTTP requests.
  -disable-redirects    Disable following of HTTP redirects
  -cpus                 Number of used cpu cores.
                        (default for current machine is 4 cores)

执行如下命令察看扩容状况：

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

后果如下：

Every 2.0s: kubectl --kubeconfig /Users/han/shop_config/ack_zjk -n test get hpa/podinfo                                            East6C16G: Tue Jan 26 18:01:30 2021

NAME      REFERENCE            TARGETS           MINPODS   MAXPODS   REPLICAS   AGE
podinfo   Deployment/podinfo   10056m/10 (avg)   1         5         2          4m45s

另外两个 HPA 相似，命令如下：

kubectl --kubeconfig $USER_CONFIG -n test get hpa

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

同时，咱们能够实时在 Prometheus 中查看相干的利用级监控指标的实时数据。示意如下：

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2021-04-26

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关于xml: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

Setup HPA

1 部署 metrics-adapter

2 部署 loadtester

3 部署 HPA

3.1 依据利用申请数量扩缩容

3.2 依据均匀提早扩缩容

3.3 依据 P95 散布扩缩容

验证 HPA

1 生成负载

2 主动扩容

3 监控指标

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