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Linkerd 金丝雀部署与 A/B 测试

admin

11月 28, 2021

本指南向您展示如何使用 Linkerd 和 Flagger 来自动化金丝雀部署与 A/B 测试。

前提条件

Flagger 需要 Kubernetes 集群 v1.16 或更新版本和 Linkerd 2.10 或更新版本。

安装 Linkerd the PrometheusLinkerd Viz 的一部分):

linkerd install | kubectl apply -f -
linkerd viz install | kubectl apply -f -

linkerd 命名空间中安装 Flagger

kubectl apply -k github.com/fluxcd/flagger//kustomize/linkerd

引导程序

Flagger 采用 Kubernetes deployment 和可选的水平 Pod 自动伸缩 (HPA),然后创建一系列对象(Kubernetes 部署、ClusterIP 服务和 SMI 流量拆分)。这些对象将应用程序暴露在网格内部并驱动 Canary 分析和推广。

创建一个 test 命名空间并启用 Linkerd 代理注入:

kubectl create ns test
kubectl annotate namespace test linkerd.io/inject=enabled

安装负载测试服务以在金丝雀分析期间生成流量:

kubectl apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main

创建部署和水平 pod autoscaler:

kubectl apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main

podinfo 部署创建一个 Canary 自定义资源:

apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: podinfo
  namespace: test
spec:
  # deployment reference
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: podinfo
  # HPA reference (optional)
  autoscalerRef:
    apiVersion: autoscaling/v2beta2
    kind: HorizontalPodAutoscaler
    name: podinfo
  # the maximum time in seconds for the canary deployment
  # to make progress before it is rollback (default 600s)
  progressDeadlineSeconds: 60
  service:
    # ClusterIP port number
    port: 9898
    # container port number or name (optional)
    targetPort: 9898
  analysis:
    # schedule interval (default 60s)
    interval: 30s
    # max number of failed metric checks before rollback
    threshold: 5
    # max traffic percentage routed to canary
    # percentage (0-100)
    maxWeight: 50
    # canary increment step
    # percentage (0-100)
    stepWeight: 5
    # Linkerd Prometheus checks
    metrics:
    - name: request-success-rate
      # minimum req success rate (non 5xx responses)
      # percentage (0-100)
      thresholdRange:
        min: 99
      interval: 1m
    - name: request-duration
      # maximum req duration P99
      # milliseconds
      thresholdRange:
        max: 500
      interval: 30s
    # testing (optional)
    webhooks:
      - name: acceptance-test
        type: pre-rollout
        url: http://flagger-loadtester.test/
        timeout: 30s
        metadata:
          type: bash
          cmd: "curl -sd 'test' http://podinfo-canary.test:9898/token | grep token"
      - name: load-test
        type: rollout
        url: http://flagger-loadtester.test/
        metadata:
          cmd: "hey -z 2m -q 10 -c 2 http://podinfo-canary.test:9898/"

将上述资源另存为 podinfo-canary.yaml 然后应用:

kubectl apply -f ./podinfo-canary.yaml

Canary 分析开始时,Flagger 将在将流量路由到 Canary 之前调用 pre-rollout webhooks。 金丝雀分析将运行五分钟,同时每半分钟验证一次 HTTP 指标和 rollout(推出) hooks

几秒钟后,Flager 将创建 canary 对象:

# applied
deployment.apps/podinfo
horizontalpodautoscaler.autoscaling/podinfo
ingresses.extensions/podinfo
canary.flagger.app/podinfo

# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
trafficsplits.split.smi-spec.io/podinfo

boostrap 之后,podinfo 部署将被缩放到零,
并且到 podinfo.test 的流量将被路由到主 pod
Canary 分析过程中,可以使用 podinfo-canary.test 地址直接定位 Canary Pod

自动金丝雀推进

Flagger 实施了一个控制循环,在测量 HTTP 请求成功率、请求平均持续时间和 Pod 健康状况等关键性能指标的同时,逐渐将流量转移到金丝雀。
根据对 KPI 的分析,提升或中止 Canary,并将分析结果发布到 Slack


Flagger 金丝雀阶段

通过更新容器镜像触发金丝雀部署:

kubectl -n test set image deployment/podinfo 
podinfod=stefanprodan/podinfo:3.1.1

Flagger 检测到部署修订已更改并开始新的部署:

kubectl -n test describe canary/podinfo

Status:
  Canary Weight:         0
  Failed Checks:         0
  Phase:                 Succeeded
Events:
 New revision detected! Scaling up podinfo.test
 Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
 Pre-rollout check acceptance-test passed
 Advance podinfo.test canary weight 5
 Advance podinfo.test canary weight 10
 Advance podinfo.test canary weight 15
 Advance podinfo.test canary weight 20
 Advance podinfo.test canary weight 25
 Waiting for podinfo.test rollout to finish: 1 of 2 updated replicas are available
 Advance podinfo.test canary weight 30
 Advance podinfo.test canary weight 35
 Advance podinfo.test canary weight 40
 Advance podinfo.test canary weight 45
 Advance podinfo.test canary weight 50
 Copying podinfo.test template spec to podinfo-primary.test
 Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
 Promotion completed! Scaling down podinfo.test

请注意,如果您在 Canary 分析期间对部署应用新更改,Flagger 将重新开始分析。

金丝雀部署由以下任何对象的更改触发:

  • Deployment PodSpec(容器镜像container image、命令command、端口ports、环境env、资源resources等)
  • ConfigMaps 作为卷挂载或映射到环境变量
  • Secrets 作为卷挂载或映射到环境变量

您可以通过以下方式监控所有金丝雀:

watch kubectl get canaries --all-namespaces

NAMESPACE   NAME      STATUS        WEIGHT   LASTTRANSITIONTIME
test        podinfo   Progressing   15       2019-06-30T14:05:07Z
prod        frontend  Succeeded     0        2019-06-30T16:15:07Z
prod        backend   Failed        0        2019-06-30T17:05:07Z

自动回滚

在金丝雀分析期间,您可以生成 HTTP 500 错误和高延迟来测试 Flagger 是否暂停并回滚有故障的版本。

触发另一个金丝雀部署:

kubectl -n test set image deployment/podinfo 
podinfod=stefanprodan/podinfo:3.1.2

使用以下命令执行负载测试器 pod

kubectl -n test exec -it flagger-loadtester-xx-xx sh

生成 HTTP 500 错误:

watch -n 1 curl http://podinfo-canary.test:9898/status/500

生成延迟:

watch -n 1 curl http://podinfo-canary.test:9898/delay/1

当失败的检查次数达到金丝雀分析阈值时,流量将路由回主服务器,金丝雀缩放为零,并将推出标记为失败。

kubectl -n test describe canary/podinfo

Status:
  Canary Weight:         0
  Failed Checks:         10
  Phase:                 Failed
Events:
 Starting canary analysis for podinfo.test
 Pre-rollout check acceptance-test passed
 Advance podinfo.test canary weight 5
 Advance podinfo.test canary weight 10
 Advance podinfo.test canary weight 15
 Halt podinfo.test advancement success rate 69.17% < 99%
 Halt podinfo.test advancement success rate 61.39% < 99%
 Halt podinfo.test advancement success rate 55.06% < 99%
 Halt podinfo.test advancement request duration 1.20s > 0.5s
 Halt podinfo.test advancement request duration 1.45s > 0.5s
 Rolling back podinfo.test failed checks threshold reached 5
 Canary failed! Scaling down podinfo.test

自定义指标

Canary analysis 可以通过 Prometheus 查询进行扩展。

让我们定义一个未找到错误的检查。编辑 canary analysis 并添加以下指标:

  analysis:
    metrics:
    - name: "404s percentage"
      threshold: 3
      query: |
        100 - sum(
            rate(
                response_total{
                    namespace="test",
                    deployment="podinfo",
                    status_code!="404",
                    direction="inbound"
                }[1m]
            )
        )
        /
        sum(
            rate(
                response_total{
                    namespace="test",
                    deployment="podinfo",
                    direction="inbound"
                }[1m]
            )
        )
        * 100

上述配置通过检查 HTTP 404 req/sec 百分比是否低于总流量的 3% 来验证金丝雀版本。
如果 404s 率达到 3% 阈值,则分析将中止,金丝雀被标记为失败。

通过更新容器镜像触发金丝雀部署:

kubectl -n test set image deployment/podinfo 
podinfod=stefanprodan/podinfo:3.1.3

生成 404

watch -n 1 curl http://podinfo-canary:9898/status/404

监视 Flagger 日志:

kubectl -n linkerd logs deployment/flagger -f | jq .msg

Starting canary deployment for podinfo.test
Pre-rollout check acceptance-test passed
Advance podinfo.test canary weight 5
Halt podinfo.test advancement 404s percentage 6.20 > 3
Halt podinfo.test advancement 404s percentage 6.45 > 3
Halt podinfo.test advancement 404s percentage 7.22 > 3
Halt podinfo.test advancement 404s percentage 6.50 > 3
Halt podinfo.test advancement 404s percentage 6.34 > 3
Rolling back podinfo.test failed checks threshold reached 5
Canary failed! Scaling down podinfo.test

如果您配置了 SlackFlager 将发送一条通知,说明金丝雀失败的原因。

Linkerd Ingress

有两个入口控制器与 FlaggerLinkerd 兼容:NGINXGloo

安装 NGINX:

helm upgrade -i nginx-ingress stable/nginx-ingress 
--namespace ingress-nginx

podinfo 创建一个 ingress 定义,将传入标头重写为内部服务名称(Linkerd 需要):

apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  name: podinfo
  namespace: test
  labels:
    app: podinfo
  annotations:
    kubernetes.io/ingress.class: "nginx"
    nginx.ingress.kubernetes.io/configuration-snippet: |
      proxy_set_header l5d-dst-override $service_name.$namespace.svc.cluster.local:9898;
      proxy_hide_header l5d-remote-ip;
      proxy_hide_header l5d-server-id;
spec:
  rules:
    - host: app.example.com
      http:
        paths:
          - backend:
              serviceName: podinfo
              servicePort: 9898

使用 ingress controller 时,Linkerd 流量拆分不适用于传入流量,因为 NGINX 在网格之外运行。 为了对前端应用程序运行金丝雀分析,Flagger 创建了一个 shadow ingress 并设置了 NGINX 特定的注释(annotations)。

A/B 测试

除了加权路由,Flagger 还可以配置为根据 HTTP 匹配条件将流量路由到金丝雀。 在 A/B 测试场景中,您将使用 HTTP headerscookies 来定位您的特定用户群。 这对于需要会话关联的前端应用程序特别有用。


Flagger Linkerd Ingress

编辑 podinfo 金丝雀分析,将提供者设置为 nginx,添加 ingress 引用,移除 max/step 权重并添加匹配条件和 iterations

apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: podinfo
  namespace: test
spec:
  # ingress reference
  provider: nginx
  ingressRef:
    apiVersion: extensions/v1beta1
    kind: Ingress
    name: podinfo
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: podinfo
  autoscalerRef:
    apiVersion: autoscaling/v2beta2
    kind: HorizontalPodAutoscaler
    name: podinfo
  service:
    # container port
    port: 9898
  analysis:
    interval: 1m
    threshold: 10
    iterations: 10
    match:
      # curl -H 'X-Canary: always' http://app.example.com
      - headers:
          x-canary:
            exact: "always"
      # curl -b 'canary=always' http://app.example.com
      - headers:
          cookie:
            exact: "canary"
    # Linkerd Prometheus checks
    metrics:
    - name: request-success-rate
      thresholdRange:
        min: 99
      interval: 1m
    - name: request-duration
      thresholdRange:
        max: 500
      interval: 30s
    webhooks:
      - name: acceptance-test
        type: pre-rollout
        url: http://flagger-loadtester.test/
        timeout: 30s
        metadata:
          type: bash
          cmd: "curl -sd 'test' http://podinfo-canary:9898/token | grep token"
      - name: load-test
        type: rollout
        url: http://flagger-loadtester.test/
        metadata:
          cmd: "hey -z 2m -q 10 -c 2 -H 'Cookie: canary=always' http://app.example.com"

上述配置将运行 10 分钟的分析,目标用户是:canary cookie 设置为 always 或使用 X-Canary: always header 调用服务。

请注意,负载测试现在针对外部地址并使用 canary cookie

通过更新容器镜像触发金丝雀部署:

kubectl -n test set image deployment/podinfo 
podinfod=stefanprodan/podinfo:3.1.4

Flagger 检测到部署修订已更改并开始 A/B 测试:

kubectl -n test describe canary/podinfo

Events:
 Starting canary deployment for podinfo.test
 Pre-rollout check acceptance-test passed
 Advance podinfo.test canary iteration 1/10
 Advance podinfo.test canary iteration 2/10
 Advance podinfo.test canary iteration 3/10
 Advance podinfo.test canary iteration 4/10
 Advance podinfo.test canary iteration 5/10
 Advance podinfo.test canary iteration 6/10
 Advance podinfo.test canary iteration 7/10
 Advance podinfo.test canary iteration 8/10
 Advance podinfo.test canary iteration 9/10
 Advance podinfo.test canary iteration 10/10
 Copying podinfo.test template spec to podinfo-primary.test
 Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
 Promotion completed! Scaling down podinfo.test
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