Linkerd Canary Deployments

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This guide shows you how to use Linkerd and Flagger to automate canary deployments.
Flagger Linkerd Traffic Split
Prerequisites
Flagger requires a Kubernetes cluster v1.16 or newer and Linkerd 2.10 or newer.
Install Linkerd the Promethues (part of Linkerd Viz):
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linkerd install | kubectl apply -f -
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linkerd viz install | kubectl apply -f -
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Install Flagger in the linkerd namespace:
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kubectl apply -k github.com/fluxcd/flagger//kustomize/linkerd
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Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA), then creates a series of objects (Kubernetes deployments, ClusterIP services and SMI traffic split). These objects expose the application inside the mesh and drive the canary analysis and promotion.
Create a test namespace and enable Linkerd proxy injection:
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kubectl create ns test
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kubectl annotate namespace test linkerd.io/inject=enabled
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Install the load testing service to generate traffic during the canary analysis:
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kubectl apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main
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Create a deployment and a horizontal pod autoscaler:
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kubectl apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main
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Create a canary custom resource for the podinfo deployment:
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apiVersion: flagger.app/v1beta1
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kind: Canary
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metadata:
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  name: podinfo
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  namespace: test
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spec:
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  # deployment reference
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  targetRef:
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    apiVersion: apps/v1
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    kind: Deployment
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    name: podinfo
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  # HPA reference (optional)
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  autoscalerRef:
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    apiVersion: autoscaling/v2beta2
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    kind: HorizontalPodAutoscaler
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    name: podinfo
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  # the maximum time in seconds for the canary deployment
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  # to make progress before it is rollback (default 600s)
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  progressDeadlineSeconds: 60
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  service:
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    # ClusterIP port number
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    port: 9898
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    # container port number or name (optional)
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    targetPort: 9898
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  analysis:
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    # schedule interval (default 60s)
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    interval: 30s
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    # max number of failed metric checks before rollback
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    threshold: 5
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    # max traffic percentage routed to canary
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    # percentage (0-100)
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    maxWeight: 50
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    # canary increment step
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    # percentage (0-100)
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    stepWeight: 5
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    # Linkerd Prometheus checks
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    metrics:
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    - name: request-success-rate
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      # minimum req success rate (non 5xx responses)
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      # percentage (0-100)
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      thresholdRange:
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        min: 99
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      interval: 1m
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    - name: request-duration
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      # maximum req duration P99
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      # milliseconds
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      thresholdRange:
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        max: 500
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      interval: 30s
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    # testing (optional)
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    webhooks:
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      - name: acceptance-test
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        type: pre-rollout
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        url: http://flagger-loadtester.test/
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        timeout: 30s
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        metadata:
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          type: bash
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          cmd: "curl -sd 'test' http://podinfo-canary.test:9898/token | grep token"
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      - name: load-test
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        type: rollout
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        url: http://flagger-loadtester.test/
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        metadata:
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          cmd: "hey -z 2m -q 10 -c 2 http://podinfo-canary.test:9898/"
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Save the above resource as podinfo-canary.yaml and then apply it:
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kubectl apply -f ./podinfo-canary.yaml
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When the canary analysis starts, Flagger will call the pre-rollout webhooks before routing traffic to the canary. The canary analysis will run for five minutes while validating the HTTP metrics and rollout hooks every half a minute.
After a couple of seconds Flagger will create the canary objects:
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# applied
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deployment.apps/podinfo
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horizontalpodautoscaler.autoscaling/podinfo
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ingresses.extensions/podinfo
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canary.flagger.app/podinfo
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​
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# generated
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deployment.apps/podinfo-primary
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horizontalpodautoscaler.autoscaling/podinfo-primary
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service/podinfo
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service/podinfo-canary
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service/podinfo-primary
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trafficsplits.split.smi-spec.io/podinfo
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After the boostrap, the podinfo deployment will be scaled to zero and the traffic to podinfo.test will be routed to the primary pods. During the canary analysis, the podinfo-canary.test address can be used to target directly the canary pods.
Automated canary promotion
Flagger implements a control loop that gradually shifts traffic to the canary while measuring key performance indicators like HTTP requests success rate, requests average duration and pod health. Based on analysis of the KPIs a canary is promoted or aborted, and the analysis result is published to Slack.
Flagger Canary Stages
Trigger a canary deployment by updating the container image:
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kubectl -n test set image deployment/podinfo \
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podinfod=stefanprodan/podinfo:3.1.1
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Flagger detects that the deployment revision changed and starts a new rollout:
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kubectl -n test describe canary/podinfo
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​
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Status:
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  Canary Weight:         0
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  Failed Checks:         0
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  Phase:                 Succeeded
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Events:
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 New revision detected! Scaling up podinfo.test
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 Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
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 Pre-rollout check acceptance-test passed
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 Advance podinfo.test canary weight 5
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 Advance podinfo.test canary weight 10
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 Advance podinfo.test canary weight 15
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 Advance podinfo.test canary weight 20
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 Advance podinfo.test canary weight 25
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 Waiting for podinfo.test rollout to finish: 1 of 2 updated replicas are available
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 Advance podinfo.test canary weight 30
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 Advance podinfo.test canary weight 35
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 Advance podinfo.test canary weight 40
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 Advance podinfo.test canary weight 45
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 Advance podinfo.test canary weight 50
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 Copying podinfo.test template spec to podinfo-primary.test
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 Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
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 Promotion completed! Scaling down podinfo.test
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Note that if you apply new changes to the deployment during the canary analysis, Flagger will restart the analysis.
A canary deployment is triggered by changes in any of the following objects:
Deployment PodSpec (container image, command, ports, env, resources, etc)
ConfigMaps mounted as volumes or mapped to environment variables
Secrets mounted as volumes or mapped to environment variables
You can monitor all canaries with:
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watch kubectl get canaries --all-namespaces
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​
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NAMESPACE   NAME      STATUS        WEIGHT   LASTTRANSITIONTIME
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test        podinfo   Progressing   15       2019-06-30T14:05:07Z
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prod        frontend  Succeeded     0        2019-06-30T16:15:07Z
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prod        backend   Failed        0        2019-06-30T17:05:07Z
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Automated rollback
During the canary analysis you can generate HTTP 500 errors and high latency to test if Flagger pauses and rolls back the faulted version.
Trigger another canary deployment:
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kubectl -n test set image deployment/podinfo \
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podinfod=stefanprodan/podinfo:3.1.2
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Exec into the load tester pod with:
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kubectl -n test exec -it flagger-loadtester-xx-xx sh
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Generate HTTP 500 errors:
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watch -n 1 curl http://podinfo-canary.test:9898/status/500
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Generate latency:
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watch -n 1 curl http://podinfo-canary.test:9898/delay/1
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When the number of failed checks reaches the canary analysis threshold, the traffic is routed back to the primary, the canary is scaled to zero and the rollout is marked as failed.
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kubectl -n test describe canary/podinfo
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​
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Status:
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  Canary Weight:         0
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  Failed Checks:         10
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  Phase:                 Failed
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Events:
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 Starting canary analysis for podinfo.test
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 Pre-rollout check acceptance-test passed
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 Advance podinfo.test canary weight 5
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 Advance podinfo.test canary weight 10
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 Advance podinfo.test canary weight 15
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 Halt podinfo.test advancement success rate 69.17% < 99%
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 Halt podinfo.test advancement success rate 61.39% < 99%
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 Halt podinfo.test advancement success rate 55.06% < 99%
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 Halt podinfo.test advancement request duration 1.20s > 0.5s
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 Halt podinfo.test advancement request duration 1.45s > 0.5s
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 Rolling back podinfo.test failed checks threshold reached 5
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 Canary failed! Scaling down podinfo.test
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Custom metrics
The canary analysis can be extended with Prometheus queries.
Let's a define a check for not found errors. Edit the canary analysis and add the following metric:
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  analysis:
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    metrics:
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    - name: "404s percentage"
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      threshold: 3
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      query: |
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        100 - sum(
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            rate(
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                response_total{
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                    namespace="test",
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                    deployment="podinfo",
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                    status_code!="404",
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                    direction="inbound"
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                }[1m]
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            )
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        )
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        /
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        sum(
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            rate(
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                response_total{
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                    namespace="test",
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                    deployment="podinfo",
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                    direction="inbound"
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                }[1m]
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            )
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        )
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        * 100
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The above configuration validates the canary version by checking if the HTTP 404 req/sec percentage is below three percent of the total traffic. If the 404s rate reaches the 3% threshold, then the analysis is aborted and the canary is marked as failed.
Trigger a canary deployment by updating the container image:
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kubectl -n test set image deployment/podinfo \
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podinfod=stefanprodan/podinfo:3.1.3
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Generate 404s:
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watch -n 1 curl http://podinfo-canary:9898/status/404
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Watch Flagger logs:
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kubectl -n linkerd logs deployment/flagger -f | jq .msg
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​
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Starting canary deployment for podinfo.test
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Pre-rollout check acceptance-test passed
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Advance podinfo.test canary weight 5
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Halt podinfo.test advancement 404s percentage 6.20 > 3
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Halt podinfo.test advancement 404s percentage 6.45 > 3
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Halt podinfo.test advancement 404s percentage 7.22 > 3
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Halt podinfo.test advancement 404s percentage 6.50 > 3
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Halt podinfo.test advancement 404s percentage 6.34 > 3
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Rolling back podinfo.test failed checks threshold reached 5
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Canary failed! Scaling down podinfo.test
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If you have Slack configured, Flagger will send a notification with the reason why the canary failed.
Linkerd Ingress
There are two ingress controllers that are compatible with both Flagger and Linkerd: NGINX and Gloo.
Install NGINX:
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helm upgrade -i nginx-ingress stable/nginx-ingress \
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--namespace ingress-nginx
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Create an ingress definition for podinfo that rewrites the incoming header to the internal service name (required by Linkerd):
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apiVersion: extensions/v1beta1
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kind: Ingress
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metadata:
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  name: podinfo
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  namespace: test
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  labels:
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    app: podinfo
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  annotations:
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    kubernetes.io/ingress.class: "nginx"
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    nginx.ingress.kubernetes.io/configuration-snippet: |
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      proxy_set_header l5d-dst-override $service_name.$namespace.svc.cluster.local:9898;
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      proxy_hide_header l5d-remote-ip;
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      proxy_hide_header l5d-server-id;
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spec:
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  rules:
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    - host: app.example.com
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      http:
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        paths:
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          - backend:
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              serviceName: podinfo
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              servicePort: 9898
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When using an ingress controller, the Linkerd traffic split does not apply to incoming traffic since NGINX in running outside of the mesh. In order to run a canary analysis for a frontend app, Flagger creates a shadow ingress and sets the NGINX specific annotations.
A/B Testing
Besides weighted routing, Flagger can be configured to route traffic to the canary based on HTTP match conditions. In an A/B testing scenario, you'll be using HTTP headers or cookies to target a certain segment of your users. This is particularly useful for frontend applications that require session affinity.
Flagger Linkerd Ingress
Edit podinfo canary analysis, set the provider to nginx, add the ingress reference, remove the max/step weight and add the match conditions and iterations:
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apiVersion: flagger.app/v1beta1
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kind: Canary
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metadata:
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  name: podinfo
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  namespace: test
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spec:
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  # ingress reference
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  provider: nginx
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  ingressRef:
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    apiVersion: extensions/v1beta1
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    kind: Ingress
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    name: podinfo
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  targetRef:
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    apiVersion: apps/v1
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    kind: Deployment
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    name: podinfo
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  autoscalerRef:
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    apiVersion: autoscaling/v2beta2
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    kind: HorizontalPodAutoscaler
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    name: podinfo
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  service:
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    # container port
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    port: 9898
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  analysis:
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    interval: 1m
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    threshold: 10
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    iterations: 10
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    match:
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      # curl -H 'X-Canary: always' http://app.example.com
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      - headers:
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          x-canary:
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            exact: "always"
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      # curl -b 'canary=always' http://app.example.com
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      - headers:
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          cookie:
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            exact: "canary"
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    # Linkerd Prometheus checks
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    metrics:
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    - name: request-success-rate
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      thresholdRange:
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        min: 99
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      interval: 1m
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    - name: request-duration
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      thresholdRange:
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        max: 500
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      interval: 30s
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    webhooks:
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      - name: acceptance-test
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        type: pre-rollout
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        url: http://flagger-loadtester.test/
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        timeout: 30s
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        metadata:
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          type: bash
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          cmd: "curl -sd 'test' http://podinfo-canary:9898/token | grep token"
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      - name: load-test
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        type: rollout
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        url: http://flagger-loadtester.test/
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        metadata:
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          cmd: "hey -z 2m -q 10 -c 2 -H 'Cookie: canary=always' http://app.example.com"
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The above configuration will run an analysis for ten minutes targeting users that have a canary cookie set to always or those that call the service using the X-Canary: always header.
Note that the load test now targets the external address and uses the canary cookie.
Trigger a canary deployment by updating the container image:
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kubectl -n test set image deployment/podinfo \
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podinfod=stefanprodan/podinfo:3.1.4
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Flagger detects that the deployment revision changed and starts the A/B testing:
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kubectl -n test describe canary/podinfo
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​
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Events:
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 Starting canary deployment for podinfo.test
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 Pre-rollout check acceptance-test passed
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 Advance podinfo.test canary iteration 1/10
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 Advance podinfo.test canary iteration 2/10
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 Advance podinfo.test canary iteration 3/10
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 Advance podinfo.test canary iteration 4/10
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 Advance podinfo.test canary iteration 5/10
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 Advance podinfo.test canary iteration 6/10
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 Advance podinfo.test canary iteration 7/10
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 Advance podinfo.test canary iteration 8/10
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 Advance podinfo.test canary iteration 9/10
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 Advance podinfo.test canary iteration 10/10
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 Copying podinfo.test template spec to podinfo-primary.test
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 Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
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 Promotion completed! Scaling down podinfo.test
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The above procedure can be extended with custom metrics checks, webhooks, manual promotion approval and Slack or MS Teams notifications.
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App Mesh Canary Deployments
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Contents
Prerequisites
Bootstrap
Automated canary promotion