Gloo Canary Deployments

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This guide shows you how to use the Gloo Edge ingress controller and Flagger to automate canary releases and A/B testing.
Flagger Gloo Ingress Controller
Prerequisites
Flagger requires a Kubernetes cluster v1.16 or newer and Gloo Edge ingress 1.6.0 or newer.
This guide was written for Flagger version 1.6.0 or higher. Prior versions of Flagger used Gloo UpstreamGroups to handle canaries, but newer versions of Flagger use Gloo RouteTables to handle canaries as well as A/B testing.
Install Gloo with Helm v3:
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helm repo add gloo https://storage.googleapis.com/solo-public-helm
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kubectl create ns gloo-system
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helm upgrade -i gloo gloo/gloo \
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--namespace gloo-system
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Install Flagger and the Prometheus add-on in the same namespace as Gloo:
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helm repo add flagger https://flagger.app
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​
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helm upgrade -i flagger flagger/flagger \
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--namespace gloo-system \
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--set prometheus.install=true \
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--set meshProvider=gloo
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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, Gloo route tables and upstreams). These objects expose the application outside the cluster and drive the canary analysis and promotion.
Create a test namespace:
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kubectl create ns test
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Create a deployment and a horizontal pod autoscaler:
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kubectl -n test apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main
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Deploy the load testing service to generate traffic during the canary analysis:
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kubectl -n test apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main
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Create a virtual service definition that references a route table that will be generated by Flagger (replace app.example.com with your own domain):
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apiVersion: gateway.solo.io/v1
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kind: VirtualService
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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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  virtualHost:
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    domains:
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      - 'app.example.com'
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    routes:
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      - matchers:
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         - prefix: /
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        delegateAction:
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          ref:
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            name: podinfo
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            namespace: test
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Save the above resource as podinfo-virtualservice.yaml and then apply it:
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kubectl apply -f ./podinfo-virtualservice.yaml
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Create a canary custom resource (replace app.example.com with your own domain):
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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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  # upstreamRef (optional)
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  # defines an upstream to copy the spec from when flagger generates new upstreams.
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  # necessary to copy over TLS config, circuit breakers, etc. (anything nonstandard)
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#  upstreamRef:
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#    apiVersion: gloo.solo.io/v1
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#    kind: Upstream
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#    name: podinfo-upstream
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#    namespace: gloo-system
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  provider: gloo
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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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  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: 10s
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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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    # Gloo 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: 10s
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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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        url: http://flagger-loadtester.test/
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        timeout: 5s
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        metadata:
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          type: cmd
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          cmd: "hey -z 2m -q 5 -c 2 -host app.example.com http://gateway-proxy.gloo-system"
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Note: when using upstreamRef the following fields are copied over from the original upstream: Labels, SslConfig, CircuitBreakers, ConnectionConfig, UseHttp2, InitialStreamWindowSize
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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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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virtualservices.gateway.solo.io/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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routetables.gateway.solo.io/podinfo
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upstreams.gloo.solo.io/test-podinfo-canaryupstream-9898
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upstreams.gloo.solo.io/test-podinfo-primaryupstream-9898
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When the bootstrap finishes Flagger will set the canary status to initialized:
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kubectl -n test get canary podinfo
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​
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NAME      STATUS        WEIGHT   LASTTRANSITIONTIME
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podinfo   Initialized   0        2019-05-17T08:09:51Z
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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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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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  Type     Reason  Age   From     Message
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  ----     ------  ----  ----     -------
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  Normal   Synced  3m    flagger  New revision detected podinfo.test
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  Normal   Synced  3m    flagger  Scaling up podinfo.test
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  Warning  Synced  3m    flagger  Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
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  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 5
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  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 10
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  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 15
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  Normal   Synced  2m    flagger  Advance podinfo.test canary weight 20
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  Normal   Synced  2m    flagger  Advance podinfo.test canary weight 25
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  Normal   Synced  1m    flagger  Advance podinfo.test canary weight 30
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  Normal   Synced  1m    flagger  Advance podinfo.test canary weight 35
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  Normal   Synced  55s   flagger  Advance podinfo.test canary weight 40
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  Normal   Synced  45s   flagger  Advance podinfo.test canary weight 45
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  Normal   Synced  35s   flagger  Advance podinfo.test canary weight 50
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  Normal   Synced  25s   flagger  Copying podinfo.test template spec to podinfo-primary.test
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  Warning  Synced  15s   flagger  Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
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  Normal   Synced  5s    flagger  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.
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       2019-05-17T14:05:07Z
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prod        frontend  Succeeded     0        2019-05-17T16:15:07Z
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prod        backend   Failed        0        2019-05-17T17:05:07Z
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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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Generate HTTP 500 errors:
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watch curl -H 'Host: app.example.com' http://gateway-proxy.gloo-system/status/500
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Generate high latency:
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watch curl -H 'Host: app.example.com' http://gateway-proxy.gloo-system/delay/2
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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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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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  Type     Reason  Age   From     Message
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  ----     ------  ----  ----     -------
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  Normal   Synced  3m    flagger  Starting canary deployment for podinfo.test
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  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 5
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  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 10
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  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 15
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  Normal   Synced  3m    flagger  Halt podinfo.test advancement success rate 69.17% < 99%
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  Normal   Synced  2m    flagger  Halt podinfo.test advancement success rate 61.39% < 99%
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  Normal   Synced  2m    flagger  Halt podinfo.test advancement success rate 55.06% < 99%
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  Normal   Synced  2m    flagger  Halt podinfo.test advancement success rate 47.00% < 99%
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  Normal   Synced  2m    flagger  (combined from similar events): Halt podinfo.test advancement success rate 38.08% < 99%
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  Warning  Synced  1m    flagger  Rolling back podinfo.test failed checks threshold reached 10
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  Warning  Synced  1m    flagger  Canary failed! Scaling down podinfo.test
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Custom metrics
The canary analysis can be extended with Prometheus queries.
The demo app is instrumented with Prometheus so you can create a custom check that will use the HTTP request duration histogram to validate the canary.
Create a metric template and apply it on the cluster:
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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  name: not-found-percentage
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  namespace: test
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spec:
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  provider:
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    type: prometheus
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    address: http://flagger-prometheus.gloo-system:9090
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  query: |
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    100 - sum(
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        rate(
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            http_request_duration_seconds_count{
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              kubernetes_namespace="{{ namespace }}",
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              kubernetes_pod_name=~"{{ target }}-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
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              status!="{{ interval }}"
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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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            http_request_duration_seconds_count{
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              kubernetes_namespace="{{ namespace }}",
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              kubernetes_pod_name=~"{{ target }}-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
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            }[{{ interval }}]
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        )
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    ) * 100
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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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        templateRef:
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          name: not-found-percentage
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        thresholdRange:
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          max: 5
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        interval: 1m
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The above configuration validates the canary by checking if the HTTP 404 req/sec percentage is below 5 percent of the total traffic. If the 404s rate reaches the 5% threshold, then the canary fails.
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 curl -H 'Host: app.example.com' http://gateway-proxy.gloo-system/status/404
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Watch Flagger logs:
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kubectl -n gloo-system logs deployment/flagger -f | jq .msg
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​
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Starting canary deployment for podinfo.test
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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 404s percentage 6.20 > 5
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Halt podinfo.test advancement 404s percentage 6.45 > 5
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Halt podinfo.test advancement 404s percentage 7.60 > 5
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Halt podinfo.test advancement 404s percentage 8.69 > 5
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Halt podinfo.test advancement 404s percentage 9.70 > 5
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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 alerting configured, Flagger will send a notification with the reason why the canary failed.
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 A/B Testing Stages
Edit the canary analysis, remove the max/step weight and add the match conditions and iterations:
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analysis:
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  interval: 1m
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  threshold: 5
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  iterations: 10
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  match:
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  - headers:
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      x-canary:
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        exact: "insider"
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  webhooks:
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  - name: load-test
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    url: http://flagger-loadtester.test/
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    metadata:
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      cmd: "hey -z 1m -q 5 -c 5 -H 'X-Canary: insider' -host app.example.com http://gateway-proxy.gloo-system"
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The above configuration will run an analysis for ten minutes targeting users that have a X-Canary: insider header.
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 test:
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kubectl -n gloo-system logs deploy/flagger -f | jq .msg
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New revision detected! Progressing canary analysis for podinfo.test
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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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Routing all traffic to primary
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Promotion completed! Scaling down podinfo.test
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The web browser user agent header allows user segmentation based on device or OS.
For example, if you want to route all mobile users to the canary instance:
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match:
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- headers:
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    user-agent:
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      regex: ".*Mobile.*"
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Or if you want to target only Android users:
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match:
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- headers:
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    user-agent:
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      regex: ".*Android.*"
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Or a specific browser version:
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match:
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- headers:
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    user-agent:
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      regex: ".*Firefox.*"
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For an in-depth look at the analysis process read the usage docs.
Tutorials - Previous
Contour Canary Deployments
Next - Tutorials
NGINX Canary Deployments
Last modified 4mo ago
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Contents
Prerequisites
Bootstrap
Automated canary promotion
Automated rollback
Custom metrics
A/B Testing