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# Contour Canary Deployments
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This guide shows you how to use the Contour ingress controller and Flagger to automate canary releases and A/B testing.
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### Prerequisites
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Flagger requires a Kubernetes cluster **v1.11** or newer and Contour **v1.0** or newer.
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Install Contour on a cluster with LoadBalancer support:
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```bash
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kubectl apply -f https://projectcontour.io/quickstart/contour.yaml
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```
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The above command will deploy Contour and an Envoy daemonset in the `projectcontour` namespace.
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Install Flagger using Kustomize (kubectl 1.14) in the `projectcontour` namespace:
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```bash
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kubectl apply -k github.com/weaveworks/flagger//kustomize/contour
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```
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The above command will deploy Flagger and Prometheus configured to scrape the Contour's Envoy instances.
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You can also enable Slack or MS Teams notifications,
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see the Kustomize install [docs](https://docs.flagger.app/install/flagger-install-on-kubernetes#install-flagger-with-kustomize).
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You can install Flagger using Helm v2 or v3:
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```sh
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helm repo add flagger https://flagger.app
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helm upgrade -i flagger flagger/flagger \
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--namespace projectcontour \
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--set meshProvider=contour \
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--set prometheus.install=true \
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--set slack.url=https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK \
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--set slack.channel=general \
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--set slack.user=flagger
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```
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### Bootstrap
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Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
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then creates a series of objects (Kubernetes deployments, ClusterIP services and Contour HTTPProxy).
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These objects expose the application in the cluster and drive the canary analysis and promotion.
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Create a test namespace:
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```bash
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kubectl create ns test
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```
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Install the load testing service to generate traffic during the canary analysis:
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```bash
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kubectl apply -k github.com/weaveworks/flagger//kustomize/tester
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```
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Create a deployment and a horizontal pod autoscaler:
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```bash
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kubectl apply -k github.com/weaveworks/flagger//kustomize/podinfo
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```
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Create a canary custom resource:
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```yaml
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apiVersion: flagger.app/v1alpha3
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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
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autoscalerRef:
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apiVersion: autoscaling/v2beta1
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kind: HorizontalPodAutoscaler
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name: podinfo
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service:
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# service port
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port: 80
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# container port
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targetPort: 9898
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# Contour request timeout
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timeout: 15s
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# Contour retry policy
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retries:
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attempts: 3
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perTryTimeout: 5s
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# define the canary analysis timing and KPIs
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canaryAnalysis:
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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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# Contour 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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threshold: 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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threshold: 500
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interval: 30s
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# testing
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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/token | grep token"
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- name: load-test
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url: http://flagger-loadtester.test/
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type: rollout
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timeout: 5s
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metadata:
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cmd: "hey -z 1m -q 10 -c 2 -host app.example.com http://envoy.projectcontour"
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```
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Save the above resource as podinfo-canary.yaml and then apply it:
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```bash
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kubectl apply -f ./podinfo-canary.yaml
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```
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When the canary analysis starts, Flagger will call the pre-rollout webhooks before routing traffic to the canary.
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The canary analysis will run for five minutes while validating the HTTP metrics and rollout hooks every half a minute.
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After a couple of seconds Flagger will create the canary objects:
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```bash
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# applied
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deployment.apps/podinfo
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horizontalpodautoscaler.autoscaling/podinfo
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canary.flagger.app/podinfo
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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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httpproxy.projectcontour.io/podinfo
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```
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After the boostrap, the podinfo deployment will be scaled to zero and the traffic to `podinfo.test` will be routed
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to the primary pods. During the canary analysis, the `podinfo-canary.test` address can be used to target directly the canary pods.
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### Expose the app outside the cluster
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Find the external address of Contour's Envoy load balancer:
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```bash
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export ADDRESS="$(kubectl -n projectcontour get svc/envoy -ojson | jq -r ".status.loadBalancer.ingress[].hostname")"
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echo $ADDRESS
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```
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Configure your DNS server with a CNAME record (AWS) or A record (GKE) and point a domain e.g. `app.example.com` to the LB address.
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Create a HTTPProxy definition and include the podinfo proxy generated by Flagger (replace `app.example.com` with your own domain):
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```yaml
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apiVersion: projectcontour.io/v1
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kind: HTTPProxy
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metadata:
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name: podinfo-ingress
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namespace: test
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spec:
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virtualhost:
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fqdn: app.example.com
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includes:
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- name: podinfo
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namespace: test
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conditions:
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- prefix: /
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```
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Save the above resource as podinfo-ingress.yaml and then apply it:
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```bash
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kubectl apply -f ./podinfo-ingress.yaml
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```
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Verify that Contour processed the proxy definition with:
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```sh
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kubectl -n test get httpproxies
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NAME FQDN STATUS
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podinfo valid
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podinfo-ingress app.example.com valid
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```
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Now you can access podinfo using your domain address.
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### Automated canary promotion
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Flagger implements a control loop that gradually shifts traffic to the canary while measuring key performance indicators
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like HTTP requests success rate, requests average duration and pod health.
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Based on analysis of the KPIs a canary is promoted or aborted.
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A canary deployment is triggered by changes in any of the following objects:
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* Deployment PodSpec (container image, command, ports, env, resources, etc)
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* ConfigMaps and Secrets mounted as volumes or mapped to environment variables
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Trigger a canary deployment by updating the container image:
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```bash
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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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```
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Flagger detects that the deployment revision changed and starts a new rollout:
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```text
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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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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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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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Routing all traffic to primary
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Promotion completed! Scaling down podinfo.test
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```
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When the canary analysis starts, Flagger will call the pre-rollout webhooks before routing traffic to the canary.
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**Note** that if you apply new changes to the deployment during the canary analysis, Flagger will restart the analysis.
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You can monitor all canaries with:
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```bash
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watch kubectl get canaries --all-namespaces
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NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME
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test podinfo Progressing 15 2019-12-20T14:05:07Z
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```
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If you’ve enabled the Slack notifications, you should receive the following messages:
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### Automated rollback
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During the canary analysis you can generate HTTP 500 errors or high latency to test if Flagger pauses the rollout.
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Trigger a canary deployment:
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```bash
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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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```
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Exec into the load tester pod with:
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```bash
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kubectl -n test exec -it deploy/flagger-loadtester bash
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```
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Generate HTTP 500 errors:
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```bash
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hey -z 1m -c 5 -q 5 http://app.example.com/status/500
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```
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Generate latency:
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```bash
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watch -n 1 curl http://app.example.com/delay/1
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```
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When the number of failed checks reaches the canary analysis threshold, the traffic is routed back to the primary,
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the canary is scaled to zero and the rollout is marked as failed.
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```text
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kubectl -n projectcontour logs deploy/flagger -f | jq .msg
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New revision detected! 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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```
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If you’ve enabled the Slack notifications, you’ll receive a message if the progress deadline is exceeded,
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or if the analysis reached the maximum number of failed checks:
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### A/B Testing
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Besides weighted routing, Flagger can be configured to route traffic to the canary based on HTTP match conditions.
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In an A/B testing scenario, you'll be using HTTP headers or cookies to target a certain segment of your users.
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This is particularly useful for frontend applications that require session affinity.
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Edit the canary analysis, remove the max/step weight and add the match conditions and iterations:
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```yaml
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canaryAnalysis:
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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://envoy.projectcontour"
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```
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The above configuration will run an analysis for ten minutes targeting users that have a `X-Canary: insider` header.
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You can also use a HTTP cookie. To target all users with a cookie set to `insider`, the match condition should be:
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```yaml
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match:
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- headers:
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cookie:
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suffix: "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 'Cookie: canary=insider' -host app.example.com http://envoy.projectcontour"
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```
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Trigger a canary deployment by updating the container image:
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```bash
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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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```
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Flagger detects that the deployment revision changed and starts the A/B test:
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```text
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kubectl -n appmesh-system logs deploy/flagger -f | jq .msg
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New revision detected! Starting 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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```
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