Blue/Green Deployments

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This guide shows you how to automate Blue/Green deployments with Flagger and Kubernetes.
For applications that are not deployed on a service mesh, Flagger can orchestrate Blue/Green style deployments with Kubernetes L4 networking. When using a service mesh blue/green can be used as specified here.
Flagger Blue/Green Stages
Prerequisites
Flagger requires a Kubernetes cluster v1.16 or newer.
Install Flagger and the Prometheus add-on:
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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 flagger \
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--set prometheus.install=true \
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--set meshProvider=kubernetes
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If you already have a Prometheus instance running in your cluster, you can point Flagger to the ClusterIP service with:
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helm upgrade -i flagger flagger/flagger \
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--namespace flagger \
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--set metricsServer=http://prometheus.monitoring:9090
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Optionally you can enable Slack notifications:
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helm upgrade -i flagger flagger/flagger \
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--reuse-values \
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--namespace flagger \
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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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Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA), then creates a series of objects (Kubernetes deployment and ClusterIP services). These objects expose the application inside the cluster and drive the canary analysis and Blue/Green 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 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 analysis:
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kubectl apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main
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Create a canary custom resource:
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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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  # service mesh provider can be: kubernetes, istio, appmesh, nginx, gloo
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  provider: kubernetes
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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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  # the maximum time in seconds for the canary deployment
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  # to make progress before rollback (default 600s)
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  progressDeadlineSeconds: 60
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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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    port: 9898
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    portDiscovery: true
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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 checks before rollback
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    threshold: 2
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    # number of checks to run before rollback
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    iterations: 10
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    # Prometheus checks based on 
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    # http_request_duration_seconds histogram
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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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    # acceptance/load testing hooks
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    webhooks:
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      - name: smoke-test
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        type: pre-rollout
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        url: http://flagger-loadtester.test/
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        timeout: 15s
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        metadata:
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          type: bash
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          cmd: "curl -sd 'anon' http://podinfo-canary.test: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 1m -q 10 -c 2 http://podinfo-canary.test:9898/"
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The above configuration will run an analysis for five minutes.
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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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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Blue/Green scenario:
on bootstrap, Flagger will create three ClusterIP services (app-primary,app-canary, app)
and a shadow deployment named app-primary that represents the blue version
when a new version is detected, Flagger would scale up the green version and run the conformance tests
(the tests should target the app-canary ClusterIP service to reach the green version)
if the conformance tests are passing, Flagger would start the load tests and validate them with custom Prometheus queries
if the load test analysis is successful, Flagger will promote the new version to app-primary and scale down the green version
Automated Blue/Green promotion
Trigger a 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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Events:
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​
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New revision detected 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 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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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   100      2019-06-16T14:05:07Z
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prod        frontend  Succeeded     0        2019-06-15T16:15:07Z
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prod        backend   Failed        0        2019-06-14T17:05:07Z
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Automated rollback
During the analysis you can generate HTTP 500 errors and high latency to test Flagger's rollback.
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 curl http://podinfo-canary.test:9898/status/500
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Generate latency:
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watch curl http://podinfo-canary.test:9898/delay/1
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When the number of failed checks reaches the analysis threshold, the green version 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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  Failed Checks:         2
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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  New revision detected podinfo.test
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  Normal   Synced  3m    flagger  Advance podinfo.test canary iteration 1/10
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  Normal   Synced  3m    flagger  Advance podinfo.test canary iteration 2/10
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  Normal   Synced  3m    flagger  Advance podinfo.test canary iteration 3/10
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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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  Warning  Synced  2m    flagger  Rolling back podinfo.test failed checks threshold reached 2
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  Warning  Synced  1m    flagger  Canary failed! Scaling down podinfo.test
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Custom metrics
The 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 (green version).
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.flagger: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 (green version) 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 rollout is rolled back.
Trigger a 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 http://podinfo-canary.test:9898/status/400
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Watch Flagger logs:
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kubectl -n flagger logs deployment/flagger -f | jq .msg
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New revision detected podinfo.test
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Scaling up podinfo.test
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Advance podinfo.test canary iteration 1/10
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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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Rolling back podinfo.test failed checks threshold reached 2
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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.
Conformance Testing with Helm
Flagger comes with a testing service that can run Helm tests when configured as a pre-rollout webhook.
Deploy the Helm test runner in the kube-system namespace using the tiller service account:
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helm repo add flagger https://flagger.app
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helm upgrade -i flagger-helmtester flagger/loadtester \
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--namespace=kube-system \
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--set serviceAccountName=tiller
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When deployed the Helm tester API will be available at http://flagger-helmtester.kube-system/.
Add a helm test pre-rollout hook to your chart:
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  analysis:
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    webhooks:
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      - name: "conformance testing"
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        type: pre-rollout
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        url: http://flagger-helmtester.kube-system/
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        timeout: 3m
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        metadata:
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          type: "helm"
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          cmd: "test {{ .Release.Name }} --cleanup"
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When the canary analysis starts, Flagger will call the pre-rollout webhooks. If the helm test fails, Flagger will retry until the analysis threshold is reached and the canary is rolled back.
For an in-depth look at the analysis process read the usage docs.
Tutorials - Previous
Open Service Mesh Deployments
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Canary analysis with Prometheus Operator
Last modified 9mo ago
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Contents
Prerequisites
Bootstrap
Automated Blue/Green promotion
Automated rollback
Custom metrics
Conformance Testing with Helm