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Gateway API Canary Deployments

This guide shows you how to use Gateway API and Flagger to automate canary deployments and A/B testing.
Flagger Canary Stages
Prerequisites
Flagger requires a Kubernetes cluster v1.19 or newer and any mesh/ingress that implements the v1beta1 version of Gateway API. We'll be using Contour for the sake of this tutorial, but you can use any other implementation.
Note: Flagger supports v1alpha2 version of Gateway API, but the alpha version has been deprecated and support will be dropped in a future release.
Install Contour, its Gateway provisioner and Gateway API CRDs in the projectcontour namespace:
https://raw.githubusercontent.com/projectcontour/contour/release-1.23/examples/render/contour-gateway-provisioner.yaml
Alternatively, you can also install the Gateway API CRDs from the upstream project:
kubectl apply -k github.com/kubernetes-sigs/gateway-api/config/crd?ref=v0.6.0
Install Flagger in the flagger-system namespace:
kubectl apply -k github.com/fluxcd/flagger//kustomize/gatewayapi
Create a GatewayClass that specifies information about the Gateway controller:
kind: GatewayClass
apiVersion: gateway.networking.k8s.io/v1beta1
metadata:
  name: contour
spec:
  controllerName: projectcontour.io/gateway-controller
Create a Gateway that configures load balancing, traffic ACL, etc:
kind: Gateway
apiVersion: gateway.networking.k8s.io/v1beta1
metadata:
  name: contour
  namespace: projectcontour
spec:
  gatewayClassName: contour
  listeners:
    - name: http
      protocol: HTTP
      port: 80
      allowedRoutes:
        namespaces:
          from: All
Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA), then creates a series of objects (Kubernetes deployments, ClusterIP services, HTTPRoutes for the Gateway). These objects expose the application inside the mesh and drive the canary analysis and promotion.
Create a test namespace:
kubectl create ns test
Create a deployment and a horizontal pod autoscaler:
kubectl apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main
Deploy the load testing service to generate traffic during the canary analysis:
kubectl apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main
Create metric templates targeting the Prometheus server in the flagger-system namespace. The PromQL queries below are meant for Envoy, but you can change it to your ingress/mesh provider accordingly.
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: latency
  namespace: flagger-system
spec:
  provider:
    type: prometheus
    address: http://flagger-prometheus:9090
  query: |
    histogram_quantile(0.99,
      sum(
        rate(
          envoy_cluster_upstream_rq_time_bucket{
            envoy_cluster_name=~"{{ namespace }}_{{ target }}-canary_[0-9a-zA-Z-]+",
          }[{{ interval }}]
        )
      ) by (le)
    )/1000
---
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: error-rate
  namespace: flagger-system
spec:
  provider:
    type: prometheus
    address: http://flagger-prometheus:9090
  query: |
    100 - sum(
      rate(
        envoy_cluster_upstream_rq{
          envoy_cluster_name=~"{{ namespace }}_{{ target }}-canary_[0-9a-zA-Z-]+",
          envoy_response_code!~"5.*"
        }[{{ interval }}]
      )
    )
    /
    sum(
      rate(
        envoy_cluster_upstream_rq{
          envoy_cluster_name=~"{{ namespace }}_{{ target }}-canary_[0-9a-zA-Z-]+",
        }[{{ interval }}]
      )
    )
    * 100
Save the above resource as metric-templates.yaml and then apply it:
kubectl apply -f metric-templates.yaml
Create a canary custom resource (replace "localproject.contour.io" with your own domain):
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: podinfo
  namespace: test
spec:
  # deployment reference
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: podinfo
  # the maximum time in seconds for the canary deployment
  # to make progress before it is rollback (default 600s)
  progressDeadlineSeconds: 60
  # HPA reference (optional)
  autoscalerRef:
    apiVersion: autoscaling/v2
    kind: HorizontalPodAutoscaler
    name: podinfo
  service:
    # service port number
    port: 9898
    # container port number or name (optional)
    targetPort: 9898
    # Gateway API HTTPRoute host names
    hosts:
     - localproject.contour.io
    # Reference to the Gateway that the generated HTTPRoute would attach to.
    gatewayRefs:
      - name: contour
        namespace: projectcontour
  analysis:
    # schedule interval (default 60s)
    interval: 1m
    # max number of failed metric checks before rollback
    threshold: 5
    # max traffic percentage routed to canary
    # percentage (0-100)
    maxWeight: 50
    # canary increment step
    # percentage (0-100)
    stepWeight: 10
    metrics:
    - name: error-rate
      # max error rate (5xx responses)
      # percentage (0-100)
      templateRef:
        name: error-rate
        namespace: flagger-system
      thresholdRange:
        max: 1
      interval: 1m
    - name: latency
      templateRef:
        name: latency
        namespace: flagger-system
      # seconds
      thresholdRange:
         max: 0.5
      interval: 30s
    # testing (optional)
    webhooks:
      - name: smoke-test
        type: pre-rollout
        url: http://flagger-loadtester.test/
        timeout: 15s
        metadata:
          type: bash
          cmd: "curl -sd 'anon' http://podinfo-canary.test:9898/token | grep token"
      - name: load-test
        url: http://flagger-loadtester.test/
        timeout: 5s
        metadata:
          cmd: "hey -z 2m -q 10 -c 2 -host localproject.contour.io http://envoy.projectcontour/"
Save the above resource as podinfo-canary.yaml and then apply it:
kubectl apply -f ./podinfo-canary.yaml
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 minute.
After a couple of seconds Flagger will create the canary objects:
# applied 
deployment.apps/podinfo
horizontalpodautoscaler.autoscaling/podinfo
canary.flagger.app/podinfo
​
# generated 
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
httproutes.gateway.networking.k8s.io/podinfo
Expose the app outside the cluster
Find the external address of Contour's Envoy load balancer:
export ADDRESS="$(kubectl -n projectcontour get svc/envoy -ojson \
| jq -r ".status.loadBalancer.ingress[].hostname")"
echo $ADDRESS
Configure your DNS server with a CNAME record (AWS) or A record (GKE/AKS/DOKS) and point a domain e.g. localproject.contour.io to the LB address.
Now you can access the podinfo UI using your domain address.
Note that you should be using HTTPS when exposing production workloads on internet. You can obtain free TLS certs from Let's Encrypt, read this guide on how to configure cert-manager to secure Contour with TLS certificates.
If you're using a local cluster via kind/k3s you can port forward the Envoy LoadBalancer service:
kubectl port-forward -n projectcontour svc/envoy 8080:80
Now you can access podinfo via curl -H "Host: localproject.contour.io" localhost:8080
Automated canary promotion
Trigger a canary deployment by updating the container image:
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:6.0.1
Flagger detects that the deployment revision changed and starts a new rollout:
kubectl -n test describe canary/podinfo
​
Status:
  Canary Weight:         0
  Failed Checks:         0
  Phase:                 Succeeded
Events:
  Type     Reason  Age   From     Message
  ----     ------  ----  ----     -------
  Normal   Synced  3m    flagger  New revision detected podinfo.test
  Normal   Synced  3m    flagger  Scaling up podinfo.test
  Warning  Synced  3m    flagger  Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 5
  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 10
  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 15
  Normal   Synced  2m    flagger  Advance podinfo.test canary weight 20
  Normal   Synced  2m    flagger  Advance podinfo.test canary weight 25
  Normal   Synced  1m    flagger  Advance podinfo.test canary weight 30
  Normal   Synced  1m    flagger  Advance podinfo.test canary weight 35
  Normal   Synced  55s   flagger  Advance podinfo.test canary weight 40
  Normal   Synced  45s   flagger  Advance podinfo.test canary weight 45
  Normal   Synced  35s   flagger  Advance podinfo.test canary weight 50
  Normal   Synced  25s   flagger  Copying podinfo.test template spec to podinfo-primary.test
  Warning  Synced  15s   flagger  Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
  Normal   Synced  5s    flagger  Promotion completed! Scaling down podinfo.test
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 how Flagger progressively changes the weights of the HTTPRoute object that is attahed to the Gateway with:
watch kubectl get httproute -n test podinfo -o=jsonpath='{.spec.rules}'
You can monitor all canaries with:
watch kubectl get canaries --all-namespaces
​
NAMESPACE   NAME      STATUS        WEIGHT   LASTTRANSITIONTIME
test        podinfo   Progressing   15       2022-01-16T14:05:07Z
prod        frontend  Succeeded     0        2022-01-15T16:15:07Z
prod        backend   Failed        0        2022-01-14T17:05:07Z
Automated rollback
During the canary analysis you can generate HTTP 500 errors and high latency to test if Flagger pauses the rollout.
Trigger another canary deployment:
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:6.0.2
Exec into the load tester pod with:
kubectl -n test exec -it flagger-loadtester-xx-xx sh
Generate HTTP 500 errors:
watch curl http://podinfo-canary:9898/status/500
Generate latency:
watch curl http://podinfo-canary:9898/delay/1
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.
kubectl -n test describe canary/podinfo
​
Status:
  Canary Weight:         0
  Failed Checks:         10
  Phase:                 Failed
Events:
  Type     Reason  Age   From     Message
  ----     ------  ----  ----     -------
  Normal   Synced  3m    flagger  Starting canary deployment for podinfo.test
  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 5
  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 10
  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 15
  Normal   Synced  3m    flagger  Halt podinfo.test advancement error rate 69.17% > 1%
  Normal   Synced  2m    flagger  Halt podinfo.test advancement error rate 61.39% > 1%
  Normal   Synced  2m    flagger  Halt podinfo.test advancement error rate 55.06% > 1%
  Normal   Synced  2m    flagger  Halt podinfo.test advancement error rate 47.00% > 1%
  Normal   Synced  2m    flagger  (combined from similar events): Halt podinfo.test advancement error rate 38.08% > 1%
  Warning  Synced  1m    flagger  Rolling back podinfo.test failed checks threshold reached 10
  Warning  Synced  1m    flagger  Canary failed! Scaling down podinfo.test
Session Affinity
While Flagger can perform weighted routing and A/B testing individually, with Gateway API it can combine the two leading to a Canary release with session affinity. For more information you can read the deployment strategies docs.
Note: The implementation must have support for the ResponseHeaderModifier API.
Create a canary custom resource (replace localproject.contour.io with your own domain):
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: podinfo
  namespace: test
spec:
  # deployment reference
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: podinfo
  # the maximum time in seconds for the canary deployment
  # to make progress before it is rollback (default 600s)
  progressDeadlineSeconds: 60
  # HPA reference (optional)
  autoscalerRef:
    apiVersion: autoscaling/v2
    kind: HorizontalPodAutoscaler
    name: podinfo
  service:
    # service port number
    port: 9898
    # container port number or name (optional)
    targetPort: 9898
    # Gateway API HTTPRoute host names
    hosts:
     - localproject.contour.io
    # Reference to the Gateway that the generated HTTPRoute would attach to.
    gatewayRefs:
      - name: contour
        namespace: projectcontour
  analysis:
    # schedule interval (default 60s)
    interval: 1m
    # max number of failed metric checks before rollback
    threshold: 5
    # max traffic percentage routed to canary
    # percentage (0-100)
    maxWeight: 50
    # canary increment step
    # percentage (0-100)
    stepWeight: 10
    # session affinity config
    sessionAffinity:
      # name of the cookie used
      cookieName: flagger-cookie
      # max age of the cookie (in seconds)
      # optional; defaults to 86400
      maxAge: 21600
    metrics:
    - name: error-rate
      # max error rate (5xx responses)
      # percentage (0-100)
      templateRef:
        name: error-rate
        namespace: flagger-system
      thresholdRange:
        max: 1
      interval: 1m
    - name: latency
      templateRef:
        name: latency
        namespace: flagger-system
      # seconds
      thresholdRange:
         max: 0.5
      interval: 30s
    # testing (optional)
    webhooks:
      - name: smoke-test
        type: pre-rollout
        url: http://flagger-loadtester.test/
        timeout: 15s
        metadata:
          type: bash
          cmd: "curl -sd 'anon' http://podinfo-canary.test:9898/token | grep token"
      - name: load-test
        url: http://flagger-loadtester.test/
        timeout: 5s
        metadata:
          cmd: "hey -z 2m -q 10 -c 2 -host localproject.contour.io http://envoy.projectcontour/"
Save the above resource as podinfo-canary-session-affinity.yaml and then apply it:
kubectl apply -f ./podinfo-canary-session-affinity.yaml
Trigger a canary deployment by updating the container image:
kubectl -n test set image deployment/podinfo \
podinfod=ghcr.io/stefanprodan/podinfo:6.0.1
You can load localproject.contour.io in your browser and refresh it until you see the requests being served by podinfo:6.0.1. All subsequent requests after that will be served by podinfo:6.0.1 and not podinfo:6.0.0 because of the session affinity configured by Flagger in the HTTPRoute object.
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
Create a canary custom resource (replace "localproject.contour.io" with your own domain):
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: podinfo
  namespace: test
spec:
  # deployment reference
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: podinfo
  # the maximum time in seconds for the canary deployment
  # to make progress before it is rollback (default 600s)
  progressDeadlineSeconds: 60
  # HPA reference (optional)
  autoscalerRef:
    apiVersion: autoscaling/v2
    kind: HorizontalPodAutoscaler
    name: podinfo
  service:
    # service port number
    port: 9898
    # container port number or name (optional)
    targetPort: 9898
    # Gateway API HTTPRoute host names
    hosts:
     - localproject.contour.io
    # Reference to the Gateway that the generated HTTPRoute would attach to.
    gatewayRefs:
      - name: contour
        namespace: projectcontour
  analysis:
    # schedule interval (default 60s)
    interval: 1m
    # max number of failed metric checks before rollback
    threshold: 5
    # max traffic percentage routed to canary
    # percentage (0-100)
    maxWeight: 50
    # canary increment step
    # percentage (0-100)
    stepWeight: 10
    metrics:
    - name: error-rate
      # max error rate (5xx responses)
      # percentage (0-100)
      templateRef:
        name: error-rate
        namespace: flagger-system
      thresholdRange:
        max: 1
      interval: 1m
    - name: latency
      templateRef:
        name: latency
        namespace: flagger-system
      # seconds
      thresholdRange:
         max: 0.5
      interval: 30s
    # testing (optional)
    webhooks:
      - name: smoke-test
        type: pre-rollout
        url: http://flagger-loadtester.test/
        timeout: 15s
        metadata:
          type: bash
          cmd: "curl -sd 'anon' http://podinfo-canary.test:9898/token | grep token"
      - name: load-test
        url: http://flagger-loadtester.test/
        timeout: 5s
        metadata:
          cmd: "hey -z 2m -q 10 -c 2 -host localproject.contour.io -H 'X-Canary: insider' http://envoy.projectcontour/"
The above configuration will run an analysis for ten minutes targeting those users that have an insider cookie.
Save the above resource as podinfo-ab-canary.yaml and then apply it:
kubectl apply -f ./podinfo-ab-canary.yaml
Trigger a canary deployment by updating the container image:
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:6.0.3
Flagger detects that the deployment revision changed and starts a new rollout:
kubectl -n test describe canary/podinfo
​
Status:
  Failed Checks:         0
  Phase:                 Succeeded
Events:
  Type     Reason  Age   From     Message
  ----     ------  ----  ----     -------
  Normal   Synced  3m    flagger  New revision detected podinfo.test
  Normal   Synced  3m    flagger  Scaling up podinfo.test
  Warning  Synced  3m    flagger  Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
  Normal   Synced  3m    flagger  Advance podinfo.test canary iteration 1/10
  Normal   Synced  3m    flagger  Advance podinfo.test canary iteration 2/10
  Normal   Synced  3m    flagger  Advance podinfo.test canary iteration 3/10
  Normal   Synced  2m    flagger  Advance podinfo.test canary iteration 4/10
  Normal   Synced  2m    flagger  Advance podinfo.test canary iteration 5/10
  Normal   Synced  1m    flagger  Advance podinfo.test canary iteration 6/10
  Normal   Synced  1m    flagger  Advance podinfo.test canary iteration 7/10
  Normal   Synced  55s   flagger  Advance podinfo.test canary iteration 8/10
  Normal   Synced  45s   flagger  Advance podinfo.test canary iteration 9/10
  Normal   Synced  35s   flagger  Advance podinfo.test canary iteration 10/10
  Normal   Synced  25s   flagger  Copying podinfo.test template spec to podinfo-primary.test
  Warning  Synced  15s   flagger  Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
  Normal   Synced  5s    flagger  Promotion completed! Scaling down podinfo.test
Traffic mirroring
Flagger Canary Traffic Shadowing
For applications that perform read operations, Flagger can be configured to do B/G tests with traffic mirroring. Gateway API traffic mirroring will copy each incoming request, sending one request to the primary and one to the canary service. The response from the primary is sent back to the user and the response from the canary is discarded. Metrics are collected on both requests so that the deployment will only proceed if the canary metrics are within the threshold values.
Note that mirroring should be used for requests that are idempotent or capable of being processed twice (once by the primary and once by the canary).
You can enable mirroring by replacing stepWeight with iterations and by setting analysis.mirror to true:
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: podinfo
  namespace: test
spec:
  # deployment reference
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: podinfo
  service:
    # service port number
    port: 9898
    # container port number or name (optional)
    targetPort: 9898
    # Gateway API HTTPRoute host names
    hosts:
     - localproject.contour.io
    # Reference to the Gateway that the generated HTTPRoute would attach to.
    gatewayRefs:
      - name: contour
        namespace: projectcontour
  analysis:
    # schedule interval
    interval: 1m
    # max number of failed metric checks before rollback
    threshold: 5
    # total number of iterations
    iterations: 10
    # enable traffic shadowing
    mirror: true
    # Gateway API HTTPRoute host names
    metrics:
      - name: request-success-rate
        thresholdRange:
          min: 99
        interval: 1m
      - name: request-duration
        thresholdRange:
          max: 500
        interval: 1m
    webhooks:
      - name: load-test
        url: http://flagger-loadtester.test/
        timeout: 5s
        metadata:
          cmd: "hey -z 2m -q 10 -c 2 -host localproject.contour.io http://envoy.projectcontour/"
With the above configuration, Flagger will run a canary release with the following steps:
detect new revision (deployment spec, secrets or configmaps changes)
scale from zero the canary deployment
wait for the HPA to set the canary minimum replicas
check canary pods health
run the acceptance tests
abort the canary release if tests fail
start the load tests
mirror 100% of the traffic from primary to canary
check request success rate and request duration every minute
abort the canary release if the metrics check failure threshold is reached
stop traffic mirroring after the number of iterations is reached
route live traffic to the canary pods
promote the canary (update the primary secrets, configmaps and deployment spec)
wait for the primary deployment rollout to finish
wait for the HPA to set the primary minimum replicas
check primary pods health
switch live traffic back to primary
scale to zero the canary
send notification with the canary analysis result
The above procedures can be extended with custom metrics checks, webhooks, manual promotion approval and Slack or MS Teams notifications.
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