Gloo Canary Deployments

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 upstream groups to handle canaries, but newer versions of Flagger use Gloo route tables to handle canaries as well as A/B testing.
Install Gloo with Helm v3:
helm repo add gloo https://storage.googleapis.com/solo-public-helm
kubectl create ns gloo-system
helm upgrade -i gloo gloo/gloo \
--namespace gloo-system
Install Flagger and the Prometheus add-on in the same namespace as Gloo:
helm repo add flagger https://flagger.app
​
helm upgrade -i flagger flagger/flagger \
--namespace gloo-system \
--set prometheus.install=true \
--set meshProvider=gloo
Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA), then creates a series of objects (Kubernetes deployments, ClusterIP services and Gloo route tables groups). These objects expose the application outside the cluster and drive the canary analysis and promotion.
Create a test namespace:
kubectl create ns test
Create a deployment and a horizontal pod autoscaler:
kubectl -n test apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main
Deploy the load testing service to generate traffic during the canary analysis:
kubectl -n test apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main
Create a virtual service definition that references a route table that will be generated by Flagger (replace app.example.com with your own domain):
apiVersion: gateway.solo.io/v1
kind: VirtualService
metadata:
  name: podinfo
  namespace: test
spec:
  virtualHost:
    domains:
      - 'app.example.com'
    routes:
      - matchers:
         - prefix: /
        delegateAction:
          ref:
            name: podinfo
            namespace: test
Save the above resource as podinfo-virtualservice.yaml and then apply it:
kubectl apply -f ./podinfo-virtualservice.yaml
Create a canary custom resource (replace app.example.com with your own domain):
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: podinfo
  namespace: test
spec:
  provider: gloo
  # deployment reference
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: podinfo
  # HPA reference (optional)
  autoscalerRef:
    apiVersion: autoscaling/v2beta2
    kind: HorizontalPodAutoscaler
    name: podinfo
  service:
    # ClusterIP port number
    port: 9898
    # container port number or name (optional)
    targetPort: 9898
  analysis:
    # schedule interval (default 60s)
    interval: 10s
    # 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: 5
    # Gloo Prometheus checks
    metrics:
    - name: request-success-rate
      # minimum req success rate (non 5xx responses)
      # percentage (0-100)
      thresholdRange:
        min: 99
      interval: 1m
    - name: request-duration
      # maximum req duration P99
      # milliseconds
      thresholdRange:
        max: 500
      interval: 30s
    # testing (optional)
    webhooks:
      - name: acceptance-test
        type: pre-rollout
        url: http://flagger-loadtester.test/
        timeout: 10s
        metadata:
          type: bash
          cmd: "curl -sd 'test' http://podinfo-canary:9898/token | grep token"
      - name: load-test
        url: http://flagger-loadtester.test/
        timeout: 5s
        metadata:
          type: cmd
          cmd: "hey -z 2m -q 5 -c 2 -host app.example.com http://gateway-proxy.gloo-system"
Save the above resource as podinfo-canary.yaml and then apply it:
kubectl apply -f ./podinfo-canary.yaml
After a couple of seconds Flagger will create the canary objects:
# applied 
deployment.apps/podinfo
horizontalpodautoscaler.autoscaling/podinfo
virtualservices.gateway.solo.io/podinfo
canary.flagger.app/podinfo
​
# generated 
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
routetables.gateway.solo.io/podinfo
When the bootstrap finishes Flagger will set the canary status to initialized:
kubectl -n test get canary podinfo
​
NAME      STATUS        WEIGHT   LASTTRANSITIONTIME
podinfo   Initialized   0        2019-05-17T08:09:51Z
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:
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.1
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.
You can monitor all canaries with:
watch kubectl get canaries --all-namespaces
​
NAMESPACE   NAME      STATUS        WEIGHT   LASTTRANSITIONTIME
test        podinfo   Progressing   15       2019-05-17T14:05:07Z
prod        frontend  Succeeded     0        2019-05-17T16:15:07Z
prod        backend   Failed        0        2019-05-17T17:05:07Z
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:
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.2
Generate HTTP 500 errors:
watch curl -H 'Host: app.example.com' http://gateway-proxy.gloo-system/status/500
Generate high latency:
watch curl -H 'Host: app.example.com' http://gateway-proxy.gloo-system/delay/2
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 success rate 69.17% < 99%
  Normal   Synced  2m    flagger  Halt podinfo.test advancement success rate 61.39% < 99%
  Normal   Synced  2m    flagger  Halt podinfo.test advancement success rate 55.06% < 99%
  Normal   Synced  2m    flagger  Halt podinfo.test advancement success rate 47.00% < 99%
  Normal   Synced  2m    flagger  (combined from similar events): Halt podinfo.test advancement success rate 38.08% < 99%
  Warning  Synced  1m    flagger  Rolling back podinfo.test failed checks threshold reached 10
  Warning  Synced  1m    flagger  Canary failed! Scaling down podinfo.test
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:
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: not-found-percentage
  namespace: test
spec:
  provider:
    type: prometheus
    address: http://flagger-prometheus.gloo-system:9090
  query: |
    100 - sum(
        rate(
            http_request_duration_seconds_count{
              kubernetes_namespace="{{ namespace }}",
              kubernetes_pod_name=~"{{ target }}-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
              status!="{{ interval }}"
            }[1m]
        )
    )
    /
    sum(
        rate(
            http_request_duration_seconds_count{
              kubernetes_namespace="{{ namespace }}",
              kubernetes_pod_name=~"{{ target }}-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
            }[{{ interval }}]
        )
    ) * 100
Edit the canary analysis and add the following metric:
  analysis:
    metrics:
      - name: "404s percentage"
        templateRef:
          name: not-found-percentage
        thresholdRange:
          max: 5
        interval: 1m
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:
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.3
Generate 404s:
watch curl -H 'Host: app.example.com' http://gateway-proxy.gloo-system/status/404
Watch Flagger logs:
kubectl -n gloo-system logs deployment/flagger -f | jq .msg
​
Starting canary deployment for podinfo.test
Advance podinfo.test canary weight 5
Advance podinfo.test canary weight 10
Advance podinfo.test canary weight 15
Halt podinfo.test advancement 404s percentage 6.20 > 5
Halt podinfo.test advancement 404s percentage 6.45 > 5
Halt podinfo.test advancement 404s percentage 7.60 > 5
Halt podinfo.test advancement 404s percentage 8.69 > 5
Halt podinfo.test advancement 404s percentage 9.70 > 5
Rolling back podinfo.test failed checks threshold reached 5
Canary failed! Scaling down podinfo.test
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:
analysis:
  interval: 1m
  threshold: 5
  iterations: 10
  match:
  - headers:
      x-canary:
        exact: "insider"
  webhooks:
  - name: load-test
    url: http://flagger-loadtester.test/
    metadata:
      cmd: "hey -z 1m -q 5 -c 5 -H 'X-Canary: insider' -host app.example.com http://gateway-proxy.gloo-system"
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:
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.4
Flagger detects that the deployment revision changed and starts the A/B test:
kubectl -n gloo-system logs deploy/flagger -f | jq .msg
​
New revision detected! Progressing canary analysis for podinfo.test
Advance podinfo.test canary iteration 1/10
Advance podinfo.test canary iteration 2/10
Advance podinfo.test canary iteration 3/10
Advance podinfo.test canary iteration 4/10
Advance podinfo.test canary iteration 5/10
Advance podinfo.test canary iteration 6/10
Advance podinfo.test canary iteration 7/10
Advance podinfo.test canary iteration 8/10
Advance podinfo.test canary iteration 9/10
Advance podinfo.test canary iteration 10/10
Copying podinfo.test template spec to podinfo-primary.test
Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
Routing all traffic to primary
Promotion completed! Scaling down podinfo.test
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:
match:
- headers:
    user-agent:
      regex: ".*Mobile.*"
Or if you want to target only Android users:
match:
- headers:
    user-agent:
      regex: ".*Android.*"
Or a specific browser version:
match:
- headers:
    user-agent:
      regex: ".*Firefox.*"
For an in-depth look at the analysis process read the usage docs.
Tutorials - Previous
Contour Canary Deployments
Next - Tutorials
NGINX Canary Deployments
Last updated 1 week ago