--- weight: 9 title: Open Service Mesh Canary Deployments --- This guide shows you how to use Open Service Mesh (OSM) and Flagger to automate canary deployments. ![Flagger OSM Traffic Split](/img/diagrams/flagger-osm-traffic-split.png) ## Prerequisites Flagger requires a Kubernetes cluster **v1.16** or newer and Open Service Mesh **0.9.1** or newer. OSM must have permissive traffic policy enabled and have an instance of Prometheus for metrics. - If the OSM CLI is being used for installation, install OSM using the following command: ```bash osm install \ --set=OpenServiceMesh.deployPrometheus=true \ --set=OpenServiceMesh.enablePermissiveTrafficPolicy=true ``` - If a managed instance of OSM is being used: - [Bring your own instance](https://docs.openservicemesh.io/docs/guides/observability/metrics/#bring-your-own) of Prometheus, setting the namespace to match the managed OSM controller namespace - Enable permissive traffic policy after installation by updating the OSM MeshConfig resource: ```bash # Replace with OSM controller's namespace kubectl patch meshconfig osm-mesh-config -n -p '{"spec":{"traffic":{"enablePermissiveTrafficPolicyMode":true}}}' --type=merge ``` To install Flagger in the default `osm-system` namespace, use: ```bash kubectl apply -k https://github.com/fluxcd/flagger//kustomize/osm?ref=main ``` Alternatively, if a non-default namespace or managed instance of OSM is in use, install Flagger with Helm, replacing the values as appropriate. If a custom instance of Prometheus is being used, replace `osm-prometheus` with the relevant Prometheus service name. ```bash helm upgrade -i flagger flagger/flagger \ --namespace= \ --set meshProvider=osm \ --set metricsServer=http://osm-prometheus..svc:7070 ``` ## Bootstrap Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA), then creates a series of objects (Kubernetes deployments, ClusterIP services and SMI traffic split). These objects expose the application inside the mesh and drive the canary analysis and promotion. Create a `test` namespace and enable OSM namespace monitoring and metrics scraping for the namespace. ```bash kubectl create namespace test osm namespace add test osm metrics enable --namespace test ``` Create a `podinfo` deployment and a horizontal pod autoscaler: ```bash kubectl apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main ``` Install the load testing service to generate traffic during the canary analysis: ```bash kubectl apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main ``` Create a canary custom resource for the `podinfo` deployment. The following `podinfo` canary custom resource instructs Flagger to: 1. monitor any changes to the `podinfo` deployment created earlier, 2. detect `podinfo` deployment revision changes, and 3. start a Flagger canary analysis, rollout, and promotion if there were deployment revision changes. ```yaml apiVersion: flagger.app/v1beta1 kind: Canary metadata: name: podinfo namespace: test spec: provider: osm # deployment reference targetRef: apiVersion: apps/v1 kind: Deployment name: podinfo # HPA reference (optional) autoscalerRef: apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler name: podinfo # the maximum time in seconds for the canary deployment # to make progress before it is rolled back (default 600s) progressDeadlineSeconds: 60 service: # ClusterIP port number port: 9898 # container port number or name (optional) targetPort: 9898 analysis: # schedule interval (default 60s) interval: 30s # 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 # OSM 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: 30s metadata: type: bash cmd: "curl -sd 'test' http://podinfo-canary.test:9898/token | grep token" - name: load-test type: rollout url: http://flagger-loadtester.test/ timeout: 5s metadata: cmd: "hey -z 2m -q 10 -c 2 http://podinfo-canary.test:9898/" ``` Save the above resource as podinfo-canary.yaml and then apply it: ```bash 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 half a minute. After a couple of seconds Flagger will create the canary objects. ```bash # applied deployment.apps/podinfo horizontalpodautoscaler.autoscaling/podinfo ingresses.extensions/podinfo canary.flagger.app/podinfo # generated deployment.apps/podinfo-primary horizontalpodautoscaler.autoscaling/podinfo-primary service/podinfo service/podinfo-canary service/podinfo-primary trafficsplits.split.smi-spec.io/podinfo ``` After the bootstrap, the `podinfo` deployment will be scaled to zero and the traffic to `podinfo.test` will be routed to the primary pods. During the canary analysis, the `podinfo-canary.test` address can be used to target directly the canary pods. ## 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. ![Flagger Canary Stages](/img/diagrams/flagger-canary-steps.png) Trigger a canary deployment by updating the container image: ```bash kubectl -n test set image deployment/podinfo \ podinfod=ghcr.io/stefanprodan/podinfo:6.0.1 ``` Flagger detects that the deployment revision changed and starts a new rollout. ```text kubectl -n test describe canary/podinfo Status: Canary Weight: 0 Failed Checks: 0 Phase: Succeeded Events: New revision detected! Scaling up podinfo.test Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available Pre-rollout check acceptance-test passed Advance podinfo.test canary weight 5 Advance podinfo.test canary weight 10 Advance podinfo.test canary weight 15 Advance podinfo.test canary weight 20 Advance podinfo.test canary weight 25 Waiting for podinfo.test rollout to finish: 1 of 2 updated replicas are available Advance podinfo.test canary weight 30 Advance podinfo.test canary weight 35 Advance podinfo.test canary weight 40 Advance podinfo.test canary weight 45 Advance podinfo.test canary weight 50 Copying podinfo.test template spec to podinfo-primary.test Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available Promotion completed! Scaling down podinfo.test ``` **Note** that if you apply any new changes to the `podinfo` 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 all canaries with: ```bash watch kubectl get canaries --all-namespaces NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME test podinfo Progressing 15 2019-06-30T14:05:07Z prod frontend Succeeded 0 2019-06-30T16:15:07Z prod backend Failed 0 2019-06-30T17:05:07Z ``` ## 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: ```bash kubectl -n test set image deployment/podinfo \ podinfod=ghcr.io/stefanprodan/podinfo:6.0.2 ``` Exec into the load tester pod with: ```bash kubectl -n test exec -it flagger-loadtester-xx-xx sh ``` Repeatedly generate HTTP 500 errors until the `kubectl describe` output below shows canary rollout failure: ```bash watch -n 0.1 curl http://podinfo-canary.test:9898/status/500 ``` Repeatedly generate latency until canary rollout fails: ```bash watch -n 0.1 curl http://podinfo-canary.test:9898/delay/1 ``` When the number of failed checks reaches the canary analysis thresholds defined in the `podinfo` canary custom resource earlier, the traffic is routed back to the primary, the canary is scaled to zero and the rollout is marked as failed. ```text kubectl -n test describe canary/podinfo Status: Canary Weight: 0 Failed Checks: 10 Phase: Failed Events: Starting canary analysis for podinfo.test Pre-rollout check acceptance-test passed Advance podinfo.test canary weight 5 Advance podinfo.test canary weight 10 Advance podinfo.test canary weight 15 Halt podinfo.test advancement success rate 69.17% < 99% Halt podinfo.test advancement success rate 61.39% < 99% Halt podinfo.test advancement success rate 55.06% < 99% Halt podinfo.test advancement request duration 1.20s > 0.5s Halt podinfo.test advancement request duration 1.45s > 0.5s Rolling back podinfo.test failed checks threshold reached 5 Canary failed! Scaling down podinfo.test ``` ## Custom Metrics The canary analysis can be extended with Prometheus queries. Let's define a check for 404 not found errors. Edit the canary analysis (`podinfo-canary.yaml` file) and add the following metric. For more information on creating additional custom metrics using OSM metrics, please check the [metrics available in OSM](https://docs.openservicemesh.io/docs/guides/observability/metrics/#available-metrics). ```yaml analysis: metrics: - name: "404s percentage" threshold: 3 query: | 100 - ( sum( rate( osm_request_total{ destination_namespace="test", destination_kind="Deployment", destination_name="podinfo", response_code!="404" }[1m] ) ) / sum( rate( osm_request_total{ destination_namespace="test", destination_kind="Deployment", destination_name="podinfo" }[1m] ) ) * 100 ) ``` The above configuration validates the canary version by checking if the HTTP 404 req/sec percentage is below three percent of the total traffic. If the 404s rate reaches the 3% threshold, then the analysis is aborted and the canary is marked as failed. Trigger a canary deployment by updating the container image: ```bash kubectl -n test set image deployment/podinfo \ podinfod=ghcr.io/stefanprodan/podinfo:6.0.3 ``` Exec into the load tester pod with: ```bash kubectl -n test exec -it flagger-loadtester-xx-xx sh ``` Repeatedly generate 404s until canary rollout fails: ```bash watch -n 0.1 curl http://podinfo-canary.test:9898/status/404 ``` Watch Flagger logs to confirm successful canary rollback. ```text kubectl -n osm-system logs deployment/flagger -f | jq .msg Starting canary deployment for podinfo.test Pre-rollout check acceptance-test passed Advance podinfo.test canary weight 5 Halt podinfo.test advancement 404s percentage 6.20 > 3 Halt podinfo.test advancement 404s percentage 6.45 > 3 Halt podinfo.test advancement 404s percentage 7.22 > 3 Halt podinfo.test advancement 404s percentage 6.50 > 3 Halt podinfo.test advancement 404s percentage 6.34 > 3 Rolling back podinfo.test failed checks threshold reached 5 Canary failed! Scaling down podinfo.test ```