# Kuma Canary Deployments This guide shows you how to use Kuma and Flagger to automate canary deployments. ![Flagger Kuma Canary](https://raw.githubusercontent.com/fluxcd/flagger/main/docs/diagrams/flagger-kuma-canary.png) ## Prerequisites Flagger requires a Kubernetes cluster **v1.19** or newer and Kuma **1.7** or newer. Install Kuma and Prometheus (part of Kuma Metrics): ```bash kumactl install control-plane | kubectl apply -f - kumactl install observability --components "grafana,prometheus" | kubectl apply -f - ``` Install Flagger in the `kong-mesh-system` namespace: ```bash kubectl apply -k github.com/fluxcd/flagger//kustomize/kuma ``` ## Bootstrap Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA), then creates a series of objects (Kubernetes deployments, ClusterIP services and Kuma `TrafficRoute`). These objects expose the application inside the mesh and drive the canary analysis and promotion. Create a test namespace and enable Kuma sidecar injection: ```bash kubectl create ns test kubectl annotate namespace test kuma.io/sidecar-injection=enabled ``` 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 deployment and a horizontal pod autoscaler: ```bash kubectl apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main ``` Create a canary custom resource for the `podinfo` deployment: ```yaml apiVersion: flagger.app/v1beta1 kind: Canary metadata: name: podinfo namespace: test annotations: kuma.io/mesh: default spec: targetRef: apiVersion: apps/v1 kind: Deployment name: podinfo progressDeadlineSeconds: 60 service: port: 9898 targetPort: 9898 apex: annotations: 9898.service.kuma.io/protocol: "http" canary: annotations: 9898.service.kuma.io/protocol: "http" primary: annotations: 9898.service.kuma.io/protocol: "http" 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 metrics: - name: request-success-rate threshold: 99 interval: 1m - name: request-duration threshold: 500 interval: 30s 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/ 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 trafficroutes.kuma.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, and the analysis result is published to Slack. ![Flagger Canary Stages](https://raw.githubusercontent.com/fluxcd/flagger/main/docs/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: ``` 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 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 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 ``` Generate HTTP 500 errors: ```bash watch -n 1 curl http://podinfo-canary.test:9898/status/500 ``` Generate latency: ```bash watch -n 1 curl http://podinfo-canary.test: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: 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 ``` The above procedures can be extended with [custom metrics](https://docs.flagger.app/usage/metrics) checks, [webhooks](https://docs.flagger.app/usage/webhooks), [manual promotion](https://docs.flagger.app/usage/webhooks#manual-gating) approval and [Slack or MS Teams](https://docs.flagger.app/usage/alerting) notifications.