# Apache APISIX Canary Deployments This guide shows you how to use the [Apache APISIX](https://apisix.apache.org/) and Flagger to automate canary deployments. ![Flagger Apache APISIX Overview](https://raw.githubusercontent.com/fluxcd/flagger/main/docs/diagrams/flagger-apisix-overview.png) ## Prerequisites Flagger requires a Kubernetes cluster **v1.19** or newer and Apache APISIX **v2.15** or newer and Apache APISIX Ingress Controller **v1.5.0** or newer. Install Apache APISIX and Apache APISIX Ingress Controller with Helm v3: ```bash helm repo add apisix https://charts.apiseven.com kubectl create ns apisix helm upgrade -i apisix apisix/apisix --version=0.11.3 \ --namespace apisix \ --set apisix.podAnnotations."prometheus\.io/scrape"=true \ --set apisix.podAnnotations."prometheus\.io/port"=9091 \ --set apisix.podAnnotations."prometheus\.io/path"=/apisix/prometheus/metrics \ --set pluginAttrs.prometheus.export_addr.ip=0.0.0.0 \ --set pluginAttrs.prometheus.export_addr.port=9091 \ --set pluginAttrs.prometheus.export_uri=/apisix/prometheus/metrics \ --set pluginAttrs.prometheus.metric_prefix=apisix_ \ --set ingress-controller.enabled=true \ --set ingress-controller.config.apisix.serviceNamespace=apisix ``` Install Flagger and the Prometheus add-on in the same namespace as Apache APISIX: ```bash helm repo add flagger https://flagger.app helm upgrade -i flagger flagger/flagger \ --namespace apisix \ --set prometheus.install=true \ --set meshProvider=apisix ``` ## Bootstrap Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA), then creates a series of objects (Kubernetes deployments, ClusterIP services and an ApisixRoute). These objects expose the application outside the cluster and drive the canary analysis and promotion. Create a test namespace: ```bash kubectl create ns test ``` Create a deployment and a horizontal pod autoscaler: ```bash kubectl apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main ``` Deploy the load testing service to generate traffic during the canary analysis: ```bash helm upgrade -i flagger-loadtester flagger/loadtester \ --namespace=test ``` Create an Apache APISIX `ApisixRoute`, Flagger will reference and generate the canary Apache APISIX `ApisixRoute` (replace `app.example.com` with your own domain): ```yaml apiVersion: apisix.apache.org/v2 kind: ApisixRoute metadata: name: podinfo namespace: test spec: http: - backends: - serviceName: podinfo servicePort: 80 match: hosts: - app.example.com methods: - GET paths: - /* name: method plugins: - name: prometheus enable: true config: disable: false prefer_name: true ``` Save the above resource as podinfo-apisixroute.yaml and then apply it: ```bash kubectl apply -f ./podinfo-apisixroute.yaml ``` Create a canary custom resource (replace `app.example.com` with your own domain): ```yaml apiVersion: flagger.app/v1beta1 kind: Canary metadata: name: podinfo namespace: test spec: provider: apisix targetRef: apiVersion: apps/v1 kind: Deployment name: podinfo # apisix route reference routeRef: apiVersion: apisix.apache.org/v2 kind: ApisixRoute name: podinfo # the maximum time in seconds for the canary deployment # to make progress before it is rollback (default 600s) progressDeadlineSeconds: 60 service: # ClusterIP port number port: 80 # container port number or name targetPort: 9898 analysis: # schedule interval (default 60s) interval: 10s # max number of failed metric checks before rollback threshold: 10 # max traffic percentage routed to canary # percentage (0-100) maxWeight: 50 # canary increment step # percentage (0-100) stepWeight: 10 # APISIX 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 # builtin Prometheus check # maximum req duration P99 # milliseconds thresholdRange: max: 500 interval: 30s webhooks: - name: load-test url: http://flagger-loadtester.test/ timeout: 5s type: rollout metadata: cmd: |- hey -z 1m -q 10 -c 2 -h2 -host app.example.com http://apisix-gateway.apisix/api/info ``` Save the above resource as podinfo-canary.yaml and then apply it: ```bash kubectl apply -f ./podinfo-canary.yaml ``` After a couple of seconds Flagger will create the canary objects: ```bash # applied deployment.apps/podinfo horizontalpodautoscaler.autoscaling/podinfo apisixroute/podinfo canary.flagger.app/podinfo # generated deployment.apps/podinfo-primary horizontalpodautoscaler.autoscaling/podinfo-primary service/podinfo service/podinfo-canary service/podinfo-primary apisixroute/podinfo-podinfo-canary ``` ## 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 or MS Teams. ![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=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 Conditions: Message: Canary analysis completed successfully, promotion finished. Reason: Succeeded Status: True Type: Promoted Failed Checks: 1 Iterations: 0 Phase: Succeeded Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning Synced 2m59s flagger podinfo-primary.test not ready: waiting for rollout to finish: observed deployment generation less than desired generation Warning Synced 2m50s flagger podinfo-primary.test not ready: waiting for rollout to finish: 0 of 1 (readyThreshold 100%) updated replicas are available Normal Synced 2m40s (x3 over 2m59s) flagger all the metrics providers are available! Normal Synced 2m39s flagger Initialization done! podinfo.test Normal Synced 2m20s flagger New revision detected! Scaling up podinfo.test Warning Synced 2m (x2 over 2m10s) flagger canary deployment podinfo.test not ready: waiting for rollout to finish: 0 of 1 (readyThreshold 100%) updated replicas are available Normal Synced 110s flagger Starting canary analysis for podinfo.test Normal Synced 109s flagger Advance podinfo.test canary weight 10 Warning Synced 100s flagger Halt advancement no values found for apisix metric request-success-rate probably podinfo.test is not receiving traffic: running query failed: no values found Normal Synced 90s flagger Advance podinfo.test canary weight 20 Normal Synced 80s flagger Advance podinfo.test canary weight 30 Normal Synced 69s flagger Advance podinfo.test canary weight 40 Normal Synced 59s flagger Advance podinfo.test canary weight 50 Warning Synced 30s (x2 over 40s) flagger podinfo-primary.test not ready: waiting for rollout to finish: 1 old replicas are pending termination Normal Synced 9s (x3 over 50s) flagger (combined from similar events): 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: ```bash watch kubectl get canaries --all-namespaces NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME test podinfo-2 Progressing 10 2022-11-23T05:00:54Z test podinfo Succeeded 0 2022-11-23T06:00:54Z ``` ## Automated rollback During the canary analysis you can generate HTTP 500 errors to test if Flagger pauses and rolls back the faulted version. Trigger another canary deployment: ```bash kubectl -n test set image deployment/podinfo \ podinfod=stefanprodan/podinfo:6.0.2 ``` Exec into the load tester pod with: ```bash kubectl -n test exec -it deploy/flagger-loadtester bash ``` Generate HTTP 500 errors: ```bash hey -z 1m -c 5 -q 5 -host app.example.com http://apisix-gateway.apisix/status/500 ``` Generate latency: ```bash watch -n 1 curl -H \"host: app.example.com\" http://apisix-gateway.apisix/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 apisix logs deploy/flagger -f | jq .msg "New revision detected! Scaling up podinfo.test" "canary deployment podinfo.test not ready: waiting for rollout to finish: 0 of 1 (readyThreshold 100%) updated replicas are available" "Starting canary analysis for podinfo.test" "Advance podinfo.test canary weight 10" "Halt podinfo.test advancement success rate 0.00% < 99%" "Halt podinfo.test advancement success rate 26.76% < 99%" "Halt podinfo.test advancement success rate 34.19% < 99%" "Halt podinfo.test advancement success rate 37.32% < 99%" "Halt podinfo.test advancement success rate 39.04% < 99%" "Halt podinfo.test advancement success rate 40.13% < 99%" "Halt podinfo.test advancement success rate 48.28% < 99%" "Halt podinfo.test advancement success rate 50.35% < 99%" "Halt podinfo.test advancement success rate 56.92% < 99%" "Halt podinfo.test advancement success rate 67.70% < 99%" "Rolling back podinfo.test failed checks threshold reached 10" "Canary failed! Scaling down podinfo.test" ``` ## Custom metrics The canary analysis can be extended with Prometheus queries. Create a metric template and apply it on the cluster: ```yaml apiVersion: flagger.app/v1beta1 kind: MetricTemplate metadata: name: not-found-percentage namespace: test spec: provider: type: prometheus address: http://flagger-prometheus.apisix:9090 query: | sum( rate( apisix_http_status{ route=~"{{ namespace }}_{{ route }}-{{ target }}-canary_.+", code!~"4.." }[{{ interval }}] ) ) / sum( rate( apisix_http_status{ route=~"{{ namespace }}_{{ route }}-{{ target }}-canary_.+" }[{{ interval }}] ) ) * 100 ``` Edit the canary analysis and add the not found error rate check: ```yaml 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. The above procedures can be extended with more [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.