# Canary analysis with KEDA ScaledObjects This guide shows you how to use Flagger with KEDA ScaledObjects to autoscale workloads during a Canary analysis run. We will be using a Blue/Green deployment strategy with the Kubernetes provider for the sake of this tutorial, but you can use any deployment strategy combined with any supported provider. ## Prerequisites Flagger requires a Kubernetes cluster **v1.16** or newer. For this tutorial, we'll need KEDA **2.7.1** or newer. Install KEDA: ```bash helm repo add kedacore https://kedacore.github.io/charts kubectl create namespace keda helm install keda kedacore/keda --namespace keda ``` Install Flagger: ```bash helm repo add flagger https://flagger.app kubectl create namespace flagger helm upgrade -i flagger flagger/flagger \ --namespace flagger \ --set prometheus.install=true \ --set meshProvider=kubernetes ``` ## Bootstrap Flagger takes a Kubernetes deployment and a KEDA ScaledObject targeting the deployment. It then creates a series of objects (Kubernetes deployments, ClusterIP services and another KEDA ScaledObject targeting the created Deployment). These objects expose the application inside the mesh and drive the Canary analysis and Blue/Green promotion. Create a test namespace: ```bash kubectl create ns test ``` Create a deployment named `podinfo`: ```bash kubectl apply -n test -f https://raw.githubusercontent.com/fluxcd/flagger/main/kustomize/podinfo/deployment.yaml ``` Deploy the load testing service to generate traffic during the analysis: ```bash kubectl apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main ``` Create a ScaledObject which targets the `podinfo` deployment and uses Prometheus as a trigger: ```yaml apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: podinfo-so namespace: test spec: scaleTargetRef: name: podinfo pollingInterval: 10 cooldownPeriod: 20 minReplicaCount: 1 maxReplicaCount: 3 triggers: - type: prometheus metadata: name: prom-trigger serverAddress: http://flagger-prometheus.flagger:9090 metricName: http_requests_total query: sum(rate(http_requests_total{ app="podinfo" }[30s])) threshold: '5' ``` Create a canary custom resource for the `podinfo` deployment: ```yaml apiVersion: flagger.app/v1beta1 kind: Canary metadata: name: podinfo namespace: test spec: provider: kubernetes # deployment reference targetRef: apiVersion: apps/v1 kind: Deployment name: podinfo # Scaler reference autoscalerRef: apiVersion: keda.sh/v1alpha1 kind: ScaledObject # ScaledObject targeting the canary deployment name: podinfo-so # Mapping between trigger names and the related query to use for the generated # ScaledObject targeting the primary deployment. (Optional) primaryScalerQueries: prom-trigger: sum(rate(http_requests_total{ app="podinfo-primary" }[30s])) # Overriding replica scaling configuration for the generated ScaledObject # targeting the primary deployment. (Optional) primaryScalerReplicas: minReplicas: 2 maxReplicas: 5 # the maximum time in seconds for the canary deployment # to make progress before rollback (default 600s) progressDeadlineSeconds: 60 service: port: 80 targetPort: 9898 name: podinfo-svc portDiscovery: true analysis: # schedule interval (default 60s) interval: 15s # max number of failed checks before rollback threshold: 5 # number of checks to run before promotion iterations: 5 # Prometheus checks based on # http_request_duration_seconds histogram metrics: - name: request-success-rate interval: 1m thresholdRange: min: 99 - name: request-duration interval: 30s thresholdRange: max: 500 # load testing hooks webhooks: - name: load-test url: http://flagger-loadtester.test/ timeout: 5s metadata: type: cmd cmd: "hey -z 2m -q 20 -c 2 http://podinfo-svc-canary.test/" ``` 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 scaledobject.keda.sh/podinfo-so canary.flagger.app/podinfo # generated deployment.apps/podinfo-primary horizontalpodautoscaler.autoscaling/podinfo-primary service/podinfo service/podinfo-canary service/podinfo-primary scaledobject.keda.sh/podinfo-so-primary ``` We refer to our ScaledObject for the canary deployment using `.spec.autoscalerRef`. Flagger will use this to generate a ScaledObject which will scale the primary deployment. By default, Flagger will try to guess the query to use for the primary ScaledObject, by replacing all mentions of `.spec.targetRef.Name` and `{.spec.targetRef.Name}-canary` with `{.spec.targetRef.Name}-primary`, for all triggers. For eg, if your ScaledObject has a trigger query defined as: `sum(rate(http_requests_total{ app="podinfo" }[30s]))` or `sum(rate(http_requests_total{ app="podinfo-primary" }[30s]))`, then the primary ScaledObject will have the same trigger with a query defined as `sum(rate(http_requests_total{ app="podinfo-primary" }[30s]))`. If, the generated query does not meet your requirements, you can specify the query for autoscaling the primary deployment explicitly using `.spec.autoscalerRef.primaryScalerQueries`, which lets you define a query for each trigger. Please note that, your ScaledObject's `.spec.triggers[@].name` must not be blank, as Flagger needs that to identify each trigger uniquely. In the situation when it is desired to have different scaling replica configuration between the canary and primary deployment ScaledObject you can use the `.spec.autoscalerRef.primaryScalerReplicas` to override these values for the generated primary ScaledObject. After the boostrap, the podinfo deployment will be scaled to zero and the traffic to `podinfo.test` will be routed to the primary pods. To keep the podinfo deployment at 0 replicas and pause auto scaling, Flagger will add an annotation to your ScaledObject: `autoscaling.keda.sh/paused-replicas: 0`. During the canary analysis, the annotation is removed, to enable auto scaling for the podinfo deployment. The `podinfo-canary.test` address can be used to target directly the canary pods. When the canary analysis starts, Flagger will call the pre-rollout webhooks before routing traffic to the canary. The Blue/Green deployment will run for five iterations while validating the HTTP metrics and rollout hooks every 15 seconds. ## Automated Blue/Green promotion Trigger a 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 Events: New revision detected 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 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 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 Progressing 100 2019-06-16T14:05:07Z ``` You can monitor the scaling of the deployments with: ```bash watch kubectl -n test get deploy podinfo NAME READY UP-TO-DATE AVAILABLE AGE flagger-loadtester 1/1 1 1 4m21s podinfo 3/3 3 3 4m28s podinfo-primary 3/3 3 3 3m14s ``` You can mointor how Flagger edits the annotations of your ScaledObject with: ```bash watch "kubectl get -n test scaledobjects podinfo-so -o=jsonpath='{.metadata.annotations}'" ```