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Merge pull request #1216 from aryan9600/keda-scaled-objects
Add support for KEDA ScaledObjects as an auto scaler
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@@ -32,6 +32,7 @@ jobs: | |
| - skipper | ||
| - kubernetes | ||
| - gatewayapi | ||
| - keda | ||
| steps: | ||
| - name: Checkout | ||
| uses: actions/checkout@v2 | ||
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| Original file line number | Diff line number | Diff line change |
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| # Canary analysis with KEDA ScaledObjects | ||
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| 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. | ||
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| ## Prerequisites | ||
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| Flagger requires a Kubernetes cluster **v1.16** or newer. For this tutorial, we'll need KEDA **2.71** or newer. | ||
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| Install KEDA: | ||
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| ```bash | ||
| helm repo add kedacore https://kedacore.github.io/charts | ||
| kubectl create namespace keda | ||
| helm install keda kedacore/keda --namespace keda | ||
| ``` | ||
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| Install Flagger: | ||
| ```bash | ||
| helm repo add flagger https://flagger.app | ||
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| helm upgrade -i flagger flagger/flagger \ | ||
| --namespace flagger \ | ||
| --set prometheus.install=true \ | ||
| --set meshProvider=kubernetes | ||
| ``` | ||
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| ## Bootstrap | ||
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| 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. | ||
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| Create a test namespace: | ||
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| ```bash | ||
| kubectl create ns test | ||
| ``` | ||
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| Create a deployment named `podinfo`: | ||
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| ```bash | ||
| kubectl apply -n test -f https://raw.githubusercontent.com/fluxcd/flagger/main/kustomize/podinfo/deployment.yaml | ||
| ``` | ||
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| Deploy the load testing service to generate traffic during the analysis: | ||
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| ```bash | ||
| kubectl apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main | ||
| ``` | ||
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| 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-system: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: | ||
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| ```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])) | ||
| # 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/" | ||
| ``` | ||
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| Save the above resource as `podinfo-canary.yaml` and then apply it: | ||
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| ```bash | ||
| kubectl apply -f ./podinfo-canary.yaml | ||
| ``` | ||
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| After a couple of seconds Flagger will create the canary objects: | ||
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| ```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 | ||
| ``` | ||
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| 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]))`. | ||
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| 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. | ||
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| 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. | ||
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| ## Automated Blue/Green promotion | ||
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| Trigger a deployment by updating the container image: | ||
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| ```bash | ||
| kubectl -n test set image deployment/podinfo \ | ||
| podinfod=ghcr.io/stefanprodan/podinfo:6.0.1 | ||
| ``` | ||
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| Flagger detects that the deployment revision changed and starts a new rollout: | ||
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| ```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 | ||
| ``` | ||
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| **Note** that if you apply new changes to the deployment during the canary analysis, Flagger will restart the analysis. | ||
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| You can monitor all canaries with: | ||
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| ```bash | ||
| watch kubectl get canaries --all-namespaces | ||
| NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME | ||
| test podinfo Progressing 100 2019-06-16T14:05:07Z | ||
| ``` | ||
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| 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 | ||
| ``` | ||
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| 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}'" | ||
| ``` |
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