mirror of https://github.com/fluxcd/flagger.git
add docs
Signed-off-by: Sanskar Jaiswal <jaiswalsanskar078@gmail.com>
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Sanskar Jaiswal
Sanskar Jaiswal
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438877674a
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@ -15,3 +15,4 @@ redirects:
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usage/traefik-progressive-delivery: tutorials/traefik-progressive-delivery.md
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usage/osm-progressive-delivery: tutorials/osm-progressive-delivery.md
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usage/kuma-progressive-delivery: tutorials/kuma-progressive-delivery.md
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usage/gatewayapi-progressive-delivery: tutorials/gatewayapi-progressive-delivery.md
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@ -32,6 +32,7 @@
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* [Traefik Canary Deployments](tutorials/traefik-progressive-delivery.md)
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* [Open Service Mesh Deployments](tutorials/osm-progressive-delivery.md)
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* [Kuma Canary Deployments](tutorials/kuma-progressive-delivery.md)
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* [Gateway API Canary Deployments](tutorials/gatewayapi-progressive-delivery.md)
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* [Blue/Green Deployments](tutorials/kubernetes-blue-green.md)
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* [Canary analysis with Prometheus Operator](tutorials/prometheus-operator.md)
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* [Zero downtime deployments](tutorials/zero-downtime-deployments.md)
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@ -0,0 +1,314 @@
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# Gateway API Canary Deployments
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This guide shows you how to use Gateway API and Flagger to automate canary deployments.
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## Prerequisites
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Flagger requires a Kubernetes cluster **v1.16** or newer and any mesh/ingress that implements the `v1alpha2` of Gateway API. We'll be using Contour for the sake of this tutorial, but you can use any other implementation.
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Install Contour with GatewayAPI and create a GatewayClass and a Gateway object:
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```bash
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kubectl apply -f https://raw.githubusercontent.com/projectcontour/contour/release-1.20/examples/render/contour-gateway.yaml
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```
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Install Flagger in the `flagger-system` namespace:
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```bash
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kubectl apply -k github.com/fluxcd/flagger//kustomize/gatewayapi
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```
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## Bootstrap
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Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler \(HPA\), then creates a series of objects \(Kubernetes deployments, ClusterIP services, HTTPRoutes for the Gateway\). These objects expose the application inside the mesh and drive the canary analysis and promotion.
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Create a test namespace:
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```bash
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kubectl create ns test
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```
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Create a deployment and a horizontal pod autoscaler:
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```bash
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kubectl apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main
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```
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Deploy the load testing service to generate traffic during the canary analysis:
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```bash
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kubectl apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main
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```
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Create metric templates targeting the Prometheus server in the `flagger-system` namespace. The PromQL query below is meant for `Envoy`, but you can [change it to your ingress/mesh provider](https://docs.flagger.app/faq#metrics) accordingly.
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```yaml
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: latency
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namespace: flagger-system
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spec:
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provider:
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type: prometheus
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address: http://flagger-prometheus:9090
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query: |
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histogram_quantile(0.99,
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sum(
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rate(
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envoy_cluster_upstream_rq_time_bucket{
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envoy_cluster_name=~"{{ namespace }}_{{ target }}-canary_[0-9a-zA-Z-]+",
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}[{{ interval }}]
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)
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) by (le)
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)/1000
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---
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: request-success-rate
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namespace: flagger-system
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spec:
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provider:
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type: prometheus
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address: http://flagger-prometheus:9090
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query: |
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sum(
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rate(
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envoy_cluster_upstream_rq{
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envoy_cluster_name=~"{{ namespace }}_{{ target }}-canary_[0-9a-zA-Z-]+",
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envoy_response_code!~"5.*"
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}[{{ interval }}]
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)
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)
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/
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sum(
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rate(
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envoy_cluster_upstream_rq{
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envoy_cluster_name=~"{{ namespace }}_{{ target }}-canary_[0-9a-zA-Z-]+",
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}[{{ interval }}]
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)
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)
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* 100
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```
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Save the above resource as metric-templates.yaml and then apply it:
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```bash
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kubectl apply -f metric-templates.yaml
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```
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Create a canary custom resource \(replace example.com with your own domain\):
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```yaml
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apiVersion: flagger.app/v1beta1
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kind: Canary
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metadata:
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name: podinfo
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namespace: test
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spec:
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# deployment reference
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targetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: podinfo
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# the maximum time in seconds for the canary deployment
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# to make progress before it is rollback (default 600s)
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progressDeadlineSeconds: 60
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# HPA reference (optional)
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autoscalerRef:
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apiVersion: autoscaling/v2beta2
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kind: HorizontalPodAutoscaler
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name: podinfo
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service:
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# service port number
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port: 9898
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# container port number or name (optional)
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targetPort: 9898
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# Gateway API HTTPRoute host names
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hosts:
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- localproject.contour.io
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# Reference to the Gateway that the generated HTTPRoute would attach to.
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gatewayRefs:
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- name: contour
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namespace: projectcontour
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analysis:
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# schedule interval (default 60s)
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interval: 1m
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# max number of failed metric checks before rollback
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threshold: 5
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# max traffic percentage routed to canary
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# percentage (0-100)
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maxWeight: 50
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# canary increment step
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# percentage (0-100)
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stepWeight: 10
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metrics:
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- name: request-success-rate
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# minimum req success rate (non 5xx responses)
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# percentage (0-100)
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templateRef:
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name: request-success-rate
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namespace: flagger-system
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thresholdRange:
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min: 99
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interval: 1m
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- name: latency
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templateRef:
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name: latency
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namespace: flagger-system
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# seconds
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thresholdRange:
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max: 0.5
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interval: 30s
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# testing (optional)
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webhooks:
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- name: load-test
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url: http://flagger-loadtester.test/
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timeout: 5s
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metadata:
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cmd: "hey -z 2m -q 10 -c 2 -host localproject.contour.io http://envoy.projectcontour/"
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```
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Save the above resource as podinfo-canary.yaml and then apply it:
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```bash
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kubectl apply -f ./podinfo-canary.yaml
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```
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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 minute.
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After a couple of seconds Flagger will create the canary objects:
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```bash
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# applied
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deployment.apps/podinfo
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horizontalpodautoscaler.autoscaling/podinfo
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canary.flagger.app/podinfo
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# generated
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deployment.apps/podinfo-primary
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horizontalpodautoscaler.autoscaling/podinfo-primary
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service/podinfo
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service/podinfo-canary
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service/podinfo-primary
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httproutes.gateway.networking.k8s.io/podinfo
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```
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## Automated canary promotion
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Trigger a canary deployment by updating the container image:
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```bash
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kubectl -n test set image deployment/podinfo \
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podinfod=stefanprodan/podinfo:3.1.1
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```
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Flagger detects that the deployment revision changed and starts a new rollout:
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```text
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kubectl -n test describe canary/podinfo
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Status:
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Canary Weight: 0
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Failed Checks: 0
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Phase: Succeeded
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Events:
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Type Reason Age From Message
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---- ------ ---- ---- -------
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Normal Synced 3m flagger New revision detected podinfo.test
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Normal Synced 3m flagger Scaling up podinfo.test
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Warning Synced 3m flagger Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
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Normal Synced 3m flagger Advance podinfo.test canary weight 5
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Normal Synced 3m flagger Advance podinfo.test canary weight 10
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Normal Synced 3m flagger Advance podinfo.test canary weight 15
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Normal Synced 2m flagger Advance podinfo.test canary weight 20
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Normal Synced 2m flagger Advance podinfo.test canary weight 25
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Normal Synced 1m flagger Advance podinfo.test canary weight 30
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Normal Synced 1m flagger Advance podinfo.test canary weight 35
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Normal Synced 55s flagger Advance podinfo.test canary weight 40
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Normal Synced 45s flagger Advance podinfo.test canary weight 45
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Normal Synced 35s flagger Advance podinfo.test canary weight 50
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Normal Synced 25s flagger Copying podinfo.test template spec to podinfo-primary.test
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Warning Synced 15s flagger Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
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Normal Synced 5s flagger Promotion completed! Scaling down podinfo.test
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```
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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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A canary deployment is triggered by changes in any of the following objects:
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* Deployment PodSpec \(container image, command, ports, env, resources, etc\)
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* ConfigMaps mounted as volumes or mapped to environment variables
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* Secrets mounted as volumes or mapped to environment variables
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You can monitor all canaries with:
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```bash
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watch kubectl get canaries --all-namespaces
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NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME
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test podinfo Progressing 15 2019-01-16T14:05:07Z
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prod frontend Succeeded 0 2019-01-15T16:15:07Z
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prod backend Failed 0 2019-01-14T17:05:07Z
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```
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## Automated rollback
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During the canary analysis you can generate HTTP 500 errors and high latency to test if Flagger pauses the rollout.
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Trigger another canary deployment:
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```bash
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kubectl -n test set image deployment/podinfo \
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podinfod=stefanprodan/podinfo:3.1.2
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```
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Exec into the load tester pod with:
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```bash
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kubectl -n test exec -it flagger-loadtester-xx-xx sh
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```
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Generate HTTP 500 errors:
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```bash
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watch curl http://podinfo-canary:9898/status/500
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```
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Generate latency:
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```bash
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watch curl http://podinfo-canary:9898/delay/1
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```
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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.
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```text
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kubectl -n test describe canary/podinfo
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Status:
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Canary Weight: 0
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Failed Checks: 10
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Phase: Failed
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Events:
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Type Reason Age From Message
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---- ------ ---- ---- -------
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Normal Synced 3m flagger Starting canary deployment for podinfo.test
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Normal Synced 3m flagger Advance podinfo.test canary weight 5
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Normal Synced 3m flagger Advance podinfo.test canary weight 10
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Normal Synced 3m flagger Advance podinfo.test canary weight 15
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Normal Synced 3m flagger Halt podinfo.test advancement success rate 69.17% < 99%
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Normal Synced 2m flagger Halt podinfo.test advancement success rate 61.39% < 99%
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Normal Synced 2m flagger Halt podinfo.test advancement success rate 55.06% < 99%
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Normal Synced 2m flagger Halt podinfo.test advancement success rate 47.00% < 99%
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Normal Synced 2m flagger (combined from similar events): Halt podinfo.test advancement success rate 38.08% < 99%
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Warning Synced 1m flagger Rolling back podinfo.test failed checks threshold reached 10
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Warning Synced 1m flagger Canary failed! Scaling down podinfo.test
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```
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The above procedures can be extended with [custom metrics](../usage/metrics.md) checks, [webhooks](../usage/webhooks.md), [manual promotion](../usage/webhooks.md#manual-gating) approval and [Slack or MS Teams](../usage/alerting.md) notifications.
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@ -3,11 +3,11 @@
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Flagger can run automated application analysis, promotion and rollback for the following deployment strategies:
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* **Canary Release** \(progressive traffic shifting\)
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* Istio, Linkerd, App Mesh, NGINX, Skipper, Contour, Gloo Edge, Traefik, Open Service Mesh, Kuma
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* Istio, Linkerd, App Mesh, NGINX, Skipper, Contour, Gloo Edge, Traefik, Open Service Mesh, Kuma, Gateway API
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* **A/B Testing** \(HTTP headers and cookies traffic routing\)
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* Istio, App Mesh, NGINX, Contour, Gloo Edge
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* Istio, App Mesh, NGINX, Contour, Gloo Edge, Gateway API
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* **Blue/Green** \(traffic switching\)
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* Kubernetes CNI, Istio, Linkerd, App Mesh, NGINX, Contour, Gloo Edge, Open Service Mesh
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* Kubernetes CNI, Istio, Linkerd, App Mesh, NGINX, Contour, Gloo Edge, Open Service Mesh, Gateway API
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* **Blue/Green Mirroring** \(traffic shadowing\)
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* Istio
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