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# App Mesh Canary Deployments
This guide shows you how to use App Mesh and Flagger to automate canary deployments.
You'll need an EKS cluster configured with App Mesh, you can find the install guide
[here](https://docs.flagger.app/install/flagger-install-on-eks-appmesh).
### Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
then creates a series of objects (Kubernetes deployments, ClusterIP services, App Mesh virtual nodes and services).
These objects expose the application on the mesh and drive the canary analysis and promotion.
The only App Mesh object you need to create by yourself is the mesh resource.
Create a mesh called `global`:
```bash
cat << EOF | kubectl apply -f -
apiVersion: appmesh.k8s.aws/v1beta1
kind: Mesh
metadata:
name: global
spec:
serviceDiscoveryType: dns
EOF
```
Create a test namespace with App Mesh sidecar injection enabled:
```bash
cat << EOF | kubectl apply -f -
apiVersion: v1
kind: Namespace
metadata:
name: test
labels:
appmesh.k8s.aws/sidecarInjectorWebhook: enabled
EOF
```
Create a deployment and a horizontal pod autoscaler:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/podinfo
```
Deploy the load testing service to generate traffic during the canary analysis:
```bash
helm upgrade -i flagger-loadtester flagger/loadtester \
--namespace=test \
--set meshName=global \
--set "backends[0]=podinfo.test" \
--set "backends[1]=podinfo-canary.test"
```
Create a canary custom resource:
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# the maximum time in seconds for the canary deployment
# to make progress before it is rollback (default 600s)
progressDeadlineSeconds: 60
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
name: podinfo
service:
# container port
port: 9898
# container port name (optional)
# can be http or grpc
portName: http
# App Mesh reference
meshName: global
# App Mesh ingress (optional)
hosts:
- "*"
# App Mesh ingress timeout (optional)
timeout: 5s
# App Mesh egress (optional)
backends:
- backend.test
# App Mesh retry policy (optional)
retries:
attempts: 3
perTryTimeout: 5s
retryOn: "gateway-error,client-error,stream-error"
# define the canary analysis timing and KPIs
canaryAnalysis:
# schedule interval (default 60s)
interval: 1m
# 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
# App Mesh Prometheus checks
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
- name: request-duration
# maximum req duration P99
# milliseconds
threshold: 500
interval: 30s
# testing (optional)
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
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
cmd: "hey -z 1m -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
```
After a couple of seconds Flagger will create the canary objects:
```bash
# applied
deployment.apps/podinfo
horizontalpodautoscaler.autoscaling/podinfo
canary.flagger.app/podinfo
# generated Kubernetes objects
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
# generated App Mesh objects
virtualnode.appmesh.k8s.aws/podinfo
virtualnode.appmesh.k8s.aws/podinfo-canary
virtualnode.appmesh.k8s.aws/podinfo-primary
virtualservice.appmesh.k8s.aws/podinfo.test
virtualservice.appmesh.k8s.aws/podinfo-canary.test
```
After the boostrap, 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.
The App Mesh specific settings are:
```yaml
service:
port: 9898
meshName: global
backends:
- backend1.test
- backend2.test
```
App Mesh blocks all egress traffic by default. If your application needs to call another service, you have to create an
App Mesh virtual service for it and add the virtual service name to the backend list.
### Setup App Mesh Gateway (optional)
In order to expose the podinfo app outside the mesh you'll be using an Envoy-powered ingress gateway and an AWS network load balancer.
The gateway binds to an internet domain and forwards the calls into the mesh through the App Mesh sidecar.
If podinfo becomes unavailable due to a cluster downscaling or a node restart,
the gateway will retry the calls for a short period of time.
Deploy the gateway behind an AWS NLB:
```bash
helm upgrade -i appmesh-gateway flagger/appmesh-gateway \
--namespace test \
--set mesh.name=global
```
Find the gateway public address:
```bash
export URL="http://$(kubectl -n test get svc/appmesh-gateway -ojson | jq -r ".status.loadBalancer.ingress[].hostname")"
echo $URL
```
Wait for the NLB to become active:
```bash
watch curl -sS $URL
```
Open your browser and navigate to the ingress address to access podinfo UI.
### Automated canary promotion
A canary deployment is triggered by changes in any of the following objects:
* Deployment PodSpec (container image, command, ports, env, resources, etc)
* ConfigMaps and Secrets mounted as volumes or mapped to environment variables
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.1
```
Flagger detects that the deployment revision changed and starts a new rollout:
```text
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
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
Routing all traffic to primary
Promotion completed! Scaling down podinfo.test
```
When the canary analysis starts, Flagger will call the pre-rollout webhooks before routing traffic to the canary.
**Note** that if you apply new changes to the deployment during the canary analysis, Flagger will restart the analysis.
During the analysis the canary’s progress can be monitored with Grafana. The App Mesh dashboard URL is
http://localhost:3000/d/flagger-appmesh/appmesh-canary?refresh=10s&orgId=1&var-namespace=test&var-primary=podinfo-primary&var-canary=podinfo
![App Mesh Canary Dashboard](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/screens/flagger-grafana-appmesh.png)
You can monitor all canaries with:
```bash
watch kubectl get canaries --all-namespaces
NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME
test podinfo Progressing 15 2019-10-02T14:05:07Z
prod frontend Succeeded 0 2019-10-02T16:15:07Z
prod backend Failed 0 2019-10-02T17:05:07Z
```
If you’ve enabled the Slack notifications, you should receive the following messages:
![Flagger Slack Notifications](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/screens/slack-canary-notifications.png)
### Automated rollback
During the canary analysis you can generate HTTP 500 errors or high latency to test if Flagger pauses the rollout.
Trigger a canary deployment:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.2
```
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 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.
```text
kubectl -n appmesh-system logs deploy/flagger -f | jq .msg
New revision detected! 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
```
If you’ve enabled the Slack notifications, you’ll receive a message if the progress deadline is exceeded,
or if the analysis reached the maximum number of failed checks:
![Flagger Slack Notifications](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/screens/slack-canary-failed.png)
### A/B Testing
Besides weighted routing, Flagger can be configured to route traffic to the canary based on HTTP match conditions.
In an A/B testing scenario, you'll be using HTTP headers or cookies to target a certain segment of your users.
This is particularly useful for frontend applications that require session affinity.
![Flagger A/B Testing Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-abtest-steps.png)
Edit the canary analysis, remove the max/step weight and add the match conditions and iterations:
```yaml
canaryAnalysis:
interval: 1m
threshold: 5
iterations: 10
match:
- headers:
x-canary:
exact: "insider"
webhooks:
- name: load-test
url: http://flagger-loadtester.test/
metadata:
cmd: "hey -z 1m -q 10 -c 2 -H 'X-Canary: insider' http://podinfo.test:9898/"
```
The above configuration will run an analysis for ten minutes targeting users that have a `X-Canary: insider` header.
You can also use a HTTP cookie, to target all users with a `canary` cookie set to `insider` the match condition should be:
```yaml
match:
- headers:
cookie:
regex: "^(.*?;)?(canary=insider)(;.*)?$"
webhooks:
- name: load-test
url: http://flagger-loadtester.test/
metadata:
cmd: "hey -z 1m -q 10 -c 2 -H 'Cookie: canary=insider' http://podinfo.test:9898/"
```
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.3
```
Flagger detects that the deployment revision changed and starts the A/B test:
```text
kubectl -n appmesh-system logs deploy/flagger -f | jq .msg
New revision detected! Starting canary analysis for podinfo.test
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
Routing all traffic to primary
Promotion completed! Scaling down podinfo.test
```
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# Contour Canary Deployments
This guide shows you how to use [Contour](https://projectcontour.io/) ingress controller and Flagger to automate canary releases and A/B testing.
![Flagger Contour Overview](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-contour-overview.png)
### Prerequisites
Flagger requires a Kubernetes cluster **v1.11** or newer and Contour **v1.0** or newer.
Install Contour on a cluster with LoadBalancer support:
```bash
kubectl apply -f https://projectcontour.io/quickstart/contour.yaml
```
The above command will deploy Contour and an Envoy daemonset in the `projectcontour` namespace.
Install Flagger using Kustomize (kubectl 1.14) in the `projectcontour` namespace:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/contour
```
The above command will deploy Flagger and Prometheus configured to scrape the Contour's Envoy instances.
You can also enable Slack or MS Teams notifications,
see the Kustomize install [docs](https://docs.flagger.app/install/flagger-install-on-kubernetes#install-flagger-with-kustomize).
Or you can install Flagger using Helm:
```sh
helm repo add flagger https://flagger.app
helm upgrade -i flagger flagger/flagger \
--namespace projectcontour \
--set meshProvider=contour \
--set prometheus.install=true \
--set slack.url=https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK \
--set slack.channel=general \
--set slack.user=flagger
```
### Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
then creates a series of objects (Kubernetes deployments, ClusterIP services and Contour HTTPProxy).
These objects expose the application in the cluster and drive the canary analysis and promotion.
Create a test namespace:
```bash
kubectl create ns test
```
Install the load testing service to generate traffic during the canary analysis:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/tester
```
Create a deployment and a horizontal pod autoscaler:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/podinfo
```
Create a canary custom resource (replace `app.example.com` with your own domain):
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# HPA reference
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
name: podinfo
service:
# service port
port: 80
# container port
targetPort: 9898
# Contour request timeout
timeout: 15s
# Contour retry policy
retries:
attempts: 3
perTryTimeout: 5s
# define the canary analysis timing and KPIs
canaryAnalysis:
# 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
# Contour Prometheus checks
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
- name: request-duration
# maximum req duration P99 in milliseconds
threshold: 500
interval: 30s
# testing
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/token | grep token"
- name: load-test
url: http://flagger-loadtester.test/
type: rollout
timeout: 5s
metadata:
cmd: "hey -z 1m -q 10 -c 2 -host app.example.com http://envoy.projectcontour"
```
Save the above resource as podinfo-canary.yaml and then apply it:
```bash
kubectl apply -f ./podinfo-canary.yaml
```
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
canary.flagger.app/podinfo
# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
httpproxy.projectcontour.io/podinfo
```
After the boostrap, 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.
### Expose the app outside the cluster
Find the external address of Contour's Envoy load balancer:
```bash
export ADDRESS="$(kubectl -n projectcontour get svc/envoy -ojson \
| jq -r ".status.loadBalancer.ingress[].hostname")"
echo $ADDRESS
```
Configure your DNS server with a CNAME record (AWS) or A record (GKE/AKS/DOKS) and point a domain e.g. `app.example.com` to the LB address.
Create a HTTPProxy definition and include the podinfo proxy generated by Flagger (replace `app.example.com` with your own domain):
```yaml
apiVersion: projectcontour.io/v1
kind: HTTPProxy
metadata:
name: podinfo-ingress
namespace: test
spec:
virtualhost:
fqdn: app.example.com
includes:
- name: podinfo
namespace: test
conditions:
- prefix: /
```
Save the above resource as podinfo-ingress.yaml and then apply it:
```bash
kubectl apply -f ./podinfo-ingress.yaml
```
Verify that Contour processed the proxy definition with:
```sh
kubectl -n test get httpproxies
NAME FQDN STATUS
podinfo valid
podinfo-ingress app.example.com valid
```
Now you can access podinfo UI using your domain address.
Note that you should be using HTTPS when exposing production workloads on internet.
You can obtain free TLS certs from Let's Encrypt, read this [guide](https://github.com/stefanprodan/eks-contour-ingress)
on how to configure cert-manager to secure Contour with TLS certificates.
### 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.
![Flagger Canary Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-canary-steps.png)
A canary deployment is triggered by changes in any of the following objects:
* Deployment PodSpec (container image, command, ports, env, resources, etc)
* ConfigMaps and Secrets mounted as volumes or mapped to environment variables
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.1
```
Flagger detects that the deployment revision changed and starts a new rollout:
```text
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
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
Routing all traffic to primary
Promotion completed! Scaling down podinfo.test
```
When the canary analysis starts, Flagger will call the pre-rollout webhooks before routing traffic to the canary.
**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 15 2019-12-20T14:05:07Z
```
If you’ve enabled the Slack notifications, you should receive the following messages:
![Flagger Slack Notifications](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/screens/slack-canary-notifications.png)
### Automated rollback
During the canary analysis you can generate HTTP 500 errors or high latency to test if Flagger pauses the rollout.
Trigger a canary deployment:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.2
```
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 http://app.example.com/status/500
```
Generate latency:
```bash
watch -n 1 curl http://app.example.com/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.
```text
kubectl -n projectcontour logs deploy/flagger -f | jq .msg
New revision detected! 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 > 500ms
Halt podinfo.test advancement request duration 1.45s > 500ms
Rolling back podinfo.test failed checks threshold reached 5
Canary failed! Scaling down podinfo.test
```
If you’ve enabled the Slack notifications, you’ll receive a message if the progress deadline is exceeded,
or if the analysis reached the maximum number of failed checks:
![Flagger Slack Notifications](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/screens/slack-canary-failed.png)
### A/B Testing
Besides weighted routing, Flagger can be configured to route traffic to the canary based on HTTP match conditions.
In an A/B testing scenario, you'll be using HTTP headers or cookies to target a certain segment of your users.
This is particularly useful for frontend applications that require session affinity.
![Flagger A/B Testing Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-abtest-steps.png)
Edit the canary analysis, remove the max/step weight and add the match conditions and iterations:
```yaml
canaryAnalysis:
interval: 1m
threshold: 5
iterations: 10
match:
- headers:
x-canary:
exact: "insider"
webhooks:
- name: load-test
url: http://flagger-loadtester.test/
metadata:
cmd: "hey -z 1m -q 5 -c 5 -H 'X-Canary: insider' -host app.example.com http://envoy.projectcontour"
```
The above configuration will run an analysis for ten minutes targeting users that have a `X-Canary: insider` header.
You can also use a HTTP cookie. To target all users with a cookie set to `insider`, the match condition should be:
```yaml
match:
- headers:
cookie:
suffix: "insider"
webhooks:
- name: load-test
url: http://flagger-loadtester.test/
metadata:
cmd: "hey -z 1m -q 5 -c 5 -H 'Cookie: canary=insider' -host app.example.com http://envoy.projectcontour"
```
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.3
```
Flagger detects that the deployment revision changed and starts the A/B test:
```text
kubectl -n appmesh-system logs deploy/flagger -f | jq .msg
New revision detected! Starting canary analysis for podinfo.test
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
Routing all traffic to primary
Promotion completed! Scaling down podinfo.test
```
The web browser user agent header allows user segmentation based on device or OS.
For example, if you want to route all mobile users to the canary instance:
```yaml
match:
- headers:
user-agent:
prefix: "Mobile"
```
Or if you want to target only Android users:
```yaml
match:
- headers:
user-agent:
prefix: "Android"
```
Or a specific browser version:
```yaml
match:
- headers:
user-agent:
suffix: "Firefox/71.0"
```
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# Envoy/Crossover Canary Deployments
This guide shows you how to use Envoy, [Crossover](https://github.com/mumoshu/crossover) and Flagger to automate canary deployments.
Crossover is a minimal Envoy xDS implementation supports [Service Mesh Interface](https://smi-spec.io/).
### Prerequisites
Flagger requires a Kubernetes cluster **v1.11** or newer and Envoy paired with [Crossover](https://github.com/mumoshu/crossover) sidecar.
Create a test namespace:
```bash
kubectl create ns test
```
Install Envoy along with the Crossover sidecar with Helm:
```bash
helm repo add crossover https://mumoshu.github.io/crossover
helm upgrade --install envoy crossover/envoy \
--namespace test \
-f <(cat <<EOF
smi:
apiVersions:
trafficSplits: v1alpha1
upstreams:
podinfo:
smi:
enabled: true
backends:
podinfo-primary:
port: 9898
weight: 100
podinfo-canary:
port: 9898
weight: 0
EOF
)
```
Install Flagger and the Prometheus add-on in the same namespace as Envoy:
```bash
helm repo add flagger https://flagger.app
helm upgrade -i flagger flagger/flagger \
--namespace test \
--set prometheus.install=true \
--set meshProvider=smi:crossover
```
Optionally you can enable Slack notifications:
```bash
helm upgrade -i flagger flagger/flagger \
--reuse-values \
--namespace test \
--set slack.url=https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK \
--set slack.channel=general \
--set slack.user=flagger
```
### Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
then creates a series of objects (Kubernetes deployments, ClusterIP services, SMI traffic splits).
These objects expose the application on the mesh and drive the canary analysis and promotion.
There's no SMI object you need to create by yourself.
Create a deployment and a horizontal pod autoscaler:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/podinfo
```
Deploy the load testing service to generate traffic during the canary analysis:
```bash
helm upgrade -i flagger-loadtester flagger/loadtester \
--namespace=test
```
Create a canary custom resource:
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
# specify mesh provider if it isn't the default one
# provider: "smi:crossover"
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# the maximum time in seconds for the canary deployment
# to make progress before it is rollback (default 600s)
progressDeadlineSeconds: 60
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
name: podinfo
service:
# ClusterIP port number
port: 9898
# container port number or name (optional)
targetPort: 9898
# define the canary analysis timing and KPIs
canaryAnalysis:
# schedule interval (default 60s)
interval: 1m
# 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
# App Mesh Prometheus checks
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
- name: request-duration
# maximum req duration P99
# milliseconds
threshold: 500
interval: 30s
# testing (optional)
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
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
cmd: "hey -z 1m -q 10 -c 2 -H 'Host: podinfo.test' http://envoy.test:10000/"
```
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
canary.flagger.app/podinfo
# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
trafficsplits.split.smi-spec.io/podinfo
```
After the boostrap, 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/weaveworks/flagger/master/docs/diagrams/flagger-canary-steps.png)
A canary deployment is triggered by changes in any of the following objects:
* Deployment PodSpec (container image, command, ports, env, resources, etc)
* ConfigMaps and Secrets mounted as volumes or mapped to environment variables
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.5
```
Flagger detects that the deployment revision changed and starts a new rollout:
```text
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
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
Routing all traffic to primary
Promotion completed! Scaling down podinfo.test
```
When the canary analysis starts, Flagger will call the pre-rollout webhooks before routing traffic to the canary.
**Note** that if you apply new changes to the deployment during the canary analysis, Flagger will restart the analysis.
During the analysis the canary’s progress can be monitored with Grafana.
Flagger comes with a Grafana dashboard made for canary analysis. Install Grafana with Helm:
```bash
helm upgrade -i flagger-grafana flagger/grafana \
--namespace=test \
--set url=http://flagger-prometheus:9090
```
Run:
```bash
kubectl port-forward --namespace test svc/flagger-grafana 3000:80
```
The Envoy dashboard URL is
http://localhost:3000/d/flagger-envoy/envoy-canary?refresh=10s&orgId=1&var-namespace=test&var-target=podinfo
![Envoy Canary Dashboard](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/screens/flagger-grafana-appmesh.png)
You can monitor all canaries with:
```bash
watch kubectl get canaries --all-namespaces
NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME
test podinfo Progressing 15 2019-10-02T14:05:07Z
prod frontend Succeeded 0 2019-10-02T16:15:07Z
prod backend Failed 0 2019-10-02T17:05:07Z
```
If you’ve enabled the Slack notifications, you should receive the following messages:
![Flagger Slack Notifications](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/screens/slack-canary-notifications.png)
### Automated rollback
During the canary analysis you can generate HTTP 500 errors or high latency to test if Flagger pauses the rollout.
Trigger a canary deployment:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.2
```
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 -H 'Host: podinfo.test' http://envoy.test:10000/status/500
```
Generate latency:
```bash
watch -n 1 curl -H 'Host: podinfo.test' http://envoy.test:10000/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.
```text
kubectl -n test logs deploy/flagger -f | jq .msg
New revision detected! 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
```
If you’ve enabled the Slack notifications, you’ll receive a message if the progress deadline is exceeded,
or if the analysis reached the maximum number of failed checks:
![Flagger Slack Notifications](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/screens/slack-canary-failed.png)
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# NGNIX Ingress Controller Canary Deployments
This guide shows you how to use the [Gloo](https://gloo.solo.io/) ingress controller and Flagger to automate canary deployments.
![Flagger Gloo Ingress Controller](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-gloo-overview.png)
### Prerequisites
Flagger requires a Kubernetes cluster **v1.11** or newer and Gloo ingress **1.3.5** or newer.
Install Gloo with Helm v3:
```bash
helm repo add gloo https://storage.googleapis.com/solo-public-helm
kubectl create ns gloo-system
helm upgrade -i gloo gloo/gloo \
--namespace gloo-system
```
Install Flagger and the Prometheus add-on in the same namespace as Gloo:
```bash
helm repo add flagger https://flagger.app
helm upgrade -i flagger flagger/flagger \
--namespace gloo-system \
--set prometheus.install=true \
--set meshProvider=gloo
```
Optionally you can enable Slack notifications:
```bash
helm upgrade -i flagger flagger/flagger \
--reuse-values \
--namespace gloo-system \
--set slack.url=https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK \
--set slack.channel=general \
--set slack.user=flagger
```
### Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
then creates a series of objects (Kubernetes deployments, ClusterIP services and Gloo upstream groups).
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 -n test apply -k github.com/weaveworks/flagger//kustomize/podinfo
```
Deploy the load testing service to generate traffic during the canary analysis:
```bash
kubectl -n test apply -k github.com/weaveworks/flagger//kustomize/tester
```
Create an virtual service definition that references an upstream group that will be generated by Flagger
(replace `app.example.com` with your own domain):
```yaml
apiVersion: gateway.solo.io/v1
kind: VirtualService
metadata:
name: podinfo
namespace: test
spec:
virtualHost:
domains:
- 'app.example.com'
routes:
- matchers:
- prefix: /
routeAction:
upstreamGroup:
name: podinfo
namespace: test
```
Save the above resource as podinfo-virtualservice.yaml and then apply it:
```bash
kubectl apply -f ./podinfo-virtualservice.yaml
```
Create a canary custom resource (replace `app.example.com` with your own domain):
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
provider: gloo
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
name: podinfo
service:
# ClusterIP port number
port: 9898
# container port number or name (optional)
targetPort: 9898
canaryAnalysis:
# schedule interval (default 60s)
interval: 10s
# 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
# Gloo Prometheus checks
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
- name: request-duration
# maximum req duration P99
# milliseconds
threshold: 500
interval: 30s
# testing (optional)
webhooks:
- name: acceptance-test
type: pre-rollout
url: http://flagger-loadtester.test/
timeout: 10s
metadata:
type: bash
cmd: "curl -sd 'test' http://podinfo-canary:9898/token | grep token"
- name: load-test
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
type: cmd
cmd: "hey -z 2m -q 5 -c 2 -host app.example.com http://gateway-proxy.gloo-system"
```
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
virtualservices.gateway.solo.io/podinfo
canary.flagger.app/podinfo
# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
upstreamgroups.gloo.solo.io/podinfo
```
When the bootstrap finishes Flagger will set the canary status to initialized:
```bash
kubectl -n test get canary podinfo
NAME STATUS WEIGHT LASTTRANSITIONTIME
podinfo Initialized 0 2019-05-17T08:09:51Z
```
### 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/weaveworks/flagger/master/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:3.1.1
```
Flagger detects that the deployment revision changed and starts a new rollout:
```text
kubectl -n test describe canary/podinfo
Status:
Canary Weight: 0
Failed Checks: 0
Phase: Succeeded
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger New revision detected podinfo.test
Normal Synced 3m flagger Scaling up podinfo.test
Warning Synced 3m flagger Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
Normal Synced 3m flagger Advance podinfo.test canary weight 5
Normal Synced 3m flagger Advance podinfo.test canary weight 10
Normal Synced 3m flagger Advance podinfo.test canary weight 15
Normal Synced 2m flagger Advance podinfo.test canary weight 20
Normal Synced 2m flagger Advance podinfo.test canary weight 25
Normal Synced 1m flagger Advance podinfo.test canary weight 30
Normal Synced 1m flagger Advance podinfo.test canary weight 35
Normal Synced 55s flagger Advance podinfo.test canary weight 40
Normal Synced 45s flagger Advance podinfo.test canary weight 45
Normal Synced 35s flagger Advance podinfo.test canary weight 50
Normal Synced 25s flagger Copying podinfo.test template spec to podinfo-primary.test
Warning Synced 15s flagger Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
Normal Synced 5s flagger 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 15 2019-05-17T14:05:07Z
prod frontend Succeeded 0 2019-05-17T16:15:07Z
prod backend Failed 0 2019-05-17T17:05:07Z
```
### 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=stefanprodan/podinfo:3.1.2
```
Generate HTTP 500 errors:
```bash
watch curl -H 'Host: app.example.com' http://gateway-proxy-v2.gloo-system/status/500
```
Generate high latency:
```bash
watch curl -H 'Host: app.example.com' http://gateway-proxy-v2.gloo-system/delay/2
```
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.
```text
kubectl -n test describe canary/podinfo
Status:
Canary Weight: 0
Failed Checks: 10
Phase: Failed
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger Starting canary deployment for podinfo.test
Normal Synced 3m flagger Advance podinfo.test canary weight 5
Normal Synced 3m flagger Advance podinfo.test canary weight 10
Normal Synced 3m flagger Advance podinfo.test canary weight 15
Normal Synced 3m flagger Halt podinfo.test advancement success rate 69.17% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 61.39% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 55.06% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 47.00% < 99%
Normal Synced 2m flagger (combined from similar events): Halt podinfo.test advancement success rate 38.08% < 99%
Warning Synced 1m flagger Rolling back podinfo.test failed checks threshold reached 10
Warning Synced 1m flagger Canary failed! Scaling down podinfo.test
```
### Custom metrics
The canary analysis can be extended with Prometheus queries.
The demo app is instrumented with Prometheus so you can create a custom check that will use the HTTP request duration
histogram to validate the canary.
Edit the canary analysis and add the following metric:
```yaml
canaryAnalysis:
metrics:
- name: "404s percentage"
threshold: 5
query: |
100 - sum(
rate(
http_request_duration_seconds_count{
kubernetes_namespace="test",
kubernetes_pod_name=~"podinfo-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
status!="404"
}[1m]
)
)
/
sum(
rate(
http_request_duration_seconds_count{
kubernetes_namespace="test",
kubernetes_pod_name=~"podinfo-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
}[1m]
)
) * 100
```
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.
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.3
```
Generate 404s:
```bash
watch curl -H 'Host: app.example.com' http://gateway-proxy.gloo-system/status/400
```
Watch Flagger logs:
```
kubectl -n gloo-system logs deployment/flagger -f | jq .msg
Starting canary deployment for podinfo.test
Advance podinfo.test canary weight 5
Advance podinfo.test canary weight 10
Advance podinfo.test canary weight 15
Halt podinfo.test advancement 404s percentage 6.20 > 5
Halt podinfo.test advancement 404s percentage 6.45 > 5
Halt podinfo.test advancement 404s percentage 7.60 > 5
Halt podinfo.test advancement 404s percentage 8.69 > 5
Halt podinfo.test advancement 404s percentage 9.70 > 5
Rolling back podinfo.test failed checks threshold reached 5
Canary failed! Scaling down podinfo.test
```
If you have Slack configured, Flagger will send a notification with the reason why the canary failed.
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# Istio A/B Testing
This guide shows you how to automate A/B testing with Istio and Flagger.
Besides weighted routing, Flagger can be configured to route traffic to the canary based on HTTP match conditions.
In an A/B testing scenario, you'll be using HTTP headers or cookies to target a certain segment of your users.
This is particularly useful for frontend applications that require session affinity.
![Flagger A/B Testing Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-abtest-steps.png)
### Bootstrap
Create a test namespace with Istio sidecar injection enabled:
```bash
kubectl create ns test
kubectl label namespace test istio-injection=enabled
```
Create a deployment and a horizontal pod autoscaler:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/podinfo
```
Deploy the load testing service to generate traffic during the canary analysis:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/tester
```
Create a canary custom resource (replace example.com with your own domain):
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# the maximum time in seconds for the canary deployment
# to make progress before it is rollback (default 600s)
progressDeadlineSeconds: 60
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
name: podinfo
service:
# container port
port: 9898
# Istio gateways (optional)
gateways:
- public-gateway.istio-system.svc.cluster.local
# Istio virtual service host names (optional)
hosts:
- app.example.com
# Istio traffic policy (optional)
trafficPolicy:
tls:
# use ISTIO_MUTUAL when mTLS is enabled
mode: DISABLE
canaryAnalysis:
# schedule interval (default 60s)
interval: 1m
# total number of iterations
iterations: 10
# max number of failed iterations before rollback
threshold: 2
# canary match condition
match:
- headers:
user-agent:
regex: "^(?!.*Chrome).*Safari.*"
- headers:
cookie:
regex: "^(.*?;)?(type=insider)(;.*)?$"
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
- name: request-duration
# maximum req duration P99
# milliseconds
threshold: 500
interval: 30s
# generate traffic during analysis
webhooks:
- name: load-test
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
cmd: "hey -z 1m -q 10 -c 2 -H 'Cookie: type=insider' http://podinfo.test:9898/"
```
The above configuration will run an analysis for ten minutes targeting Safari users and those that have an insider cookie.
Save the above resource as podinfo-abtest.yaml and then apply it:
```bash
kubectl apply -f ./podinfo-abtest.yaml
```
After a couple of seconds Flagger will create the canary objects:
```bash
# applied
deployment.apps/podinfo
horizontalpodautoscaler.autoscaling/podinfo
canary.flagger.app/podinfo
# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
destinationrule.networking.istio.io/podinfo-canary
destinationrule.networking.istio.io/podinfo-primary
virtualservice.networking.istio.io/podinfo
```
### Automated canary promotion
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/abtest \
podinfod=stefanprodan/podinfo:3.1.1
```
Flagger detects that the deployment revision changed and starts a new rollout:
```text
kubectl -n test describe canary/abtest
Status:
Failed Checks: 0
Phase: Succeeded
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger New revision detected podinfo.test
Normal Synced 3m flagger Scaling up podinfo.test
Warning Synced 3m flagger Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
Normal Synced 3m flagger Advance podinfo.test canary iteration 1/10
Normal Synced 3m flagger Advance podinfo.test canary iteration 2/10
Normal Synced 3m flagger Advance podinfo.test canary iteration 3/10
Normal Synced 2m flagger Advance podinfo.test canary iteration 4/10
Normal Synced 2m flagger Advance podinfo.test canary iteration 5/10
Normal Synced 1m flagger Advance podinfo.test canary iteration 6/10
Normal Synced 1m flagger Advance podinfo.test canary iteration 7/10
Normal Synced 55s flagger Advance podinfo.test canary iteration 8/10
Normal Synced 45s flagger Advance podinfo.test canary iteration 9/10
Normal Synced 35s flagger Advance podinfo.test canary iteration 10/10
Normal Synced 25s flagger Copying podinfo.test template spec to abtest-primary.test
Warning Synced 15s flagger Waiting for abtest-primary.test rollout to finish: 1 of 2 updated replicas are available
Normal Synced 5s flagger 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 abtest Progressing 100 2019-03-16T14:05:07Z
prod frontend Succeeded 0 2019-03-15T16:15:07Z
prod backend Failed 0 2019-03-14T17:05:07Z
```
### Automated rollback
During the canary analysis you can generate HTTP 500 errors and high latency to test Flagger's rollback.
Generate HTTP 500 errors:
```bash
watch curl -b 'type=insider' http://app.example.com/status/500
```
Generate latency:
```bash
watch curl -b 'type=insider' http://app.example.com/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.
```text
kubectl -n test describe canary/abtest
Status:
Failed Checks: 2
Phase: Failed
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger Starting canary deployment for podinfo.test
Normal Synced 3m flagger Advance podinfo.test canary iteration 1/10
Normal Synced 3m flagger Advance podinfo.test canary iteration 2/10
Normal Synced 3m flagger Advance podinfo.test canary iteration 3/10
Normal Synced 3m flagger Halt podinfo.test advancement success rate 69.17% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 61.39% < 99%
Warning Synced 2m flagger Rolling back podinfo.test failed checks threshold reached 2
Warning Synced 1m flagger Canary failed! Scaling down podinfo.test
```
docs/gitbook/tutorials/istio-progressive-delivery.md
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# Istio Canary Deployments
This guide shows you how to use Istio and Flagger to automate canary deployments.
![Flagger Canary Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-canary-steps.png)
### Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
then creates a series of objects (Kubernetes deployments, ClusterIP services, Istio destination rules and virtual services).
These objects expose the application inside the mesh and drive the canary analysis and promotion.
Create a test namespace with Istio sidecar injection enabled:
```bash
kubectl create ns test
kubectl label namespace test istio-injection=enabled
```
Create a deployment and a horizontal pod autoscaler:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/podinfo
```
Deploy the load testing service to generate traffic during the canary analysis:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/tester
```
Create a canary custom resource (replace example.com with your own domain):
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# the maximum time in seconds for the canary deployment
# to make progress before it is rollback (default 600s)
progressDeadlineSeconds: 60
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
name: podinfo
service:
# service port number
port: 9898
# container port number or name (optional)
targetPort: 9898
# Istio gateways (optional)
gateways:
- public-gateway.istio-system.svc.cluster.local
# Istio virtual service host names (optional)
hosts:
- app.example.com
# Istio traffic policy (optional)
trafficPolicy:
tls:
# use ISTIO_MUTUAL when mTLS is enabled
mode: DISABLE
# Istio retry policy (optional)
retries:
attempts: 3
perTryTimeout: 1s
retryOn: "gateway-error,connect-failure,refused-stream"
canaryAnalysis:
# schedule interval (default 60s)
interval: 1m
# 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: 10
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
- name: request-duration
# maximum req duration P99
# milliseconds
threshold: 500
interval: 30s
# testing (optional)
webhooks:
- name: acceptance-test
type: pre-rollout
url: http://flagger-loadtester.test/
timeout: 30s
metadata:
type: bash
cmd: "curl -sd 'test' http://podinfo-canary:9898/token | grep token"
- name: load-test
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
cmd: "hey -z 1m -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 minute.
![Flagger Canary Process](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-canary-hpa.png)
After a couple of seconds Flagger will create the canary objects:
```bash
# applied
deployment.apps/podinfo
horizontalpodautoscaler.autoscaling/podinfo
canary.flagger.app/podinfo
# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
destinationrule.networking.istio.io/podinfo-canary
destinationrule.networking.istio.io/podinfo-primary
virtualservice.networking.istio.io/podinfo
```
### Automated canary promotion
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.1
```
Flagger detects that the deployment revision changed and starts a new rollout:
```text
kubectl -n test describe canary/podinfo
Status:
Canary Weight: 0
Failed Checks: 0
Phase: Succeeded
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger New revision detected podinfo.test
Normal Synced 3m flagger Scaling up podinfo.test
Warning Synced 3m flagger Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
Normal Synced 3m flagger Advance podinfo.test canary weight 5
Normal Synced 3m flagger Advance podinfo.test canary weight 10
Normal Synced 3m flagger Advance podinfo.test canary weight 15
Normal Synced 2m flagger Advance podinfo.test canary weight 20
Normal Synced 2m flagger Advance podinfo.test canary weight 25
Normal Synced 1m flagger Advance podinfo.test canary weight 30
Normal Synced 1m flagger Advance podinfo.test canary weight 35
Normal Synced 55s flagger Advance podinfo.test canary weight 40
Normal Synced 45s flagger Advance podinfo.test canary weight 45
Normal Synced 35s flagger Advance podinfo.test canary weight 50
Normal Synced 25s flagger Copying podinfo.test template spec to podinfo-primary.test
Warning Synced 15s flagger Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
Normal Synced 5s flagger 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 2019-01-16T14:05:07Z
prod frontend Succeeded 0 2019-01-15T16:15:07Z
prod backend Failed 0 2019-01-14T17:05:07Z
```
### Automated rollback
During the canary analysis you can generate HTTP 500 errors and high latency to test if Flagger pauses the rollout.
Trigger another canary deployment:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.2
```
Exec into the load tester pod with:
```bash
kubectl -n test exec -it flagger-loadtester-xx-xx sh
```
Generate HTTP 500 errors:
```bash
watch curl http://podinfo-canary:9898/status/500
```
Generate latency:
```bash
watch curl http://podinfo-canary: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.
```text
kubectl -n test describe canary/podinfo
Status:
Canary Weight: 0
Failed Checks: 10
Phase: Failed
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger Starting canary deployment for podinfo.test
Normal Synced 3m flagger Advance podinfo.test canary weight 5
Normal Synced 3m flagger Advance podinfo.test canary weight 10
Normal Synced 3m flagger Advance podinfo.test canary weight 15
Normal Synced 3m flagger Halt podinfo.test advancement success rate 69.17% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 61.39% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 55.06% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 47.00% < 99%
Normal Synced 2m flagger (combined from similar events): Halt podinfo.test advancement success rate 38.08% < 99%
Warning Synced 1m flagger Rolling back podinfo.test failed checks threshold reached 10
Warning Synced 1m flagger Canary failed! Scaling down podinfo.test
```
### Traffic mirroring
![Flagger Canary Traffic Shadowing](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-canary-traffic-mirroring.png)
For applications that perform read operations, Flagger can be configured to drive canary releases with traffic mirroring.
Istio traffic mirroring will copy each incoming request, sending one request to the primary and one to the canary service.
The response from the primary is sent back to the user and the response from the canary is discarded.
Metrics are collected on both requests so that the deployment will only proceed if the canary metrics are within the threshold values.
Note that mirroring should be used for requests that are **idempotent** or capable of being processed twice
(once by the primary and once by the canary).
You can enable mirroring by replacing `stepWeight/maxWeight` with `iterations` and
by setting `canaryAnalysis.mirror` to `true`:
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
canaryAnalysis:
# schedule interval
interval: 1m
# max number of failed metric checks before rollback
threshold: 5
# total number of iterations
iterations: 10
# enable traffic shadowing
mirror: true
metrics:
- name: request-success-rate
threshold: 99
interval: 1m
- name: request-duration
threshold: 500
interval: 1m
webhooks:
- name: acceptance-test
type: pre-rollout
url: http://flagger-loadtester.test/
timeout: 30s
metadata:
type: bash
cmd: "curl -sd 'test' http://podinfo-canary:9898/token | grep token"
- name: load-test
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
cmd: "hey -z 1m -q 10 -c 2 http://podinfo.test:9898/"
```
With the above configuration, Flagger will run a canary release with the following steps:
* detect new revision (deployment spec, secrets or configmaps changes)
* scale from zero the canary deployment
* wait for the HPA to set the canary minimum replicas
* check canary pods health
* run the acceptance tests
* abort the canary release if tests fail
* start the load tests
* mirror traffic from primary to canary
* check request success rate and request duration every minute
* abort the canary release if the metrics check failure threshold is reached
* stop traffic mirroring after the number of iterations is reached
* route live traffic to the canary pods
* promote the canary (update the primary secrets, configmaps and deployment spec)
* wait for the primary deployment rollout to finish
* wait for the HPA to set the primary minimum replicas
* check primary pods health
* switch live traffic back to primary
* scale to zero the canary
* send notification with the canary analysis result
The above procedure can be extended with [custom metrics](https://docs.flagger.app/how-it-works#custom-metrics) checks,
[webhooks](https://docs.flagger.app/how-it-works#webhooks),
[manual promotion](https://docs.flagger.app/how-it-works#manual-gating) approval and
[Slack or MS Teams](https://docs.flagger.app/usage/alerting) notifications.
docs/gitbook/tutorials/kubernetes-blue-green.md
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# Blue/Green Deployments
This guide shows you how to automate Blue/Green deployments with Flagger and Kubernetes.
For applications that are not deployed on a service mesh, Flagger can orchestrate Blue/Green style deployments
with Kubernetes L4 networking.
When using a service mesh blue/green can be used as specified [here](https://docs.flagger.app/how-it-works#blue-green-deployments).
![Flagger Blue/Green Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-bluegreen-steps.png)
### Prerequisites
Flagger requires a Kubernetes cluster **v1.11** or newer.
Install Flagger and the Prometheus add-on:
```bash
helm repo add flagger https://flagger.app
helm upgrade -i flagger flagger/flagger \
--namespace flagger \
--set prometheus.install=true \
--set meshProvider=kubernetes
```
If you already have a Prometheus instance running in your cluster,
you can point Flagger to the ClusterIP service with:
```bash
helm upgrade -i flagger flagger/flagger \
--namespace flagger \
--set metricsServer=http://prometheus.monitoring:9090
```
Optionally you can enable Slack notifications:
```bash
helm upgrade -i flagger flagger/flagger \
--reuse-values \
--namespace flagger \
--set slack.url=https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK \
--set slack.channel=general \
--set slack.user=flagger
```
### Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
then creates a series of objects (Kubernetes deployment and ClusterIP services).
These objects expose the application inside the cluster and drive the canary analysis and Blue/Green promotion.
Create a test namespace:
```bash
kubectl create ns test
```
Create a deployment and a horizontal pod autoscaler:
```bash
export REPO=https://raw.githubusercontent.com/weaveworks/flagger/master
kubectl apply -f ${REPO}/artifacts/canaries/deployment.yaml
kubectl apply -f ${REPO}/artifacts/canaries/hpa.yaml
```
Deploy the load testing service to generate traffic during the analysis:
```bash
kubectl -n test apply -f ${REPO}/artifacts/loadtester/deployment.yaml
kubectl -n test apply -f ${REPO}/artifacts/loadtester/service.yaml
```
Create a canary custom resource:
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
# service mesh provider can be: kubernetes, istio, appmesh, nginx, gloo
provider: kubernetes
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# the maximum time in seconds for the canary deployment
# to make progress before rollback (default 600s)
progressDeadlineSeconds: 60
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
name: podinfo
service:
port: 9898
portDiscovery: true
canaryAnalysis:
# schedule interval (default 60s)
interval: 30s
# max number of failed checks before rollback
threshold: 2
# number of checks to run before rollback
iterations: 10
# Prometheus checks based on
# http_request_duration_seconds histogram
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
- name: request-duration
# maximum req duration P99
# milliseconds
threshold: 500
interval: 30s
# acceptance/load testing hooks
webhooks:
- name: smoke-test
type: pre-rollout
url: http://flagger-loadtester.test/
timeout: 15s
metadata:
type: bash
cmd: "curl -sd 'anon' http://podinfo-canary.test:9898/token | grep token"
- name: load-test
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
type: cmd
cmd: "hey -z 1m -q 10 -c 2 http://podinfo-canary.test:9898/"
```
The above configuration will run an analysis for five minutes.
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
canary.flagger.app/podinfo
# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
```
Blue/Green scenario:
* on bootstrap, Flagger will create three ClusterIP services (`app-primary`,` app-canary`, `app`) and a shadow deployment named `app-primary` that represents the blue version
* when a new version is detected, Flagger would scale up the green version and run the conformance tests (the tests should target the `app-canary` ClusterIP service to reach the green version)
* if the conformance tests are passing, Flagger would start the load tests and validate them with custom Prometheus queries
* if the load test analysis is successful, Flagger will promote the new version to `app-primary` and scale down the green version
### Automated Blue/Green promotion
Trigger a deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.1
```
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
prod frontend Succeeded 0 2019-06-15T16:15:07Z
prod backend Failed 0 2019-06-14T17:05:07Z
```
### Automated rollback
During the analysis you can generate HTTP 500 errors and high latency to test Flagger's rollback.
Exec into the load tester pod with:
```bash
kubectl -n test exec -it flagger-loadtester-xx-xx sh
```
Generate HTTP 500 errors:
```bash
watch curl http://podinfo-canary.test:9898/status/500
```
Generate latency:
```bash
watch curl http://podinfo-canary.test:9898/delay/1
```
When the number of failed checks reaches the analysis threshold,
the green version is scaled to zero and the rollout is marked as failed.
```text
kubectl -n test describe canary/podinfo
Status:
Failed Checks: 2
Phase: Failed
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger New revision detected podinfo.test
Normal Synced 3m flagger Advance podinfo.test canary iteration 1/10
Normal Synced 3m flagger Advance podinfo.test canary iteration 2/10
Normal Synced 3m flagger Advance podinfo.test canary iteration 3/10
Normal Synced 3m flagger Halt podinfo.test advancement success rate 69.17% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 61.39% < 99%
Warning Synced 2m flagger Rolling back podinfo.test failed checks threshold reached 2
Warning Synced 1m flagger Canary failed! Scaling down podinfo.test
```
### Custom metrics
The analysis can be extended with Prometheus queries. The demo app is instrumented with Prometheus
so you can create a custom check that will use the HTTP request duration histogram to validate the canary (green version).
Edit the canary analysis and add the following metric:
```yaml
canaryAnalysis:
metrics:
- name: "404s percentage"
threshold: 5
query: |
100 - sum(
rate(
http_request_duration_seconds_count{
kubernetes_namespace="test",
kubernetes_pod_name=~"podinfo-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
status!="404"
}[1m]
)
)
/
sum(
rate(
http_request_duration_seconds_count{
kubernetes_namespace="test",
kubernetes_pod_name=~"podinfo-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
}[1m]
)
) * 100
```
The above configuration validates the canary (green version) 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 rollout is rolled back.
Trigger a deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.3
```
Generate 404s:
```bash
watch curl http://podinfo-canary.test:9898/status/400
```
Watch Flagger logs:
```
kubectl -n flagger logs deployment/flagger -f | jq .msg
New revision detected podinfo.test
Scaling up podinfo.test
Advance podinfo.test canary iteration 1/10
Halt podinfo.test advancement 404s percentage 6.20 > 5
Halt podinfo.test advancement 404s percentage 6.45 > 5
Rolling back podinfo.test failed checks threshold reached 2
Canary failed! Scaling down podinfo.test
```
If you have Slack configured, Flagger will send a notification with the reason why the canary failed.
### Conformance Testing with Helm
Flagger comes with a testing service that can run Helm tests when configured as a pre-rollout webhook.
Deploy the Helm test runner in the `kube-system` namespace using the `tiller` service account:
```bash
helm repo add flagger https://flagger.app
helm upgrade -i flagger-helmtester flagger/loadtester \
--namespace=kube-system \
--set serviceAccountName=tiller
```
When deployed the Helm tester API will be available at `http://flagger-helmtester.kube-system/`.
Add a helm test pre-rollout hook to your chart:
```yaml
canaryAnalysis:
webhooks:
- name: "conformance testing"
type: pre-rollout
url: http://flagger-helmtester.kube-system/
timeout: 3m
metadata:
type: "helm"
cmd: "test {{ .Release.Name }} --cleanup"
```
When the canary analysis starts, Flagger will call the pre-rollout webhooks.
If the helm test fails, Flagger will retry until the analysis threshold is reached and the canary is rolled back.
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# Linkerd Canary Deployments
This guide shows you how to use Linkerd and Flagger to automate canary deployments.
![Flagger Linkerd Traffic Split](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-linkerd-traffic-split.png)
### Prerequisites
Flagger requires a Kubernetes cluster **v1.11** or newer and Linkerd **2.4** or newer.
Install Flagger in the linkerd namespace:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/linkerd
```
Note that you'll need kubectl 1.14 or newer to run the above command.
To enable Slack or MS Teams notifications,
see Flagger's [install docs](https://docs.flagger.app/install/flagger-install-on-kubernetes) for Kustomize or Helm options.
### Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
then creates a series of objects (Kubernetes deployments, ClusterIP services and SMI traffic split).
These objects expose the application inside the mesh and drive the canary analysis and promotion.
Create a test namespace and enable Linkerd proxy injection:
```bash
kubectl create ns test
kubectl annotate namespace test linkerd.io/inject=enabled
```
Install the load testing service to generate traffic during the canary analysis:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/tester
```
Create a deployment and a horizontal pod autoscaler:
```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/podinfo
```
Create a canary custom resource for the podinfo deployment:
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
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: 9898
# container port number or name (optional)
targetPort: 9898
canaryAnalysis:
# 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
# Linkerd Prometheus checks
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
- name: request-duration
# maximum req duration P99
# milliseconds
threshold: 500
interval: 30s
# testing (optional)
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
trafficsplits.split.smi-spec.io/podinfo
```
After the boostrap, 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/weaveworks/flagger/master/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:3.1.1
```
Flagger detects that the deployment revision changed and starts a new rollout:
```text
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=stefanprodan/podinfo:3.1.2
```
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.
```text
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
```
### Custom metrics
The canary analysis can be extended with Prometheus queries.
Let's a define a check for not found errors. Edit the canary analysis and add the following metric:
```yaml
canaryAnalysis:
metrics:
- name: "404s percentage"
threshold: 3
query: |
100 - sum(
rate(
response_total{
namespace="test",
deployment="podinfo",
status_code!="404",
direction="inbound"
}[1m]
)
)
/
sum(
rate(
response_total{
namespace="test",
deployment="podinfo",
direction="inbound"
}[1m]
)
)
* 100
```
The above configuration validates the canary version by checking if the HTTP 404 req/sec percentage is below
three percent of the total traffic. If the 404s rate reaches the 3% threshold, then the analysis is aborted and the
canary is marked as failed.
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.3
```
Generate 404s:
```bash
watch -n 1 curl http://podinfo-canary:9898/status/404
```
Watch Flagger logs:
```
kubectl -n linkerd logs deployment/flagger -f | jq .msg
Starting canary deployment for podinfo.test
Pre-rollout check acceptance-test passed
Advance podinfo.test canary weight 5
Halt podinfo.test advancement 404s percentage 6.20 > 3
Halt podinfo.test advancement 404s percentage 6.45 > 3
Halt podinfo.test advancement 404s percentage 7.22 > 3
Halt podinfo.test advancement 404s percentage 6.50 > 3
Halt podinfo.test advancement 404s percentage 6.34 > 3
Rolling back podinfo.test failed checks threshold reached 5
Canary failed! Scaling down podinfo.test
```
If you have Slack configured, Flagger will send a notification with the reason why the canary failed.
### Linkerd Ingress
There are two ingress controllers that are compatible with both Flagger and Linkerd: NGINX and Gloo.
Install NGINX:
```bash
helm upgrade -i nginx-ingress stable/nginx-ingress \
--namespace ingress-nginx
```
Create an ingress definition for podinfo that rewrites the incoming header to the internal service name (required by Linkerd):
```yaml
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
name: podinfo
namespace: test
labels:
app: podinfo
annotations:
kubernetes.io/ingress.class: "nginx"
nginx.ingress.kubernetes.io/configuration-snippet: |
proxy_set_header l5d-dst-override $service_name.$namespace.svc.cluster.local:9898;
proxy_hide_header l5d-remote-ip;
proxy_hide_header l5d-server-id;
spec:
rules:
- host: app.example.com
http:
paths:
- backend:
serviceName: podinfo
servicePort: 9898
```
When using an ingress controller, the Linkerd traffic split does not apply to incoming traffic since NGINX in running outside of
the mesh. In order to run a canary analysis for a frontend app, Flagger creates a shadow ingress and sets the NGINX specific annotations.
### A/B Testing
Besides weighted routing, Flagger can be configured to route traffic to the canary based on HTTP match conditions.
In an A/B testing scenario, you'll be using HTTP headers or cookies to target a certain segment of your users.
This is particularly useful for frontend applications that require session affinity.
![Flagger Linkerd Ingress](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-nginx-linkerd.png)
Edit podinfo canary analysis, set the provider to `nginx`, add the ingress reference, remove the max/step weight and add the match conditions and iterations:
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
# ingress reference
provider: nginx
ingressRef:
apiVersion: extensions/v1beta1
kind: Ingress
name: podinfo
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
name: podinfo
service:
# container port
port: 9898
canaryAnalysis:
interval: 1m
threshold: 10
iterations: 10
match:
# curl -H 'X-Canary: always' http://app.example.com
- headers:
x-canary:
exact: "always"
# curl -b 'canary=always' http://app.example.com
- headers:
cookie:
exact: "canary"
# Linkerd Prometheus checks
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:9898/token | grep token"
- name: load-test
type: rollout
url: http://flagger-loadtester.test/
metadata:
cmd: "hey -z 2m -q 10 -c 2 -H 'Cookie: canary=always' http://app.example.com"
```
The above configuration will run an analysis for ten minutes targeting users that have a `canary` cookie set to `always` or
those that call the service using the `X-Canary: always` header.
**Note** that the load test now targets the external address and uses the canary cookie.
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.4
```
Flagger detects that the deployment revision changed and starts the A/B testing:
```text
kubectl -n test describe canary/podinfo
Events:
Starting canary deployment for podinfo.test
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
```
docs/gitbook/tutorials/nginx-progressive-delivery.md
+427
View File
@@ -0,0 +1,427 @@
# NGNIX Ingress Controller Canary Deployments
This guide shows you how to use the NGINX ingress controller and Flagger to automate canary deployments and A/B testing.
![Flagger NGINX Ingress Controller](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-nginx-overview.png)
### Prerequisites
Flagger requires a Kubernetes cluster **v1.11** or newer and NGINX ingress **0.24** or newer.
Install NGINX with Helm v3:
```bash
kubectl create ns ingress-nginx
helm upgrade -i nginx-ingress stable/nginx-ingress \
--namespace ingress-nginx \
--set controller.stats.enabled=true \
--set controller.metrics.enabled=true \
--set controller.podAnnotations."prometheus\.io/scrape"=true \
--set controller.podAnnotations."prometheus\.io/port"=10254
```
Install Flagger and the Prometheus add-on in the same namespace as NGINX:
```bash
helm repo add flagger https://flagger.app
helm upgrade -i flagger flagger/flagger \
--namespace ingress-nginx \
--set prometheus.install=true \
--set meshProvider=nginx
```
Optionally you can enable Slack notifications:
```bash
helm upgrade -i flagger flagger/flagger \
--reuse-values \
--namespace ingress-nginx \
--set slack.url=https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK \
--set slack.channel=general \
--set slack.user=flagger
```
### Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
then creates a series of objects (Kubernetes deployments, ClusterIP services and canary ingress).
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 github.com/weaveworks/flagger//kustomize/podinfo
```
Deploy the load testing service to generate traffic during the canary analysis:
```bash
helm upgrade -i flagger-loadtester flagger/loadtester \
--namespace=test
```
Create an ingress definition (replace `app.example.com` with your own domain):
```yaml
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
name: podinfo
namespace: test
labels:
app: podinfo
annotations:
kubernetes.io/ingress.class: "nginx"
spec:
rules:
- host: app.example.com
http:
paths:
- backend:
serviceName: podinfo
servicePort: 80
```
Save the above resource as podinfo-ingress.yaml and then apply it:
```bash
kubectl apply -f ./podinfo-ingress.yaml
```
Create a canary custom resource (replace `app.example.com` with your own domain):
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
provider: nginx
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# ingress reference
ingressRef:
apiVersion: extensions/v1beta1
kind: Ingress
name: podinfo
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
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
canaryAnalysis:
# 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: 5
# NGINX Prometheus checks
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
# testing (optional)
webhooks:
- name: acceptance-test
type: pre-rollout
url: http://flagger-loadtester.test/
timeout: 30s
metadata:
type: bash
cmd: "curl -sd 'test' http://podinfo-canary/token | grep token"
- name: load-test
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
cmd: "hey -z 1m -q 10 -c 2 http://app.example.com/"
```
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
ingresses.extensions/podinfo
canary.flagger.app/podinfo
# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
ingresses.extensions/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.
![Flagger Canary Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/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:3.1.1
```
Flagger detects that the deployment revision changed and starts a new rollout:
```text
kubectl -n test describe canary/podinfo
Status:
Canary Weight: 0
Failed Checks: 0
Phase: Succeeded
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger New revision detected podinfo.test
Normal Synced 3m flagger Scaling up podinfo.test
Warning Synced 3m flagger Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
Normal Synced 3m flagger Advance podinfo.test canary weight 5
Normal Synced 3m flagger Advance podinfo.test canary weight 10
Normal Synced 3m flagger Advance podinfo.test canary weight 15
Normal Synced 2m flagger Advance podinfo.test canary weight 20
Normal Synced 2m flagger Advance podinfo.test canary weight 25
Normal Synced 1m flagger Advance podinfo.test canary weight 30
Normal Synced 1m flagger Advance podinfo.test canary weight 35
Normal Synced 55s flagger Advance podinfo.test canary weight 40
Normal Synced 45s flagger Advance podinfo.test canary weight 45
Normal Synced 35s flagger Advance podinfo.test canary weight 50
Normal Synced 25s flagger Copying podinfo.test template spec to podinfo-primary.test
Warning Synced 15s flagger Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
Normal Synced 5s flagger 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 15 2019-05-06T14:05:07Z
prod frontend Succeeded 0 2019-05-05T16:15:07Z
prod backend Failed 0 2019-05-04T17:05:07Z
```
### 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:3.1.2
```
Generate HTTP 500 errors:
```bash
watch curl http://app.example.com/status/500
```
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.
```text
kubectl -n test describe canary/podinfo
Status:
Canary Weight: 0
Failed Checks: 10
Phase: Failed
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger Starting canary deployment for podinfo.test
Normal Synced 3m flagger Advance podinfo.test canary weight 5
Normal Synced 3m flagger Advance podinfo.test canary weight 10
Normal Synced 3m flagger Advance podinfo.test canary weight 15
Normal Synced 3m flagger Halt podinfo.test advancement success rate 69.17% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 61.39% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 55.06% < 99%
Normal Synced 2m flagger Halt podinfo.test advancement success rate 47.00% < 99%
Normal Synced 2m flagger (combined from similar events): Halt podinfo.test advancement success rate 38.08% < 99%
Warning Synced 1m flagger Rolling back podinfo.test failed checks threshold reached 10
Warning Synced 1m flagger Canary failed! Scaling down podinfo.test
```
### Custom metrics
The canary analysis can be extended with Prometheus queries.
The demo app is instrumented with Prometheus so you can create a custom check that will use the HTTP request duration
histogram to validate the canary.
Edit the canary analysis and add the following metric:
```yaml
canaryAnalysis:
metrics:
- name: "latency"
threshold: 0.5
interval: 1m
query: |
histogram_quantile(0.99,
sum(
rate(
http_request_duration_seconds_bucket{
kubernetes_namespace="test",
kubernetes_pod_name=~"podinfo-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
}[1m]
)
) by (le)
)
```
The threshold is set to 500ms so if the average request duration in the last minute
goes over half a second then the analysis will fail and the canary will not be promoted.
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.3
```
Generate high response latency:
```bash
watch curl http://app.exmaple.com/delay/2
```
Watch Flagger logs:
```
kubectl -n nginx-ingress logs deployment/flagger -f | jq .msg
Starting canary deployment for podinfo.test
Advance podinfo.test canary weight 5
Advance podinfo.test canary weight 10
Advance podinfo.test canary weight 15
Halt podinfo.test advancement latency 1.20 > 0.5
Halt podinfo.test advancement latency 1.45 > 0.5
Halt podinfo.test advancement latency 1.60 > 0.5
Halt podinfo.test advancement latency 1.69 > 0.5
Halt podinfo.test advancement latency 1.70 > 0.5
Rolling back podinfo.test failed checks threshold reached 5
Canary failed! Scaling down podinfo.test
```
If you have Slack configured, Flagger will send a notification with the reason why the canary failed.
### A/B Testing
Besides weighted routing, Flagger can be configured to route traffic to the canary based on HTTP match conditions.
In an A/B testing scenario, you'll be using HTTP headers or cookies to target a certain segment of your users.
This is particularly useful for frontend applications that require session affinity.
![Flagger A/B Testing Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-abtest-steps.png)
Edit the canary analysis, remove the max/step weight and add the match conditions and iterations:
```yaml
canaryAnalysis:
interval: 1m
threshold: 10
iterations: 10
match:
# curl -H 'X-Canary: insider' http://app.example.com
- headers:
x-canary:
exact: "insider"
# curl -b 'canary=always' http://app.example.com
- headers:
cookie:
exact: "canary"
metrics:
- name: request-success-rate
threshold: 99
interval: 1m
webhooks:
- name: load-test
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
cmd: "hey -z 1m -q 10 -c 2 -H 'Cookie: canary=always' http://app.example.com/"
```
The above configuration will run an analysis for ten minutes targeting users that have a `canary` cookie set to `always` or
those that call the service using the `X-Canary: insider` header.
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.4
```
Flagger detects that the deployment revision changed and starts the A/B testing:
```text
kubectl -n test describe canary/podinfo
Status:
Failed Checks: 0
Phase: Succeeded
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Synced 3m flagger New revision detected podinfo.test
Normal Synced 3m flagger Scaling up podinfo.test
Warning Synced 3m flagger Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
Normal Synced 3m flagger Advance podinfo.test canary iteration 1/10
Normal Synced 3m flagger Advance podinfo.test canary iteration 2/10
Normal Synced 3m flagger Advance podinfo.test canary iteration 3/10
Normal Synced 2m flagger Advance podinfo.test canary iteration 4/10
Normal Synced 2m flagger Advance podinfo.test canary iteration 5/10
Normal Synced 1m flagger Advance podinfo.test canary iteration 6/10
Normal Synced 1m flagger Advance podinfo.test canary iteration 7/10
Normal Synced 55s flagger Advance podinfo.test canary iteration 8/10
Normal Synced 45s flagger Advance podinfo.test canary iteration 9/10
Normal Synced 35s flagger Advance podinfo.test canary iteration 10/10
Normal Synced 25s flagger Copying podinfo.test template spec to podinfo-primary.test
Warning Synced 15s flagger Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
Normal Synced 5s flagger Promotion completed! Scaling down podinfo.test
```