NGINX 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
Flagger requires a Kubernetes cluster v1.19 or newer and NGINX ingress v1.0.2 or newer.
Install the NGINX ingress controller with Helm v3:
helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
kubectl create ns ingress-nginx
helm upgrade -i ingress-nginx ingress-nginx/ingress-nginx \
--namespace ingress-nginx \
--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 the ingress controller:
helm repo add flagger https://flagger.app
â
helm upgrade -i flagger flagger/flagger \
--namespace ingress-nginx \
--set prometheus.install=true \
--set meshProvider=nginx
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:
kubectl create ns test
Create a deployment and a horizontal pod autoscaler:
kubectl apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main
Deploy the load testing service to generate traffic during the canary analysis:
helm upgrade -i flagger-loadtester flagger/loadtester \
--namespace=test
Create an ingress definition (replace
app.example.com with your own domain):apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: podinfo
namespace: test
labels:
app: podinfo
annotations:
kubernetes.io/ingress.class: "nginx"
spec:
rules:
- host: "app.example.com"
http:
paths:
- pathType: Prefix
path: "/"
backend:
service:
name: podinfo
port:
number: 80
Save the above resource as podinfo-ingress.yaml and then apply it:
kubectl apply -f ./podinfo-ingress.yaml
Create a canary custom resource (replace
app.example.com with your own domain):apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
provider: nginx
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# ingress reference
ingressRef:
apiVersion: networking.k8s.io/v1
kind: Ingress
name: podinfo
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta2
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
analysis:
# schedule interval (default 60s)
interval: 10s
# max number of failed metric checks before rollback
threshold: 10
# max traffic percentage routed to canary
# percentage (0-100)
maxWeight: 50
# canary increment step
# percentage (0-100)
stepWeight: 5
# NGINX Prometheus checks
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
thresholdRange:
min: 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:
kubectl apply -f ./podinfo-canary.yaml
After a couple of seconds Flagger will create the canary objects:
# applied
deployment.apps/podinfo
horizontalpodautoscaler.autoscaling/podinfo
ingresses.extensions/podinfo
canary.flagger.app/podinfo
â
# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
ingresses.extensions/podinfo-canary
Flagger implements a control loop that gradually shifts traffic to the canary while measuring key performance indicators like HTTP requests success rate, requests average duration and pod health. Based on analysis of the KPIs a canary is promoted or aborted, and the analysis result is published to Slack or MS Teams.
Flagger Canary Stages
Trigger a canary deployment by updating the container image:
kubectl -n test set image deployment/podinfo \
podinfod=ghcr.io/stefanprodan/podinfo:6.0.1
Flagger detects that the deployment revision changed and starts a new rollout:
kubectl -n test describe canary/podinfo
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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:
watch kubectl get canaries --all-namespaces
â
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
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:
kubectl -n test set image deployment/podinfo \
podinfod=ghcr.io/stefanprodan/podinfo:6.0.2
Generate HTTP 500 errors:
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.
kubectl -n test describe canary/podinfo
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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
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.
Create a metric template and apply it on the cluster:
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: latency
namespace: test
spec:
provider:
type: prometheus
address: http://flagger-prometheus.ingress-nginx:9090
query: |
histogram_quantile(0.99,
sum(
rate(
http_request_duration_seconds_bucket{
kubernetes_namespace="{{ namespace }}",
kubernetes_pod_name=~"{{ target }}-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
}[1m]
)
) by (le)
)
Edit the canary analysis and add the latency check:
analysis:
metrics:
- name: "latency"
templateRef:
name: latency
thresholdRange:
max: 0.5
interval: 1m
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:
kubectl -n test set image deployment/podinfo \
podinfod=ghcr.io/stefanprodan/podinfo:6.0.3
Generate high response latency:
watch curl http://app.example.com/delay/2
Watch Flagger logs:
kubectl -n nginx-ingress logs deployment/flagger -f | jq .msg
â
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 alerting configured, Flagger will send a notification with the reason why the canary failed.
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
Edit the canary analysis, remove the max/step weight and add the match conditions and iterations:
analysis:
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
thresholdRange:
min: 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:
kubectl -n test set image deployment/podinfo \
podinfod=ghcr.io/stefanprodan/podinfo:6.0.4
Flagger detects that the deployment revision changed and starts the A/B testing:
kubectl -n test describe canary/podinfo
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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
The above procedure can be extended with custom metrics checks, webhooks, manual promotion approval and Slack or MS Teams notifications.