Skipper Canary Deployments

This guide shows you how to use the Skipper ingress controller and Flagger to automate canary deployments.

Flagger Skipper Ingress Controller

Prerequisites

Flagger requires a Kubernetes cluster v1.14 or newer and Skipper ingress 0.11.40 or newer.

Install Skipper ingress-controller using upstream definition.

Certain arguments are relevant:

- -enable-connection-metrics
- -histogram-metric-buckets=.01,1,10,100
- -kubernetes
- -kubernetes-in-cluster
- -kubernetes-path-mode=path-prefix
- -metrics-exp-decay-sample
- -metrics-flavour=prometheus
- -route-backend-metrics
- -route-backend-error-counters
- -route-response-metrics
- -serve-host-metrics
- -serve-route-metrics
- -whitelisted-healthcheck-cidr=0.0.0.0/0 # permit Kind source health checks

Install Flagger using kustomize:

kustomize build https://github.com/weaveworks/flagger/kustomize/kubernetes | kubectl apply -f -

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:

kubectl create ns test

Create a deployment and a horizontal pod autoscaler:

kubectl apply -k github.com/weaveworks/flagger//kustomize/podinfo

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/v1beta1
kind: Ingress
metadata:
name: podinfo
namespace: test
labels:
app: podinfo
annotations:
kubernetes.io/ingress.class: "skipper"
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:

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: skipper
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
# ingress reference
ingressRef:
apiVersion: networking.k8s.io/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
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
# Skipper Prometheus checks
metrics:
- name: request-success-rate
interval: 1m
# minimum req success rate (non 5xx responses)
# percentage (0-100)
thresholdRange:
min: 99
- name: request-duration
interval: 1m
# maximum req duration P99
# milliseconds
thresholdRange:
max: 500
webhooks:
- name: gate
type: confirm-rollout
url: http://flagger-loadtester.test/gate/approve
- name: acceptance-test
type: pre-rollout
url: http://flagger-loadtester.test/
timeout: 10s
metadata:
type: bash
cmd: "curl -sd 'test' http://podinfo-canary/token | grep token"
- name: load-test
type: rollout
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
type: cmd
cmd: "hey -z 10m -q 10 -c 2 -host app.example.com http://skipper-ingress.kube-system"
logCmdOutput: "true"

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
ingress.networking.k8s.io/podinfo-ingress
canary.flagger.app/podinfo
# generated
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
ingress.networking.k8s.io/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 or MS Teams.

Flagger Canary Stages

Trigger a canary deployment by updating the container image:

kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:4.0.6

Flagger detects that the deployment revision changed and starts a new rollout:

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

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
NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME
test podinfo-2 Progressing 30 2020-08-14T12:32:12Z
test podinfo Succeeded 0 2020-08-14T11:23:88Z

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:

kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:4.0.6

Exec into the load tester pod with:

kubectl -n test exec -it deploy/flagger-loadtester bash

Generate HTTP 500 errors:

hey -z 1m -c 5 -q 5 http://app.example.com/status/500

Generate latency:

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.

kubectl -n flagger-system logs deploy/flagger -f | jq .msg
New revision detected! Scaling up podinfo.test
Canary deployment podinfo.test not ready: waiting for rollout to finish: 0 of 1 updated replicas are available
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
Advance podinfo.test canary weight 20
Halt podinfo.test advancement success rate 53.42% < 99%
Halt podinfo.test advancement success rate 53.19% < 99%
Halt podinfo.test advancement success rate 48.05% < 99%
Rolling back podinfo.test failed checks threshold reached 3
Canary failed! Scaling down podinfo.test

Custom metrics

The canary analysis can be extended with Prometheus queries.

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.flagger-system:9090
query: |
histogram_quantile(0.99,
sum(
rate(
skipper_serve_route_duration_seconds_bucket{
route=~"{{ printf "kube(ew)?_%s__%s_canary__.*__%s_canary(_[0-9]+)?" namespace ingress service }}",
le="+Inf"
}[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=stefanprodan/podinfo:4.0.6

Generate high response latency:

watch curl http://app.example.com/delay/2

Watch Flagger logs:

kubectl -n flagger-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 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.