Canary analysis with Prometheus Operator
Flagger requires a Kubernetes cluster v1.16 or newer and Prometheus Operator v0.40 or newer.
Install Prometheus Operator with Helm v3:
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
kubectl create ns monitoring
helm upgrade -i prometheus prometheus-community/kube-prometheus-stack \
--namespace monitoring \
--set prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false \
--set fullnameOverride=prometheus
The
prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false option allows Prometheus Operator to watch serviceMonitors outside of its namespace.Install Flagger by setting the metrics server to Prometheus:
helm repo add flagger https://flagger.app
kubectl create ns flagger-system
helm upgrade -i flagger flagger/flagger \
--namespace flagger-system \
--set metricsServer=http://prometheus-prometheus.monitoring:9090 \
--set meshProvider=kubernetes
Install Flagger's tester:
helm upgrade -i loadtester flagger/loadtester \
--namespace flagger-system
helm repo add podinfo https://stefanprodan.github.io/podinfo
kubectl create ns test
helm upgrade -i podinfo podinfo/podinfo \
--namespace test \
--set service.enabled=false
The demo app is instrumented with Prometheus, so you can create a
ServiceMonitor objects to scrape podinfo's metrics endpoint:apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: podinfo-canary
namespace: test
spec:
endpoints:
- path: /metrics
port: http
interval: 5s
selector:
matchLabels:
app: podinfo-canary
---
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: podinfo-primary
namespace: test
spec:
endpoints:
- path: /metrics
port: http
interval: 5s
selector:
matchLabels:
app: podinfo
We are setting
interval: 5s to have a more aggressive scraping. If you do not define it, you should use a longer interval in the Canary object.Create a metric template to measure the HTTP requests error rate:
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: error-rate
namespace: test
spec:
provider:
address: http://prometheus-prometheus.monitoring:9090
type: prometheus
query: |
100 - rate(
http_requests_total{
namespace="{{ namespace }}",
job="{{ target }}-canary",
status!~"5.*"
}[{{ interval }}])
/
rate(
http_requests_total{
namespace="{{ namespace }}",
job="{{ target }}-canary"
}[{{ interval }}]
) * 100
Create a metric template to measure the HTTP requests average duration:
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: latency
namespace: test
spec:
provider:
address: http://prometheus-prometheus.monitoring:9090
type: prometheus
query: |
histogram_quantile(0.99,
sum(
rate(
http_request_duration_seconds_bucket{
namespace="{{ namespace }}",
job="{{ target }}-canary"
}[{{ interval }}]
)
) by (le)
)
Using the metrics template you can configure the canary analysis with HTTP error rate and latency checks:
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
provider: kubernetes
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
progressDeadlineSeconds: 60
service:
port: 80
targetPort: http
name: podinfo
analysis:
interval: 30s
iterations: 10
threshold: 2
metrics:
- name: error-rate
templateRef:
name: error-rate
thresholdRange:
max: 1
interval: 30s
- name: latency
templateRef:
name: latency
thresholdRange:
max: 0.5
interval: 30s
webhooks:
- name: load-test
type: rollout
url: "http://loadtester.flagger-system/"
timeout: 5s
metadata:
type: cmd
cmd: "hey -z 1m -q 10 -c 2 http://podinfo-canary.test/"
Based on the above specification, Flagger creates the primary and canary Kubernetes ClusterIP service.
During the canary analysis, Prometheus will scrape the canary service and Flagger will use the HTTP error rate and latency queries to determine if the release should be promoted or rolled back.