💡 Quick Answer: deployments

The Problem

This is a fundamental Kubernetes topic that engineers search for frequently. A comprehensive reference with production-ready examples saves hours of trial and error.

The Solution

Native Kubernetes Canary

# Stable: 90% traffic
apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-stable
spec:
  replicas: 9
  selector:
    matchLabels:
      app: web
      version: stable
  template:
    metadata:
      labels:
        app: web
        version: stable
    spec:
      containers:
        - name: web
          image: my-app:v1
---
# Canary: 10% traffic (1 out of 10 pods)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-canary
spec:
  replicas: 1
  selector:
    matchLabels:
      app: web
      version: canary
  template:
    metadata:
      labels:
        app: web
        version: canary
    spec:
      containers:
        - name: web
          image: my-app:v2
---
# Service selects BOTH (app: web)
apiVersion: v1
kind: Service
metadata:
  name: web
spec:
  selector:
    app: web     # Matches both stable and canary
  ports:
    - port: 80

Argo Rollouts (Automated Canary)

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: web-app
spec:
  replicas: 10
  strategy:
    canary:
      steps:
        - setWeight: 10        # 10% to canary
        - pause: {duration: 5m}
        - setWeight: 30        # 30%
        - pause: {duration: 5m}
        - setWeight: 50        # 50%
        - pause: {duration: 10m}
      # Automated analysis
      analysis:
        templates:
          - templateName: success-rate
        startingStep: 1
        args:
          - name: service-name
            value: web
  selector:
    matchLabels:
      app: web
  template:
    metadata:
      labels:
        app: web
    spec:
      containers:
        - name: web
          image: my-app:v2
---
# Auto-rollback if error rate > 5%
apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
  name: success-rate
spec:
  metrics:
    - name: success-rate
      interval: 60s
      successCondition: result[0] >= 0.95
      provider:
        prometheus:
          address: http://prometheus:9090
          query: |
            sum(rate(http_requests_total{service="{{args.service-name}}",status=~"2.."}[5m]))
            / sum(rate(http_requests_total{service="{{args.service-name}}"}[5m]))

graph LR
    A[100% v1] -->|Step 1| B[90% v1 + 10% v2]
    B -->|Analyze 5m| C{Metrics OK?}
    C -->|Yes| D[70% v1 + 30% v2]
    C -->|No| E[Rollback to 100% v1]
    D -->|Analyze 5m| F[50% v1 + 50% v2]
    F -->|All clear| G[100% v2]

Frequently Asked Questions

Native canary vs Argo Rollouts?

Native K8s canary is manual (adjust replica counts). Argo Rollouts automates the process with traffic splitting, metric analysis, and automatic rollback. Use Argo Rollouts for production.

How many canary pods do I need?

At least 1 pod gets ~10% of traffic (if you have 10 total). For more precise traffic splitting, use Istio or a service mesh that supports weighted routing.

Best Practices

Start with the simplest configuration that meets your needs
Test changes in staging before production
Use kubectl describe and events for troubleshooting
Document your decisions for the team

Key Takeaways

This is essential Kubernetes knowledge for production operations
Follow the principle of least privilege and minimal configuration
Monitor and iterate based on real-world behavior
Automation reduces human error and improves consistency

Canary Deployment in Kubernetes