Learn how to implement blue-green deployments in Kubernetes for zero-downtime releases. Complete guide with examples.
Blue-Green Deployments: Zero-Downtime Releases
Blue-green deployments enable zero-downtime releases. This guide shows you how to implement them.
Run two identical environments:
- Blue: Current production
- Green: New version
# Blue deployment
apiVersion: apps/v1
kind: Deployment
metadata:
name: app-blue
spec:
replicas: 3
selector:
matchLabels:
app: app
version: blue
---
# Green deployment
apiVersion: apps/v1
kind: Deployment
metadata:
name: app-green
spec:
replicas: 3
selector:
matchLabels:
app: app
version: green
apiVersion: v1
kind: Service
metadata:
name: app-service
spec:
selector:
app: app
version: blue # Switch to green for new version
ports:
- port: 80
Simply switch service selector back to blue.
- Test green environment thoroughly
- Use health checks
- Monitor metrics
- Keep blue running during switch
- Document procedures
Blue-green deployments provide safe, zero-downtime releases. Implement with proper testing and monitoring.
For Blue-Green Deployments: Zero-Downtime Releases, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Blue-Green Deployments: Zero-Downtime Releases, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Blue-Green Deployments: Zero-Downtime Releases, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Blue-Green Deployments: Zero-Downtime Releases, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
