Blue-Green Deployments#

A blue-green deployment runs two identical production environments. One (blue) serves live traffic. The other (green) is idle or running the new version. When the green environment passes validation, you switch traffic from blue to green. If something goes wrong, you switch back. The old environment stays running until you are confident the new version is stable.

The fundamental advantage over rolling updates is atomicity. Traffic switches from 100% old to 100% new in a single operation. There is no period where some users see the old version and others see the new one.

DNS Is Not a Load Balancer#

This needs to be said upfront: DNS was designed for name resolution, not traffic management. Using DNS for failover is a pragmatic hack that works well enough for most use cases, but it has fundamental limitations.

DNS responses are cached at multiple levels (recursive resolvers, OS caches, application caches, browser caches). You cannot force a client to re-resolve. You can set a TTL, but clients and resolvers are free to ignore it (and some do). Java applications, for example, cache DNS indefinitely by default in some JVM versions unless you explicitly set networkaddress.cache.ttl.

Infrastructure Disaster Recovery with Terraform#

Application disaster recovery is well-understood: replicate data, failover traffic, restore from backups. Infrastructure disaster recovery is different — you are recovering the platform that applications run on. If your Terraform state is lost, your VPC is deleted, or an entire region goes down, how do you rebuild?

This article covers the DR patterns specific to Terraform-managed infrastructure: protecting state, recovering from state loss, designing infrastructure for regional failover, and the runbooks that agents and operators need when things go wrong.

Kubernetes Deployment Strategies#

Every deployment strategy answers the same question: how do you replace running pods with new ones without breaking things for users? The answer depends on your tolerance for downtime, risk appetite, and infrastructure complexity.

Rolling Update (Default)#

Rolling updates replace pods incrementally. Kubernetes creates new pods before killing old ones, keeping the service available throughout. This is the default strategy for Deployments.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-api
spec:
  replicas: 4
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 1
  minReadySeconds: 10
  selector:
    matchLabels:
      app: web-api
  template:
    metadata:
      labels:
        app: web-api
    spec:
      containers:
      - name: web-api
        image: web-api:2.1.0
        readinessProbe:
          httpGet:
            path: /healthz
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5

Key parameters:

Release Management Patterns#

Releasing software is more than merging to main and deploying. A disciplined release process ensures that every version is identifiable, every change is documented, every deployment is reversible, and failures are contained before they reach all users. This operational sequence walks through each phase of a production release workflow.

Phase 1 – Semantic Versioning#

Step 1: Adopt Semantic Versioning#

Semantic versioning (semver) communicates the impact of changes through the version number itself: MAJOR.MINOR.PATCH.