Deployment Patterns

Production deployment workflows and CI/CD best practices.

When to Activate

Setting up CI/CD pipelines
Dockerizing an application
Planning deployment strategy (blue-green, canary, rolling)
Implementing health checks and readiness probes
Preparing for a production release
Configuring environment-specific settings

Deployment Strategies

Rolling Deployment (Default)

Replace instances gradually — old and new versions run simultaneously during rollout.

Instance 1: v1 → v2  (update first)
Instance 2: v1        (still running v1)
Instance 3: v1        (still running v1)
Instance 1: v2
Instance 2: v1 → v2  (update second)
Instance 3: v1Instance 1: v2
Instance 2: v2
Instance 3: v1 → v2  (update last)

Pros: Zero downtime, gradual rollout Cons: Two versions run simultaneously — requires backward-compatible changes Use when: Standard deployments, backward-compatible changes

Blue-Green Deployment

Run two identical environments. Switch traffic atomically.

Blue  (v1) ← traffic
Green (v2)   idle, running new version
After verification:
Blue  (v1)   idle (becomes standby)
Green (v2) ← traffic

Pros: Instant rollback (switch back to blue), clean cutover Cons: Requires 2x infrastructure during deployment Use when: Critical services, zero-tolerance for issues

Canary Deployment

Route a small percentage of traffic to the new version first.

v1: 95% of traffic
v2:  5% of traffic  (canary)
If metrics look good:
v1: 50% of traffic
v2: 50% of traffic
Final:
v2: 100% of traffic

Pros: Catches issues with real traffic before full rollout Cons: Requires traffic splitting infrastructure, monitoring Use when: High-traffic services, risky changes, feature flags

Docker

Multi-Stage Dockerfile (Node.js)

# Stage 1: Install dependencies FROM node:22-alpine AS deps WORKDIR /app COPY package.json package-lock.json ./ RUN npm ci --production=false Stage 2: Build FROM node:22-alpine AS builder WORKDIR /app COPY --from=deps /app/node_modules ./node_modules COPY . . RUN npm run build RUN npm prune --production Stage 3: Production image FROM node:22-alpine AS runner WORKDIR /app RUN addgroup -g 1001 -S appgroup && adduser -S appuser -u 1001 USER appuser COPY --from=builder --chown=appuser:appgroup /app/node_modules ./node_modules COPY --from=builder --chown=appuser:appgroup /app/dist ./dist COPY --from=builder --chown=appuser:appgroup /app/package.json ./ ENV NODE_ENV=production EXPOSE 3000 HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ CMD wget --no-verbose --tries=1 --spider http://localhost:3000/health || exit 1

CMD ["node", "dist/server.js"]

Multi-Stage Dockerfile (Go)

FROM golang:1.22-alpine AS builder WORKDIR /app COPY go.mod go.sum ./ RUN go mod download COPY . . RUN CGO_ENABLED=0 GOOS=linux go build -ldflags="-s -w" -o /server ./cmd/server FROM alpine:3.19 AS runner RUN apk --no-cache add ca-certificates RUN adduser -D -u 1001 appuser USER appuser COPY --from=builder /server /server

EXPOSE 8080 HEALTHCHECK --interval=30s --timeout=3s CMD wget -qO- http://localhost:8080/health || exit 1 CMD ["/server"]

Multi-Stage Dockerfile (Python/Django)

FROM python:3.12-slim AS builder
WORKDIR /app
RUN pip install --no-cache-dir uv
COPY requirements.txt .
RUN uv pip install --system --no-cache -r requirements.txt
FROM python:3.12-slim AS runner
WORKDIR /app
RUN useradd -r -u 1001 appuser
USER appuser
COPY --from=builder /usr/local/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages
COPY --from=builder /usr/local/bin /usr/local/bin
COPY . .
ENV PYTHONUNBUFFERED=1
EXPOSE 8000HEALTHCHECK --interval=30s --timeout=3s CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health/')" || exit 1
CMD ["gunicorn", "config.wsgi:application", "--bind", "0.0.0.0:8000", "--workers", "4"]

Docker Best Practices

# GOOD practices
Use specific version tags (node:22-alpine, not node:latest)
Multi-stage builds to minimize image size
Run as non-root user
Copy dependency files first (layer caching)
Use .dockerignore to exclude node_modules, .git, tests
Add HEALTHCHECK instruction
Set resource limits in docker-compose or k8s

BAD practices
Running as root
Using :latest tags
Copying entire repo in one COPY layer
Installing dev dependencies in production image
Storing secrets in image (use env vars or secrets manager)

CI/CD Pipeline

GitHub Actions (Standard Pipeline)

name: CI/CD
on:
  push:
    branches: [main]
  pull_request:
    branches: [main]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 22
          cache: npm
      - run: npm ci
      - run: npm run lint
      - run: npm run typecheck
      - run: npm test -- --coverage
      - uses: actions/upload-artifact@v4
        if: always()
        with:
          name: coverage
          path: coverage/
  build:
    needs: test
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    steps:
      - uses: actions/checkout@v4
      - uses: docker/setup-buildx-action@v3
      - uses: docker/login-action@v3
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      - uses: docker/build-push-action@v5
        with:
          push: true
          tags: ghcr.io/${{ github.repository }}:${{ github.sha }}
          cache-from: type=gha
          cache-to: type=gha,mode=max  deploy:
    needs: build
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - name: Deploy to production
        run: |
          # Platform-specific deployment command
          # Railway: railway up
          # Vercel: vercel --prod
          # K8s: kubectl set image deployment/app app=ghcr.io/${{ github.repository }}:${{ github.sha }}
          echo "Deploying ${{ github.sha }}"

Pipeline Stages

PR opened: lint → typecheck → unit tests → integration tests → preview deploy

Merged to main: lint → typecheck → unit tests → integration tests → build image → deploy staging → smoke tests → deploy production

Health Checks

Health Check Endpoint

// Simple health check
app.get("/health", (req, res) => {
  res.status(200).json({ status: "ok" });
});
// Detailed health check (for internal monitoring)
app.get("/health/detailed", async (req, res) => {
  const checks = {
    database: await checkDatabase(),
    redis: await checkRedis(),
    externalApi: await checkExternalApi(),
  };
  const allHealthy = Object.values(checks).every(c => c.status === "ok");
  res.status(allHealthy ? 200 : 503).json({
    status: allHealthy ? "ok" : "degraded",
    timestamp: new Date().toISOString(),
    version: process.env.APP_VERSION || "unknown",
    uptime: process.uptime(),
    checks,
  });
});async function checkDatabase(): Promise<HealthCheck> {
  try {
    await db.query("SELECT 1");
    return { status: "ok", latency_ms: 2 };
  } catch (err) {
    return { status: "error", message: "Database unreachable" };
  }
}

Kubernetes Probes

livenessProbe: httpGet: path: /health port: 3000 initialDelaySeconds: 10 periodSeconds: 30 failureThreshold: 3 readinessProbe: httpGet: path: /health port: 3000 initialDelaySeconds: 5 periodSeconds: 10 failureThreshold: 2

startupProbe: httpGet: path: /health port: 3000 initialDelaySeconds: 0 periodSeconds: 5 failureThreshold: 30 # 30 * 5s = 150s max startup time

Environment Configuration

Twelve-Factor App Pattern

# All config via environment variables — never in code
DATABASE_URL=postgres://user:pass@host:5432/db
REDIS_URL=redis://host:6379/0
API_KEY=${API_KEY}           # injected by secrets manager
LOG_LEVEL=info
PORT=3000
Environment-specific behavior
NODE_ENV=production          # or staging, development
APP_ENV=production           # explicit app environment

Configuration Validation

import { z } from "zod";
const envSchema = z.object({
  NODE_ENV: z.enum(["development", "staging", "production"]),
  PORT: z.coerce.number().default(3000),
  DATABASE_URL: z.string().url(),
  REDIS_URL: z.string().url(),
  JWT_SECRET: z.string().min(32),
  LOG_LEVEL: z.enum(["debug", "info", "warn", "error"]).default("info"),
});// Validate at startup — fail fast if config is wrong
export const env = envSchema.parse(process.env);

Rollback Strategy

Instant Rollback

# Docker/Kubernetes: point to previous image
kubectl rollout undo deployment/app
Vercel: promote previous deployment
vercel rollback
Railway: redeploy previous commit
railway up --commit <previous-sha>
Database: rollback migration (if reversible)
npx prisma migrate resolve --rolled-back <migration-name>

Rollback Checklist

[ ] Previous image/artifact is available and tagged
[ ] Database migrations are backward-compatible (no destructive changes)
[ ] Feature flags can disable new features without deploy
[ ] Monitoring alerts configured for error rate spikes
[ ] Rollback tested in staging before production release

Production Readiness Checklist

Before any production deployment:

Application

[ ] All tests pass (unit, integration, E2E)
[ ] No hardcoded secrets in code or config files
[ ] Error handling covers all edge cases
[ ] Logging is structured (JSON) and does not contain PII
[ ] Health check endpoint returns meaningful status

Infrastructure

[ ] Docker image builds reproducibly (pinned versions)
[ ] Environment variables documented and validated at startup
[ ] Resource limits set (CPU, memory)
[ ] Horizontal scaling configured (min/max instances)
[ ] SSL/TLS enabled on all endpoints

Monitoring

[ ] Application metrics exported (request rate, latency, errors)
[ ] Alerts configured for error rate > threshold
[ ] Log aggregation set up (structured logs, searchable)
[ ] Uptime monitoring on health endpoint

Security

[ ] Dependencies scanned for CVEs
[ ] CORS configured for allowed origins only
[ ] Rate limiting enabled on public endpoints
[ ] Authentication and authorization verified
[ ] Security headers set (CSP, HSTS, X-Frame-Options)

Operations

[ ] Rollback plan documented and tested
[ ] Database migration tested against production-sized data
[ ] Runbook for common failure scenarios
[ ] On-call rotation and escalation path defined