Deployment Patterns

Production deployment workflows and CI/CD best practices.

When to Activate

See instead:

• Container configuration → docker-patterns
• CI pipeline setup → ci-cd-patterns
• Kubernetes manifests → kubernetes-patterns

Use this skill when:

• Choosing or implementing a deployment strategy (blue-green, canary, rolling, feature flags)
• Orchestrating a release across environments (staging → production promotion)
• Designing a rollback plan for a risky schema change or infrastructure update
• Planning traffic shifting and progressive delivery steps
• Validating environment variable configuration at application startup to fail fast on misconfiguration
• Preparing a production readiness checklist before go-live

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: v1

Instance 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:24-alpine AS deps
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm ci --production=false

# Stage 2: Build
FROM node:24-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:24-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:1.26-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:3.23 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:3.14-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:3.14-slim AS runner
WORKDIR /app

RUN useradd -r -u 1001 appuser
USER appuser

COPY --from=builder /usr/local/lib/python3.14/site-packages /usr/local/lib/python3.14/site-packages
COPY --from=builder /usr/local/bin /usr/local/bin
COPY . .

ENV PYTHONUNBUFFERED=1
EXPOSE 8000

HEALTHCHECK --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:24-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: 24
          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

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Edge & Serverless

Modern deployment targets beyond containers:

Edge Functions

Run code at the network edge — globally distributed, sub-millisecond cold starts, V8 isolates instead of containers.

Platforms: Cloudflare Workers, Vercel Edge Middleware, Deno Deploy

When to use edge:

• Request routing, geo-targeting, A/B testing
• Auth checks before origin (reduce origin load)
• Response transformation (add headers, modify HTML)
• API proxying with caching

Key constraints:

• No fs, child_process, net — only Web Standard APIs
• Bundle size limits (Cloudflare: 1MB, Vercel: 4MB)
• CPU limits (Cloudflare: 50ms per request)

Deep dive: see skill edge-patterns for Cloudflare Workers (KV, D1, R2, Durable Objects), Vercel Edge Middleware, and testing with Miniflare.

Serverless Functions (AWS Lambda)

Event-driven compute — pay per invocation, infinite scale, no servers to manage.

When to use Lambda:

• APIs with spiky or unpredictable traffic
• Event processing (S3, SQS, DynamoDB Streams)
• Scheduled jobs, background tasks

Cold start by runtime: Rust/Go ~1ms, Node.js ~100ms, Python ~200ms, Java (SnapStart) ~150ms

Mitigation: Provisioned Concurrency (eliminates cold starts), SnapStart (JVM), keep-warm pings

Deep dive: see skill serverless-patterns for cold starts, Step Functions, Lambda Powertools, and idempotency.