deployment-patterns
Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications. Use when setting up deployment infrastructure or planning releases.
Resumen
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: 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) ← trafficPros: 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 trafficPros: 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 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: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 ${{ github.sha }}"Pipeline Stages
PR opened:
lint → typecheck → unit tests → integration tests → preview deploy
Merged to main:
lint → typecheck → unit tests → integration tests → build image → deploy staging → smoke tests → deploy productionHealth Checks
Health Check Endpoint
// Simple health check
app.get("/health", (req, res) => {
res.status(200).json({ status: "ok" });
});
// Detailed health check (for internal monitoring)
app.get("/health/detailed", async (req, res) => {
const checks = {
database: await checkDatabase(),
redis: await checkRedis(),
externalApi: await checkExternalApi(),
};
const allHealthy = Object.values(checks).every(c => c.status === "ok");
res.status(allHealthy ? 200 : 503).json({
status: allHealthy ? "ok" : "degraded",
timestamp: new Date().toISOString(),
version: process.env.APP_VERSION || "unknown",
uptime: process.uptime(),
checks,
});
});
async function checkDatabase(): Promise<HealthCheck> {
try {
await db.query("SELECT 1");
return { status: "ok", latency_ms: 2 };
} catch (err) {
return { status: "error", message: "Database unreachable" };
}
}Kubernetes Probes
livenessProbe:
httpGet:
path: /health
port: 3000
initialDelaySeconds: 10
periodSeconds: 30
failureThreshold: 3
readinessProbe:
httpGet:
path: /health
port: 3000
initialDelaySeconds: 5
periodSeconds: 10
failureThreshold: 2
startupProbe:
httpGet:
path: /health
port: 3000
initialDelaySeconds: 0
periodSeconds: 5
failureThreshold: 30 # 30 * 5s = 150s max startup timeEnvironment Configuration
Twelve-Factor App Pattern
# All config via environment variables — never in code
DATABASE_URL=postgres://user:pass@host:5432/db
REDIS_URL=redis://host:6379/0
API_KEY=${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 environmentConfiguration 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
When to Use This Skill
- Setting up CI/CD pipelines
- Dockerizing applications
- Planning deployment strategies
- Implementing health checks
- Preparing for production releases
- Troubleshooting deployment issues
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