Sheshiyer 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) ← 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 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 ${{ github.sha }}"
Pipeline Stages
PR opened:
lint → typecheck → unit tests → integration tests → preview deploy
Merged to main:
lint → typecheck → unit tests → integration tests → build image → deploy staging → smoke tests → deploy production
Health Checks
Health Check Endpoint
// Simple health check
app.get("/health", (req, res) => {
res.status(200).json({ status: "ok" });
});
// Detailed health check (for internal monitoring)
app.get("/health/detailed", async (req, res) => {
const checks = {
database: await checkDatabase(),
redis: await checkRedis(),
externalApi: await checkExternalApi(),
};
const allHealthy = Object.values(checks).every(c => c.status === "ok");
res.status(allHealthy ? 200 : 503).json({
status: allHealthy ? "ok" : "degraded",
timestamp: new Date().toISOString(),
version: process.env.APP_VERSION || "unknown",
uptime: process.uptime(),
checks,
});
});
async function checkDatabase(): Promise<HealthCheck> {
try {
await db.query("SELECT 1");
return { status: "ok", latency_ms: 2 };
} catch (err) {
return { status: "error", message: "Database unreachable" };
}
}
Kubernetes Probes
livenessProbe:
httpGet:
path: /health
port: 3000
initialDelaySeconds: 10
periodSeconds: 30
failureThreshold: 3
readinessProbe:
httpGet:
path: /health
port: 3000
initialDelaySeconds: 5
periodSeconds: 10
failureThreshold: 2
startupProbe:
httpGet:
path: /health
port: 3000
initialDelaySeconds: 0
periodSeconds: 5
failureThreshold: 30 # 30 * 5s = 150s max startup time
Environment Configuration
Twelve-Factor App Pattern
# All config via environment variables — never in code
DATABASE_URL=postgres://user:pass@host:5432/db
REDIS_URL=redis://host:6379/0
API_KEY=${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