DevOps & Infrastructure

Docker Compose Patterns

Design multi-service Docker Compose architectures with networking, volumes, health checks, and production-ready patterns for complex applications.

Claude Code Codex Copilot Cursor Gemini CLI

Overview

Docker Compose is the standard tool for defining and running multi-container applications. While basic Compose files are straightforward, production patterns require health checks, proper networking, volume management, environment variable handling, and service dependencies. AI agents can generate sophisticated Compose configurations that incorporate all these best practices from a simple description of your architecture.

Advanced Docker Compose patterns include multi-stage builds for smaller images, named networks for service isolation, health check-based dependency ordering, profiles for conditional service startup, and extends for shared configuration. Your AI agent understands these patterns and can generate Compose files that use depends_on with service_healthy conditions, configure proper logging drivers, and set resource limits for each service.

This skill is particularly valuable for local development environments that mirror production. Your AI agent can create Compose configurations with databases, caches, message queues, and your application services all properly networked and configured, with volume mounts for live reloading during development and optimized builds for production.

Who Is This For?

Full-stack developers creating local dev environments with databases and caches
Teams standardizing multi-service architectures with Docker Compose
DevOps engineers implementing health check-based dependency ordering
Developers creating production Compose files with resource limits and logging

Installation

Setup for Claude Code

Install Docker Desktop (includes Compose): https://docs.docker.com/get-docker/
Claude Code generates docker-compose.yml and runs docker compose commands

Configuration

# docker-compose.yml
services:
  app:
    build:
      context: .
      target: production
    ports: ["3000:3000"]
    depends_on:
      db:
        condition: service_healthy
    environment:
      DATABASE_URL: postgres://user:pass@db:5432/myapp
  db:
    image: postgres:16-alpine
    volumes: [pgdata:/var/lib/postgresql/data]
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U user"]
      interval: 5s
      timeout: 5s
      retries: 5
volumes:
  pgdata: