Docker Compose: AI-Optimized Technical Reference

Executive Summary

Docker Compose is a single-host container orchestration tool that simplifies multi-container application management through YAML configuration. Critical Limitation: Single-host only - when the server dies, everything dies. No high availability or multi-host clustering capability.

Configuration: Production-Ready Settings

Memory Limits (CRITICAL - Always Required)

services:
  web:
    image: myapp:latest
    deploy:
      resources:
        limits:
          memory: 512M
          cpus: '0.5'

Failure Impact: Without memory limits, one container with memory leak kills entire stack by consuming all RAM.

Health Checks (Essential for Recovery)

services:
  web:
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

Critical Context: Only automated recovery mechanism in single-host deployments. PostgreSQL containers can appear "running" in docker ps while refusing connections.

Version Selection

• Use: Compose file version 3.8
• Avoid: Version 3.9 (breaks health checks in Docker Engine 20.10.x)
• Tool: Use docker compose (no hyphen) - v2, not legacy docker-compose Python version

Startup Dependencies

services:
  web:
    depends_on:
      - postgres
    # GOTCHA: depends_on only waits for container start, not service readiness
    # PostgreSQL takes 5-10 seconds to initialize, app connects in 2 seconds

Solution: Implement retry logic in application or use health checks with condition.

Resource Requirements

Financial Costs

Deployment Type	Monthly Cost	Operational Overhead
Docker Compose	$200-300 (single server) + $50 monitoring	Few hours/month maintenance
Kubernetes	$500+ (cluster) + monitoring tools	Dedicated DevOps engineer required

Crossover Point: When downtime costs exceed Kubernetes complexity investment.

Hardware Requirements

• AI/ML Workloads: 8GB RAM per model, Linux + NVIDIA GPU required
• Development: 16GB RAM minimum (avoid swapping with multiple models)
• Production: 16-core, 64GB RAM handles thousands of users
• GPU Support: Linux only, Windows/macOS support unreliable

Time Investments

• Learning Curve: Simple (vs. Kubernetes steep learning curve)
• Setup Time: 2 minutes for new team members (git clone + docker-compose up)
• Migration to K8s: Weeks with Compose Bridge conversion tool

Critical Warnings

Single Host Failure Points

• No Failover: Server crash = complete outage
• No Load Distribution: Cannot spread across multiple machines
• Resource Contention: One service affects all others on same host

Production Limitations

• Scaling: Vertical only (bigger server, not more servers)
• Updates: Always cause downtime (no rolling updates)
• High Availability: Impossible with single-host architecture

Security Vulnerabilities

• Default Network: Bridge network allows all container communication
• Secrets in YAML: Common mistake leading to credential exposure
• Root Containers: Default containers run as root user

Development Gotchas

• Watch Mode: Breaks with symlinks and nested Docker builds
• Startup Race: Apps try connecting before databases are ready
• Log Chaos: Multi-service debugging without proper log aggregation

Technical Specifications

Network Architecture

• Service Discovery: Automatic DNS (service names as hostnames)
• Isolation: Custom networks prevent unwanted service communication
• Overhead: Minimal - containers communicate over Docker bridge network

AI/ML Integration (2025 Features)

services:
  llm:
    image: docker/model-runner:llama3.1-8b
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Limitations: Linux + NVIDIA only, 45-second inference on M1 Max (unusable), Windows driver issues

Watch Mode (Hot Reloading)

services:
  web:
    develop:
      watch:
        - action: rebuild
          path: ./src
        - action: sync
          path: ./static
          target: /app/static

Reliability Issues: Breaks with symlinks, silently fails in monorepos

Migration Strategies

When to Migrate from Compose

Triggers:

• Traffic outgrows single server capacity
• High availability requirements
• Compliance demanding disaster recovery
• Team growth enables Kubernetes expertise

Migration Tools

1. Compose Bridge: Converts YAML to Kubernetes manifests (imperfect but functional)
2. Kompose: Free alternative conversion tool
3. Service-by-Service: Gradual migration using load balancers

Cloud Integration Options

• Google Cloud Run: Direct Compose file deployment
• Azure Container Apps: Native Compose support
• AWS ECS: Via conversion tools

Failure Scenarios and Solutions

Common Failures

Symptom	Root Cause	Solution
Containers restart constantly	App crashes, OOM kills, health check failures	Check logs, set memory limits, fix health checks
ECONNREFUSED errors	Service not ready, wrong port/hostname	Add health checks, verify service names
Memory exhaustion	No resource limits set	Always set memory/CPU limits
Connection timeouts	PostgreSQL still initializing	Implement app retry logic

Debugging Commands

docker-compose logs service-name     # Check specific service logs
docker-compose ps                    # View container status and exit codes
docker-compose exec service ping target  # Test inter-service connectivity

Alternatives Comparison

Tool	Best For	Avoid When
Docker Compose	Development, small production, internal tools	High availability needed
Kubernetes	Enterprise scale, multi-host, advanced features	Small teams, simple apps
Docker Swarm	Mid-scale production with clustering	Complex networking requirements
Manual Docker	Learning, minimal overhead	Any multi-container scenario

Security Best Practices

Essential Configurations

# Network isolation
networks:
  frontend:
  backend:

# Non-root user
services:
  web:
    user: "1000:1000"

# Secrets management
secrets:
  db_password:
    external: true

Security Vulnerabilities

• Credential Exposure: Passwords in YAML files committed to git
• Network Exposure: Default bridge network allows unrestricted communication
• Root Privilege: Containers running as root enable privilege escalation

Production Checklist

Must-Have for Production

• Memory and CPU limits on all services
• Health checks with proper retry logic
• Named volumes for persistent data
• Backup strategy for volumes
• Monitoring (Prometheus/Grafana recommended)
• Log aggregation system
• Security scanning of images
• Secrets management (never in YAML)

Monitoring Requirements

• CPU, memory, disk usage alerts
• Service health endpoint monitoring
• Container restart frequency tracking
• Log aggregation for debugging

Decision Framework

Use Docker Compose When:

• Single-server deployment acceptable
• Development environment setup
• Internal tools with limited users
• Prototyping and MVPs
• Budget constraints prevent Kubernetes investment

Migrate to Kubernetes When:

• High availability requirements
• Need horizontal scaling across servers
• Compliance requires disaster recovery
• Team has Kubernetes expertise
• Downtime costs exceed infrastructure complexity

Financial Threshold

Docker Compose becomes cost-ineffective when:

• Downtime costs > $500/incident
• Need 24/7 availability
• Scaling requires frequent server upgrades
• Compliance mandates redundancy

Integration Patterns

CI/CD Integration

• GitHub Actions: Native Docker support for testing
• GitLab CI: Built-in registry and pipeline integration
• Jenkins: Plugin available but adds complexity

Monitoring Stack

services:
  prometheus:
    image: prom/prometheus
  grafana:
    image: grafana/grafana
  cadvisor:
    image: gcr.io/cadvisor/cadvisor

Backup Strategy

# Database backup
docker-compose exec db pg_dump -U user dbname > backup.sql

# Volume backup
docker run --rm -v myapp_data:/data -v $(pwd):/backup alpine tar czf /backup/data.tar.gz /data

Troubleshooting Guide

Startup Issues

1. Check service dependencies with docker-compose ps
2. Verify logs with docker-compose logs service-name
3. Test connectivity with docker-compose exec service ping target
4. Validate YAML syntax and indentation

Performance Problems

1. Monitor resource usage with docker stats
2. Check for memory leaks in application logs
3. Verify network latency between services
4. Review container resource limits

Security Incidents

1. Audit container permissions and users
2. Check for exposed ports and services
3. Review secrets management implementation
4. Validate network isolation configuration

This technical reference provides the operational intelligence needed for informed Docker Compose adoption and implementation decisions, including failure modes, resource requirements, and migration strategies.