Docker Compose

Core concept: real systems are multi-service. Compose captures service topology in a single versioned spec.

services:
  api:
    build: .
    ports: ["8000:8000"]
    depends_on: [postgres, redis, mlflow]
  postgres:
    image: postgres:16
  redis:
    image: redis:7
  mlflow:
    image: ghcr.io/mlflow/mlflow:v2.14.1
    volumes: ["pgdata:/var/lib/postgresql/data"]
volumes:
  pgdata:

Architecture Diagram

Compose file
    -> api service
    -> database service
    -> shared network + shared volume contracts
    -> one-command startup for local integration testing

Theory Deepening

• Declarative topology: service dependencies become reviewable infrastructure code.
• Service discovery: containers communicate using service names.
• Parity testing: validate interactions before orchestrator-level deployment.

Interview-Ready Deepening

Source-backed reinforcement: these points add detail beyond short-duration UI hints and emphasize production tradeoffs.

• Declarative orchestration for multi-container application stacks and dependency coordination.
• Container isolation improves dependency safety, but operational complexity grows around networking and storage.
• Docker Compose: Interaction example: a forecasting platform can run API, Postgres, Redis, and MLflow in one Compose stack so the full training-to-serving loop is validated locally before promotion.
• Declarative topology: service dependencies become reviewable infrastructure code.
• Interaction example: a forecasting platform can run API, Postgres, Redis, and MLflow in one Compose stack so the full training-to-serving loop is validated locally before promotion.
• Pinning versions stabilizes releases, but can delay security upgrades if dependency refresh cycles are weak.
• Compose captures service topology in a single versioned spec.
• Immutable images improve reproducibility, but frequent rebuilds increase CI cost without layer optimization.

Tradeoffs You Should Be Able to Explain

• Immutable images improve reproducibility, but frequent rebuilds increase CI cost without layer optimization.
• Container isolation improves dependency safety, but operational complexity grows around networking and storage.
• Pinning versions stabilizes releases, but can delay security upgrades if dependency refresh cycles are weak.

First-time learner note: Learn Docker as a systems flow, not a command list: image design, container runtime, storage, networking, and orchestration each solve a different problem.

Production note: Treat containers as release artifacts with runtime contracts: version tags, explicit config, health checks, dependency connectivity, and rollback strategy.