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Docker Compose for Local Multi-Service Stacks

Run BookStore + PostgreSQL + Redis locally with one compose file and health checks.

Why this matters

  • This topic directly affects how reliably BookStore reaches production — compose runs bookstore api with postgres and redis locally with one command and health-aware startup order..
  • Interviewers connect hands-on commands and manifests to real delivery stories, not buzzwords.
  • Later modules assume you can explain both the why and the concrete file or command involved.
  • Platform maturity shows up when teams automate this instead of relying on tribal knowledge.
Git repoSource of truth
Maven buildJAR artefact
Docker imageImmutable package
CI/CD pipelineTest + push
KubernetesScale + route
ObservabilityMonitor + alert
The same Spring Boot app progresses through every module — from source code to production operations.

Multi-service stack

API + Postgres + Redis: BookStore engineers treat this as part of the standard path from laptop to docker compose local dev readiness. Document decisions in the team runbook so on-call knows which knobs exist.

Key ideas

  • Multi-service stack — primary idea for docker-compose-local-dev
  • BookStore context — catalog API, checkout, and inventory services share the same pattern
  • Automation — prefer pipeline jobs over manual SSH steps
  • Verification — staging must prove the change before prod traffic

Health-aware depends_on

Local parity with staging: When staging matches production architecture, BookStore catches misconfigurations early. Pair this section's practice with observability dashboards to confirm behavior under load.

Key ideas

  • Health-aware depends_on — operational detail
  • Rollback — know how to revert without rebuilding artefacts
  • Security — least privilege for deploy roles
  • Documentation — link runbooks from the service README

Production checklist

Before promoting BookStore changes tied to this topic, run automated tests, inspect artefact immutability (image digest or JAR checksum), execute a staging smoke test on /actuator/health, and watch error-rate dashboards for thirty minutes after prod rollout.

Key ideas

  • Staging soak — validate under synthetic load
  • Change ticket — attach pipeline URL and artefact digest
  • On-call — page owner stays on dashboards during rollout
  • Post-deploy — record metrics baseline for comparison
Java
services:
  api:
    build: .
    ports: ["8080:8080"]
    depends_on:
      postgres:
        condition: service_healthy
  postgres:
    image: postgres:16
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U bookstore"]

Quick recall

Everything you need if you only revisit this box.

  • BookStore uses docker compose local dev as a standard delivery practice.
  • Prefer automation and versioned config over manual server changes.
  • Staging proves changes before customer-facing promotion.
  • Observability confirms success — do not rely on silence alone.
  • Rollback plans must be tested, not invented during an outage.
  • Security and least privilege apply to every pipeline and cluster role.

Test yourself

Answer these before moving on — recall is what makes it stick.