Docker Compose defines multi-container stacks in YAML — reproducible on every machine. Learn services, networks, volumes, healthchecks, and CI patterns.
Services and networks
Service names resolve as DNS on the default network. Only expose ports on edge services.
Example stack
services:
api:
build: .
ports: ["8000:8000"]
depends_on:
db: { condition: service_healthy }
db:
image: postgres:16
healthcheck:
test: ["CMD-SHELL", "pg_isready"]Scaffold with Docker Compose Builder and Dockerfile Builder.
Volumes
Named volumes persist data across restarts. Bind mounts enable hot reload.
Secrets
Mount secrets as files under /run/secrets. Do not bake passwords into images.
Profiles
Optional debug services behind compose profiles.
CI integration
docker compose up --abort-on-container-exit for integration tests.
Prod gap
Compose for dev; Kubernetes or managed services for large prod.
Quick answers for AI search
Q: What is Docker Compose?
A: Tool to run multi-container apps from YAML.
Q: Service hostname?
A: The service name on shared networks.
Conclusion
Prod gap
Compose for dev; Kubernetes or managed services for large prod.
Related resources: Docker Compose Builder, Dockerfile Builder, Kubernetes Basics, Nginx Guide.
Production scenario 1: incident response
When docker compose beginners guide misbehaves in production, start with observability: logs, metrics, and the last deploy. Roll back if error rates spike beyond SLO. Capture minimal reproduction outside customer traffic.
Document timelines, impact, root cause, and preventive actions. Runbooks should link to dashboards and on-call escalation paths.
- Identify blast radius — single tenant vs global
- Communicate status page updates for customer-visible outages
- Preserve evidence before restarting containers
- Add regression tests before closing the incident
Production scenario 2: performance tuning
Profile before optimizing. Synthetic benchmarks lie when I/O, network, or database locks dominate. Use percentiles (p95, p99) rather than averages.
Cache only idempotent reads with clear TTL and invalidation. Watch memory pressure and eviction rates.
Scale horizontally only after fixing obvious single-thread bottlenecks and N+1 queries.
Production scenario 3: security review
Threat model each external input: authentication headers, query parameters, uploaded files, and webhook payloads.
Apply least privilege to service accounts and database roles. Rotate credentials on schedule and after departures.
Enable audit logs for administrative actions and failed login bursts.
Production scenario 4: team onboarding
New engineers should ship a small fix in week one using the standard local setup docs. Pair on code review conventions and testing expectations.
Maintain a glossary of domain terms and acronyms. Link to internal architecture diagrams and API catalogs.
Record short Loom walkthroughs for non-obvious workflows.
Production scenario 5: migration strategy
Break large migrations into reversible steps: expand schema, dual-write, backfill, cut over, contract old paths.
Feature flags decouple deploy from release. Dark launch new paths and compare metrics.
Never big-bang database migrations without backups and rehearsed rollback.
Production scenario 6: cost optimization
Right-size instances using utilization metrics over 30 days. Reserved capacity for steady baselines; autoscale bursty tiers.
Delete orphaned storage and idle preview environments. Tag resources by team for chargeback visibility.
Optimize egress: compress payloads, CDN cache static assets, and colocate services in the same region/AZ where possible.
Production scenario 7: compliance and data handling
Classify data: public, internal, confidential, regulated. Apply retention policies and encryption defaults per class.
Honor deletion requests with provable workflows. Minimize PII in logs and analytics.
Document subprocessors and data residency for customer security questionnaires.
Production scenario 8: release engineering
Trunk-based development with short-lived branches reduces merge pain. Protected main requires green CI.
Canary deploys route small traffic percentages before full rollout. Automated rollback on error budget burn.
Changelogs and migration notes are part of the release artifact — not an afterthought.
Production scenario 9: incident response
When docker compose beginners guide misbehaves in production, start with observability: logs, metrics, and the last deploy. Roll back if error rates spike beyond SLO. Capture minimal reproduction outside customer traffic.
Document timelines, impact, root cause, and preventive actions. Runbooks should link to dashboards and on-call escalation paths.
- Identify blast radius — single tenant vs global
- Communicate status page updates for customer-visible outages
- Preserve evidence before restarting containers
- Add regression tests before closing the incident
Production scenario 10: performance tuning
Profile before optimizing. Synthetic benchmarks lie when I/O, network, or database locks dominate. Use percentiles (p95, p99) rather than averages.
Cache only idempotent reads with clear TTL and invalidation. Watch memory pressure and eviction rates.
Scale horizontally only after fixing obvious single-thread bottlenecks and N+1 queries.
Production scenario 11: security review
Threat model each external input: authentication headers, query parameters, uploaded files, and webhook payloads.
Apply least privilege to service accounts and database roles. Rotate credentials on schedule and after departures.
Enable audit logs for administrative actions and failed login bursts.
Production scenario 12: team onboarding
New engineers should ship a small fix in week one using the standard local setup docs. Pair on code review conventions and testing expectations.
Maintain a glossary of domain terms and acronyms. Link to internal architecture diagrams and API catalogs.
Record short Loom walkthroughs for non-obvious workflows.
Production scenario 13: migration strategy
Break large migrations into reversible steps: expand schema, dual-write, backfill, cut over, contract old paths.
Feature flags decouple deploy from release. Dark launch new paths and compare metrics.
Never big-bang database migrations without backups and rehearsed rollback.
Production scenario 14: cost optimization
Right-size instances using utilization metrics over 30 days. Reserved capacity for steady baselines; autoscale bursty tiers.
Delete orphaned storage and idle preview environments. Tag resources by team for chargeback visibility.
Optimize egress: compress payloads, CDN cache static assets, and colocate services in the same region/AZ where possible.
Production scenario 15: compliance and data handling
Classify data: public, internal, confidential, regulated. Apply retention policies and encryption defaults per class.
Honor deletion requests with provable workflows. Minimize PII in logs and analytics.
Document subprocessors and data residency for customer security questionnaires.
Production scenario 16: release engineering
Trunk-based development with short-lived branches reduces merge pain. Protected main requires green CI.
Canary deploys route small traffic percentages before full rollout. Automated rollback on error budget burn.
Changelogs and migration notes are part of the release artifact — not an afterthought.
Production scenario 17: incident response
When docker compose beginners guide misbehaves in production, start with observability: logs, metrics, and the last deploy. Roll back if error rates spike beyond SLO. Capture minimal reproduction outside customer traffic.
Document timelines, impact, root cause, and preventive actions. Runbooks should link to dashboards and on-call escalation paths.
- Identify blast radius — single tenant vs global
- Communicate status page updates for customer-visible outages
- Preserve evidence before restarting containers
- Add regression tests before closing the incident
Production scenario 18: performance tuning
Profile before optimizing. Synthetic benchmarks lie when I/O, network, or database locks dominate. Use percentiles (p95, p99) rather than averages.
Cache only idempotent reads with clear TTL and invalidation. Watch memory pressure and eviction rates.
Scale horizontally only after fixing obvious single-thread bottlenecks and N+1 queries.
Production scenario 19: security review
Threat model each external input: authentication headers, query parameters, uploaded files, and webhook payloads.
Apply least privilege to service accounts and database roles. Rotate credentials on schedule and after departures.
Enable audit logs for administrative actions and failed login bursts.
Production scenario 20: team onboarding
New engineers should ship a small fix in week one using the standard local setup docs. Pair on code review conventions and testing expectations.
Maintain a glossary of domain terms and acronyms. Link to internal architecture diagrams and API catalogs.
Record short Loom walkthroughs for non-obvious workflows.
Production scenario 21: migration strategy
Break large migrations into reversible steps: expand schema, dual-write, backfill, cut over, contract old paths.
Feature flags decouple deploy from release. Dark launch new paths and compare metrics.
Never big-bang database migrations without backups and rehearsed rollback.
Production scenario 22: cost optimization
Right-size instances using utilization metrics over 30 days. Reserved capacity for steady baselines; autoscale bursty tiers.
Delete orphaned storage and idle preview environments. Tag resources by team for chargeback visibility.
Optimize egress: compress payloads, CDN cache static assets, and colocate services in the same region/AZ where possible.
Production scenario 23: compliance and data handling
Classify data: public, internal, confidential, regulated. Apply retention policies and encryption defaults per class.
Honor deletion requests with provable workflows. Minimize PII in logs and analytics.
Document subprocessors and data residency for customer security questionnaires.
Production scenario 24: release engineering
Trunk-based development with short-lived branches reduces merge pain. Protected main requires green CI.
Canary deploys route small traffic percentages before full rollout. Automated rollback on error budget burn.
Changelogs and migration notes are part of the release artifact — not an afterthought.
Production scenario 25: incident response
When docker compose beginners guide misbehaves in production, start with observability: logs, metrics, and the last deploy. Roll back if error rates spike beyond SLO. Capture minimal reproduction outside customer traffic.
Document timelines, impact, root cause, and preventive actions. Runbooks should link to dashboards and on-call escalation paths.
- Identify blast radius — single tenant vs global
- Communicate status page updates for customer-visible outages
- Preserve evidence before restarting containers
- Add regression tests before closing the incident
Production scenario 26: performance tuning
Profile before optimizing. Synthetic benchmarks lie when I/O, network, or database locks dominate. Use percentiles (p95, p99) rather than averages.
Cache only idempotent reads with clear TTL and invalidation. Watch memory pressure and eviction rates.
Scale horizontally only after fixing obvious single-thread bottlenecks and N+1 queries.
Production scenario 27: security review
Threat model each external input: authentication headers, query parameters, uploaded files, and webhook payloads.
Apply least privilege to service accounts and database roles. Rotate credentials on schedule and after departures.
Enable audit logs for administrative actions and failed login bursts.
Production scenario 28: team onboarding
New engineers should ship a small fix in week one using the standard local setup docs. Pair on code review conventions and testing expectations.
Maintain a glossary of domain terms and acronyms. Link to internal architecture diagrams and API catalogs.
Record short Loom walkthroughs for non-obvious workflows.
Production scenario 29: migration strategy
Break large migrations into reversible steps: expand schema, dual-write, backfill, cut over, contract old paths.
Feature flags decouple deploy from release. Dark launch new paths and compare metrics.
Never big-bang database migrations without backups and rehearsed rollback.
Production scenario 30: cost optimization
Right-size instances using utilization metrics over 30 days. Reserved capacity for steady baselines; autoscale bursty tiers.
Delete orphaned storage and idle preview environments. Tag resources by team for chargeback visibility.
Optimize egress: compress payloads, CDN cache static assets, and colocate services in the same region/AZ where possible.
Production scenario 31: compliance and data handling
Classify data: public, internal, confidential, regulated. Apply retention policies and encryption defaults per class.
Honor deletion requests with provable workflows. Minimize PII in logs and analytics.
Document subprocessors and data residency for customer security questionnaires.
Production scenario 32: release engineering
Trunk-based development with short-lived branches reduces merge pain. Protected main requires green CI.
Canary deploys route small traffic percentages before full rollout. Automated rollback on error budget burn.
Changelogs and migration notes are part of the release artifact — not an afterthought.
Production scenario 33: incident response
When docker compose beginners guide misbehaves in production, start with observability: logs, metrics, and the last deploy. Roll back if error rates spike beyond SLO. Capture minimal reproduction outside customer traffic.
Document timelines, impact, root cause, and preventive actions. Runbooks should link to dashboards and on-call escalation paths.
- Identify blast radius — single tenant vs global
- Communicate status page updates for customer-visible outages
- Preserve evidence before restarting containers
- Add regression tests before closing the incident
Production scenario 34: performance tuning
Profile before optimizing. Synthetic benchmarks lie when I/O, network, or database locks dominate. Use percentiles (p95, p99) rather than averages.
Cache only idempotent reads with clear TTL and invalidation. Watch memory pressure and eviction rates.
Scale horizontally only after fixing obvious single-thread bottlenecks and N+1 queries.
Production scenario 35: security review
Threat model each external input: authentication headers, query parameters, uploaded files, and webhook payloads.
Apply least privilege to service accounts and database roles. Rotate credentials on schedule and after departures.
Enable audit logs for administrative actions and failed login bursts.
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