Microservices Design Patterns: Essential Architecture and Design Guide
1) Decomposition Design Patterns
1.1 Decompose by Business Capability
Definition: Split services around high‑level business capabilities (e.g., Billing, Catalog, Shipping).
Problem: Monoliths couple unrelated features; teams step on each other; slow, risky releases.
Solution: Align service boundaries with capabilities owned by dedicated teams; each service has its own API, data, and lifecycle. Example: “Payments” service owns all payment logic and data.
1.2 Decompose by Subdomain
Definition: Use Domain-Driven Design to slice by domain subdomains (Core, Supporting, Generic) and bounded contexts.
Problem: Business language/logic varies across contexts; shared models cause ambiguity and tight coupling.
Solution: Create services per bounded context with explicit contracts/anti-corruption layers between them. Example: “Orders” vs “Inventory” as separate contexts.
1.3 Strangler Pattern
Definition: Incrementally replace a legacy system by routing specific features to new services while the old system continues.
Problem: Big‑bang rewrites are risky/slow; migration halts delivery.
Solution: Add a routing façade; strangler services implement slices; traffic for those slices moves to new code until legacy can be retired.
2) Integration Patterns
2.1 API Gateway Pattern
Definition: A single entry point that fronts many services, handling routing, auth, rate limiting, and protocol translation.
Problem: Clients must call many services, manage auth, deal with versioning/latency.
Solution: Put a gateway in front; it exposes a simpler client API and forwards/aggregates to internal services.
2.2 Aggregator Pattern
Definition: One component composes data from multiple services and returns a unified response.
Problem: Client or upstream layer must orchestrate many calls, increasing latency and complexity.
Solution: Centralize orchestration in an aggregator (gateway, BFF, or dedicated service) to fan‑out, collect, and shape the response.
2.3 Client-Side UI Composition
Definition: The UI composes a page from multiple service- or widget-specific endpoints (micro‑frontends).
Problem: A server aggregator becomes a bottleneck; teams can’t ship UI independently.
Solution: Split UI into independently deployable fragments that fetch their own data; compose at the client (or edge/CDN).
3) Database Patterns
3.1 Database per Service
Definition: Each service owns its data store and schema.
Problem: Shared databases create coupling; schema changes break unrelated services.
Solution: Strict ownership of data per service; inter-service communication via APIs/events. Tradeoff: handle cross-entity queries via composition or projections.
3.2 Shared Database per Service
Definition: Multiple services share a single physical database (often separate schemas/tables).
Problem: Easy to start but hard to evolve; tight coupling and unsafe cross-service joins.
Solution: If unavoidable (legacy/migration), enforce schema boundaries, read-only views, and change-control; plan migration to per-service DBs.
3.3 Command Query Responsibility Segregation (CQRS)
Definition: Split write (command) models from read (query) models, often with different schemas/stores.
Problem: One schema can’t serve both transactional writes and varied, fast reads efficiently.
Solution: Use a normalized write model + one or more read models (projections) fed by events/CDC; optimize each side independently. Expect eventual consistency.
3.4 Saga Pattern
Definition: A sequence of local transactions across services coordinated via events or a controller, with compensating actions for failures.
Problem: No ACID transactions across services/datastores; 2PC is fragile.
Solution: Model business workflows as sagas (orchestration or choreography), ensure idempotency, define compensations, and track state.
4) Observability Patterns
4.1 Log Aggregation
Definition: Centralize logs from all services (structured JSON) into a searchable store.
Problem: Debugging distributed issues is impossible with siloed logs.
Solution: Standardize log format/correlation IDs; ship logs to a centralized system (e.g., ELK/OpenSearch); set retention and alerts.
4.2 Performance Metrics
Definition: Collect time‑series metrics (RED/USE/Golden signals) per service and infra.
Problem: You can’t detect regressions or capacity issues without quantitative signals.
Solution: Expose metrics endpoints; scrape/push to TSDB; build SLOs/alerts and dashboards.
4.3 Distributed Tracing
Definition: End‑to‑end traces of requests across services with spans and context propagation.
Problem: Hard to locate bottlenecks and failing hops in call chains.
Solution: Instrument with OpenTelemetry (trace IDs in logs/headers); visualize traces; sample wisely.
4.4 Health Check
Definition: Endpoints that report service health (liveness/readiness/startup).
Problem: Orchestrators need to know when to start, stop, or route traffic; naive checks cause flapping.
Solution: Provide separate checks; readiness covers dependencies; liveness is lightweight; integrate with deployment/auto‑scaling.
5) Cross‑Cutting Concern Patterns
5.1 Externalized Configuration
Definition: Store config outside the binary (files, env vars, config service) with versioning and secrets management.
Problem: Rebuilds/redeploys for simple config changes; secrets end up in code.
Solution: Twelve‑Factor style config, config servers, secret managers, dynamic reload with safety gates.
5.2 Service Discovery
Definition: Dynamically find service instances at runtime via a registry/DNS.
Problem: IPs/ports change in elastic environments; hardcoded endpoints break.
Solution: Use a registry (e.g., Consul/Eureka) or DNS‑based discovery; health‑checked registrations; clients or mesh perform lookups.
5.3 Circuit Breakers
Definition: Guard calls to remote dependencies; open when failures spike; try after a cool‑down.
Problem: Cascading failures when one dependency slows or fails.
Solution: Implement circuit breakers with timeouts, bulkheads, retries, and fallbacks; monitor error rates/latency to trip.
5.4 Blue‑Green Deployments
Definition: Run two production environments (Blue and Green); switch traffic to the new one when ready.
Problem: In‑place deploys cause downtime and risky rollouts.
Solution: Deploy to idle environment, run checks, shift traffic (router/ALB), and roll back by flipping back; pair with DB expand‑contract migrations.
Quick usage hints
- Prefer business capability + subdomain for boundaries, then choose API Gateway/Aggregators for client simplicity.
- Default to Database per Service; add CQRS + Sagas when complexity warrants.
- Bake in logs/metrics/traces/health from day one.
- Use externalized config, discovery, circuit breakers, blue‑green to stay resilient and ship safely.