Microservices Design Patterns and Anti‑Patterns

Microservices promise autonomy, scalability, and resilience, but they also add complexity. Patterns help teams build solid blocks, while anti-patterns warn about common traps. This guide covers practical patterns, with clear notes on when to use them and what to watch for in real projects.

Common Design Patterns

• Decomposition by business capability (bounded context): align services to real domains; avoid too many tiny services or ill‑defined boundaries.
• API Gateway: a single entry point for routing, auth, rate limits, and cross‑cutting concerns.
• Database per service with data ownership: each service owns its data; use events or APIs to synchronize interesting changes.
• Saga for distributed transactions: choose choreography or orchestration to keep data consistent without distributed locks.
• Event‑driven architecture: services publish and react to events, increasing decoupling and resilience.
• Asynchronous messaging: queues and streams absorb bursts and failures, with clear delivery guarantees.
• Service discovery and load balancing: services find each other, scale, and recover gracefully.
• Resilience patterns: circuit breakers, bulkheads, timeouts to limit cascading failures.
• Observability by design: structured logs, metrics, tracing to debug and optimize.

Anti‑Patterns to Avoid

• Shared database across services: creates tight coupling and data drift.
• Chatty inter‑service calls: many small requests add latency and failure risk.
• God services: large, multifunction components slow to evolve and hard to test.
• Tight coupling via contracts without versioning: breaking changes disrupt consumers.
• Hidden data stores and unclear ownership: teams argue over who controls what.
• Circular calls and leaked dependencies: tight loops escalate latency and faults.
• Ignoring observability: incidents become mysterious and slow to fix.
• Unmanaged eventual consistency: soft guarantees without a plan cause data surprises.

Practical tips for teams

• Start with domain‑driven decomposition; define clear bounded contexts.
• Create stable API contracts and plan versioning; use consumer‑driven contracts when possible.
• Favor event sources to align state changes across services.
• Implement basics of resilience early: timeouts, retries, circuit breakers.
• Build observability from day one: trace IDs, correlated logs, dashboards.
• Test at multiple levels: unit, contract, and end‑to‑end tests that cover failure scenarios.

Key Takeaways

• Choose a few core patterns aligned with your domain; scale patterns as teams grow.
• Avoid shared databases and noisy inter‑service calls to keep services independent.
• Prioritize observability and contracts to detect issues quickly and safely evolve your system.