Building Microservices: Designing Fine-Grained Systems

Building Microservices: Designing Fine-Grained Systems

Author: Sam Newman

Quick Overview

A comprehensive guide to designing, building, and managing microservices architectures. Sam Newman provides practical advice on breaking down monolithic systems, managing distributed systems complexity, and building scalable, resilient services that can evolve independently.

Key Ideas

When to Use Microservices

• Organizational readiness matters: Teams need DevOps maturity, monitoring capabilities, and automation before adopting microservices
• Start with a monolith: For new projects, begin monolithic and split later when domain boundaries become clear
• Team size indicator: If you can’t support independent service ownership with autonomous teams, you’re not ready

Decomposition Strategies

• Domain-Driven Design (DDD): Use bounded contexts to identify service boundaries
• Data first, not UI: Decompose around data ownership and business capabilities, not interface layers
• Avoid distributed monoliths: Ensure services can be deployed independently without cascading changes
• The “two-pizza team” rule: Services should be small enough for a team of 6-8 people to own

Integration Patterns

• Orchestration vs Choreography: Choose between centralized control and event-driven collaboration
• API Gateway pattern: Centralize cross-cutting concerns like authentication, rate limiting, and routing
• Avoid chatty services: Minimize inter-service calls; consider data duplication over excessive coupling
• Versioning strategy: Use tolerant readers and semantic versioning for backward compatibility

Resilience and Reliability

• Embrace failure: Design for failure using circuit breakers, timeouts, and bulkheads
• Observability is non-negotiable: Distributed tracing, centralized logging, and metrics are essential
• Test in production: Use canary deployments, feature flags, and progressive rollouts
• Retry with exponential backoff: Protect services from cascading failures with intelligent retry logic

Data Management

• Database per service: Each microservice should own its data store
• Eventual consistency: Accept that distributed systems can’t maintain strict consistency
• Saga pattern: Manage distributed transactions through choreographed workflows
• CQRS when appropriate: Separate read and write models for complex domains

Organizational Impact

• Conway’s Law is real: Your architecture will mirror your communication structures
• Autonomous teams: Give teams end-to-end ownership of services
• Shared governance: Establish standards for cross-cutting concerns while allowing team autonomy
• Evolutionary architecture: Build systems that can change incrementally over time

Practical Takeaways for Staff Engineers

1. Start with the domain, not the technology: Map business capabilities before choosing technical implementations
2. Invest in platform capabilities: Self-service deployment, monitoring, and testing infrastructure are prerequisites
3. Define clear contracts: Use consumer-driven contracts and API specifications to maintain service boundaries
4. Monitor everything: You can’t troubleshoot what you can’t see; invest in observability from day one
5. Incremental migration: When breaking up a monolith, use the strangler fig pattern to gradually migrate functionality
6. Cost of coordination: Microservices trade development complexity for operational complexity—ensure your team is ready

Who Should Read This

• Staff engineers designing distributed systems
• Technical leaders evaluating microservices adoption
• Architects planning system modernization
• Engineering managers scaling engineering organizations

Bottom Line

Microservices aren’t a silver bullet—they’re a trade-off. This book provides the mental models and practical patterns needed to make informed decisions about when, how, and why to adopt microservices architecture. Essential reading for anyone designing systems at scale.