Everything You Need to Know About Microservices

Microservices are a popular architecture style in software development, though they remain widely misunderstood. Adopting microservices can lead to faster, independent releases for some teams, while others may end up with a complex, difficult-to-manage distributed system instead. The key to success lies in understanding what microservices trade away.

This article delves into what microservices are, why teams choose them, their costs, design principles, and how to tell if they are the right fit for your situation. It also highlights how TDK CLI aids in running microservice systems on your own machine during development.

A microservice is a small, independently deployable service that owns one specific business capability. It includes its own code, data store (ideally), and network interface for other services to call. This style is characterized by three properties: independent deployment, single responsibility by business capability, and decentralized data ownership.

The term "micro" is misleading; the goal is not to create the smallest possible service, but rather a service that one team can understand, change, and operate without coordination with everyone else.

Monoliths are a single deployable unit with all modules sharing a process, codebase, and usually a database. They are the default choice for many reasons: fast function calls, simple transactions, straightforward debugging, and a single thing to deploy. Microservices, on the other hand, trade these advantages for independence. The trade-off includes independent deployment, single responsibility by business capability, and decentralized data ownership.

Monoliths have advantages like fast function calls, simple transactions, straightforward debugging, and a single deployment artifact. Microservices, however, offer independent scaling, better technology fit, fault isolation, and faster, safer releases. However, microservices come with their own set of costs. Teams that overlook these costs regret the move.

Network unreliability is a significant issue, as every call between services can time out, fail, or succeed on one server while the client sees an error. This necessitates retries, timeouts, idempotency, and circuit breakers. Data consistency is also more challenging, as a single business operation may span several services, requiring eventual consistency, sagas, or careful design.

Observability is mandatory, as without distributed tracing, correlated logs, and per-service metrics, a single failed request can become a search across ten log streams. Operational overhead increases as each service needs its own build, deploy, configuration, secrets, dashboards, alerts, and owner. Local development becomes harder, requiring the ability to run the entire system on a laptop.

Versioning and contracts are crucial, as changing an API now affects every consumer. Compatibility rules, schema validation, and deprecation paths are necessary. Testing also shifts, with unit tests still important, but end-to-end tests across services becoming slow and fragile. Contract tests and well-scoped integration tests are more critical.

Design principles include splitting by business capability, starting from the domain rather than technology, and identifying useful boundaries from the language your business already uses. Domain-driven design speaks to these as bounded contexts, areas where a term has a consistent meaning. A warning sign is if every feature request touches four services, indicating that your boundaries may be incorrect.

Each service should own its data, exposing it only through its API, as sharing a database between services couples their schemas, deployments, and failure modes. APIs should be treated as contracts, versioned explicitly, backward compatible where possible, and validated with payloads and documentation. Prefer asynchronous communication when appropriate, while synchronous calls fit request-response work.