Latest Microservices Articles

Running microservices in production requires more than splitting an application into independently deployed services. The architecture introduces network failures, partial availability, distributed data, asynchronous workflows, deployment coordination, and significantly more operational state.

Microservices architecture structures an application as a set of independently deployable services, each responsible for a focused business capability. Instead of deploying users, orders, payments, inventory, and notifications as one application, these capabilities can evolve, scale, and fail indepe

Choosing service boundaries is one of the hardest decisions in a microservices architecture. Services that are too large lose deployment and ownership independence, while services that are too small create excessive network communication, distributed transactions, and operational overhead.

Microservices must communicate across process and network boundaries. Choosing between REST, gRPC, and asynchronous messaging affects latency, coupling, failure handling, observability, scalability, and how services evolve independently.

Microservice architectures often contain dozens of internal APIs, but exposing those services directly to browsers, mobile applications, and external clients creates coupling between clients and the internal service topology. API Gateways and Backend-for-Frontend (BFF) services solve different parts

Distributed systems make latency and failures harder to explain because a single user request can cross several services, queues, databases, caches, and external providers. Each component may look healthy in isolation while the complete request is slow, repeatedly retried, or partially failing.

Splitting an application into services creates an important architectural problem: each service owns its data, but other services frequently need that data to complete their work .

APIs are the contracts that allow microservices to evolve independently. A well-designed API exposes stable business capabilities while hiding internal schemas, implementation details, and deployment decisions.

Distributed systems often store the same data on multiple nodes to improve availability, fault tolerance, read scalability, and geographic performance . Replication introduces a difficult question: when one copy changes, how quickly must every other copy reflect that update?

Data management becomes one of the hardest parts of microservices architecture. Inside a monolith, multiple modules can share a database, execute joins, and update related records inside a single transaction. Once business capabilities become independent services, those assumptions no longer hold.