Popular Microservices Articles

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 .

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.

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.

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

An API gateway is a service that acts as a single entry point for client requests to backend APIs. It receives HTTP requests, applies policies such as authentication and rate limiting, selects the appropriate backend service, and forwards requests to their destinations.

Service Discovery is the mechanism that allows applications to find the network locations of other services dynamically. Instead of hardcoding IP addresses and ports, a service asks a discovery system where another service is currently available.

The Saga pattern is a way to manage a business transaction that spans multiple services without using one distributed database transaction. Instead of locking every participating resource until the entire operation completes, a saga breaks the workflow into a sequence of local transactions.

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.

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.

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

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.

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.

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?