Testing
HTTP contract testing is well understood. You define the request and response, Pact records them as a contract, and the provider verifies it. But microservices don't only communicate over HTTP — they publish and consume events through Kafka topics, SQS queues, and SNS topics. Those async channels need contracts
Pact
Pact contract testing solves one of the hardest problems in microservices: how do you know that your services will work together without running a full integration test suite? This guide covers Pact from the ground up — what it is, how consumer-driven contracts work, and how to integrate Pact into
Pact
Consumer-driven contract testing with Pact is well-documented at the basics level: consumer writes expectations, provider verifies them. But real multi-team microservices environments hit edge cases that basic tutorials don't cover. This guide focuses on the advanced patterns: provider states, message contracts, Pact Broker integration, and
Approval Testing
Microservice architectures distribute the blast radius of API changes across multiple teams and deployment pipelines. A field renamed in the user service breaks the order service, which breaks the notification service, and nobody finds out until three services are deployed and the monitoring lights up. The standard advice is "
Testing
Service meshes like Istio and Linkerd add a powerful layer of infrastructure between your microservices: traffic management, mTLS encryption, observability, and resilience features. But they also add complexity to testing. A service that works fine in unit tests may behave unexpectedly when running behind a mesh proxy. This guide covers
Testing
Microservices testing is fundamentally different from testing monolithic applications. When your system spans dozens of independently deployable services, a bug in one can cascade through the entire platform. This guide covers the testing strategies that actually work in production microservices environments. Why Microservices Testing Is Hard In a monolith, you
Testing
When a test fails in a distributed system, the error message tells you what happened but not where. A 500 response from your API gateway might be caused by a timeout in service C, waiting on a database query in service B, initiated by a bad request from service A.
Testing
When teams develop microservices independently, the biggest risk is one team changing an API that another team depends on. Contract testing solves this — without requiring a shared test environment or coordinating release schedules. What Is Contract Testing? A contract is a formal specification of the interactions between two services: what
Testing
Distributed systems fail in ways you don't expect. Chaos engineering is the practice of intentionally introducing failures into your system to discover weaknesses before they cause production incidents. For microservices, it's not optional — it's how you gain confidence that your system actually works under
Shift-Left Testing
Microservices solve organizational problems — independent deployments, team autonomy, technology flexibility. But they create a testing problem: how do you verify that services that evolve independently still work together? Integration test suites require running all services simultaneously. End-to-end tests are slow and brittle. Neither catches API contract breaks early
Testing
Testing rate limiting is deceptively hard. The implementation often works correctly in isolation but fails under concurrent load, leaks when clients use different keys that should share a limit, or applies the wrong limit tier to authenticated users. Rate limiting testing requires concurrent request generation, careful timing, and validation of
Testing
API backward compatibility testing is one of the most critical — and most overlooked — disciplines in microservices engineering. A single breaking change pushed to a shared API can cascade into production failures across a dozen downstream services, and the failure often surfaces hours after the deployment in a way that'