Contract Testing
Pact is a consumer-driven contract testing library that lets you define what a consumer (e.g., a frontend app or microservice) expects from a provider (e.g., an API), generate a contract file from those expectations, and verify the provider actually fulfills them — without spinning up both services at
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
Testing
Consumer-driven contract testing solves one of the trickiest problems in distributed systems: how do you know that changes to a provider API won't silently break every consumer that depends on it? Schema validation tells you the shape of a response. Contract testing tells you whether the response
Testing
Generating a Pact contract locally is a useful proof of concept. But contracts only deliver their value when they're shared automatically between teams, versioned alongside code, and verified on every pull request. That's what the Pact Broker is for. This post covers running a Pact Broker,
Testing
The Pact Broker is the hub of your contract testing workflow. Consumers publish pacts to it. Providers fetch pacts from it, run verification, and publish results back. The broker tracks which consumer/provider version pairs are compatible and answers the critical question: "can this version be safely deployed?"
Contract Testing
Pact, Dredd, and Schemathesis are three different approaches to API contract testing, each solving a different part of the problem. Pact validates what individual consumers expect. Dredd validates that an implementation matches its OpenAPI/API Blueprint spec. Schemathesis uses property-based testing to automatically generate edge-case requests from an
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
Integration testing microservices is notoriously painful. You need real instances of every dependent service, a shared environment where everyone's changes play nicely together, and the patience to debug failures caused by a schema change three teams made two weeks ago. Contract testing — specifically consumer-driven contract testing — cuts
GraphQL
GraphQL schemas evolve, and every change carries the risk of breaking clients. Contract testing catches breaking changes before deployment by comparing schema versions and validating that client queries still work against the new schema. This post covers what counts as a breaking change, how to use GraphQL Inspector for schema
Testing
Micro-frontends communicate through defined interfaces: federated module exports, custom events, shared state, and props passed from shell to remote. When one team changes their interface without notifying consumers, the entire application breaks — and in production. Contract testing formalizes these interfaces so both sides can verify compatibility independently, without deploying
Testing
Every third-party API integration needs tests — but mocking, contract testing, and real integration testing each have different tradeoffs. Mocks are fast but drift from reality. Real integration tests are accurate but slow and expensive. Contract tests are the middle ground that most teams miss. This guide covers when to