Redis
Testing code that depends on Redis presents a fundamental choice: simulate Redis in-process with a mock library, or spin up a real Redis instance via Testcontainers. Both approaches have distinct trade-offs that affect test speed, reliability, and the bugs you catch. This guide walks through both options in
Testing
Integration testing for microservices is where good intentions go to die. Teams start with aspirations of comprehensive test coverage across service boundaries, then hit the reality: standing up five services, three databases, a message queue, and a cache just to run a single test is slow, fragile, and expensive to
TestContainers
Testcontainers eliminates the gap between mocked database behavior and real database behavior by spinning up actual Docker containers during test runs. This post covers the full setup for Postgres, Redis, and MongoDB in both Node.js and Python, including parallel container execution, GitHub Actions integration, and concrete techniques to keep
Testing
MinIO is an S3-compatible object storage server you can run locally or in production. Testing it properly means running a real MinIO instance — TestContainers makes this easy in both Java and Python by spinning up a MinIO Docker container per test suite and tearing it down automatically. Key Takeaways
Database Testing
PostgreSQL is the world's most popular open-source relational database — and testing PostgreSQL-backed applications is one of the most important skills a backend engineer can have. This guide covers the full spectrum: fast in-memory tests with pg-mem, realistic integration tests with Testcontainers, and patterns that
gRPC
Unit tests are fast but incomplete — they mock dependencies that can behave differently in production. Integration tests with Testcontainers close this gap by running real databases, caches, and dependent services in Docker containers that your test starts and manages. For gRPC services, this means testing against real PostgreSQL instead of