temporal-python-testing

To test Temporal workflows with pytest using time-skipping, you apply deterministic testing techniques that mock activities and manipulate time, allowing long-running workflows to execute quickly. This approach validates workflow logic without waiting for real time to pass.

Setting up a local Temporal server for pytest testing involves starting a local server instance and configuring pytest with unit and integration markers. This creates a fast feedback loop for exercising time-skipping and mocked activities during local development.

Mocking activities in Temporal Python workflows for integration testing requires injecting mocked activity implementations into the workflow context. This validates workflow determinism against live server contexts while isolating external dependencies.

Yes, you can use pytest markers to separate Temporal unit and integration tests. By applying markers, you can selectively run unit tests with time-skipping or integration and replay tests against a live server, ensuring fast and targeted feedback loops.

Temporal workflow tests are brittle and slow without time-skipping because they rely on real time progression and live external dependencies. Implementing time-skipping and activity mocking provides deterministic, fast testing by controlling workflow time automatically.

Replay testing in Temporal Python can utilize a live server context to validate determinism with mocked activities. While local development setups often use a local server, replay testing focuses on ensuring workflow history yields consistent results.