temporal-python-testing

Test Temporal workflows with pytest by leveraging time-skipping to advance workflow clocks automatically, enabling fast and reliable unit tests without waiting for real timers to elapse. This approach uses mocks for activities to isolate workflow logic.

Mocking activities in Temporal workflow integration tests involves replacing actual activity implementations with mocked functions using pytest. This isolates workflow logic from external dependencies, ensuring tests run quickly and reliably without hitting real services.

Yes, a running Temporal server is required for integration and end-to-end replay testing. Replay testing validates workflow execution history against the current workflow code, ensuring deterministic behavior and detecting non-deterministic changes.

Yes, you can use this approach for local development setups. It supports fast feedback loops through time-skipping unit tests and integration tests with activity mocks, streamlining local development and debugging of Temporal workflows.

Temporal workflow tests are slow and brittle without time-skipping because they rely on real-time timers and actual activity executions. Time-skipping and pytest activity mocks solve this by advancing clocks instantly and isolating workflow logic.

Yes, Temporal Python SDK testing with pytest requires asyncio support because Temporal workflows run asynchronously. Pytest with asyncio support ensures proper execution and handling of async workflow tests using time-skipping and activity mocks.