temporal-python-testing

Testing Temporal workflows in Python involves unit testing individual activities and integration testing the workflow orchestration logic using pytest fixtures and mock environments to ensure reliability.

Time-skipping with WorkflowEnvironment allows you to simulate the passage of time in unit tests, enabling rapid verification of long-running Temporal workflows without actual delays. This isolates and tests individual workflows efficiently.

Replay testing is the best way to ensure backward-compatibility by validating determinism against production histories. This guarantees that new code changes safely process existing execution data before deployment without breaking running workflows.

Yes, you can use pytest with a local Temporal Docker Compose setup for integration testing. This configuration simulates real-world scenarios by validating interactions between workflows and mocked activities in an isolated environment.

You need ActivityEnvironment for mocking to isolate and test individual activities deterministically within your Temporal Python testing workflow. It allows you to validate activity logic independently of the broader workflow orchestration.

Yes, validating determinism against production histories before deployment is essential for safe deployment of changes. Replay testing catches non-deterministic code changes that could break existing, in-progress Temporal workflow executions.