What is time-skipping in Temporal Python testing?

Time-skipping allows the test environment to automatically skip periods of inactivity, enabling you to test workflows that sleep for days or weeks in a matter of milliseconds.

Does this skill support async testing with pytest?

Yes, it specifically focuses on using pytest-asyncio and provides patterns for managing the asynchronous lifecycle of Temporal clients and workers.

Why is replay testing necessary for Temporal?

Temporal workflows must be deterministic. Replay testing runs your updated code against existing workflow histories to ensure that your changes won't break currently running executions.

How do I mock activities in a Temporal workflow test?

You can use the integration testing resources provided by this skill to replace real activity implementations with mocks during worker setup, allowing you to test workflow logic in isolation.

Temporal Python Testing

Name: Temporal Python Testing
Author: HermeticOrmus

byHermeticOrmus

Seguridad y Pruebas

Implements robust testing strategies for Temporal workflows using pytest, time-skipping, and activity mocking.

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This skill provides a comprehensive framework for testing Temporal workflows within the Python ecosystem. It enables developers to implement fast unit tests using time-skipping, isolate workflow logic through sophisticated activity mocking, and ensure production-readiness with replay testing for determinism validation. Whether you are setting up a local development environment or integrating automated tests into a CI/CD pipeline, this skill offers the patterns and fixtures needed to achieve high test coverage for complex, long-running distributed processes.

Características Principales

Virtual time-skipping to test month-long workflows in seconds
Async pytest fixtures for automated Temporal environment setup
0 GitHub stars
Activity mocking and error injection for isolated integration testing
Replay testing to validate workflow determinism against production history
Pre-configured local development setups including Docker Compose

Casos de Uso

Validating workflow versioning and compatibility before deploying code changes
Simulating external service failures to verify workflow retry and compensation logic
Testing complex business logic in long-running workflows without real-time waiting

Acerca de

Características Principales

Virtual time-skipping to test month-long workflows in seconds
Async pytest fixtures for automated Temporal environment setup
0 GitHub stars
Activity mocking and error injection for isolated integration testing
Replay testing to validate workflow determinism against production history
Pre-configured local development setups including Docker Compose

Casos de Uso

Validating workflow versioning and compatibility before deploying code changes
Simulating external service failures to verify workflow retry and compensation logic
Testing complex business logic in long-running workflows without real-time waiting