How do I mock activities in Temporal Python tests?

This skill provides patterns for using mocked activities within a Worker during integration tests, allowing you to isolate workflow logic from external API calls or database operations.

Why is replay testing important for Temporal workflows?

Replay testing ensures that your workflow code remains deterministic. It validates that updated code can still successfully recreate the state of existing workflows from production history without causing non-deterministic errors.

What is the recommended testing strategy for Temporal?

The recommended approach is to write a majority of tests as integration tests with mocked activities, supplemented by unit tests for isolated logic and replay tests for deployment safety.

What is time-skipping in Temporal testing?

Time-skipping is a feature of the Temporal test environment that allows you to advance the virtual clock, enabling you to test workflows that would normally take days or months in just a few seconds.

Temporal Python Testing

Name: Temporal Python Testing
Author: pur3v4d3r

bypur3v4d3r

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Security & Testing

Streamlines testing for Temporal workflows in Python using pytest, time-skipping, and determinism validation.

About

This skill provides a comprehensive framework for testing Temporal workflows using the Python SDK. It guides developers through implementing fast unit tests using time-skipping, creating robust integration tests with mocked activities, and ensuring production safety through replay testing for determinism validation. It is particularly useful for debugging complex workflow failures, setting up CI/CD pipelines for backend agents, and achieving high code coverage for distributed systems, ensuring that even month-long workflows can be validated in seconds.

Key Features

Local development setup with Docker and coverage tool integration
Pre-configured pytest fixtures for async workflow environments
Time-skipping for accelerated testing of long-running workflows
Comprehensive activity mocking and error injection strategies
1 GitHub stars
Determinism validation through production history replay testing

Use Cases

Validating business logic in long-running workflows without waiting for real time
Setting up automated testing pipelines in CI/CD for Temporal-based backends
Ensuring workflow determinism before deploying code changes to production

About

Key Features

Local development setup with Docker and coverage tool integration
Pre-configured pytest fixtures for async workflow environments
Time-skipping for accelerated testing of long-running workflows
Comprehensive activity mocking and error injection strategies
1 GitHub stars
Determinism validation through production history replay testing

Use Cases

Validating business logic in long-running workflows without waiting for real time
Setting up automated testing pipelines in CI/CD for Temporal-based backends
Ensuring workflow determinism before deploying code changes to production