How to Mock a Compute-Collect Action Trigger in Python
Mock a compute-collect action trigger using Python's unittest.mock to simulate Spark-style job execution and assert trigger behavior.
pip install pyspark
Python code
1 lineHere's a Python code sample for the problem title "Action trigger compute collect mock":Output
Triggered compute with 100 records\nTriggered collect with 5 partitions\nCollected results: [0, 1, 2, 3, 4]\nTrigger called 2 times\nCompute mock called with 100\nCollect mock called with 5How it works
This pattern uses unittest.mock.patch to replace heavy compute and collect functions, letting you test the action-trigger logic without real data processing. The mock objects record calls, so you can assert that the trigger fires compute and collect exactly as expected. By patching at the module level with patch('module.compute'), you isolate the trigger function from actual Spark operations, making tests fast and deterministic. The key insight is that a mock's call_count and call_args give you visibility into the flow, while the side_effect lets you return canned data for downstream assertions.
Common mistakes
- Patching the wrong module path (must match where the name is looked up)
- Forgetting to return a value from the mock, causing NoneType errors in downstream code
- Not asserting on call arguments, so interface mismatches go unnoticed
- Using mock without context manager, leaving patches active across tests
Variations
- Use `unittest.mock.patch.object` to patch a method on a class instance
- Use `pytest-mock`'s `mocker` fixture for cleaner setup and teardown
Real-world use cases
- Unit-testing an ETL orchestrator that triggers Spark actions on a schedule without launching a cluster.
- Verifying that a data pipeline's action trigger calls compute and collect in the right order before deploying to production.
- Simulating expensive big-data operations in CI tests to keep builds fast and free of infrastructure dependencies.
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