A test framework that allows you to write unit and functional tests for Data Factory pipelines against the git integrated json resource files.
Supporting currently:
Planned:
This unit test framework is not officially supported. It is currently in an experimental state and has not been tested with every single data factory resource. It should support all activities out-of-the-box but has not been thoroughly tested, please report any issues in the issues section and include an example of the pipeline that is not working as expected.
If there's a lot of interest in this framework, then we will continue to improve it and move it to a production-ready state.
Goal: Validate that the evaluated pipeline configuration with its expressions is behaving as expected on runtime.
- Evaluate expressions with their functions and arguments instantly by using the framework's internal expression parser.
- Test a pipeline or activity against any state to assert the expected outcome. A state can be configured with pipeline parameters, global parameters, variables and activity outputs.
- Simulate a pipeline run and evaluate the execution flow and outcome of each activity.
- Dynamically supports all activity types with all their attributes.
Pipelines and activities are not executed on any Data Factory environment, but the evaluation of the pipeline configuration is validated locally. This is different from the "validation" functionality present in the UI, which only validates the syntax of the pipeline configuration.
Data Factory does not support unit testing out of the box. The only way to validate your changes is through manual testing or running e2e tests against a deployed data factory. These tests are great to have, but miss the following benefits that unit tests, like using this unit test framework, provide:
- Shift left with immediate feedback on changes - Evaluate any individual data factory resource (pipelines, activities, triggers, datasets, linked services etc..), including (complex) expressions
- Allows testing individual resources (e.g. activity) for many different input values to cover more scenarios.
- Less issues in production - due to the fast nature of writing and running unit tests, you will write more tests in less time and therefore have a higher test coverage. This means more confidence in new changes, fewer risks in breaking existing features (regression tests), and thus far fewer issues in production.
Even though Data Factory is UI-driven writing unit tests, and might not be in the nature of it. How can you be confident that your changes will work as expected, and that existing pipelines will not break, without writing unit tests?
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Set up an empty Python project with your favorite testing library
More information: docs_Setup
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Install the package using your preferred package manager:
Pip:
pip install data-factory-testing-framework -
Create a Folder in your project and copy the JSON Files with the pipeline definitions locally.
More information: docs Json
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Start writing tests
The samples seen below are the only code that you need to write! The framework will take care of the rest.
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Evaluate activities (e.g. a WebActivity that calls Azure Batch API)
# Arrange activity: Activity = pipeline.get_activity_by_name("Trigger Azure Batch Job") state = PipelineRunState( parameters=[ RunParameter(RunParameterType.Global, "BaseUrl", "https://example.com"), RunParameter(RunParameterType.Pipeline, "JobId", "123"), ], variables=[ PipelineRunVariable("JobName", "Job-123"), ]) state.add_activity_result("Get version", DependencyCondition.SUCCEEDED, {"Version": "version1"}) # Act activity.evaluate(state) # Assert assert "https://example.com/jobs" == activity.type_properties["url"].value assert "POST" == activity.type_properties["method"].value body = activity.type_properties["body"].get_json_value() assert "123" == body["JobId"] assert "Job-123" == body["JobName"] assert "version1" == body["Version"]
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Evaluate Pipelines and test the flow of activities given a specific input
# Arrange pipeline: PipelineResource = test_framework.repository.get_pipeline_by_name("batch_job") # Runs the pipeline with the provided parameters activities = test_framework.evaluate_pipeline(pipeline, [ RunParameter(RunParameterType.Pipeline, "JobId", "123"), RunParameter(RunParameterType.Pipeline, "ContainerName", "test-container"), RunParameter(RunParameterType.Global, "BaseUrl", "https://example.com"), ]) set_variable_activity: Activity = next(activities) assert set_variable_activity is not None assert "Set JobName" == set_variable_activity.name assert "JobName" == activity.type_properties["variableName"] assert "Job-123" == activity.type_properties["value"].value get_version_activity = next(activities) assert get_version_activity is not None assert "Get version" == get_version_activity.name assert "https://example.com/version" == get_version_activity.type_properties["url"].value assert "GET" == get_version_activity.type_properties["method"] get_version_activity.set_result(DependencyCondition.Succeeded,{"Version": "version1"}) create_batch_activity = next(activities) assert create_batch_activity is not None assert "Trigger Azure Batch Job" == create_batch_activity.name assert "https://example.com/jobs" == create_batch_activity.type_properties["url"].value assert "POST" == create_batch_activity.type_properties["method"] body = create_batch_activity.type_properties["body"].get_json_value() assert "123" == body["JobId"] assert "Job-123" == body["JobName"] assert "version1" == body["Version"] with pytest.raises(StopIteration): next(activities)
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See the Examples folder for more samples
As the framework is interpreting expressions containing functions, these functions are implemented in the framework, but there may be bugs in some of them. You can override their implementation through:
FunctionsRepository.register("concat", lambda arguments: "".join(arguments))
FunctionsRepository.register("trim", lambda text, trim_argument: text.strip(trim_argument[0]))- After parsing a data factory resource file, you can use the debugger to easily discover which classes are actually initialized so that you can cast them to the correct type.
- Use ADF Git integration
- Use UI to create a feature branch, build the initial pipeline, and save it to the feature branch
- Pull feature branch locally
- Start writing tests unit and functional tests, run them locally for immediate feedback, and fix bugs
- Push changes to the feature branch
- Test the new features manually through the UI in a sandbox environment
- Create PR, which will run the tests in the CI pipeline
- Approve PR
- Merge to main and start deploying to dev/test/prod environments
- Run e2e tests after each deployment to validate all happy flows work on that specific environment
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