At Qxf2, we’ve successfully integrated Great Expectations into majority of our projects. We now have GitHub workflows in place to run Great Expectations checkpoints before deploying our applications to production. However, as our test suite expanded, we encountered a few challenges:
1. Triggering valid checkpoints.
2. Aggregating checkpoint results.
To address these issues, we turned to pytest. In this post, I’ll demonstrate how to modify your checkpoint scripts to make them compatible with pytest for seamless integration.
Modifying Checkpoint Scripts for pytest Compatibility:
This is how your Python checkpoint script will mostly look after you generate it using this command great_expectations checkpoint script github_stats_checkpoint
# Contents of github_stats_checkpoint.py module from great_expectations.data_context import DataContext data_context: DataContext = DataContext(context_root_dir="great_expectations") result: Checkpointresult = data_context.run_checkpoint(checkpoint_name="github_stats_checkpoint", batch_request=None, run_name=None,) if not result["success"]: print ("Validation failed!") sys.exit (1) print ("Validation succeeded!") sys.exit(0) |
To ensure compatibility with pytest, you’ll need to implement the following changes in your checkpoint scripts:
1. Rename the checkpoint script
2. Create a test function
3. Create a test marker
1. Rename the checkpoint script:
To enable pytest to identify and execute the test, modify the checkpoint script’s name. It should either start with test_name.py or end with name_test.py, i.e if the name of the checkpoint script is github_stats_checkpoint.py rename it to test_github_stats_checkpoint.py.
2. Create a test function:
Revise the checkpoint script to include:
a. A test function, the function’s name should be test_function_name.
b. An assert statement in the test function to validate the test status.
# Contents of the new test_github_stats_checkpoint.py module from great_expectations.data_context import DataContext def test_validate_stats(): # a. Test function adhering to the naming convention added data_context: DataContext = DataContext(context_root_dir="great_expectations") result: Checkpointresult = data_context.run_checkpoint(checkpoint_name="github_stats_checkpoint", batch_request=None, run_name=None,) assert result["success"], "Validation failed" # b. Assert statement to validate the status |
Note: For further information on Python test discovery conventions you can refer pytest conventions
3. Create a test marker:
Enhance your test function by adding a marker. This allows you to select and run tests belonging to the same category. @pytest.mark.marker_name adds a marker for the test function.
from great_expectations.data_context import DataContext import pytest @pytest.mark.github_stats def test_validate_stats(): data_context: DataContext = DataContext(context_root_dir="great_expectations") result: Checkpointresult = data_context.run_checkpoint(checkpoint_name="github_stats_checkpoint", batch_request=None, run_name=None,) assert result["success"], "Validation failed" |
How to run tests:
Now that you’ve adjusted the checkpoint scripts to align with pytest, running it becomes a straightforward process.
To validate that the new test is now discoverable run the following command:
$ pytest --collect-only tests/data_validation/great_expectations/utils ===================================================== test session starts ===================================================== platform xxxxxx -- Python 3.9.17, pytest-7.2.1, pluggy-1.0.0 rootdir: /project_root_dir_location, configfile: pytest.ini plugins: anyio-3.6.2 collected 1 items <Package utils> <Module test_github_data_checkpoint.py> <Function test_validate_stats> |
Once you have confirmed that the test is discoverable, use the following command to trigger the appropriate checkpoints and streamline the aggregation of checkpoint results:
$ pytest -v -m github_stats tests/data_validation/great_expectations/utils =========================== test session starts ============================ platform xxxxxx -- Python 3.9.17, pytest-7.2.1, pluggy-1.0.0 rootdir: /project_root_dir_location, configfile: pytest.ini plugins: anyio-3.6.2 collected 1 item test_github_stats_checkpoint.py::test_validate_stats PASSED [100%] ===================== 1 passed in 0.12s ====================== |
There you have it—a suitable test runner for executing your checkpoints with just a few simple modifications.
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My expertise lies in engineering high-quality software. I began my career as a manual tester at Cognizant Technology Solutions, where I worked on a healthcare project. However, due to personal reasons, I eventually left CTS and tried my hand at freelancing as a trainer. During this time, I mentored aspiring engineers on employability skills. As a hobby, I enjoyed exploring various applications and always sought out testing jobs that offered a good balance of exploratory, scripted, and automated testing.
In 2015, I joined Qxf2 and was introduced to Python, my first programming language. Over the years, I have also had the opportunity to learn other languages like JavaScript, Shell scripting (if it can be called a language at all), and more recently, Rust. Despite this exposure, Python remains my favorite language due to its simplicity and the extensive support it offers for libraries.
In my free time, I like to watch football (I support Arsenal Football Club), play football myself, and read books.
