When one of your production pipelines is broken, causing your data to be missing or inaccurate, this downtime affects everyone in your company. Your data engineers and data scientists waste time fixing these issues and cause low morale in your team. More importantly, your data analysts or customers lose trust in your insight and products.
By regularly testing your production pipelines, your MLops team can ensure pipelines run as expected and quickly detect and fix any errors. While using LineaPy to convert code into a pipeline reduces errors by removing extraneous code, testing and confirming that the generated code doesn’t introduce any unexpected behaviors is always a good idea.
LineaPy offers a tool that generates unit tests to simplify this testing process. We’re excited to introduce it, and you can learn more about the tool here.
By dedicating time to do testing early and often in pipeline development, you can more easily create a production-ready pipeline that delivers reliable results.
Some best practices include ensuring production pipelines are well-documented, regularly monitoring performance, and setting up alerts that report any unexpected behaviors. These techniques help developers prevent errors from reaching production in the first place while also making it easier for them to identify and fix issues that do make it to deployment.
A key factor for ensuring pipeline robustness is establishing a firm contract between producers and consumers of the pipeline.
A data contract defines the schema and values of any data and how changes to the pipeline are monitored and communicated. A data contract for a pipeline should define the expected input and output for a task, including information like variable names, descriptions, data types, and expected values. It might also include the statistical characteristics of the expected values, such as their minimum and maximum value, range, or mean. With LineaPy, you can test your pipeline to ensure that it remains consistent with its data contract after the code is transformed!
LineaPy’s API offers a turnkey approach for confirming that transformed code works as expected every time.
You can configure LineaPy to generate tests when building a pipeline by setting the
generate_test parameter to True. LineaPy will automatically create unit tests for each task in the pipeline, where each test method checks whether the target function generates the same value as the original code. This tool is described in more detail in LineaPy’s testing documentation.
These tests can be valuable tools, but they have important limitations to remember during development. The generated tests are designed to provide a scaffold for testing rather than a final testing solution, so you may need to adapt the generated tests for your use case.
For example, the equality comparison used in the generated tests is insufficient for certain complex object types. These tests use the
assertEqual() method to check for object equality, but calling this method on two NumPy arrays would return an array of Booleans rather than a single value. In some cases, too, simply checking for equality between values might not be sufficient to ensure unchanged performances. In these cases, you may want to add some tests of your own!
With those limitations in mind, the LineaPy testing library provides a powerful toolset for testing your pipeline. These testing tools can get you one step closer to developing a production-ready data pipeline!