Hi all, I'd like to start a discussion about implementing an official PySpark test framework. Currently, there's no official test framework, but only various open-source repos and blog posts.
Many of these open-source resources are very popular, which demonstrates user-demand for PySpark testing capabilities. spark-testing-base <https://github.com/holdenk/spark-testing-base> has 1.4k stars, and chispa <https://github.com/MrPowers/chispa> has 532k downloads/month. However, it can be confusing for users to piece together disparate resources to write their own PySpark tests (see The Elephant in the Room: How to Write PySpark Tests <https://towardsdatascience.com/the-elephant-in-the-room-how-to-write-pyspark-unit-tests-a5073acabc34> ). We can streamline and simplify the testing process by incorporating test features, such as a PySpark Test Base class (which allows tests to share Spark sessions) and test util functions (for example, asserting dataframe and schema equality). Please see the SPIP document attached: https://docs.google.com/document/d/1OkyBn3JbEHkkQgSQ45Lq82esXjr9rm2Vj7Ih_4zycRc/edit#heading=h.f5f0u2riv07vAnd the JIRA ticket: https://issues.apache.org/jira/browse/SPARK-44042 I would appreciate it if you could share your thoughts on this proposal. Thank you! Amanda Liu
