When should you explicitly specify the join type in PySpark?

Specifying the join type explicitly in PySpark, even when it is the default, helps enhance code clarity. This practice is beneficial because it makes the code more readable and understandable for others (or for the same developer revisiting the code later). When the join type is clearly stated, it reduces ambiguity, especially in complex transforms where multiple join types might be involved.

This approach aids in maintaining the code and debugging because it provides immediate context regarding the logic behind how datasets are combined. This is particularly important in collaborative environments where multiple stakeholders need to interpret the code quickly. By spelling out the join type, one can prevent misinterpretation of intentions behind the transformations being applied.

Explicitly stating the join type is a best practice in data engineering and programming in general, aligning with principles of writing clean code, which emphasizes clarity and maintainability. It encourages a habit of mindfulness around the operations being performed, contributing to better overall software quality.