Which practices are recommended for refactoring complex logical operations in PySpark transformations?

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Refactoring complex logical operations in PySpark transformations is essential for improving code readability, maintainability, and debugging. Extracting complex logic into separate functions is a recommended practice because it promotes modularity, allowing each function to handle a specific task or logical operation. This makes the code easier to understand at a glance, as each function can be named meaningfully to reflect its purpose. By breaking down complex transformations into smaller, manageable units, developers can also simplify the testing process and reuse functions across different parts of the codebase.

Moreover, separating logic into functions can help reduce the cognitive load on someone reading the code, making it easier to follow the flow of data transformations. Efficient refactoring contributes to cleaner code, which is crucial for collaborative projects in data engineering, where multiple team members may work on the same codebase over time.

Other practices like chaining multiple filter calls or using deeply nested parentheses would complicate the code structure, making it harder to understand. Similarly, limiting logic expressions to a certain number is subjective and may not adequately address the complexity of the transformations needed, whereas encapsulating them in dedicated functions provides a structured approach to managing complexity effectively.

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