When implementing distributed processing in Foundry, which step is crucial for independent file processing by each executor?

Creating a DataFrame of FileStatus objects and using flatMap to distribute processing is crucial for enabling independent file processing by each executor in a distributed processing environment like Foundry.

The rationale behind this choice lies in how distributed computing frameworks manage workloads across various executors. When you create a DataFrame of FileStatus objects, you're essentially building a structured representation of the files available for processing. By leveraging flatMap, you can efficiently distribute these file-processing tasks across multiple executors. Each executor can then independently process its assigned file data without waiting for others, ensuring parallel processing capabilities.

This method of using flatMap establishes a clear and efficient pathway for managing how data is split and processed, as flatMap allows for creating a new dataset by mapping each element to zero or more elements in a manner that is well-suited to distributed computing. It permits the flexibility needed for dynamic partitioning of processing tasks, which is essential when handling numerous files in parallel.

The other options don't align as effectively with the goal of supporting independent file processing. For instance, buffering all files into the driver's memory can lead to scalability issues and inefficient memory usage, as it prevents the true parallel execution of tasks. Similarly, while serializing TransformInput and TransformOutput objects (the choice not