Refine and merge for PR568

[HYLcool]

As the title says.

Use persist(StorageLevel.MEMORY_AND_DISK) instead of default MEMORY_ONLY and none in the PR to balance between memory and computing speed.

[gemini-code-assist]

Summary of Changes

Hello @HYLcool, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request refines the distributed deduplication utility by implementing a more robust RDD persistence strategy and enhancing the efficiency of the change detection mechanism. The core objective is to optimize resource utilization and processing speed by explicitly managing how RDDs are stored and by streamlining the loop termination condition.

Highlights

• Persistence Strategy Optimization: The RDD persistence strategy in the find_components function has been updated from the default MEMORY_ONLY (implied by .cache()) to StorageLevel.MEMORY_AND_DISK. This change aims to balance memory usage and computation speed for improved performance in distributed deduplication.
• Efficient Change Detection: The logic for detecting changes within the while loop has been optimized. Instead of collecting all changes and checking the length of the resulting list (.collect() and len(changes)), the code now directly counts the changes (.count()) for a more efficient comparison, especially with large datasets.

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[gemini-code-assist]

Code Review

The pull request refines the distributed deduplication logic by optimizing Spark's RDD persistence strategy and improving the termination condition for the connected components algorithm. The change from cache() to persist(StorageLevel.MEMORY_AND_DISK) enhances memory management and robustness, while replacing .collect() with .count() for change detection significantly boosts scalability and prevents potential OutOfMemory errors on the driver node.