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Answer: Small files and over partitioning can lead to increased scanning overhead and reduced performance. Repartitioning and coalescing can help optimize performance.
Small files and over partitioning can lead to increased I/O operations and scanning overhead, reducing query performance. By repartitioning and coalescing data, one can reduce the number of small files and optimize partition sizes, thereby improving query performance. This approach ensures more efficient data processing and reduces overhead.
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Describe a scenario where 'smalls' (tiny files, scanning overhead, over partitioning) significantly impact Spark query performance. Provide a detailed analysis of the performance issues encountered and propose a solution involving repartitioning and coalescing to address these issues. Include a code snippet demonstrating the solution.
A
Small files do not impact query performance.
B
Over partitioning always improves query performance.
C
Small files and over partitioning can lead to increased scanning overhead and reduced performance. Repartitioning and coalescing can help optimize performance.
D
Query performance is solely dependent on the data size, not the file size.
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