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Answer: Configure spark.sql.adaptive.skewJoin.enabled to true and adjust spark.sql.adaptive.skewJoin.skewedPartitionFactor based on the skew ratio.
Configuring `spark.sql.adaptive.skewJoin.enabled` to true and adjusting `spark.sql.adaptive.skewJoin.skewedPartitionFactor` based on the skew ratio is the most effective way to handle skewed joins in Spark. This approach allows AQE to automatically detect and optimize for skewed data without the need for manual intervention. Disabling AQE (option D) removes its benefits for other optimizations, while increasing shuffle partitions (option B) is a less targeted solution. Creating a custom Spark extension (option A) is unnecessarily complex when AQE can be fine-tuned for the task.
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How can you fine-tune Adaptive Query Execution (AQE) settings in Spark to specifically address performance degradation caused by skewed data in join operations?
A
Implement a custom Spark extension to replace the AQE logic, focusing on skew detection and resolution.
B
Increase spark.sql.shuffle.partitions significantly to reduce the impact of skew on join performance.
C
Configure spark.sql.adaptive.skewJoin.enabled to true and adjust spark.sql.adaptive.skewJoin.skewedPartitionFactor based on the skew ratio.
D
Disable AQE entirely to manually handle skewed data through custom partitioning strategies.
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