Learn about the Spark property that enables automatic detection and subdivision of skewed partitions when joining DataFrames, ensuring optimal performance and efficient data processing.
Table of Contents
Question
Which of the following Spark properties is used to configure whether skewed partitions are automatically detected and subdivided into smaller partitions when joining two DataFrames together?
A. spark.sql.adaptive.skewedJoin.enabled
B. spark.sql.adaptive.coalescePartitions.enable
C. spark.sql.adaptive.skewHints.enabled
D. spark.sql.shuffle.partitions
E. spark.sql.shuffle.skewHints.enabled
Answer
A. spark.sql.adaptive.skewedJoin.enabled
Explanation
The spark.sql.adaptive.skewedJoin.enabled property is used to configure whether skewed partitions are automatically detected and subdivided into smaller partitions when joining two DataFrames. When set to true, Spark analyzes the data distribution during the join operation and identifies partitions with significantly more data than others (skewed partitions). It then subdivides these skewed partitions into smaller, more evenly distributed partitions, improving the overall performance and load balancing of the join operation. This adaptive optimization helps mitigate the impact of data skew and ensures efficient utilization of cluster resources.
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