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Answer: Partitioning the DataFrame by the window’s partitionBy column before applying the window function.
Partitioning the DataFrame by the window’s partitionBy column before applying the window function is highly effective because window functions group data by these partition columns. Pre-partitioning reduces shuffle overhead during window execution, leading to performance improvements. Other options like caching the DataFrame or increasing shuffle partitions may offer benefits in specific scenarios but do not directly optimize window function operations as effectively as pre-partitioning.
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When optimizing Spark SQL window functions over a large dataset, which technique significantly enhances performance?
A
Increasing spark.sql.shuffle.partitions to a very high number to ensure data is evenly distributed.
B
Partitioning the DataFrame by the window’s partitionBy column before applying the window function.
C
Leveraging broadcast join before applying the window function to reduce shuffle.
D
Caching the DataFrame before applying the window function.