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In complex multi-join queries with varying sizes of intermediate results, what is the most effective method to dynamically adjust shuffle partitions before each join operation to optimize performance?
A
Adjust spark.sql.adaptive.coalescePartitions.enabled before each join operation based on runtime statistics.
spark.sql.adaptive.coalescePartitions.enabled
B
Utilize repartition before each join based on anticipated result sizes, informed by profiling previous runs.
repartition
C
Pre-calculate the size of join inputs and use spark.sql.shuffle.partitions to set partitions dynamically via a UDF.
spark.sql.shuffle.partitions
D
Maintain a static number of shuffle partitions, relying on Spark's cost optimizer to automatically handle partition sizing.
