Answer-first summary for fast verification
Answer: Use broadcast variables to distribute `df2` and apply the broadcast hint in the join operation.
Broadcast variables in Spark are designed to efficiently distribute small, read-only data sets across all nodes in a cluster, eliminating the need for data shuffling across the network. By broadcasting `df2` and using the broadcast hint during the join operation, Spark ensures that `df2` is available on all nodes, significantly improving the join's performance. This approach is more efficient than repartitioning `df1`, which can be resource-intensive, or caching both DataFrames, which may not directly optimize the join operation. Manual broadcasting via RDD conversion is less efficient and not recommended when Spark's built-in broadcast mechanisms are available and optimized for such scenarios.
Author: LeetQuiz Editorial Team
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When joining a large DataFrame df1 with a small DataFrame df2 in Spark, what is the most efficient method to optimize the operation?
A
Repartition df1 to match the number of partitions in df2 before joining.
B
Convert df2 to an RDD and manually broadcast it for the join operation with df1.
C
Cache both DataFrames in memory before performing the join to speed up access.
D
Use broadcast variables to distribute df2 and apply the broadcast hint in the join operation.
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