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Answer: Use the repartition method to control the number of partitions.
The most effective strategy to reduce data shuffling and optimize Spark job performance is to use the repartition method to control the number of partitions. This approach allows for explicit definition of the number of partitions, ensuring data is evenly distributed across worker nodes and minimizing the need for shuffling during transformations. While increasing the number of partitions can distribute data initially, it may lead to more shuffling if partitions become too small. Decreasing worker nodes limits resources and is not a targeted solution for shuffling. Automatic optimization features may not always be optimal for every scenario, making manual control through repartitioning the preferred method for fine-tuning performance.
Author: LeetQuiz Editorial Team
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To optimize the performance of Spark jobs in a Databricks environment by reducing data shuffling during transformations, what strategy should a data engineering team consider?
A
Enable automatic optimization in the Databricks cluster settings.
B
Use the repartition method to control the number of partitions.
C
Increase the number of partitions in the DataFrame.
D
Decrease the number of worker nodes in the Spark cluster.
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