What are the concepts of coalesce and repartition in data processing?

Question

Accepted Answer

Coalesce and repartition are concepts used in data processing to control the number of partitions in a dataset.

Coalesce is used to reduce the number of partitions in a dataset without shuffling the data, which can improve performance.
Repartition is used to increase or decrease the number of partitions in a dataset by shuffling the data across the cluster.
Coalesce is preferred over repartition when reducing partitions to avoid unnecessary shuffling of data.
Repartition is useful when the number of partitions needs to be explicitly set for better parallelism or performance.
Example: coalesce(5) reduces the number of partitions to 5 without shuffling the data.
Example: repartition(10) increases the number of partitions to 10 by shuffling the data across the cluster.

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