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Describe the use case and benefits of using repartitionByRange in PySpark. How does this method differ from repartition and coalesce in terms of data distribution and performance?
repartitionByRange
repartition
coalesce
A
repartitionByRange is used for evenly distributing data across partitions without considering the order of data, similar to repartition.
B
repartitionByRange ensures that data is partitioned based on the range of values in a specified column, which can be beneficial for optimizing queries that filter based on ranges.
C
repartitionByRange is similar to coalesce in that it reduces the number of partitions but does not shuffle data, making it less efficient for range-based queries.
D
repartitionByRange should be avoided as it always leads to uneven data distribution, making it less efficient than repartition.
