Difference between Coalesce and Repartition and In which case we are using it ?

Question

Accepted Answer

Coalesce is used to combine multiple small partitions into a larger one, while Repartition is used to increase or decrease the number of partitions in a DataFrame.

Coalesce reduces the number of partitions in a DataFrame by combining small partitions into larger ones.
Repartition increases or decreases the number of partitions in a DataFrame by shuffling the data across partitions.
Coalesce is more efficient than Repartition as it minimizes data movement.
Coalesce is typically used when there is a need to reduce the number of partitions without shuffling data, while Repartition is used when there is a need to explicitly control the number of partitions.

Accepted Answer

•	Coalesce: Reduces partitions efficiently without a full shuffle; ideal for optimization before writing data.
	•	Repartition: Changes the number of partitions with a full shuffle; useful for load balancing and ensuring even data distribution.

Difference between Coalesce and Repartition and In which case we are using it ?

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