Difference Between Repartition & Coalesce

The coalesce and repartition methods in Apache Spark are used to control the number of partitions in an RDD, DataFrame, or Dataset. However, there are some differences between the two:

1. Functionality:

• Repartition: This method is used to increase or decrease the number of partitions in a distributed dataset. It involves a full shuffle of the data across the cluster and creates equal-sized partitions of data.
• Coalesce: This method is used to decrease the number of partitions in a distributed dataset. It combines existing partitions to avoid a full shuffle. Unlike repartition, coalesce does not guarantee equal-sized partitions.

2. Performance:

• Repartition: Since repartition involves a full shuffle of the data, it can be an expensive operation in terms of network usage and performance impact.
• Coalesce: coalesce is generally more efficient than repartition when reducing the number of partitions because it avoids shuffling the data across all partitions. However, if you want to increase the number of partitions using coalesce, you need to set the shuffle argument to true, which will result in a shuffle operation.

3. Data Distribution:

• repartition: When using repartition, the data distribution in the partitions is roughly the same size. It guarantees equal-sized partitions.
• Coalesce: Coalesce does not guarantee equal-sized partitions. It combines existing partitions, which may result in uneven data distribution.

Shuffling

In summary, Repartition is used to increase or decrease the number of partitions and involves a full shuffle of the data, while coalesce is primarily used to decrease the number of partitions and avoids shuffling the data across all partitions. However, if you want to increase the number of partitions using coalesce, you need to set the shuffle argument to true, which will result in a shuffle operation.