Understanding the Differences between repartition() and coalesce()

Introduction

• Apache Spark is an open-source big data processing framework that enables fast and efficient data processing. It offers various functions that help in organizing and reshuffling the data. In this article, we will delve into two of these functions – repartition and coalesce – and understand the difference between the two.

Repartition vs. Coalesce:

Repartition and Coalesce are two functions in Apache Spark that help in reshuffling data, but they differ in their approach and the results they produce.

Repartition

The repartition function is used to shuffle the data and redistribute it among a specified number of partitions. It involves shuffling all the data, which can be a time-consuming process, especially when dealing with large data sets. Repartition is ideal when you need to balance the data across different partitions.

Coalesce

Coalesce, on the other hand, is used to merge the existing partitions into fewer partitions. It is an optimization technique used to reduce the number of partitions while retaining the data within each partition. Coalesce is ideal when you need to reduce the number of partitions to reduce the overhead associated with shuffling large data sets.

Similarities

Both Repartition and Coalesce functions help to reshuffle the data, and both can be used to change the number of partitions.

Examples

Let’s consider a sample data set with 100 partitions and see how the repartition and coalesce functions can be used.

Repartition

val data = sc.parallelize(1 to 100, 100)
val repartitionedData = data.repartition(50)

In the above example, the data is repartitioned into 50 partitions, shuffling the data and redistributing it among the partitions.

Coalesce

val coalescedData = repartitionedData.coalesce(25)

In the above example, the repartitioned data is coalesced into 25 partitions, merging the existing partitions into fewer partitions.