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# PySpark RDD | coalesce method

schedule Aug 12, 2023
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PySpark RDD's `coalesce(~)` method returns a new RDD with the number of partitions reduced.

# Parameters

1. `numPartitions` | `int`

The number of partitions to reduce to.

2. `shuffle` | `boolean` | `optional`

Whether or not to shuffle the data such that they end up in different partitions. By default, `shuffle=False`.

# Return Value

A PySpark RDD (`pyspark.rdd.RDD`).

# Examples

Consider the following RDD with 3 partitions:

``` rdd = sc.parallelize(["A","B","C","D","A"], numSlices=3)rdd.glom().collect() [['A'], ['B', 'C'], ['D', 'A']] ```

Here:

## Reducing the number of partitions of RDD

To reduce the number of partitions to 2:

``` new_rdd = rdd.coalesce(numPartitions=2)new_rdd.glom().collect() [['A'], ['B', 'C', 'D', 'A']] ```

We can see that the 2nd partition merged with the 3rd partition.

## Balanced partitioning of RDD using shuffle

Instead of merging partitions to reduce the number partitions, we can also shuffle the data:

``` new_rdd = rdd.coalesce(numPartitions=2, shuffle=True)new_rdd.glom().collect() [['A', 'D', 'A'], ['B', 'C']] ```

As you can see, this results in a partitioning that is more balanced. The downside to shuffling, however, is that this is a costly process when your data size is large since data must be transferred from one worker node to another.

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