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# PySpark RDD | partitionBy method

schedule Aug 12, 2023
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PySpark RDD's `partitionBy(~)` method re-partitions a pair RDD into the desired number of partitions.

# Parameters

1. `numPartitions` | `int`

The desired number of partitions of the resulting RDD.

2. `partitionFunc` | `function` | `optional`

The partitioning function - the input is the key and the return value must be the hashed value. By default, a hash partitioner will be used.

# Return Value

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

# Examples

## Repartitioning a pair RDD

Consider the following RDD:

``` # Create a RDD with 3 partitionsrdd = sc.parallelize([("A",1),("B",1),("C",1),("A",1)], numSlices=3)rdd.collect() [('A', 1), ('B', 1), ('C', 1), ('A', 1)] ```

To see how this RDD is partitioned, use the `glom()` method:

``` rdd.glom().collect() [[('A', 1)], [('B', 1)], [('C', 1), ('A', 1)]] ```

We can indeed see that there are 3 partitions:

• Partition one: `('A',1)` and `('B',1)`

• Partition two: `('C',1)`

• Partition three: `('A',1)`

To re-partition into 2 partitions:

``` new_rdd = rdd.partitionBy(numPartitions=2)new_rdd.collect() [('C', 1), ('A', 1), ('B', 1), ('A', 1)] ```

To see the contents of the new partitions:

``` new_rdd.glom().collect() [[('C', 1)], [('A', 1), ('B', 1), ('A', 1)]] ```

We can indeed see that there are 2 partitions:

• Partition one: `('C',1)`

• Partition two: `('A',1)`, `('B',1)`, `('A', 1)`

Notice how the tuple with the key `A` has ended up in the same partition. This is guaranteed to happen because the hash partitioner will perform bucketing based on the tuple key.

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