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PySpark RDD | partitionBy method

Machine Learning
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PySpark
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PySpark RDD
schedule Jul 1, 2022
Last updated
local_offer PySpark
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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 partitions
rdd = 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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Published by Isshin Inada
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