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PySpark DataFrame | repartition method

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PySpark
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PySpark DataFrame
schedule Jul 1, 2022
Last updated
local_offer PySpark
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PySpark DataFrame's repartition(~) method returns a new PySpark DataFrame with the data split into the specified number of partitions. This method also allows to partition by column values.

Parameters

1. numPartitions | int

The number of patitions to break down the DataFrame.

2. cols | str or Column

The columns by which to partition the DataFrame.

Return Value

A new PySpark DataFrame.

Examples

Partitioning a PySpark DataFrame

Cosnider the following PySpark DataFrame:

df = spark.createDataFrame([("Alex", 20), ("Bob", 30), ("Cathy", 40)], ["name", "age"])
df.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 20|
| Bob| 30|
|Cathy| 40|
+-----+---+

By default, the number of partitions depends on the parallelism level of your PySpark configuration:

In my case, our PySpark DataFrame is split into 8 partitions by default.

We can see how the rows of our DataFrame are partitioned using the glom() method of the underlying RDD:

[[],
[],
[Row(name='Alex', age=20)],
[],
[],
[Row(name='Bob', age=30)],
[],
[Row(name='Cathy', age=40)]]

Here, we can see that we have indeed 8 partitions, but only 3 of the partitions have a Row in them.

Now, let's repartition our DataFrame such that the Rows are divided into only 2 partitions:

df_new = df.repartition(2)
2

The distribution of the rows in our repartitioned DataFrame is now:

df_new.rdd.glom().collect()
[[Row(name='Alex', age=20),
Row(name='Bob', age=30),
Row(name='Cathy', age=40)],
[]]

As demonstrated here, there is no guarantee that the rows will be evenly distributed in the partitions.

Partitioning a PySpark DataFrame by column values

Consider the following PySpark DataFrame:

df = spark.createDataFrame([("Alex", 20), ("Bob", 30), ("Cathy", 40), ("Alex", 50)], ["name", "age"])
df.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 20|
| Bob| 30|
|Cathy| 40|
| Alex| 50|
+-----+---+

To repartition this PySpark DataFrame by the column name into 2 partitions:

df_new = df.repartition(2, "name")
df_new.rdd.glom().collect()
[[Row(name='Alex', age=20),
Row(name='Cathy', age=40),
Row(name='Alex', age=50)],
[Row(name='Bob', age=30)]]

Here, notice how the rows with the same value for name ('Alex' in this case) end up in the same partition.

We can also repartition by multiple column values:

df_new = df.repartition(4, "name", "age")
df_new.rdd.glom().collect()
[[Row(name='Alex', age=20)],
[Row(name='Bob', age=30)],
[Row(name='Alex', age=50)],
[Row(name='Cathy', age=40)]]

Here, we are repartitioning by the name and age columns into 4 partitions.

We can also use the default number of partitions by specifying column labels only:

df_new = df.repartition("name")
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Published by Isshin Inada
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