sortWithinPartitions

Returns a new DataFrame with each partition sorted by the specified column(s).

Syntax

sortWithinPartitions(*cols: Union[int, str, Column, List[Union[int, str, Column]]], **kwargs: Any)

Parameters

Parameter	Type	Description
cols	int, str, list or Column, optional	list of Column or column names or column ordinals to sort by.
ascending	bool or list, optional, default True	boolean or list of boolean. Sort ascending vs. descending. Specify list for multiple sort orders. If a list is specified, the length of the list must equal the length of the cols.

Returns

DataFrame: DataFrame sorted by partitions.

Notes

A column ordinal starts from 1, which is different from the 0-based __getitem__. If a column ordinal is negative, it means sort descending.

Examples

Python

from pyspark.sql import functions as sf
df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
df.sortWithinPartitions("age", ascending=False)
# DataFrame[age: bigint, name: string]

df.coalesce(1).sortWithinPartitions(1).show()
# +---+-----+
# |age| name|
# +---+-----+
# |  2|Alice|
# |  5|  Bob|
# +---+-----+

df.coalesce(1).sortWithinPartitions(-1).show()
# +---+-----+
# |age| name|
# +---+-----+
# |  5|  Bob|
# |  2|Alice|
# +---+-----+