pyspark.RDD.sortByKey#

RDD.sortByKey(ascending=True, numPartitions=None, keyfunc=<function RDD.<lambda>>)[source]#

Sorts this RDD, which is assumed to consist of (key, value) pairs.

Added in version 0.9.1.

Parameters:

ascendingbool, optional, default True: sort the keys in ascending or descending order
numPartitionsint, optional: the number of partitions in new RDD
keyfuncfunction, optional, default identity mapping: a function to compute the key

Returns:

RDD: a new RDD

See also

RDD.sortBy()
pyspark.sql.DataFrame.sort()

Examples

>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
>>> sc.parallelize(tmp).sortByKey().first()
('1', 3)
>>> sc.parallelize(tmp).sortByKey(True, 1).collect()
[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
>>> sc.parallelize(tmp).sortByKey(True, 2).collect()
[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
>>> tmp2 = [('Mary', 1), ('had', 2), ('a', 3), ('little', 4), ('lamb', 5)]
>>> tmp2.extend([('whose', 6), ('fleece', 7), ('was', 8), ('white', 9)])
>>> sc.parallelize(tmp2).sortByKey(True, 3, keyfunc=lambda k: k.lower()).collect()
[('a', 3), ('fleece', 7), ('had', 2), ('lamb', 5),...('white', 9), ('whose', 6)]