## Pyspark’s AggregateByKey Method

The pyspark documentation doesn’t include an example for the aggregateByKey RDD method. I didn’t find any nice examples online, so I wrote my own.

Here’s what the documentation does say:

aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None)

Aggregate the values of each key, using given combine functions and a neutral “zero value”. This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U’s, The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.

reduceByKey and aggregateByKey are much more efficient than groupByKey and should be used for aggregations as much as possible.

In the example below, I create an RDD that is a short list of characters. My functions will aggregate the functions together with concatenation. I added brackets to the two types of concatenation to help give you an idea of what aggregateByKey is doing.

Welcome to
____              __
/ __/__  ___ _____/ /__
_\ \/ _ \/ _ / __/  '_/
/__ / .__/\_,_/_/ /_/\_\   version 1.1.0
/_/

Using Python version 2.7.5 (default, Mar  9 2014 22:15:05)
SparkContext available as sc.

In [1]: # Create rdd that is a list of characters

In [2]: sc.parallelize(list("aaaaabbbbcccdd")) \
...:         .map(lambda letter: (letter, {"value": letter})) \
...:         .aggregateByKey(
...:                    # Value to start aggregation (passed as s to lambda s, d)
...:                    "start",
...:                    # Function to join final data type (string) and rdd data type
...:                    lambda s, d: "[ %s %s ]" % (s, d["value"]),
...:                    # Function to join two final data types.
...:                    lambda s1, s2: "{ %s %s }" % (s1, s2),
...:                    ) \
...:         .collect()

Out[2]:
[('a', '{ { [ start a ] [ [ start a ] a ] } [ [ start a ] a ] }'),
('c', '{ [ start c ] [ [ start c ] c ] }'),
('b', '{ { [ [ start b ] b ] [ start b ] } [ start b ] }'),
('d', '[ [ start d ] d ]')]

`