[SPARK-13339][DOCS] Clarify commutative / associative operator requirements for reduce, fold

Clarify that reduce functions need to be commutative, and fold functions do not

See https://github.com/apache/spark/pull/11091

Author: Sean Owen <sowen@cloudera.com>

Closes #11217 from srowen/SPARK-13339.

1 files changed, 3 insertions, 4 deletions

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index fe2264a63c..4eaf589ad5 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -844,8 +844,7 @@ class RDD(object):
     def fold(self, zeroValue, op):
         """
         Aggregate the elements of each partition, and then the results for all
-        the partitions, using a given associative and commutative function and
-        a neutral "zero value."
+        the partitions, using a given associative function and a neutral "zero value."
 
         The function C{op(t1, t2)} is allowed to modify C{t1} and return it
         as its result value to avoid object allocation; however, it should not
@@ -1558,7 +1557,7 @@ class RDD(object):
 
     def reduceByKey(self, func, numPartitions=None, partitionFunc=portable_hash):
         """
-        Merge the values for each key using an associative reduce function.
+        Merge the values for each key using an associative and commutative reduce function.
 
         This will also perform the merging locally on each mapper before
         sending results to a reducer, similarly to a "combiner" in MapReduce.
@@ -1576,7 +1575,7 @@ class RDD(object):
 
     def reduceByKeyLocally(self, func):
         """
-        Merge the values for each key using an associative reduce function, but
+        Merge the values for each key using an associative and commutative reduce function, but
         return the results immediately to the master as a dictionary.
 
         This will also perform the merging locally on each mapper before