Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/5074#discussion_r26987263
--- Diff: docs/programming-guide.md ---
@@ -1086,6 +1086,62 @@ for details.
</tr>
</table>
+### Shuffle operations
+
+Certain operations within Spark trigger an event known as the shuffle. The
shuffle is Spark's
+mechanism for re-distributing data so that is grouped differently across
partitions. This typically
+involves re-arranging and copying data across executors and machines,
making shuffle a complex and
+costly operation.
+
+#### Background
+
+To understand what happens during the shuffle we can consider the example
of the
+[`reduceByKey`](#ReduceByLink) operation. The `reduceByKey` operation
generates a new RDD where all
+values for a single key are combined into a tuple - the key and the result
of executing a reduce
+function against all values associated with that key. The challenge is
that not all values for a
+single key necessarily reside on the same partition, or even the same
machine, but they must be
+co-located to present a single array per key.
+
+In Spark, data is generally not distributed across partitions to be in the
necessary place for a
--- End diff --
These first couple sentences are a little redundant with the previous
paragraph.
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