Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/5074#discussion_r26790966
--- Diff: docs/programming-guide.md ---
@@ -1086,6 +1086,29 @@ for details.
</tr>
</table>
+### Shuffle operations
+
+Certain operations within Spark trigger an operation 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 [`groupByKey`](#GroupByLink) operation. The `groupByKey` operation
generates a new RDD where all values for a single key are combined into a tuple
- the key and an `Iterable` object containing all the associated values. 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.
--- End diff --
Kind of a nit, but because we try to de-emphasize the `groupByKey`
operation, it might be better to go with a different example.
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