Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/5074#discussion_r26751143
--- Diff: docs/programming-guide.md ---
@@ -1022,6 +1022,27 @@ 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 data with the same
key becomes co-located after a shuffle.
--- End diff --
I think a shuffle is not just about collecting data by key. For example a
repartitioning can cause a shuffle. Personally I'd say that some operations
need to redistribute data so that it is grouped differently into partitions,
which typically means rearranging and copying data across executors or machines.
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