Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/5074#discussion_r26751403
--- 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.
+
+#### 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
2-tuple - the key and an Iterable object containing all the associated values.
If we think of the map and reduce steps for `groupByKey()` then we can see that
to generate the list of all values associated with a key, all of the values
must reside on the same reducer, since the output of the reduce step is the
complete array.
+
+In Spark, by default, the way data is distributed across partitions is
undefined. During computations, a single task will operate on a single
partition - thus, to organize all the data for a single `groupByKey` reduce
task to execute, Spark needs to perform an all-to-all operation. It must read
from all partitions to find all the values for all keys, and then organize
those such that all values for any key lie within the same partition - this is
called the **shuffle**.
--- End diff --
Nit: I don't even think we have to say it's undefined, since in some cases
it is. It's just that the partitioning does not in general have data already in
the same places that the operation needs it to be in.
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