Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/5074#discussion_r27305674
--- Diff: docs/programming-guide.md ---
@@ -1086,6 +1086,66 @@ for details.
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+### 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 copying data across executors and machines, making the 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 compute the result.
+
+In Spark, data is generally not distributed across partitions to be in the
necessary place for a
+specific operation. During computations, a single task will operate on a
single partition - thus, to
+organize all the data for a single `reduceByKey` 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**.
+
+Although the set of elements in each partition of newly shuffled data will
be deterministic, the
+ordering of these elements is not. If one desires predictably ordered data
following shuffle
--- End diff --
@srowen that's exactly how `HashPartitioner` works. As long as the
partition function isn't using System.currentTimeMillis or something the
ordering of partitions is deterministic.
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