Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/5074#discussion_r27292232
--- Diff: docs/programming-guide.md ---
@@ -1086,6 +1086,66 @@ 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 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 --
Fair point, yeah. This could be a dumb question, but do we know that the
partitions will always appear in the same order? I suppose it depends on the
partitioner, but, even for `HashPartitioner` -- do we know that, say,
everything hashing to 0 occurs in partition 0 every time? If this is order of
partitions is deterministic for any reasonable case, then I get it at last,
yeah.
_If_ that understanding is correct, then, here's another pass at the
paragraph:
Although the set of elements in each partition of newly shuffled data will
be deterministic, and so is the ordering of partitions themselves, the ordering
of these elements within each partition is not. If one desires predictably
ordered data following shuffle operations, then it's possible to use:
- `mapPartitions` to sort each partition using, for example, `.sorted`
- `repartitionAndSortWithinPartitions` to efficiently sort partitions while
simultaneously repartitioning
- `sortBy` to make a globally ordered RDD
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