Github user ilganeli commented on a diff in the pull request:
https://github.com/apache/spark/pull/5074#discussion_r27054643
--- Diff: docs/programming-guide.md ---
@@ -1086,6 +1086,62 @@ 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 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
+[`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 present a single array per key.
+
+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
+operations, [`mapPartitions`](#MapPartLink) can be used to sort each
partition or `sortBy` can be
+used to perform a global sort. A similar operation,
+[`repartitionAndSortWithinPartitions`](#Repartition2Link`) coupled with
`mapPartitions`,
+may be used to enact a Hadoop style shuffle.
+
+Operations which can cause a shuffle include **repartition** operations
like
+[`repartition`](#RepartitionLink), and [`coalesce`](#CoalesceLink),
**'byKey** operations
+(except for counting) like [`groupByKey`](#GroupByLink) and
[`reduceByKey`](#ReduceByLink), and
+**join** operations like [`cogroup`](#CogroupLink) and [`join`](#JoinLink).
+
+#### Performance Impact
+**Shuffle** is an expensive operation since it involves disk I/O, data
serialization, and
+network I/O. To organize data for the shuffle, Spark generates two sets of
tasks - map tasks to
--- End diff --
Sandy - could you elaborate on that first issue? Is this due to the fact
that subsequent shuffles will get coalesced via the DAGScheduler? Is that worth
mentioning here and if so, what would be the best way to phrase it?
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