Github user harishreedharan commented on a diff in the pull request:
https://github.com/apache/spark/pull/5074#discussion_r32698212
--- Diff: docs/programming-guide.md ---
@@ -1090,6 +1090,67 @@ 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, and so
+is the ordering of partitions themselves, the ordering of these elements
is not. If one desires predictably
+ordered data following shuffle 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
+
+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
+The **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 sets of
tasks - *map* tasks to
+organize the data, and a set of *reduce* tasks to aggregate it. This
nomenclature comes from
+MapReduce and does not directly relate to Spark's `map` and `reduce`
operations.
+
+Internally, results from individual map tasks are kept in memory until
they can't fit. Then, these
+are sorted based on the target partition and written to a single file. On
the reduce side, tasks
+read the relevant sorted blocks.
+
+Certain shuffle operations can consume significant amounts of heap memory
since they employ
+in-memory data structures to organize records before or after transferring
them. Specifically,
+`reduceByKey` and `aggregateByKey` create these structures on the map side
and `'ByKey` operations
+generate these on the reduce side. When data does not fit in memory Spark
will spill these tables
+to disk, incurring the additional overhead of disk I/O and increased
garbage collection.
+
+Shuffle also generates a large number of intermediate files on disk. As of
Spark 1.3, these files
+are not cleaned up from Spark's temporary storage until Spark is stopped,
which means that
+long-running Spark jobs may consume available disk space. This is done so
the shuffle doesn't need
--- End diff --
+1. I had a couple of people ask me this during Spark Summit. I was
investigating this myself today.
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