Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/5074#discussion_r26636863
--- 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-organizing data to co-locate data
associated with particular keys.
+
+#### 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, data is distributed randomly across partitions.
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**.
+
+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. A similar
operation, [`repartitionAndSortWithinPartitions`](#Repartition2Link`) coupled
with `mapPartitions`, may be used to enact a `Hadoop` style shuffle.
+
+Operations, which cause a shuffle include [`groupByKey`](#GroupByLink),
[`sortByKey`](#SortByLink), [`reduceByKey`](#ReduceByLink),
[`aggregateByKey`](#AggregateByLink), [`repartition`](#RepartitionLink),
[`repartitionAndSortWithinPartitions`](#Repartition2Link`),
[`coalesce`](#CoalesceLink), and [`countByKey`](#CountByLink).
+
+#### Performance Impact
+**Shuffle** is an expensive operation since it involves disk I/O, data
serialization, and network I/O. Shuffle operations can have a serious impact on
performance. To organize data for the shuffle, Spark will also generate lookup
tables in memory, which, for large operations, can consume significant amounts
of heap memory. When out of memory, for all shuffle operations with the
exception of `sortByKey`, Spark will spill these tables to disk, incurring the
additional overhead of disk I/O and increased garbage collection. Since
`sortByKey` does not spill these intermediate tables to disk, the shuffle
operation may cause OOM errors.
--- End diff --
I think the 2nd sentence is redundant.
For the 3rd one, you might as well be more accurate, i.e. "Internally,
Spark creates a hash table to ...".
And then instead of saying "When out of memory", it is better to say "When
data does not fit in memory"
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
