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From sryza <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-5750][SPARK-3441][SPARK-5836][CORE] Add...
Date Thu, 19 Mar 2015 19:51:23 GMT
Github user sryza commented on a diff in the pull request:

    https://github.com/apache/spark/pull/5074#discussion_r26790986
  
    --- Diff: docs/programming-guide.md ---
    @@ -1086,6 +1086,29 @@ 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-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 [`groupByKey`](#GroupByLink)
operation. The `groupByKey` operation generates a new RDD where all values for a single key
are combined into a tuple - the key and an `Iterable` object containing all the associated
values. 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 ncessary 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 `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**. 
    --- End diff --
    
    necessary


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