[
https://issues.apache.org/jira/browse/TEZ-2442?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14544283#comment-14544283
]
Kannan Rajah commented on TEZ-2442:
-----------------------------------
Not for everything, but for operations that are disk space intensive. This can
be done because MapRFS has a way to carve out node local storage within the
distributed file system namespace.
So shuffle is the main use case we are going after. But in general, by using
this abstraction, even a user job can benefit from doing operations like
sorting on local disk and work fine on Apache as well as MapR distribution.
> Support DFS based shuffle in addition to HTTP shuffle
> -----------------------------------------------------
>
> Key: TEZ-2442
> URL: https://issues.apache.org/jira/browse/TEZ-2442
> Project: Apache Tez
> Issue Type: Improvement
> Affects Versions: 0.5.3
> Reporter: Kannan Rajah
> Attachments: Tez Shuffle using DFS.pdf
>
>
> In Tez, Shuffle is a mechanism by which intermediate data can be shared
> between stages. Shuffle data is written to local disk and fetched from any
> remote node using HTTP. A DFS like MapR file system can support writing this
> shuffle data directly to its DFS using a notion of local volumes and retrieve
> it using HDFS API from remote node. The current Shuffle implementation
> assumes local data can only be managed by LocalFileSystem. So it uses
> RawLocalFileSystem and LocalDirAllocator. If we can remove this assumption
> and introduce an abstraction to manage local disks, then we can reuse most of
> the shuffle logic (store, sort) and inject a HDFS API based retrieval instead
> of HTTP.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
