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Date: Thu, 31 Jul 2014 18:09:41 +0000 (UTC)
From: "Chao (JIRA)" <jira@apache.org>
To: hive-dev@hadoop.apache.org
Message-ID: <JIRA.12651047.1370418124746.88306.1406830181216@arcas>
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Subject: [jira] [Updated] (HIVE-4660) Let there be Tez
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     [ https://issues.apache.org/jira/browse/HIVE-4660?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Chao updated HIVE-4660:
-----------------------

    Description: 
Tez is a new application framework built on Hadoop Yarn that can execute complex directed acyclic graphs of general data processing tasks. Here's the project's page: http://incubator.apache.org/projects/tez.html

The interesting thing about Tez from Hive's perspective is that it will over time allow us to overcome inefficiencies in query processing due to having to express every algorithm in the map-reduce paradigm.

The barrier to entry is pretty low as well: Tez can actually run unmodified MR jobs; But as a first step we can without much trouble start using more of Tez' features by taking advantage of the MRR pattern. 

MRR simply means that there can be any number of reduce stages following a single map stage - without having to write intermediate results to HDFS and re-read them in a new job. This is common when queries require multiple shuffles on keys without correlation (e.g.: join - grp by - window function - order by)

For more details see the design doc here

  was:
Tez is a new application framework built on Hadoop Yarn that can execute complex directed acyclic graphs of general data processing tasks. Here's the project's page: http://incubator.apache.org/projects/tez.html

The interesting thing about Tez from Hive's perspective is that it will over time allow us to overcome inefficiencies in query processing due to having to express every algorithm in the map-reduce paradigm.

The barrier to entry is pretty low as well: Tez can actually run unmodified MR jobs; But as a first step we can without much trouble start using more of Tez' features by taking advantage of the MRR pattern. 

MRR simply means that there can be any number of reduce stages following a single map stage - without having to write intermediate results to HDFS and re-read them in a new job. This is common when queries require multiple shuffles on keys without correlation (e.g.: join - grp by - window function - order by)

For more details see the design doc here: https://cwiki.apache.org/confluence/display/Hive/Hive+on+Tez


> Let there be Tez
> ----------------
>
>                 Key: HIVE-4660
>                 URL: https://issues.apache.org/jira/browse/HIVE-4660
>             Project: Hive
>          Issue Type: New Feature
>            Reporter: Gunther Hagleitner
>            Assignee: Gunther Hagleitner
>             Fix For: 0.13.0
>
>
> Tez is a new application framework built on Hadoop Yarn that can execute complex directed acyclic graphs of general data processing tasks. Here's the project's page: http://incubator.apache.org/projects/tez.html
> The interesting thing about Tez from Hive's perspective is that it will over time allow us to overcome inefficiencies in query processing due to having to express every algorithm in the map-reduce paradigm.
> The barrier to entry is pretty low as well: Tez can actually run unmodified MR jobs; But as a first step we can without much trouble start using more of Tez' features by taking advantage of the MRR pattern. 
> MRR simply means that there can be any number of reduce stages following a single map stage - without having to write intermediate results to HDFS and re-read them in a new job. This is common when queries require multiple shuffles on keys without correlation (e.g.: join - grp by - window function - order by)
> For more details see the design doc here


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