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From "Lefty Leverenz (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-4660) Let there be Tez
Date Tue, 14 Jan 2014 11:39:50 GMT

    [ https://issues.apache.org/jira/browse/HIVE-4660?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13870645#comment-13870645
] 

Lefty Leverenz commented on HIVE-4660:
--------------------------------------

... and there was Tez.  But mere mortals don't rest on the seventh day:  can the design doc
be updated now?

For example, "Functional requirements of phase I" mentions hive.optimize.tez but hasn't that
been replaced by hive.execution.engine?

Also a recent HiveConf.java doesn't contain mapreduce.framework.name which is mentioned in
the design doc, but it does contain hive.compute.splits.in.am with the comment "Whether to
generate the splits locally or in the AM (tez only)."  Are there any more Tez config params?

Here's the doc link:  https://cwiki.apache.org/confluence/display/Hive/Hive+on+Tez#HiveonTez-FunctionalrequirementsofphaseI

> 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: https://cwiki.apache.org/confluence/display/Hive/Hive+on+Tez



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