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From "Eric Hanson (JIRA)" <>
Subject [jira] [Commented] (HIVE-4660) Let there be Tez (aka mrr ftw)
Date Wed, 12 Jun 2013 23:06:20 GMT


Eric Hanson commented on HIVE-4660:

If the following use case can be handled in Tez, that would be fantastic.

With vectorized query execution (HIVE-4160) on ORC data I am seeing the following. A simple
single-table group-by/aggregate query against 218 million rows on a 6 core machine, with one
map followed by one reduce, takes about 40 seconds to run. But if you look at the Windows
task manager, you see that there is about a 20 second setup period with only 5-20% CPU consumption,
then there is a 5 second burst of 100% CPU consumption where all the mappers are running full
steam, and then it takes another 15 seconds or so to finish the query, also with only about
5-20% CPU consumption. The query is not I/O bound.

If the CPU slack could be eliminated, i.e. the CPU cores could run near 100% from start to
finish, the query could probably run in 7 seconds. 

If you could include a discussion of this use case in the spec and how Tez will help now and/or
in later Tez versions, or if other work beyond the scope of Tez is needed, that would be great.
> Let there be Tez (aka mrr ftw)
> ------------------------------
>                 Key: HIVE-4660
>                 URL:
>             Project: Hive
>          Issue Type: New Feature
>            Reporter: Gunther Hagleitner
>            Assignee: Gunther Hagleitner
>         Attachments: HiveonTez.pdf
> 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:
> 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 attached design doc.

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