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From "Yin Huai (JIRA)" <>
Subject [jira] [Updated] (HIVE-2206) add a new optimizer for query correlation discovery and optimization
Date Wed, 16 Jan 2013 19:30:15 GMT


Yin Huai updated HIVE-2206:

    Attachment: HIVE-2206.20-r1434012.patch.txt

[~ashutoshc] The only thing I have not addressed is cloning the plan at the beginning of optimization
work. Seems that we only can clone a part of the query plan tree. If we backup only a part
of the query plan tree, I think it is the same as the current patch (described below), since
in this way, we still need to link the backup part to other parts if correlation optimizer
cannot optimize the given query. 

In my patch, the original plan tree will be changed to a version without map-side aggregations,
firstly. This version of the plan tree will be only changed if there is any correlation detected
and new merged Map-side plan and reduce side dispatch have been successfully generated (this
query can be optimized). If optimizer cannot optimize this plan, it will change the plan back
to the original one. 

Any suggestion on how to address this issue? Or, did I miss any thing which can facilitate
the cloning work? I think it will be great if we can clone a ParseContext, so if a optimization
is failed, we still have a copy of the original plan.
> add a new optimizer for query correlation discovery and optimization
> --------------------------------------------------------------------
>                 Key: HIVE-2206
>                 URL:
>             Project: Hive
>          Issue Type: New Feature
>          Components: Query Processor
>    Affects Versions: 0.10.0
>            Reporter: He Yongqiang
>            Assignee: Yin Huai
>         Attachments: HIVE-2206.10-r1384442.patch.txt, HIVE-2206.11-r1385084.patch.txt,
HIVE-2206.12-r1386996.patch.txt, HIVE-2206.13-r1389072.patch.txt, HIVE-2206.14-r1389704.patch.txt,
HIVE-2206.15-r1392491.patch.txt, HIVE-2206.16-r1399936.patch.txt, HIVE-2206.17-r1404933.patch.txt,
HIVE-2206.18-r1407720.patch.txt, HIVE-2206.19-r1410581.patch.txt, HIVE-2206.1.patch.txt, HIVE-2206.20-r1434012.patch.txt,
HIVE-2206.2.patch.txt, HIVE-2206.3.patch.txt, HIVE-2206.4.patch.txt, HIVE-2206.5-1.patch.txt,
HIVE-2206.5.patch.txt, HIVE-2206.6.patch.txt, HIVE-2206.7.patch.txt, HIVE-2206.8.r1224646.patch.txt,
HIVE-2206.8-r1237253.patch.txt, testQueries.2.q, YSmartPatchForHive.patch
> This issue proposes a new logical optimizer called Correlation Optimizer, which is used
to merge correlated MapReduce jobs (MR jobs) into a single MR job. The idea is based on YSmart
( paper and slides of YSmart are linked at the bottom.
> Since Hive translates queries in a sentence by sentence fashion, for every operation
which may need to shuffle the data (e.g. join and aggregation operations), Hive will generate
a MapReduce job for that operation. However, for those operations which may need to shuffle
the data, they may involve correlations explained below and thus can be executed in a single
MR job.
> # Input Correlation: Multiple MR jobs have input correlation (IC) if their input relation
sets are not disjoint;
> # Transit Correlation: Multiple MR jobs have transit correlation (TC) if they have not
only input correlation, but also the same partition key;
> # Job Flow Correlation: An MR has job flow correlation (JFC) with one of its child nodes
if it has the same partition key as that child node.
> The current implementation of correlation optimizer only detect correlations among MR
jobs for reduce-side join operators and reduce-side aggregation operators (not map only aggregation).
A query will be optimized if it satisfies following conditions.
> # There exists a MR job for reduce-side join operator or reduce side aggregation operator
which have JFC with all of its parents MR jobs (TCs will be also exploited if JFC exists);
> # All input tables of those correlated MR job are original input tables (not intermediate
tables generated by sub-queries); and 
> # No self join is involved in those correlated MR jobs.
> Correlation optimizer is implemented as a logical optimizer. The main reasons are that
it only needs to manipulate the query plan tree and it can leverage the existing component
on generating MR jobs.
> Current implementation can serve as a framework for correlation related optimizations.
I think that it is better than adding individual optimizers. 
> There are several work that can be done in future to improve this optimizer. Here are
three examples.
> # Support queries only involve TC;
> # Support queries in which input tables of correlated MR jobs involves intermediate tables;
> # Optimize queries involving self join. 
> References:
> Paper and presentation of YSmart.
> Paper:
> Slides:

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