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Posted to issues@ignite.apache.org by "Andrew Mashenkov (JIRA)" <ji...@apache.org> on 2016/12/06 11:11:25 UTC

[jira] [Comment Edited] (IGNITE-4106) SQL: parallelize sql queries over cache local partitions

    [ https://issues.apache.org/jira/browse/IGNITE-4106?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15722743#comment-15722743 ] 

Andrew Mashenkov edited comment on IGNITE-4106 at 12/6/16 9:08 AM:
-------------------------------------------------------------------

* Segmented tree index implementation moved to separate class. 
* ReduceQueryExecutor now sends only one query request per node. 
* MapQueryExecutor bothered with starting query threads if needed.
* Corresponding configuration options added.




was (Author: amashenkov):
Segmented tree index implementation moved to separate class. 
ReduceQueryExecutor now sends only one query request per node. 
MapQueryExecutor bothered with starting query threads if needed.
Corresponding configuration options added.



> SQL: parallelize sql queries over cache local partitions
> --------------------------------------------------------
>
>                 Key: IGNITE-4106
>                 URL: https://issues.apache.org/jira/browse/IGNITE-4106
>             Project: Ignite
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 1.6, 1.7
>            Reporter: Andrew Mashenkov
>            Assignee: Andrew Mashenkov
>              Labels: performance, sql
>             Fix For: 2.0
>
>         Attachments: 1node-4thread.jfr, 4node-1thread.jfr
>
>
> If we run SQL query on cache partitioned over several cluster nodes, it will be split into several queries running in parallel. But really we will have one thread per query on each node.
> So, for now, to improve SQL query performance we need to run more Ignite instances or split caches manually.
> It seems to be better to split local SQL queries over cache partitions, so we would be able to parallelize SQL query on every single node and utilize CPU more efficiently.



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