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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (DRILL-4363) Apply row count based pruning for parquet table in LIMIT n query
Date Fri, 12 Feb 2016 00:33:18 GMT

    [ https://issues.apache.org/jira/browse/DRILL-4363?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15143781#comment-15143781

ASF GitHub Bot commented on DRILL-4363:

Github user asfgit closed the pull request at:


> Apply row count based pruning for parquet table in LIMIT n query
> ----------------------------------------------------------------
>                 Key: DRILL-4363
>                 URL: https://issues.apache.org/jira/browse/DRILL-4363
>             Project: Apache Drill
>          Issue Type: Improvement
>            Reporter: Jinfeng Ni
>            Assignee: Jinfeng Ni
>             Fix For: 1.6.0
> In interactive data exploration use case, one common and probably first query that users
would use is " SELECT * from table LIMIT n", where n is a small number. Such query will give
user idea about the columns in the table.
> Normally, user would expect such query should be completed in very short time, since
it's just asking for small amount of rows, without any sort/aggregation.
> When table is small, there is no big problem for Drill. However, when the table is extremely
large,  Drill's response time is not as fast as what user would expect.
> In case of parquet table, it seems that query planner could do a bit better job : by
applying row count based pruning for such LIMIT n query.  The pruning is kind of similar to
what partition pruning will do, except that it uses row count, in stead of partition column
values. Since row count is available in parquet table, it's possible to do such pruning.
> The benefit of doing such pruning is clear: 1) for small "n",  such pruning would end
up with a few parquet files, in stead of thousands, or millions of files to scan. 2) execution
probably does not have to put scan into multiple minor fragments and start reading the files
concurrently, which will cause big IO overhead. 3) the physical plan itself is much smaller,
since it does not include the long list of parquet files, reduce rpc cost of sending the fragment
plans to multiple drillbits, and the overhead to serialize/deserialize the fragment plans.

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