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https://issues.apache.org/jira/browse/DRILL-4982?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15621361#comment-15621361
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ASF GitHub Bot commented on DRILL-4982:
---------------------------------------
GitHub user chunhui-shi opened a pull request:
https://github.com/apache/drill/pull/638
DRILL-4982: Separate Hive reader classes for different data formats t…
…o improve performance.
1, Separating Hive reader classes allows optimization to apply on different
classes in optimized ways. This separation effectively avoid the performance
degradation of scan.
2, Do not apply Skip footer/header mechanism on most Hive formats. This
skip mechanism introduces extra checks on each incoming records.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/chunhui-shi/drill DRILL-4982
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/drill/pull/638.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #638
----
commit 1e240a0dfea7970798bd550204388fc0e9bc3d42
Author: chunhui-shi <[email protected]>
Date: 2016-10-30T08:29:06Z
DRILL-4982: Separate Hive reader classes for different data formats to
improve performance.
1, Separating Hive reader classes allows optimization to apply on different
classes in optimized ways. This separation effectively avoid the performance
degradation of scan.
2, Do not apply Skip footer/header mechanism on most Hive formats. This
skip mechanism introduces extra checks on each incoming records.
----
> Hive Queries degrade when queries switch between different formats
> ------------------------------------------------------------------
>
> Key: DRILL-4982
> URL: https://issues.apache.org/jira/browse/DRILL-4982
> Project: Apache Drill
> Issue Type: Bug
> Reporter: Chunhui Shi
> Assignee: Chunhui Shi
> Priority: Critical
>
> We have seen degraded performance by doing these steps:
> 1) generate the repro data:
> python script repro.py as below:
> import string
> import random
>
> for i in range(30000000):
> x1 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _
> in range(random.randrange(19, 27)))
> x2 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _
> in range(random.randrange(19, 27)))
> x3 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _
> in range(random.randrange(19, 27)))
> x4 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _
> in range(random.randrange(19, 27)))
> x5 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _
> in range(random.randrange(19, 27)))
> x6 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _
> in range(random.randrange(19, 27)))
> print
> "{0}".format(x1),"{0}".format(x2),"{0}".format(x3),"{0}".format(x4),"{0}".format(x5),"{0}".format(x6)
> python repro.py > repro.csv
> 2) put these files in a dfs directory e.g. '/tmp/hiveworkspace/plain'. Under
> hive prompt, use the following sql command to create an external table:
> CREATE EXTERNAL TABLE `hiveworkspace`.`plain` (`id1` string, `id2` string,
> `id3` string, `id4` string, `id5` string, `id6` string) ROW FORMAT SERDE
> 'org.apache.hadoop.hive.serde2.OpenCSVSerde' STORED AS TEXTFILE LOCATION
> '/tmp/hiveworkspace/plain'
> 3) create Hive's table of ORC|PARQUET format:
> CREATE TABLE `hiveworkspace`.`plainorc` STORED AS ORC AS SELECT
> id1,id2,id3,id4,id5,id6 from `hiveworkspace`.`plain`;
> CREATE TABLE `hiveworkspace`.`plainparquet` STORED AS PARQUET AS SELECT
> id1,id2,id3,id4,id5,id6 from `hiveworkspace`.`plain`;
> 4) Query switch between these two tables, then the query time on the same
> table significantly lengthened. On my setup, for ORC, it was 15sec -> 26secs.
> Queries on table of other formats, after injecting a query to other formats,
> all have significant slow down.
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