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https://issues.apache.org/jira/browse/DRILL-4982?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15637501#comment-15637501
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ASF GitHub Bot commented on DRILL-4982:
---------------------------------------
Github user chunhui-shi commented on the issue:
https://github.com/apache/drill/pull/638
The performance degradation is worse with larger tables. The degradation is
>300% on a 15GB table. After this fix, this degradation disappears.
There are other visible performance gains of this fix even when there is no
degradation:
For a simple query on ORC/Parquet table through HiveReaders, I observed the
improvement is about 10%-25% and the average improved percentage is 17.6%.
For TPCH tests on a 10 node cluster with Parquet tables + HiveReaders, the
average improvement (of two runs) is 5.1%
> 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: Jinfeng Ni
> 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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