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https://issues.apache.org/jira/browse/TAJO-2063?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15125611#comment-15125611
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ASF GitHub Bot commented on TAJO-2063:
--------------------------------------
GitHub user blrunner opened a pull request:
https://github.com/apache/tajo/pull/952
TAJO-2063: Refactor FileTablespace::commitOutputData.
I implemented this PR as following:
- Refactor ``FileTablespace::commitOutputData`` into well-defined methods.
- Use catalog informs to minimize ``FileSystem::listStatus`` for
partitioned table.
It doesn't seem to have new unit test cases because there are already unit
test cases for committing output data, for example,
``TestInsertQuery``,``TestTablePartitions``, ``TestJoinOnPartitionedTables``.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/blrunner/tajo TAJO-2063
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/tajo/pull/952.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 #952
----
commit 145b8b209cee134ffdf548f2cf197cf97cf8c420
Author: JaeHwa Jung <[email protected]>
Date: 2016-02-01T01:34:01Z
TAJO-2063: Refactor FileTablespace::commitOutputData.
----
> Refactor FileTablespace::commitOutputData.
> ------------------------------------------
>
> Key: TAJO-2063
> URL: https://issues.apache.org/jira/browse/TAJO-2063
> Project: Tajo
> Issue Type: Improvement
> Components: Storage
> Reporter: Jaehwa Jung
> Assignee: Jaehwa Jung
> Fix For: 0.12.0
>
>
> {{FileTablespace::commitOutputData}} has some problems as following:
> First, it is too long and complexed because it handles various cases in a
> single method. We need to refactor this method into several small and
> well-defined methods.
> Second, {{FileSystem::listStatus}} is widely used while committing output
> data. It especially causes a lot of overhead to list partitioned directories
> in S3. Also it occurs in HDFS too with large partitioned tables. We need to
> minimize its usage.
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