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https://issues.apache.org/jira/browse/NIFI-2851?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15662836#comment-15662836
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ASF GitHub Bot commented on NIFI-2851:
--------------------------------------

GitHub user ijokarumawak opened a pull request:

    https://github.com/apache/nifi/pull/1215

    NIFI-2851: Fixed CheckStyle error.

    The latest master branch fails with CheckStyle by a test class introduced 
by NIFI-2851.
    https://travis-ci.org/apache/nifi/builds/175185419
    
    Confirmed this PR fixes the CheckStyle error locally.

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/ijokarumawak/nifi nifi-2851

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/nifi/pull/1215.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 #1215
    
----
commit 15f314c0f07df898aafb08788859a6ba4f05f177
Author: Koji Kawamura <[email protected]>
Date:   2016-11-14T05:28:05Z

    NIFI-2851: Fixed CheckStyle error.

----


> Improve performance of SplitText
> --------------------------------
>
>                 Key: NIFI-2851
>                 URL: https://issues.apache.org/jira/browse/NIFI-2851
>             Project: Apache NiFi
>          Issue Type: Improvement
>          Components: Core Framework
>            Reporter: Mark Payne
>            Assignee: Oleg Zhurakousky
>             Fix For: 1.1.0
>
>
> SplitText is fairly CPU-intensive and quite slow. A simple flow that splits a 
> 1.4 million line text file into 5k line chunks and then splits those 5k line 
> chunks into 1 line chunks is only capable of pushing through about 10k lines 
> per second. This equates to about 10 MB/sec. JVisualVM shows that the 
> majority of the time is spent in the locateSplitPoint() method. Isolating 
> this code and inspecting how it works, and using some micro-benchmarking, it 
> appears that if we refactor the calls to InputStream.read() to instead read 
> into a byte array, we can improve performance.



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