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https://issues.apache.org/jira/browse/NIFI-2851?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15555414#comment-15555414
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ASF GitHub Bot commented on NIFI-2851:
--------------------------------------

GitHub user olegz opened a pull request:

    https://github.com/apache/nifi/pull/1116

    NIFI-2851 initial comit of perf improvements on SplitText

    Thank you for submitting a contribution to Apache NiFi.
    
    In order to streamline the review of the contribution we ask you
    to ensure the following steps have been taken:
    
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You can merge this pull request into a Git repository by running:

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

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

    https://github.com/apache/nifi/pull/1116.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 #1116
    
----
commit 0818d8689cb474bb1ad81dcd672cb0e31078825e
Author: Oleg Zhurakousky <o...@suitcase.io>
Date:   2016-10-07T15:37:32Z

    NIFI-2851 initial comit of perf improvements on SplitText

----


> 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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