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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (IGNITE-9870) GridDhtPartitionsFullMessage#prepareMarshal compression parallelization.
Date Wed, 07 Nov 2018 10:46:00 GMT

    [ https://issues.apache.org/jira/browse/IGNITE-9870?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16678022#comment-16678022

ASF GitHub Bot commented on IGNITE-9870:

GitHub user voropava reopened a pull request:


    IGNITE-9870 GridDhtPartitionsFullMessage#prepareMarshal compression parallelization.


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

    $ git pull https://github.com/gridgain/apache-ignite ignite-9870

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


To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #5330
commit a988cd095c2e41a05701305a07b9140145d0bbe4
Author: Pavel Voronkin <pvoronkin@...>
Date:   2018-08-14T18:20:50Z

    IGNITE-9870 Implemented parallel GridDhtPartitionsFullMessage zip and unzip using getNetCompressionLevel()
BEST_SPEED by default.

commit cd0e4555cdca4865e0fd9b06a23afd756510518e
Author: Pavel Voronkin <pvoronkin@...>
Date:   2018-08-14T18:20:50Z

    IGNITE-9870 Fixed javadoc.

commit ecae04a6810c10e24f079422b376797f022b84b4
Author: pereslegin-pa <xxtern@...>
Date:   2018-08-14T18:20:50Z

    IGNITE-9870 Fixed imports.


> GridDhtPartitionsFullMessage#prepareMarshal compression parallelization.
> ------------------------------------------------------------------------
>                 Key: IGNITE-9870
>                 URL: https://issues.apache.org/jira/browse/IGNITE-9870
>             Project: Ignite
>          Issue Type: Improvement
>          Components: general
>    Affects Versions: 2.6
>            Reporter: Stanilovsky Evgeny
>            Assignee: Pavel Voronkin
>            Priority: Major
>             Fix For: 2.8
> In huge topologies ~ 200 cluster nodes , 100 caches , 32k partitions per cache, full
map generation takes about 3 sec, seems correct approach here in parallelization.

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