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https://issues.apache.org/jira/browse/HDFS-1093?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12910216#action_12910216
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dhruba borthakur commented on HDFS-1093:
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[exec] +1 @author. The patch does not contain any @author tags.
[exec]
[exec] -1 tests included. The patch doesn't appear to include any new
or modified tests.
[exec] Please justify why no new tests are needed
for this patch.
[exec] Also please list what manual steps were
performed to verify this patch.
[exec]
[exec] +1 javadoc. The javadoc tool did not generate any warning
messages.
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[exec] +1 javac. The applied patch does not increase the total number
of javac compiler warnings.
[exec]
[exec] +1 findbugs. The patch does not introduce any new Findbugs
warnings.
[exec]
[exec] +1 release audit. The applied patch does not increase the
total number of release audit warnings.
[exec]
[exec] +1 system tests framework. The patch passed system tests
framework compile.
> Improve namenode scalability by splitting the FSNamesystem synchronized
> section in a read/write lock
> ----------------------------------------------------------------------------------------------------
>
> Key: HDFS-1093
> URL: https://issues.apache.org/jira/browse/HDFS-1093
> Project: Hadoop HDFS
> Issue Type: Improvement
> Components: name-node
> Reporter: dhruba borthakur
> Assignee: dhruba borthakur
> Fix For: 0.22.0
>
> Attachments: NNreadwriteLock.txt, NNreadwriteLock_trunk_1.txt,
> NNreadwriteLock_trunk_2.txt, NNreadwriteLock_trunk_3.txt,
> NNreadwriteLock_trunk_4.txt, NNreadwriteLock_trunk_5.txt
>
>
> Most critical data structures in the NameNode (NN) are protected by a
> syncronized methods in the FSNamesystem class. This essentially makes
> critical code paths in the NN single-threaded. However, a large percentage of
> the NN calls are listStatus, getBlockLocations, etc which do not change
> internal data structures at all, these are read-only calls. If we change the
> FSNamesystem lock to a read/write lock, many of the above operations can
> occur in parallel, thus improving the scalability of the NN.
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