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From "Ahad Rana (JIRA)" <j...@apache.org>
Subject [jira] Updated: (HADOOP-4483) getBlockArray in DatanodeDescriptor does not not honor passed in maxblocks value
Date Tue, 21 Oct 2008 20:40:44 GMT

     [ https://issues.apache.org/jira/browse/HADOOP-4483?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Ahad Rana updated HADOOP-4483:
------------------------------

    Fix Version/s: 0.18.2
           Status: Patch Available  (was: Open)

This fixes the getBlockArray method in DatanodeDescriptor to constrained the returned Block
array to the maxBlocks values passed in.

> getBlockArray in DatanodeDescriptor does not not honor passed in maxblocks value
> --------------------------------------------------------------------------------
>
>                 Key: HADOOP-4483
>                 URL: https://issues.apache.org/jira/browse/HADOOP-4483
>             Project: Hadoop Core
>          Issue Type: Bug
>          Components: dfs
>    Affects Versions: 0.18.1
>         Environment: hadoop-0.18.1 running on a cluster of 16 nodes.
>            Reporter: Ahad Rana
>            Priority: Critical
>             Fix For: 0.18.2
>
>   Original Estimate: 1h
>  Remaining Estimate: 1h
>
> The getBlockArray method in DatanodeDescriptor.java should honor the passed in maxblocks
parameter. In its current form it passed in an array sized to min(maxblocks,blocks.size())
into the Collections.toArray method. As the javadoc for Collections.toArray indicates, the
toArray method may discard the passed in array (and allocate a new array) if the number of
elements returned by the iterator exceeds the size of the passed in array. As a result, the
flawed implementation of this method would return all the invalid blocks for a data node in
one go, and thus trigger the NameNode to send a DNA_INVALIDATE command to the DataNode with
an excessively large number of blocks. This INVALIDATE command, in turn, could potentially
take a very long time to process at the DataNode, and since DatanodeCommand(s) are processed
in between heartbeats at the DataNode, this would trigger the NameNode to consider the DataNode
to be offline / unresponsive (due to a lack of heartbeats). 
> In our use-case at CommonCrawl.org, we regularly do large scale hdfs file deletions after
certain stages of our map-reduce pipeline. These deletes would make certain DataNode(s) unresponsive,
and thus impact the cluster's capability to properly balance file-system reads / writes across
the whole available cluster. This problem only surfaced once we migrated from our 16.2 deployment
to the current 18.1 release. 

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