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https://issues.apache.org/jira/browse/HDFS-14694?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16920067#comment-16920067
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Ayush Saxena commented on HDFS-14694:
-------------------------------------

Thanx [~zhangchen] for the patch, The idea seams fair enough to me. The fix 
LGTM. But seems there are couple of changes just for testing and to trigger 
exception, May be we should try to avoid as much as possible. You may try 
Mocking to get the desired behavior, And for the test we should cover both 
scenario, with conf set on and off

> Call recoverLease on DFSOutputStream close exception
> ----------------------------------------------------
>
>                 Key: HDFS-14694
>                 URL: https://issues.apache.org/jira/browse/HDFS-14694
>             Project: Hadoop HDFS
>          Issue Type: Improvement
>          Components: hdfs-client
>            Reporter: Chen Zhang
>            Assignee: Chen Zhang
>            Priority: Major
>         Attachments: HDFS-14694.001.patch, HDFS-14694.002.patch, 
> HDFS-14694.003.patch, HDFS-14694.004.patch
>
>
> HDFS uses file-lease to manage opened files, when a file is not closed 
> normally, NN will recover lease automatically after hard limit exceeded. But 
> for a long running service(e.g. HBase), the hdfs-client will never die and NN 
> don't have any chances to recover the file.
> Usually client program needs to handle exceptions by themself to avoid this 
> condition(e.g. HBase automatically call recover lease for files that not 
> closed normally), but in our experience, most services (in our company) don't 
> process this condition properly, which will cause lots of files in abnormal 
> status or even data loss.
> This Jira propose to add a feature that call recoverLease operation 
> automatically when DFSOutputSteam close encounters exception. It should be 
> disabled by default, but when somebody builds a long-running service based on 
> HDFS, they can enable this option.
> We've add this feature to our internal Hadoop distribution for more than 3 
> years, it's quite useful according our experience.



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