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https://issues.apache.org/jira/browse/HADOOP-7753?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13136451#comment-13136451
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Nathan Roberts commented on HADOOP-7753:
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Hi Todd. I reviewed the patch and only have a few questions/suggestions
(Nothing that should hold it up):
* configure.ac, why the change to ldflags?
* Did you happen to test sync_file_range on both 32 and 64 bit architectures
with files greater than 4G? I'm pretty sure it's correct as it basically does
exactly what glibc does but I wasn't 100% certain.
* I was wondering if cancel should go ahead and remove the req from the workq.
* I think it would nice to have a way to disable readahead via configuration.
Maybe we could expose POOL_SIZE/MAX_POOL_SIZE and do it that way?
* Regarding the cancel race. It's not guaranteed to return EBADF because the fd
is likely to get reused immediately for something else. Could be another file,
a socket, pretty much anything. I tried to think of ways this could cause a
problem, but couldn't come up with any. So I think the code is safe, maybe just
a tweak to the comment saying you realize it could be a totally different file
and that's ok.
* It would be nice if ReadaheadRequest sanity checked curPos and
maxOffsetToRead as well, that way it's a little easier to guarantee the rest of
the method is behaving correctly under all conditions.
* Might be nice in the maxOffsetToRead param to indicate what the "don't care"
value is (Long.MAX_VALUE?)
> Support fadvise and sync_data_range in NativeIO, add ReadaheadPool class
> ------------------------------------------------------------------------
>
> Key: HADOOP-7753
> URL: https://issues.apache.org/jira/browse/HADOOP-7753
> Project: Hadoop Common
> Issue Type: Sub-task
> Components: io, native
> Affects Versions: 0.23.0
> Reporter: Todd Lipcon
> Assignee: Todd Lipcon
> Attachments: hadoop-7753.txt, hadoop-7753.txt
>
>
> This JIRA adds JNI wrappers for sync_data_range and posix_fadvise. It also
> implements a ReadaheadPool class for future use from HDFS and MapReduce.
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