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From "Chris Nauroth (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HADOOP-13403) AzureNativeFileSystem rename/delete performance improvements
Date Mon, 01 Aug 2016 21:15:20 GMT

    [ https://issues.apache.org/jira/browse/HADOOP-13403?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15402836#comment-15402836

Chris Nauroth commented on HADOOP-13403:

Thank you for sharing patch 003.

If the reason for the unusual executor logic is optimization, then I suggest adding more comments
in the {{executeParallel}} JavaDocs to explain that.  I'm not sure that the memory optimization
argument is true for the {{delete}} code path, where it still does a conversion from {{ArrayList}}
to array.

bq. Is there any way to achieve this through futures?

If the code had followed idiomatic usage, then the typical solution is to call {{ThreadPoolExecutor#submit}}
for each task, track every returned {{Future}} in a list, and then iterate through the list
and call {{Future#get}} on each one.  If any individual task threw an exception, then the
call to {{Future#get}} would propagate that exception.  Then, that would give you an opportunity
to call {{ThreadPoolExecutor#shutdownNow}} to cancel or interrupt all remaining tasks.  With
the current logic though, I don't really see a way to adapt this pattern.

Repeating an earlier comment, I don't see any exceptions thrown from {{getThreadPool}}, so
coding exception handling around it and tests for it looks unnecessary.  If you check validity
of {{deleteThreadCount}} and {{renameThreadCount}} in {{initialize}} (e.g. check for values
<= 0) and fail fast by throwing an exception during initialization, then even unchecked
exceptions will be impossible during calls to {{getThreadPool}}.

I still see numerous test failures in {{TestFileSystemOperationsWithThreads}}.  For the next
patch revision, would you please ensure all tests pass?

> AzureNativeFileSystem rename/delete performance improvements
> ------------------------------------------------------------
>                 Key: HADOOP-13403
>                 URL: https://issues.apache.org/jira/browse/HADOOP-13403
>             Project: Hadoop Common
>          Issue Type: Bug
>          Components: azure
>    Affects Versions: 2.7.2
>            Reporter: Subramanyam Pattipaka
>            Assignee: Subramanyam Pattipaka
>             Fix For: 2.9.0
>         Attachments: HADOOP-13403-001.patch, HADOOP-13403-002.patch, HADOOP-13403-003.patch
> WASB Performance Improvements
> Problem
> -----------
> Azure Native File system operations like rename/delete which has large number of directories
and/or files in the source directory are experiencing performance issues. Here are possible
> a)	We first list all files under source directory hierarchically. This is a serial operation.

> b)	After collecting the entire list of files under a folder, we delete or rename files
one by one serially.
> c)	There is no logging information available for these costly operations even in DEBUG
mode leading to difficulty in understanding wasb performance issues.
> Proposal
> -------------
> Step 1: Rename and delete operations will generate a list all files under the source
folder. We need to use azure flat listing option to get list with single request to azure
store. We have introduced config fs.azure.flatlist.enable to enable this option. The default
value is 'false' which means flat listing is disabled.
> Step 2: Create thread pool and threads dynamically based on user configuration. These
thread pools will be deleted after operation is over.  We are introducing introducing two
new configs
> 	a)	fs.azure.rename.threads : Config to set number of rename threads. Default value is
0 which means no threading.
> 	b)	fs.azure.delete.threads: Config to set number of delete threads. Default value is
0 which means no threading.
> 	We have provided debug log information on number of threads not used for the operation
which can be useful .
> 	Failure Scenarios:
> 	If we fail to create thread pool due to ANY reason (for example trying create with thread
count with large value such as 1000000), we fall back to serialization operation. 
> Step 3: Bob operations can be done in parallel using multiple threads executing following
> 	while ((currentIndex = fileIndex.getAndIncrement()) < files.length) {
> 		FileMetadata file = files[currentIndex];
> 		Rename/delete(file);
> 	}
> 	The above strategy depends on the fact that all files are stored in a final array and
each thread has to determine synchronized next index to do the job. The advantage of this
strategy is that even if user configures large number of unusable threads, we always ensure
that work doesn’t get serialized due to lagging threads. 
> 	We are logging following information which can be useful for tuning number of threads
> 	a) Number of unusable threads
> 	b) Time taken by each thread
> 	c) Number of files processed by each thread
> 	d) Total time taken for the operation
> 	Failure Scenarios:
> 	Failure to queue a thread execute request shouldn’t be an issue if we can ensure at
least one thread has completed execution successfully. If we couldn't schedule one thread
then we should take serialization path. Exceptions raised while executing threads are still
considered regular exceptions and returned to client as operation failed. Exceptions raised
while stopping threads and deleting thread pool shouldn't can be ignored if operation all
files are done with out any issue.

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