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From "Colin Patrick McCabe (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HDFS-347) DFS read performance suboptimal when client co-located on nodes with data
Date Thu, 21 Feb 2013 00:07:21 GMT

    [ https://issues.apache.org/jira/browse/HDFS-347?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13582695#comment-13582695

Colin Patrick McCabe commented on HDFS-347:

bq. Why some clients don't want to do short-circuit? Could you give an example?

When using short-circuit local reads, you don't get all of the metrics that you get with regular

bq. LengthInputStream is a FilterInputStream. It is easy to return the underlying input strem
from   FilterInputStream.

Can you be more specific about how you would like to do this?

bq. How to configure the existing short-circuit read (HDFS-2246) after the patch?

On the DataNode side, the configuration parameters for old-style short-circuit local reads
haven't changed.  On the client side, using old-style short-circuit local reads is not possible.
 The server-side code is there only to provide backwards compatibility.  In other words, it
is there to provide interoperability between older clients and newer servers.  We don't have
to maintain it forever, but I think we at least want the backwards compatibility code in 2.0.x.

bq. I mean we might not need to compare version since we currently only has one. If we have
a new version in the future, the server could then detect the old clients and fail them. Sound

My fear is that if we don't think through the compatibility issues now, we'll have more bugs
like HDFS-4506.
> DFS read performance suboptimal when client co-located on nodes with data
> -------------------------------------------------------------------------
>                 Key: HDFS-347
>                 URL: https://issues.apache.org/jira/browse/HDFS-347
>             Project: Hadoop HDFS
>          Issue Type: Improvement
>          Components: datanode, hdfs-client, performance
>            Reporter: George Porter
>            Assignee: Colin Patrick McCabe
>         Attachments: 2013.01.28.design.pdf, 2013.01.31.consolidated2.patch, 2013.01.31.consolidated.patch,
2013.02.15.consolidated4.patch, all.tsv, BlockReaderLocal1.txt, full.patch, HADOOP-4801.1.patch,
HADOOP-4801.2.patch, HADOOP-4801.3.patch, HDFS-347-016_cleaned.patch, HDFS-347.016.patch,
HDFS-347.017.clean.patch, HDFS-347.017.patch, HDFS-347.018.clean.patch, HDFS-347.018.patch2,
HDFS-347.019.patch, HDFS-347.020.patch, HDFS-347.021.patch, HDFS-347.022.patch, HDFS-347.024.patch,
HDFS-347.025.patch, HDFS-347.026.patch, HDFS-347.027.patch, HDFS-347.029.patch, HDFS-347.030.patch,
HDFS-347.033.patch, HDFS-347.035.patch, HDFS-347-branch-20-append.txt, hdfs-347-merge.txt,
hdfs-347-merge.txt, hdfs-347-merge.txt, hdfs-347.png, hdfs-347.txt, local-reads-doc
> One of the major strategies Hadoop uses to get scalable data processing is to move the
code to the data.  However, putting the DFS client on the same physical node as the data blocks
it acts on doesn't improve read performance as much as expected.
> After looking at Hadoop and O/S traces (via HADOOP-4049), I think the problem is due
to the HDFS streaming protocol causing many more read I/O operations (iops) than necessary.
 Consider the case of a DFSClient fetching a 64 MB disk block from the DataNode process (running
in a separate JVM) running on the same machine.  The DataNode will satisfy the single disk
block request by sending data back to the HDFS client in 64-KB chunks.  In BlockSender.java,
this is done in the sendChunk() method, relying on Java's transferTo() method.  Depending
on the host O/S and JVM implementation, transferTo() is implemented as either a sendfilev()
syscall or a pair of mmap() and write().  In either case, each chunk is read from the disk
by issuing a separate I/O operation for each chunk.  The result is that the single request
for a 64-MB block ends up hitting the disk as over a thousand smaller requests for 64-KB each.
> Since the DFSClient runs in a different JVM and process than the DataNode, shuttling
data from the disk to the DFSClient also results in context switches each time network packets
get sent (in this case, the 64-kb chunk turns into a large number of 1500 byte packet send
operations).  Thus we see a large number of context switches for each block send operation.
> I'd like to get some feedback on the best way to address this, but I think providing
a mechanism for a DFSClient to directly open data blocks that happen to be on the same machine.
 It could do this by examining the set of LocatedBlocks returned by the NameNode, marking
those that should be resident on the local host.  Since the DataNode and DFSClient (probably)
share the same hadoop configuration, the DFSClient should be able to find the files holding
the block data, and it could directly open them and send data back to the client.  This would
avoid the context switches imposed by the network layer, and would allow for much larger read
buffers than 64KB, which should reduce the number of iops imposed by each read block operation.

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