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From "Todd Lipcon (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HDFS-347) DFS read performance suboptimal when client co-located on nodes with data
Date Mon, 12 Oct 2009 08:14:31 GMT

    [ https://issues.apache.org/jira/browse/HDFS-347?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12764594#action_12764594
] 

Todd Lipcon commented on HDFS-347:
----------------------------------

Spent some more time on the implementation this weekend. Here's a benchmark including checksums:

todd@todd-laptop:~/git/hadoop-common/build/hadoop-core-0.21.0-dev$ time ./bin/hadoop fs -Ddfs.client.use.unix.sockets=false
-cat bigfile bigfile bigfile > /dev/null

real    0m13.502s
user    0m9.561s
sys     0m2.904s

todd@todd-laptop:~/git/hadoop-common/build/hadoop-core-0.21.0-dev$ time ./bin/hadoop fs -Ddfs.client.use.unix.sockets=true
-cat bigfile bigfile bigfile > /dev/null
real    0m9.644s
user    0m8.321s
sys     0m1.012s

bigfile is a 700MB file that I put on my local pseudo-distributed cluster.

For comparison, here's just catting the same file:
todd@todd-laptop:~/git/hadoop-common/build/hadoop-core-0.21.0-dev$ time ./bin/hadoop fs -cat
file:///var/www/ubuntu-8.10-desktop-amd64.iso file:///var/www/ubuntu-8.10-desktop-amd64.iso
file:///var/www/ubuntu-8.10-desktop-amd64.iso  > /dev/null
real    0m2.914s
user    0m1.760s
sys     0m1.068s

So, the result is about a 30% speedup over the current implementation, but still 3x overhead
compared to local filesystem. Profiling shows that most of this is in the copying out of the
direct FileChannels - I think a little bit of smart buffering there to create larger reads
over the native-code boundary will get us closer to 2x overhead.

Will clean up the patch tomorrow and upload (though it still needs plenty of work to be commitable)

> 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
>            Reporter: George Porter
>         Attachments: HADOOP-4801.1.patch, HADOOP-4801.2.patch, HADOOP-4801.3.patch, 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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