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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 Thu, 08 Oct 2009 00:33:31 GMT

    [ https://issues.apache.org/jira/browse/HDFS-347?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12763320#action_12763320

Todd Lipcon commented on HDFS-347:

I ran some tests on my laptop in August where I set the mtu of my loopback interface super-high
and it didn't really change DFSIO benchmarks. This was just on my laptop, though, so if someone
wanted to reproduce on a real machine that would be helpful.

Regarding the bottlenecks, here's one microbenchmark that shows that there is some significant
overhead in local network connections:

Both windows on the same machine:
Window A:
$ nc -l 1234 > /dev/null

Window B:
$ time dd if=/dev/zero of=/dev/fd/1 bs=1M count=4000 | nc localhost 1234
4194304000 bytes (4.2 GB) copied, 23.8092 s, 176 MB/s

real    0m23.818s
user    0m0.948s
sys     0m12.841s

$ time dd if=/dev/zero of=/dev/fd/1 bs=1M count=4000 | cat > /dev/null
4000+0 records in
4000+0 records out
4194304000 bytes (4.2 GB) copied, 4.69959 s, 892 MB/s

real    0m4.708s
user    0m0.268s
sys     0m4.096s

The above is with a jacked-up MTU. With standard MTU, the netcat goes 136MB/sec instead of

Granted, this is a microbenchmark, and a bit unfair since the DN uses sendfile and I'm not,
here, but it does show there's significant overhead for localhost network connections.

> 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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