[
https://issues.apache.org/jira/browse/HDFS-918?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12833579#action_12833579
]
Zlatin Balevsky commented on HDFS-918:
--------------------------------------
I see a problem with doing the disk read on the same thread that is doing the
select()-ing; the round-robining of several selector threads doesn't help you
avoid a situation where a channel is writable, but the selecting thread is
stuck in a transferTo call to another channel even if there are other selector
threads in handlers[] available. With an architecture like this you will
always perform worse than a thread-per-stream approach.
Instead you could have a single selector thread that blocks only on select()
and never does any disk io (including creation of RandomAccessFile objects).
It simply dispatches the writable channels to a threadpool that does the actual
transferTo calls.
> Use single Selector and small thread pool to replace many instances of
> BlockSender for reads
> --------------------------------------------------------------------------------------------
>
> Key: HDFS-918
> URL: https://issues.apache.org/jira/browse/HDFS-918
> Project: Hadoop HDFS
> Issue Type: Improvement
> Components: data-node
> Reporter: Jay Booth
> Fix For: 0.22.0
>
> Attachments: hdfs-918-20100201.patch, hdfs-918-20100203.patch,
> hdfs-918-20100211.patch, hdfs-multiplex.patch
>
>
> Currently, on read requests, the DataXCeiver server allocates a new thread
> per request, which must allocate its own buffers and leads to
> higher-than-optimal CPU and memory usage by the sending threads. If we had a
> single selector and a small threadpool to multiplex request packets, we could
> theoretically achieve higher performance while taking up fewer resources and
> leaving more CPU on datanodes available for mapred, hbase or whatever. This
> can be done without changing any wire protocols.
--
This message is automatically generated by JIRA.
-
You can reply to this email to add a comment to the issue online.
