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https://issues.apache.org/jira/browse/SPARK-4740?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14235036#comment-14235036
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Saisai Shao commented on SPARK-4740:
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Hi [~rxin], the difference between NIO and Netty in transfer small block is to
use transferTo or read block into ByteBuffer. I tried replacing the above code
you mentioned with the below code to keep same behavior.
{code}
ByteBuffer buf = ByteBuffer.allocate((int) length);
channel.position(offset);
while (buf.remaining() != 0) {
if (channel.read(buf) == -1) {
throw new IOException(String.format("Reached EOF before filling
buffer\n" +
"offset=%s\nfile=%s\nbuf.remaining=%s",
offset, file.getAbsoluteFile(), buf.remaining()));
}
}
buf.flip();
return buf;
{code}
I'm not sure is this what you want. IIUC, as I remembered the result is not
improved. We can test again to verify it.
> Netty's network throughput is about 1/2 of NIO's in spark-perf sortByKey
> ------------------------------------------------------------------------
>
> Key: SPARK-4740
> URL: https://issues.apache.org/jira/browse/SPARK-4740
> Project: Spark
> Issue Type: Improvement
> Components: Shuffle, Spark Core
> Affects Versions: 1.2.0
> Reporter: Zhang, Liye
> Attachments: Spark-perf Test Report.pdf, TestRunner sort-by-key -
> Thread dump for executor 1_files (48 Cores per node).zip
>
>
> When testing current spark master (1.3.0-snapshot) with spark-perf
> (sort-by-key, aggregate-by-key, etc), Netty based shuffle transferService
> takes much longer time than NIO based shuffle transferService. The network
> throughput of Netty is only about half of that of NIO.
> We tested with standalone mode, and the data set we used for test is 20
> billion records, and the total size is about 400GB. Spark-perf test is
> Running on a 4 node cluster with 10G NIC, 48 cpu cores per node and each
> executor memory is 64GB. The reduce tasks number is set to 1000.
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