Github user liyezhang556520 commented on a diff in the pull request:
https://github.com/apache/spark/pull/12083#discussion_r58287737
--- Diff:
common/network-common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java
---
@@ -44,6 +45,14 @@
private long totalBytesTransferred;
/**
+ * When the write buffer size is larger than this limit, I/O will be
done in chunks of this size.
+ * The size should not be too large as it will waste underlying memory
copy. e.g. If network
+ * avaliable buffer is smaller than this limit, the data cannot be sent
within one single write
+ * operation while it still will make memory copy with this size.
+ */
+ private static final int NIO_BUFFER_LIMIT = 512 * 1024;
--- End diff --
>What if we create DirectByteBuffer here manually for a big buf (big enough
so that we can get benefits even if creating a direct buffer is slow) and try
to write as many as possible? Then we can avoid the memory copy in IOUtil.write.
@zsxwing , Yes, redundant copy can be avoided if we give a directBuffer
directly to `WritableByteChannel.write()` because of code in line
http://www.grepcode.com/file/repository.grepcode.com/java/root/jdk/openjdk/7u40-b43/sun/nio/ch/IOUtil.java#50,
but I don't know if that's worthwhile. `IOUtil` will maintain a directBuffer
pool to avoid frequently allocate the directBuffers. I think that's why when I
made the test, the first time I run code
`sc.parallelize(Array(1,2,3),3).mapPartitions(a=>Array(new Array[Long](1024 *
1024 * 200)).iterator).reduce((a,b)=> a).length`, the network throughput is
extremely low on executor side, and if I ran this code after I ran the code
`sc.parallelize(Array(1,2,3),3).mapPartitions(a=>Array(new Array[Double](1024 *
1024 * 50)).iterator).reduce((a,b)=> a).length`, the network throughput will be
much higher.
So, If we want create direct Buffer manually in Spark, It's better also
maintain a buffer pool, but that will introduce much more complexity and have
the risk of memory leak.
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