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https://issues.apache.org/jira/browse/MAPREDUCE-6923?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16114083#comment-16114083
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Robert Schmidtke commented on MAPREDUCE-6923:
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I am using bytecode instrumentation to log every read and write request going
through the core Java I/O classes. I do this for every JVM started (Yarn, Map,
Reduce, Hdfs, ...), and log statistics over the entire TeraSort run. The
aggregated statistics from there agree to 97-99% (for reads and writes,
respectively) with what the underlying XFS file system counters report. Hence I
assume that my instrumentation is pretty accurate, giving 1169 GiB for all Yarn
I/O. I see that some 1.5 GiB is spent on reading the mapreduce jar files (in
Yarn), and another 1.2 GiB is spent reading jar files in /usr/lib/jvm. However,
there most likely is caching involved, and I wouldn't be sure about how much
I/O actually happened at this level.
I have instrumented reading zip and jar files separately, and over the course
of all map tasks (TeraGen + TeraSort), my instrumentation gives a total of
{{638 GiB / (2048 + 2048) = 159.5 MiB}} per mapper, and {{337 GiB / 2048 =
168.5 MiB}} per reducer. However I wouldn't rely too much on these numbers,
because if I added them to the regular I/O induced by reading/writing the
input/output, shuffle and spill, then my numbers wouldn't agree any longer with
the XFS counters.
{quote}
On some installations I've seen the JVM load close to 400Mb of jar files for
hadoop and its dependencies. Even on trunk my MapTask reads about 180Mb of jars
atleast.
{quote}
Do you mean that Yarn should exhibit this I/O, or would I see this in the map
and reduce JVMs (as explained above)?
> YARN Shuffle I/O for small partitions
> -------------------------------------
>
> Key: MAPREDUCE-6923
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-6923
> Project: Hadoop Map/Reduce
> Issue Type: Improvement
> Environment: Observed in Hadoop 2.7.3 and above (judging from the
> source code of future versions), and Ubuntu 16.04.
> Reporter: Robert Schmidtke
> Assignee: Robert Schmidtke
> Attachments: MAPREDUCE-6923.00.patch
>
>
> When a job configuration results in small partitions read by each reducer
> from each mapper (e.g. 65 kilobytes as in my setup: a
> [TeraSort|https://github.com/apache/hadoop/blob/branch-2.7.3/hadoop-mapreduce-project/hadoop-mapreduce-examples/src/main/java/org/apache/hadoop/examples/terasort/TeraSort.java]
> of 256 gigabytes using 2048 mappers and reducers each), and setting
> {code:xml}
> <property>
> <name>mapreduce.shuffle.transferTo.allowed</name>
> <value>false</value>
> </property>
> {code}
> then the default setting of
> {code:xml}
> <property>
> <name>mapreduce.shuffle.transfer.buffer.size</name>
> <value>131072</value>
> </property>
> {code}
> results in almost 100% overhead in reads during shuffle in YARN, because for
> each 65K needed, 128K are read.
> I propose a fix in
> [FadvisedFileRegion.java|https://github.com/apache/hadoop/blob/trunk/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-shuffle/src/main/java/org/apache/hadoop/mapred/FadvisedFileRegion.java#L114]
> as follows:
> {code:java}
> ByteBuffer byteBuffer = ByteBuffer.allocate(Math.min(this.shuffleBufferSize,
> trans > Integer.MAX_VALUE ? Integer.MAX_VALUE : (int) trans));
> {code}
> e.g.
> [here|https://github.com/apache/hadoop/compare/branch-2.7.3...robert-schmidtke:adaptive-shuffle-buffer].
> This sets the shuffle buffer size to the minimum value of the shuffle buffer
> size specified in the configuration (128K by default), and the actual
> partition size (65K on average in my setup). In my benchmarks this reduced
> the read overhead in YARN from about 100% (255 additional gigabytes as
> described above) down to about 18% (an additional 45 gigabytes). The runtime
> of the job remained the same in my setup.
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