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From "Guttadauro, Jeff" <jeff.guttada...@here.com>
Subject RE: YARN cluster underutilization
Date Wed, 25 May 2016 17:30:09 GMT
Thanks for digging into the log, Sunil, and making some interesting observations!

The heartbeat interval hasn’t been changed from its default, and I do see the yarn.resourcemanager.nodemanagers.heartbeat-interval-ms
property set to 1000 in the job configuration.  I was searching in the log for heartbeat interval
information, but I didn’t find anything.  Where do you look in the log for the heartbeats?

Also, you are correct about there being no data locality, as all the input data is in S3.
 The utilization has been fluctuating, but I can’t really see a pattern or tell why.  It
actually started out pretty low in the 20-30% range and then managed to get up into the 50-70%
range after a while, but that was short-lived, as it went back down into the 20-30% range
for quite a while.  While writing this, I saw it surprisingly hit 80%!!  First time I’ve
seen it that high in the 20 hours it’s been running…  Although looks like it may be headed
back down.  I’m perplexed.  Wouldn’t you generally expect fairly stable utilization over
the course of the job?  (This is the only job running.)


From: Sunil Govind [mailto:sunil.govind@gmail.com]
Sent: Wednesday, May 25, 2016 11:55 AM
To: Guttadauro, Jeff <jeff.guttadauro@here.com>; user@hadoop.apache.org
Subject: Re: YARN cluster underutilization

Hi Jeff.

Thanks for sharing this information. I have some observations from this logs.

- I think the node heartbeat is around 2/3 seconds here. Is it changed due to some other reasons?
- And all mappers Resource Request seems to be asking for type ANY (there is no data locality).
pls correct me if I am wrong.

If the resource request type is ANY, only one container will be allocated per heartbeat for
a node. Here node heartbeat delay is also more. And I can see that containers are released
very fast too. So when u started you application, are you seeing more better resource utilization?
And once containers started to get released/completed, you are seeing under utilization.

Pls look into this line. It may be a reason.


On Wed, May 25, 2016 at 9:59 PM Guttadauro, Jeff <jeff.guttadauro@here.com<mailto:jeff.guttadauro@here.com>>
Thanks for your thoughts thus far, Sunil.  Most grateful for any additional help you or others
can offer.  To answer your questions,

1.       This is a custom M/R job, which uses mappers only (no reduce phase) to process GPS
probe data and filter based on inclusion within a provided polygon.  There is actually a lot
of upfront work done in the driver to make that task as simple as can be (identifies a list
of tiles that are completely inside the polygon and those that fall across an edge, for which
more processing would be needed), but the job would still be more compute-intensive than wordcount,
for example.

2.       I’m running almost 84k mappers for this job.  This is actually down from ~600k
mappers, since one other thing I’ve done is increased the mapreduce.input.fileinputformat.split.minsize
to 536870912 (512M) for the job.  Data is in S3, so loss of locality isn’t really a concern.

3.       For NodeManager configuration, I’m using EMR’s default configuration for the
m3.xlarge instance type, which is yarn.scheduler.minimum-allocation-mb=32, yarn.scheduler.maximum-allocation-mb=11520,
and yarn.nodemanager.resource.memory-mb=11520.  YARN dashboard shows min/max allocations of
<memory:32, vCores:1>/<memory:11520, vCores:8>.

4.       Capacity Scheduler [MEMORY]

5.       I’ve attached 2500 lines from the RM log.  Happy to grab more, but they are pretty
big, and I thought that might be sufficient.

Any guidance is much appreciated!

From: Sunil Govind [mailto:sunil.govind@gmail.com<mailto:sunil.govind@gmail.com>]
Sent: Wednesday, May 25, 2016 10:55 AM
To: Guttadauro, Jeff <jeff.guttadauro@here.com<mailto:jeff.guttadauro@here.com>>;
Subject: Re: YARN cluster underutilization

Hi Jeff,

It looks like to you are allocating more memory for AM container. Mostly you might not need
6Gb (as per the log). Could you please help  to provide some more information.

1. What type of mapreduce application (wordcount etc) are you running? Some AMs may be CPU
intensive and some may not be. So based on the type application, memory/cpu can be tuned for
better utilization.
2. How many mappers (reducers) are you trying to run here?
3. You have mentioned that each node has 8 cores and 15GB, but how much is actually configured
for NM?
4. Which scheduler are you using?
5. Its better to attach RM log if possible.


On Wed, May 25, 2016 at 8:58 PM Guttadauro, Jeff <jeff.guttadauro@here.com<mailto:jeff.guttadauro@here.com>>
Hi, all.

I have an M/R (map-only) job that I’m running on a Hadoop 2.7.1 YARN cluster that is being
quite underutilized (utilization of around 25-30%).  The EMR cluster is 1 master + 20 core
m3.xlarge nodes, which have 8 cores each and 15G total memory (with 11.25G of that available
to YARN).  I’ve configured mapper memory with the following properties, which should allow
for 8 containers running map tasks per node:

  <!-- Container size -->
 <!-- JVM arguments for a Map task -->

It was suggested that perhaps my AppMaster was having trouble keeping up with creating all
the mapper containers and that I bulk up its resource allocation.  So I did, as shown below,
providing it 6G container memory (5G task memory), 3 cores, and 60 task listener threads.

 <!-- App Master task listener threads -->
 <!-- App Master container vcores -->
 <!-- App Master container size -->
 <!-- JVM arguments for each Application Master -->

Taking a look at the node on which the AppMaster is running, I'm seeing plenty of CPU idle
time and free memory, yet there are still nodes with no utilization (0 running containers).
 The log indicates that the AppMaster has way more memory (physical/virtual) than it appears
to need with repeated log messages like this:

2016-05-25 13:59:04,615 INFO org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl
(Container Monitor): Memory usage of ProcessTree 11265 for container-id container_1464122327865_0002_01_000001:
1.6 GB of 6.3 GB physical memory used; 6.1 GB of 31.3 GB virtual memory used

Can you please help me figure out where to go from here to troubleshoot, or any other things
to try?


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