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From jalafate <jalaf...@eng.ucsd.edu>
Subject Identify the performance bottleneck from hardware prospective
Date Tue, 17 Feb 2015 07:28:03 GMT
Hi there,

I am trying to scale up the data size that my application is handling. This
application is running on a cluster with 16 slave nodes. Each slave node has
60GB memory. It is running in standalone mode. The data is coming from HDFS
that also in same local network.

In order to have an understanding on how my program is running, I also had a
Ganglia installed on the cluster. From previous run, I know the stage that
taking longest time to run is counting word pairs (my RDD consists of
sentences from a corpus). My goal is to identify the bottleneck of my
application, then modify my program or hardware configurations according to
that.

Unfortunately, I didn't find too much information on Spark monitoring and
optimization topics. Reynold Xin gave a great talk on Spark Summit 2014 for
application tuning from tasks perspective. Basically, his focus is on tasks
that oddly slower than the average. However, it didn't solve my problem
because there is no such tasks that run way slow than others in my case.

So I tried to identify the bottleneck from hardware prospective. I want to
know what the limitation of the cluster is. I think if the executers are
running hard, either CPU, memory or network bandwidth (or maybe the
combinations) is hitting the roof. But Ganglia reports the CPU utilization
of cluster is no more than 50%, network utilization is high for several
seconds at the beginning, then drop close to 0. From Spark UI, I can see the
nodes with maximum memory usage is consuming around 6GB, while
"spark.executor.memory" is set to be 20GB. 

I am very confused that the program is not running fast enough, while
hardware resources are not in shortage. Could you please give me some hints
about what decides the performance of a Spark application from hardware
perspective?

Thanks!

Julaiti



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