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From Pat Ferrel <>
Subject Re: Problem scaling UR
Date Thu, 11 May 2017 01:09:03 GMT
What is the physical architecture? Do you have HBase, Elasticsearch, and Spark running on separate
machines? If the CPU load is low then it must be IO bound reading from Hbase or writing to
Elasticsearch. Do you have any input event load yet or are you making queries? These will
all change the equation and are why separating services to run separately makes the most sense.

On May 10, 2017, at 1:47 PM, Bolmo Joosten <> wrote:

Thanks for your suggestion. I forgot to mention in my last email that the $plus$plus stage
takes most time (95%+) and is using only 1-3 CPUs.

I will give it a try with lower driver memory and higher executor memory.

Maybe a hard question, any idea what kind of training time I should expect with this data
size on this cluster? 

We modified the default UR template to create the eventRDDs from CSV files instead of HBASE.
Hbase was unable to process this amount of data on the cluster. This means we can't provide
any personalized recommendations, but that is ok for now. 

2017-05-10 10:22 GMT-07:00 Pat Ferrel < <>>:
You can’t bypass HBase, you can import JSON to HBase directly so I assume this is what you
are saying.

Executor memory should be higher and driver memory lower. Spark loves memory and in this case
the lower limit is all your input events and BiMaps for all user and item ids. If you don’t
have an OOM you are above minimum but increasing the executor mem might help, also executor
CPUs. The lower limit for the driver mem is roughly equal to the amount per executor.

One unfortunate thing about Spark is that you can scale it to do the job in minutes but when
you go to read or write to/from HBase or Elasticsearch this large a cluster will overload
the DBs. So training in a long time is not all that bad a thing since the cluster will probably
not be overloading the IO.

On May 10, 2017, at 8:45 AM, Bolmo Joosten < <>>

Hi all,

I have trouble scaling the Universal Recommender to a dataset with 250M events (purchase,
view, atb). It trains ok on a couple of million events, but the training time becomes very
long (>48h) on the large dataset.

Hardware specs:

Standalone cluster
20 cores (40 hyper threading)
Input data size format:

We load directly from CSV files and bypass HBASE. Size of CSV is 19 GB.
PIO JSON format equivalent size: 150 GB
Train command:

pio train -- --driver-memory 64G --executor-memory 8G --executor-cores 2

I have used various variations with driver, executor memory and number of cores, but the training
time does not seem to be affected by this.

Spark UI tells me the save method (collect > $plus$plus) in URModel.scala takes a very
long time. See attached dumps of the Spark UI for details. 

Any suggestions?

Thanks, Bolmo

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