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From Gautham Acharya <gauth...@alleninstitute.org>
Subject RE: [Beginner] Run compute on large matrices and return the result in seconds?
Date Wed, 17 Jul 2019 19:13:27 GMT
Thanks for the reply, Bobby.

I’ve received notice that we can probably tolerate response times of up to 30 seconds. Would
this be more manageable? 5 seconds was an initial ask, but 20-30 seconds is also a reasonable
response time for our use case.

With the new SLA, do you think that we can easily perform this computation in spark?
--gautham

From: Bobby Evans [mailto:revans2@gmail.com]
Sent: Wednesday, July 17, 2019 7:06 AM
To: Steven Stetzler <steven.stetzler@gmail.com>
Cc: Gautham Acharya <gauthama@alleninstitute.org>; user@spark.apache.org
Subject: Re: [Beginner] Run compute on large matrices and return the result in seconds?

CAUTION: This email originated from outside the Allen Institute. Please do not click links
or open attachments unless you've validated the sender and know the content is safe.
________________________________
Let's do a few quick rules of thumb to get an idea of what kind of processing power you will
need in general to do what you want.

You need 3,000,000 ints by 50,000 rows.  Each int is 4 bytes so that ends up being about 560
GB that you need to fully process in 5 seconds.

If you are reading this from spinning disks (which average about 80 MB/s) you would need at
least 1,450 disks to just read the data in 5 seconds (that number can vary a lot depending
on the storage format and your compression ratio).
If you are reading the data over a network (let's say 10GigE even though in practice you cannot
get that in the cloud easily) you would need about 90 NICs just to read the data in 5 seconds,
again depending on the compression ration this may be lower.
If you assume you have a cluster where it all fits in main memory and have cached all of the
data in memory (which in practice I have seen on most modern systems at somewhere between
12 and 16 GB/sec) you would need between 7 and 10 machines just to read through the data once
in 5 seconds.  Spark also stores cached data compressed so you might need less as well.

All the numbers fit with things that spark should be able to handle, but a 5 second SLA is
very tight for this amount of data.

Can you make this work with Spark?  probably. Does spark have something built in that will
make this fast and simple for you?  I doubt it you have some very tight requirements and will
likely have to write something custom to make it work the way you want.


On Thu, Jul 11, 2019 at 4:12 PM Steven Stetzler <steven.stetzler@gmail.com<mailto:steven.stetzler@gmail.com>>
wrote:
Hi Gautham,

I am a beginner spark user too and I may not have a complete understanding of your question,
but I thought I would start a discussion anyway. Have you looked into using Spark's built
in Correlation function? (https://spark.apache.org/docs/latest/ml-statistics.html<https://nam05.safelinks.protection.outlook.com/?url=https%3A%2F%2Fspark.apache.org%2Fdocs%2Flatest%2Fml-statistics.html&data=02%7C01%7C%7C7d44353d2dd5420bc35108d70abff11d%7C32669cd6737f4b398bddd6951120d3fc%7C0%7C1%7C636989691818858010&sdata=UG7owx%2FyHayKECNbDbfoNV53nJCSlF06Oak1plpi4RY%3D&reserved=0>)
This might let you get what you want (per-row correlation against the same matrix) without
having to deal with parallelizing the computation yourself. Also, I think the question of
how quick you can get your results is largely a data access question vs how fast is Spark
question. As long as you can exploit data parallelism (i.e. you can partition up your data),
Spark will give you a speedup. You can imagine that if you had a large machine with many cores
and ~100 GB of RAM (e.g. a m5.12xlarge EC2 instance), you could fit your problem in main memory
and perform your computation with thread based parallelism. This might get your result relatively
quickly. For a dedicated application with well constrained memory and compute requirements,
it might not be a bad option to do everything on one machine as well. Accessing an external
database and distributing work over a large number of computers can add overhead that might
be out of your control.

Thanks,
Steven

On Thu, Jul 11, 2019 at 9:24 AM Gautham Acharya <gauthama@alleninstitute.org<mailto:gauthama@alleninstitute.org>>
wrote:
Ping? I would really appreciate advice on this! Thank you!

From: Gautham Acharya
Sent: Tuesday, July 9, 2019 4:22 PM
To: user@spark.apache.org<mailto:user@spark.apache.org>
Subject: [Beginner] Run compute on large matrices and return the result in seconds?


This is my first email to this mailing list, so I apologize if I made any errors.



My team's going to be building an application and I'm investigating some options for distributed
compute systems. We want to be performing computes on large matrices.



The requirements are as follows:



1.     The matrices can be expected to be up to 50,000 columns x 3 million rows. The values
are all integers (except for the row/column headers).

2.     The application needs to select a specific row, and calculate the correlation coefficient
( https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html<https://nam05.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpandas.pydata.org%2Fpandas-docs%2Fstable%2Freference%2Fapi%2Fpandas.DataFrame.corr.html&data=02%7C01%7C%7C7d44353d2dd5420bc35108d70abff11d%7C32669cd6737f4b398bddd6951120d3fc%7C0%7C1%7C636989691818868018&sdata=e5blX8ItE1JDJRx9D3FnmsXp4TnOKvyH6fA6%2Fw2QTbI%3D&reserved=0>
) against every other row. This means up to 3 million different calculations.

3.     A sorted list of the correlation coefficients and their corresponding row keys need
to be returned in under 5 seconds.

4.     Users will eventually request random row/column subsets to run calculations on, so
precomputing our coefficients is not an option. This needs to be done on request.



I've been looking at many compute solutions, but I'd consider Spark first due to the widespread
use and community. I currently have my data loaded into Apache Hbase for a different scenario
(random access of rows/columns). I’ve naively tired loading a dataframe from the CSV using
a Spark instance hosted on AWS EMR, but getting the results for even a single correlation
takes over 20 seconds.



Thank you!


--gautham

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