Subject: [Beginner] Run compute on large matrices and return the result in seconds?


Users can also request random rows in those columns. So a user can request a subset of the matrix (N rows and N columns) which would change the value of the correlation coefficient.

From: Jerry Vinokurov [mailto:[EMAIL PROTECTED]]
Sent: Wednesday, July 17, 2019 1:27 PM
To: [EMAIL PROTECTED]
Subject: Re: [Beginner] Run compute on large matrices and return the result in seconds?

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Maybe I'm not understanding something about this use case, but why is precomputation not an option? Is it because the matrices themselves change? Because if the matrices are constant, then I think precomputation would work for you even if the users request random correlations. You can just store the resulting column with the matrix id, row, and column as the key for retrieval.

My general impression is that while you could do this in Spark, it's probably not the correct framework for carrying out this kind of operation. This feels more like a job for something like OpenMP than for Spark.
On Wed, Jul 17, 2019 at 3:42 PM Gautham Acharya <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>> wrote:
As I said in the my initial message, precomputing is not an option.

Retrieving only the top/bottom N most correlated is an option – would that speed up the results?

Our SLAs are soft – slight variations (+- 15 seconds) will not cause issues.

--gautham
From: Patrick McCarthy [mailto:[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>]
Sent: Wednesday, July 17, 2019 12:39 PM
To: Gautham Acharya <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>>
Cc: Bobby Evans <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>>; Steven Stetzler <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>>; [EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>
Subject: Re: [Beginner] Run compute on large matrices and return the result in seconds?

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Do you really need the results of all 3MM computations, or only the top- and bottom-most correlation coefficients? Could correlations be computed on a sample and from that estimate a distribution of coefficients? Would it make sense to precompute offline and instead focus on fast key-value retrieval, like ElasticSearch or ScyllaDB?

Spark is a compute framework rather than a serving backend, I don't think it's designed with retrieval SLAs in mind and you may find those SLAs difficult to maintain.

On Wed, Jul 17, 2019 at 3:14 PM Gautham Acharya <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>> wrote:
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:[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>]
Sent: Wednesday, July 17, 2019 7:06 AM
To: Steven Stetzler <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>>
Cc: Gautham Acharya <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>>; [EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>
Subject: Re: [Beginner] Run compute on large matrices and return the result in seconds?

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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 <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>> 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%7C66627034c52c4b439bf008d70af53a3e%7C32669cd6737f4b398bddd6951120d3fc%7C0%7C0%7C