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webservice performance

User: "sgenzer"
Altair Employee
Updated by Jocelyn

Hi...question from a potential new user of RM Server:

 

"I am curious about performance of the rapidminer api - if we determine that we want to build models using that tool is it capable of then serving those results to an app with a large audience?"

 

Thoughts?  I assume the performance of any RapidMiner webservice (api) is purely dependent on the server on which it is hosted...?  Any insight or experiences using a RM Server webservice in high capacity situations?

 

Scott

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    User: "IngoRM"
    New Altair Community Member
    Accepted Answer

    Hi Scott,

     

    There is probably no generic answer to this but I will try.  First, it depends what the process behind the service is doing.  Data prep? Modeling? Scoring?  Most integrations focus more on scoring (plus the necessary data prep) so I will focus on this.  Let's also assume that you made all the smart choices (e.g. you are not using a K-NN model trained on a billion examples and hope for a fast scoring ;-)).

     

    In those situations, and of course this depends on the used machines, you can easily get up to millions of scores per second through executing the process directly.  You will get the same throughput if you are using the Java API of RapidMiner.  That is more than enough for most situations I have seen (with the notable exception of automatic trading).

     

    The web service API slows this down a bit. Hard to tell by how much since this not only depends on the machines but also on the network infrastructure. But then on the other hand you can overcome this easily by running multiple scoring instances in parallel.

     

    So in short: 10+ million / second is what you can achieve in a single instance for scoring plus simple data prep.  But this is only true if you are using the Java API.  It will be considerably less for the web service API.  But here the advantage is that you can easily scale this out horizontally if you actually need to.

     

    Hope this helps a bit,

    Ingo