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From Nicolas Long <nicolasl...@gmail.com>
Subject mllib model in production web API
Date Tue, 11 Oct 2016 15:53:18 GMT
Hi all,

so I have a model which has been stored in S3. And I have a Scala webapp
which for certain requests loads the model and transforms submitted data
against it.

I'm not sure how to run this quickly on a single instance though. At the
moment Spark is being bundled up with the web app in an uberjar (sbt
assembly).

But the process is quite slow. I'm aiming for responses < 1 sec so that the
webapp can respond quickly to requests. When I look the Spark UI I see:

Summary Metrics for 1 Completed Tasks

Metric    Min    25th percentile    Median    75th percentile    Max
Duration    94 ms    94 ms    94 ms    94 ms    94 ms
Scheduler Delay    0 ms    0 ms    0 ms    0 ms    0 ms
Task Deserialization Time    3 s    3 s    3 s    3 s    3 s
GC Time    2 s    2 s    2 s    2 s    2 s
Result Serialization Time    0 ms    0 ms    0 ms    0 ms    0 ms
Getting Result Time    0 ms    0 ms    0 ms    0 ms    0 ms
Peak Execution Memory    0.0 B    0.0 B    0.0 B    0.0 B    0.0 B

I don't really understand why deserialization and GC should take so long
when the models are already loaded. Is this evidence I am doing something
wrong? And where can I get a better understanding on how Spark works under
the hood here, and how best to do a standalone/bundled jar deployment?

Thanks!

Nic

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