HiFaced a similar issue. Our solution was to load the model, cache it after converting it to a model from mllib and then use that instead of ml model.On Tue, Oct 11, 2016 at 10:22 PM, Sean Owen <email@example.com> wrote:I don't believe it will ever scale to spin up a whole distributed job to serve one request. You can look possibly at the bits in mllib-local. You might do well to export as something like PMML either with Spark's export or JPMML and then load it into a web container and score it, without Spark (possibly also with JPMML, OpenScoring)On Tue, Oct 11, 2016, 17:53 Nicolas Long <firstname.lastname@example.org> wrote: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?