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From Sean Owen <so...@cloudera.com>
Subject Re: Serialized 3rd party libs
Date Tue, 02 Sep 2014 17:00:24 GMT
The problem is not using the drivers per se, but writing your
functions in a way that you are trying to serialize them. You can't
serialize them, and indeed don't want to.  Instead your code needs to
reopen connections and so forth when the function is instantiated on
the remote worker.

static variables are a crude way to do that, probably too crude in general.
No, there's certainly no reason you can't access these things in Spark.

Since it answers exactly this point, I don't mind promoting today's blog post:
http://blog.cloudera.com/blog/2014/09/how-to-translate-from-mapreduce-to-apache-spark/
... which repeats Tobias's good formulation of how to deal with things
like drivers in an efficient way that doesn't trip over serialization.

On Tue, Sep 2, 2014 at 5:45 PM, Matt Narrell <matt.narrell@gmail.com> wrote:
> Hello,
>
> I’m using Spark streaming to aggregate data from a Kafka topic in sliding windows.
 Usually we want to persist this aggregated data to a MongoDB cluster, or republish to a different
Kafka topic.  When I include these 3rd party drivers, I usually get a NotSerializableException
due to the parallelization of the job.  To side step this, I’ve used static class variables
which seem to help, e.g., I can run my jobs.
>
> Is this the proper way to provide 3rd party libs to Spark jobs?
> Does having these drivers declared as static prohibit me from parallelizing my job?
> Is this even a proper way to design jobs?
>
> An alternative (I assume) would be to aggregate my data into HDFS and have another process
(perhaps non-Spark?) consume it and republish/persist?
>
> Thanks,
> Matt
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