[jira] [Created] (SPARK-5037) support dynamic loading of input DStreams in pyspark streaming

["Jascha Swisher (JIRA)" <ji...@apache.org>]

Jascha Swisher created SPARK-5037:
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             Summary: support dynamic loading of input DStreams in pyspark streaming
                 Key: SPARK-5037
                 URL: https://issues.apache.org/jira/browse/SPARK-5037
             Project: Spark
          Issue Type: New Feature
          Components: PySpark, Streaming
    Affects Versions: 1.2.0
            Reporter: Jascha Swisher


The scala and java streaming APIs support "external" InputDStreams (e.g. the ZeroMQReceiver example) through a number of mechanisms, for instance by overriding ActorReceiver or just subclassing Receiver directly. The pyspark streaming API does not currently allow similar flexibility, being limited at the moment to file-backed text and binary streams or socket text streams.

It would be great to open up the pyspark streaming API to other stream sources, putting it closer to on par with the JVM APIs.

One way of doing this could be to support dynamically loading InputDStream implementations through reflection at the JVM level, analogously to what is currently done for Hadoop InputFormats in the regular pyspark context.py *Hadoop* methods. 

I'll submit a PR momentarily with my shot at this. Comments and alternative approaches more than welcome.



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