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From "Jascha Swisher (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-5037) support dynamic loading of input DStreams in pyspark streaming
Date Wed, 31 Dec 2014 17:25:13 GMT
Jascha Swisher created SPARK-5037:
-------------------------------------

             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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