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Comment by jbooth | Hacker News Reader

More like 3 tiers of support:

Scala -- native API

Java -- java wrappers for Scala API, slightly clunky but same implementation

Python+R -- janky process involving forking a process and feeding strings back and forth between JVM and python/R via pipes

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You're right about Python and R having to pass data back and forth to the JVM for certain operations, but also keep in mind that native code still runs in the native interpreter. That means you have access to the full ecosystem of the native language.

For example, if I want to convert an RDD of JSON strings into Python dictionaries:

    import json
    rdd_dict = rdd.map(lambda x: json.loads(x))

Same goes for any external Python libraries I install on the cluster and want to use in my Spark job. You can even run your Python code on PyPy [4]!

For me, working in Python generally feels like a first class experience on Spark. There are areas -- like GraphX [0], certain niche features [1] -- where Scala is definitely easier to work with, but with time that is becoming less [2] and less [3] true thanks to the DataFrame API.

[0] https://spark.apache.org/graphx/

[1] http://stackoverflow.com/q/23995040/877069

[2] https://github.com/graphframes/graphframes

[3] http://stackoverflow.com/a/37150604/877069

[4] https://github.com/apache/spark/pull/2144

Has anyone tried running Jython on top of the native API? Would that make any sense?

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