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From Patrick Wendell <>
Subject Re: [PySpark]: reading arbitrary Hadoop InputFormats
Date Fri, 25 Oct 2013 21:03:14 GMT
As a starting point, a version where people just write their own "wrapper"
functions to convert various HadoopFiles into String <K, V> files could go
a long way. We could even have a few built-in versions, such as dealing
with Sequence files that are <String, String>. Basically, the user needs to
write a translator in Java/Scala that produces textual records from
whatever format that want. Then, they make sure this is included in the
classpath when running PySpark.

As Josh is saying, I'm pretty sure this is already possible, but we may
want to document it for users. In many organizations they might have 1-2
people who can write the Java/Scala to do this but then many more people
who are comfortable using python once it's setup.

- Patrick

On Fri, Oct 25, 2013 at 11:00 AM, Josh Rosen <> wrote:

> Hi Nick,
> I've seen several requests for SequenceFile support in PySpark, so there's
> definitely demand for this feature.
> I like the idea of passing MsgPack'ed data (or some other structured
> format) from Java to the Python workers.  My early prototype of custom
> serializers (described at
> )
> might be useful for implementing this.  Proper custom serializer support
> would handle the bookkeeping for tracking each stage's input and output
> formats and supplying the appropriate deserialization functions to the
> Python worker, so the Python worker would be able to directly read the
> MsgPack'd data that's sent to it.
> Regarding a wrapper API, it's actually possible to initially transform data
> using Scala/Java and perform the remainder of the processing in PySpark.
>  This involves adding the appropriate compiled to the Java classpath and a
> bit of work in Py4J to create the Java/Scala RDD and wrap it for use by
> PySpark.  I can hack together a rough example of this if anyone's
> interested, but it would need some work to be developed into a
> user-friendly API.
> If you wanted to extend your proof-of-concept to handle the cases where
> keys and values have parseable toString() values, I think you could remove
> the need for a delimiter by creating a PythonRDD from the newHadoopFile
> JavaPairRDD and adding a new method to writeAsPickle (
> )
> to dump its contents as a pickled pair of strings.  (Aside: most of
> writeAsPickle() would probably need be eliminated or refactored when adding
> general custom serializer support).
> - Josh
> On Thu, Oct 24, 2013 at 11:18 PM, Nick Pentreath
> <>wrote:
> > Hi Spark Devs
> >
> > I was wondering what appetite there may be to add the ability for PySpark
> > users to create RDDs from (somewhat) arbitrary Hadoop InputFormats.
> >
> > In my data pipeline for example, I'm currently just using Scala (partly
> > because I love it but also because I am heavily reliant on quite custom
> > Hadoop InputFormats for reading data). However, many users may prefer to
> > use PySpark as much as possible (if not for everything). Reasons might
> > include the need to use some Python library. While I don't do it yet, I
> can
> > certainly see an attractive use case for using say scikit-learn / numpy
> to
> > do data analysis & machine learning in Python. Added to this my cofounder
> > knows Python well but not Scala so it can be very beneficial to do a lot
> of
> > stuff in Python.
> >
> > For text-based data this is fine, but reading data in from more complex
> > Hadoop formats is an issue.
> >
> > The current approach would of course be to write an ETL-style Java/Scala
> > job and then process in Python. Nothing wrong with this, but I was
> thinking
> > about ways to allow Python to access arbitrary Hadoop InputFormats.
> >
> > Here is a quick proof of concept:
> >
> > This works for simple stuff like SequenceFile with simple Writable
> > key/values.
> >
> > To work with more complex files, perhaps an approach is to manipulate
> > Hadoop JobConf via Python and pass that in. The one downside is of course
> > that the InputFormat (well actually the Key/Value classes) must have a
> > toString that makes sense so very custom stuff might not work.
> >
> > I wonder if it would be possible to take the objects that are yielded via
> > the InputFormat and convert them into some representation like ProtoBuf,
> > MsgPack, Avro, JSON, that can be read relatively more easily from Python?
> >
> > Another approach could be to allow a simple "wrapper API" such that one
> can
> > write a wrapper function T => String and pass that into an
> > InputFormatWrapper that takes an arbitrary InputFormat and yields Strings
> > for the keys and values. Then all that is required is to compile that
> > function and add it to the SPARK_CLASSPATH and away you go!
> >
> > Thoughts?
> >
> > Nick
> >

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