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From Nick Pentreath <nick.pentre...@gmail.com>
Subject Re: [PySpark]: reading arbitrary Hadoop InputFormats
Date Wed, 30 Oct 2013 21:25:49 GMT
Thanks Josh, Patrick for the feedback.

Based on Josh's pointers I have something working for JavaPairRDD ->
PySpark RDD[(String, String)]. This just calls the toString method on each
key and value as before, but without the need for a delimiter. For
SequenceFile, it uses SequenceFileAsTextInputFormat which itself calls
toString to convert to Text for keys and values. We then call toString
(again) ourselves to get Strings to feed to writeAsPickle.

Details here: https://gist.github.com/MLnick/7230588

This also illustrates where the "wrapper function" api would fit in. All
that is required is to define a T => String for key and value.

I started playing around with MsgPack and can sort of get things to work in
Scala, but am struggling with getting the raw bytes to be written properly
in PythonRDD (I think it is treating them as pickled byte arrays when they
are not, but when I removed the 'stripPickle' calls and amended the length
(-6) I got "UnpicklingError: invalid load key, ' '. ").

Another issue is that MsgPack does well at writing "structures" - like Java
classes with public fields that are fairly simple - but for example the
Writables have private fields so you end up with nothing being written.
This looks like it would require custom "Templates" (serialization
functions effectively) for many classes, which means a lot of custom code
for a user to write to use it. Fortunately for most of the common Writables
a toString does the job. Will keep looking into it though.

Anyway, Josh if you have ideas or examples on the "Wrapper API from Python"
that you mentioned, I'd be interested to hear them.

If you think this is worth working up as a Pull Request covering
SequenceFiles and custom InputFormats with default toString conversions and
the ability to specify Wrapper functions, I can clean things up more, add
some functionality and tests, and also test to see if common things like
the "normal" Writables and reading from things like HBase and Cassandra can
be made to work nicely (any other common use cases that you think make
sense?).

Thoughts, comments etc welcome.

Nick



On Fri, Oct 25, 2013 at 11:03 PM, Patrick Wendell <pwendell@gmail.com>wrote:

> 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 <rosenville@gmail.com> 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
> >
> >
> https://cwiki.apache.org/confluence/display/SPARK/PySpark+Internals#PySparkInternals-customserializers
> > )
> > 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 (
> >
> >
> https://github.com/apache/incubator-spark/blob/master/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala#L224
> > )
> > 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
> > <nick.pentreath@gmail.com>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:
> https://gist.github.com/MLnick/7150058
> > >
> > > 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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