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https://issues.apache.org/jira/browse/GIRAPH-717?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13710028#comment-13710028
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Alessandro Presta commented on GIRAPH-717:
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Quick comment on the design: having to call wrap()/unwrap() is inconvenient and
defeats part of the point of this abstraction.
I know you're thinking of deeper changes in how we serialize the IVEM types,
but if you want to ship this first, here's what I suggest in the interim:
- The user implements an interface similar to Computation, but that doesn't
extend it, so that types don't have to be Writable.
- Internally, we do the wrapping/unwrapping magic.
> HiveJythonRunner with support for pure Jython value types.
> ----------------------------------------------------------
>
> Key: GIRAPH-717
> URL: https://issues.apache.org/jira/browse/GIRAPH-717
> Project: Giraph
> Issue Type: Bug
> Reporter: Nitay Joffe
> Assignee: Nitay Joffe
>
> This adds support for pure Jython jobs. Currently this runner is hooked up to
> work with Hive. I'll make it more generic later.
> A Jython job is made up of two Jython scripts:
> 1) launcher - this script is used to configure the job, it is only
> interpreted locally.
> 2) worker - this script is distributed to every worker and is used there.
> Running a Jython job is simply:
> HIVE_HOME=<x>
> HADOOP_HOME=<y>
> $HIVE_HOME/bin/hive --service jar <giraph-hive-jar>
> org.apache.giraph.hive.jython.HiveJythonRunner jython --launcher
> <launcher.py> --worker <worker.py>
> There are examples and tests in the diff. Here is one example:
> launcher: https://gist.github.com/nitay/a62e0a5d369a5e701fa3
> worker: https://gist.github.com/nitay/7834fd2b059527e65a36
> There are a few pieces to a Jython job, I'll go over each part here.
> The launcher defines the graph types (those IVEMM writables) and sets up the
> Hive vertex/edge inputs and output. Each graph type is one of the following:
> 1) A Java type. For example the user can specify simply IntWritable
> 2) A Jython type that implements Writable. In the example above the message
> value implements Writable.
> 3) A pure Jython type. The Java code will wrap these objects in a Writable
> wrapper that serializes Jython values using Pickle (jython IO framework).
> For Hive usage - if your value type is a primitive e.g. IntWritable or
> LongWritable, then you need not do anything. The Java code will automatically
> read/write the Hive table specified and convert between Hive types and the
> primitive Writable. The vertex_id type in the example works like this.
> If your value is a custom Jython type, you must create classes which
> implement JythonHiveReader/JythonHiveWriter (or JythonHiveIO which is both).
> These objects read/write Jython types from Hive. There are wrappers in the
> Java code which take HiveIO data normally used in giraph-hive and turns them
> into Jython types. This means, for example, that getMap() will return a
> Jython dictionary instead of a Java Map.
> There is also a PageRankBenchmark (from previous diff) implemented in Jython.
> Here's a run for comparison / sanity check:
> PageRankBenchmark with 10 workers, 100M vertices, 10B edges, 10 compute
> threads
> trunk:
> https://gist.github.com/nitay/3170fa3b575d4d2e22a9
> total time: 302466
> with this diff:
> https://gist.github.com/nitay/a52b6d1d64e50ab9829e
> total time: 306517
> in jython:
> https://gist.github.com/nitay/3f2e758b2933c3521727
> total time: 434730
> So we see that existing things are not affected (is there something else I
> should test?) and that Jython has around 40% overhead.
> ReviewBoard: https://reviews.apache.org/r/12543/ (Sorry it's a big one, hard
> to split up :/)
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