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https://issues.apache.org/jira/browse/GIRAPH-717?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13721195#comment-13721195
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Eli Reisman commented on GIRAPH-717:
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+1
> 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.
> 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 jython1.py [jython2.py] ...
> You can pass in any number of scripts. They will be parsed in order and sent
> to all the workers using DistributedCache.
> 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 HiveJythonRunner will call a function called "prepare(job)" from the
> Jython scripts. This is the entry point for configuring your job.
> In this configuration you setup everything, such as your 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).
> Your computation must implement JythonComputation. Note that this does not
> actually implement Computation, but rather is a separate class so that we can
> wrap all the types passed in with a wrapper that implements Writable. The
> methods are named the same so that the user does not notice anything.
> 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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