[jira] [Created] (GIRAPH-717) Pure Jython support

["Nitay Joffe (JIRA)" <ji...@apache.org>]

Nitay Joffe created GIRAPH-717:
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             Summary: Pure Jython support
                 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 there is custom Jython types, the user must create types 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 types and turn them into Jython types so that for example getMap() returns 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.

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