wn = WordNetCorpusReader(nltk.data.find('lib/wordnet-flat.zip'))

#!/usr/bin/env python 
import os
import re
import sys
import zipimport
 
importer = zipimport.zipimporter('nltkandyaml.mod')
yaml = importer.load_module('yaml')
nltk = importer.load_module('nltk')
punct = re.compile('[^\w\s]+')
 
from nltk.corpus.reader import wordnet
from nltk.corpus.reader import WordNetCorpusReader
 
nltk.data.path += ["."]
wn = WordNetCorpusReader(nltk.data.find('lib/wordnet-flat.zip'))
 
def mapper(args):
  line = sys.stdin.readline();
  try:
    while line:
      line = line.strip()
      word = line
      all_synonyms = []
 
      string_synsets = wn.synsets(word)
 
      for synset in string_synsets:
        synonyms = [lemma.name for lemma in wn.synset(synset.name).lemmas]
        synonyms.pop(0)
        for synonym in synonyms:
          synonym = re.sub("_", " ", synonym)
          all_synonyms.append(synonym) 
 
      print "\t".join([word, ','.join(all_synonyms)])
      line = sys.stdin.readline()
  except "end of file":
    return None
 
# we're not using this, but we could
def reducer(args):
  for line in sys.stdin:
    line = line.strip()
    print line
 
if __name__ == "__main__":
  if sys.argv[1] == "mapper":
    mapper(sys.argv[2:])
  elif sys.argv[1] == "reducer":
    reducer(sys.argv[2:])

package com.xcombinator.hadoopjobs.cascadingstreamingtest;
 
import cascading.cascade.*;
import cascading.flow.Flow;
import cascading.flow.FlowConnector;
import cascading.flow.MapReduceFlow;
import cascading.operation.aggregator.Count;
import cascading.operation.regex.*;
import cascading.pipe.*;
import cascading.scheme.*;
import cascading.tap.*;
import cascading.tuple.Fields;
import cascading.operation.Identity;
import java.util.Properties;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.apache.hadoop.mapred.lib.IdentityMapper;
import org.apache.hadoop.mapred.lib.IdentityReducer;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.log4j.Logger;
import cascading.operation.Debug;
import org.apache.hadoop.streaming.StreamJob;
import java.io.IOException;
 
/**
 * An example file to use a Hadoop Streaming job in cascading
 */
public class Main extends Configured implements Tool
  {
  private static final Logger LOG = Logger.getLogger( Main.class );
 
  public int run(String[] args)
  {
    JobConf conf = new JobConf(getConf(), this.getClass());
    Properties properties = new Properties();
    FlowConnector.setApplicationJarClass(properties, this.getClass());
 
    CascadeConnector cascadeConnector = new CascadeConnector();
    FlowConnector flowConnector = new FlowConnector(properties);
 
    String inputPath  = args[0];
    String outputPath = args[1];
    String intermediatePath1 = args[1] + "-mr-input";
    String intermediatePath2 = args[1] + "-mr-output";
 
    Scheme textLineScheme = new TextLine();
 
    Tap sourceTap = new Hfs(textLineScheme, inputPath);
    Tap intermediateTap1 = new Hfs(textLineScheme, intermediatePath1);
    Tap intermediateTap2 = new Hfs(textLineScheme, intermediatePath2);
    Tap sinkTap   = new Hfs(textLineScheme, outputPath);
 
    // create our first flow, sink to the intermediateTap
    Pipe wsPipe = new Each("wordsplit", 
        new Fields("line"), 
        new RegexSplitGenerator(new Fields("word"), "\\s+"), 
        new Fields("word"));
 
    Flow parsedLogFlow = flowConnector.connect(sourceTap, intermediateTap1, wsPipe);
 
    // Create a pipe and set our mr job for it 
    Pipe importPipe = new Pipe("mr pipe");
    Flow mrFlow;
 
    try {
      JobConf streamConf = StreamJob.createJob( new String[]{
          "-input", intermediateTap1.getPath().toString(), 
          "-output", intermediateTap2.getPath().toString(),
 
          // straight unix
          // "-mapper", "/bin/cat",
          // "-reducer", "/usr/bin/wc"
 
          // ruby
          // "-mapper", "src/main/ruby/word_count_mapper.rb",
          // "-reducer", "src/main/ruby/word_count_reducer.rb",
          // "-file", "src/main/ruby/word_count_mapper.rb",
          // "-file", "src/main/ruby/word_count_reducer.rb"
 
          // python
          "-mapper", "python synsets.py mapper",
          "-reducer", "org.apache.hadoop.mapred.lib.IdentityReducer",
          "-file", "src/main/python/synsets.py",
          "-file", "resources/nltkandyaml.mod",
          "-file", "resources/lib/wordnet-flat.zip",
          });
      mrFlow = new MapReduceFlow("streaming flow", streamConf, intermediateTap1,
        intermediateTap2, false, true);
    } catch(IOException ioe) {
       ioe.printStackTrace();
       System.exit(1);
       return 1;
    }
 
    // create our third "regular" cascading pipe. this is a bit contrived, but
    // the idea is substitute all 'e's with 'x's. it's just here to show how to
    // take the input of a streaming job back into cascading
    Pipe subPipe = new Pipe("subber");
    subPipe = new Each(subPipe,
        new Fields("line"),
        new RegexReplace(new Fields("linx"), "e", "x", true),
        new Fields("linx"));
    Flow subFlow = flowConnector.connect(intermediateTap2, sinkTap, subPipe);
 
    Cascade cascade = cascadeConnector.connect(parsedLogFlow, mrFlow, subFlow);
    cascade.complete();
 
    // to get rid of the intermediate files you could do this:
    // Path tmp = tap.getPath();
    // FileSystem fs = tmp.getFileSystem(conf);
    // fs.delete(tmp, true);
 
    return 0;
  }
 
  public static void main(String[] args) throws Exception 
  {
    int res = ToolRunner.run(new Configuration(), new Main(), args);
    System.exit(res);
  }
 
  }