com.twitter.algebra.nmf.CombinerJob.java Source code

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Here is the source code for com.twitter.algebra.nmf.CombinerJob.java

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/*
Copyright 2014 Twitter, Inc.
    
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
    
http://www.apache.org/licenses/LICENSE-2.0
    
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/

package com.twitter.algebra.nmf;

import java.io.IOException;
import java.util.List;
import java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.common.AbstractJob;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.hadoop.DistributedRowMatrix;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.twitter.algebra.MergeVectorsReducer;
import com.twitter.algebra.matrix.format.MatrixOutputFormat;
import com.twitter.algebra.matrix.format.RowPartitioner;

/**
 * Combiner smaller (row) partitions to get bigger files. This would put less
 * getfileinfo load on the name node.
 * 
 * @author myabandeh
 * 
 */
public class CombinerJob extends AbstractJob {
    private static final Logger log = LoggerFactory.getLogger(CombinerJob.class);

    public static void main(String[] args) throws Exception {
        ToolRunner.run(new CombinerJob(), args);
    }

    @Override
    public int run(String[] strings) throws Exception {
        addInputOption();
        addOption("numRows", "nr", "Number of rows of the input matrix");
        addOption("numCols", "nc", "Number of columns of the input matrix");
        Map<String, List<String>> parsedArgs = parseArguments(strings);
        if (parsedArgs == null) {
            return -1;
        }

        int numRows = Integer.parseInt(getOption("numRows"));
        int numCols = Integer.parseInt(getOption("numCols"));

        DistributedRowMatrix matrix = new DistributedRowMatrix(getInputPath(), getTempPath(), numRows, numCols);
        matrix.setConf(new Configuration(getConf()));
        CombinerJob.run(getConf(), matrix, "combined-" + getInputPath().getName());
        return 0;
    }

    public static DistributedRowMatrix run(Configuration conf, DistributedRowMatrix A, String label)
            throws IOException, InterruptedException, ClassNotFoundException {
        log.info("running " + CombinerJob.class.getName());
        Path outPath = new Path(A.getOutputTempPath(), label);
        FileSystem fs = FileSystem.get(outPath.toUri(), conf);
        CombinerJob job = new CombinerJob();
        if (!fs.exists(outPath)) {
            job.run(conf, A.getRowPath(), outPath, A.numRows());
        } else {
            log.warn("----------- Skip already exists: " + outPath);
        }
        DistributedRowMatrix distRes = new DistributedRowMatrix(outPath, A.getOutputTempPath(), A.numRows(),
                A.numCols());
        distRes.setConf(conf);
        return distRes;
    }

    public void run(Configuration conf, Path matrixInputPath, Path matrixOutputPath, int aRows)
            throws IOException, InterruptedException, ClassNotFoundException {
        conf = new Configuration(conf);
        //    conf.setBoolean("mapreduce.output.compress", true);
        //    conf.setBoolean("mapreduce.output.fileoutputformat.compress", true);
        //    conf.set("mapreduce.output.fileoutputformat.compress.codec", "com.hadoop.compression.lzo.LzoCodec");
        conf.setInt("dfs.replication", 20);

        @SuppressWarnings("deprecation")
        Job job = new Job(conf);
        job.setJarByClass(CombinerJob.class);
        job.setJobName(CombinerJob.class.getSimpleName() + "-" + matrixOutputPath.getName());
        FileSystem fs = FileSystem.get(matrixInputPath.toUri(), conf);
        matrixInputPath = fs.makeQualified(matrixInputPath);
        matrixOutputPath = fs.makeQualified(matrixOutputPath);

        FileInputFormat.addInputPath(job, matrixInputPath);
        job.setInputFormatClass(SequenceFileInputFormat.class);
        FileOutputFormat.setOutputPath(job, matrixOutputPath);

        int numReducers = NMFCommon.getNumberOfReduceSlots(conf, "combiner");
        job.setNumReduceTasks(numReducers);// TODO: make it a parameter

        job.setOutputFormatClass(MatrixOutputFormat.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(VectorWritable.class);

        job.setMapperClass(IdMapper.class);
        job.setReducerClass(MergeVectorsReducer.class);

        RowPartitioner.setPartitioner(job, RowPartitioner.IntRowPartitioner.class, aRows);

        job.submit();
        boolean res = job.waitForCompletion(true);
        if (!res)
            throw new IOException("Job failed!");
    }

    public static class IdMapper extends Mapper<IntWritable, VectorWritable, IntWritable, VectorWritable> {
        @Override
        public void map(IntWritable key, VectorWritable value, Context context)
                throws IOException, InterruptedException {
            context.write(key, value);
        }
    }

}