Java tutorial
/** * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF licenses this file to You 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 org.apache.mahout.math.hadoop; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.mapred.FileOutputFormat; import org.apache.hadoop.mapred.JobConf; import org.apache.hadoop.mapred.MapReduceBase; import org.apache.hadoop.mapred.Mapper; import org.apache.hadoop.mapred.OutputCollector; import org.apache.hadoop.mapred.Reducer; import org.apache.hadoop.mapred.Reporter; import org.apache.hadoop.mapred.SequenceFileInputFormat; import org.apache.hadoop.mapred.SequenceFileOutputFormat; import org.apache.hadoop.mapred.join.CompositeInputFormat; import org.apache.hadoop.mapred.join.TupleWritable; import org.apache.hadoop.util.ToolRunner; import org.apache.mahout.common.AbstractJob; import org.apache.mahout.math.RandomAccessSparseVector; import org.apache.mahout.math.SequentialAccessSparseVector; import org.apache.mahout.math.Vector; import org.apache.mahout.math.VectorWritable; import org.apache.mahout.math.function.Functions; import java.io.IOException; import java.util.Iterator; import java.util.List; import java.util.Map; /** * This still uses the old MR api and as with all things in Mahout that are MapReduce is now part of 'mrlegacy'. * There is no plan to convert the old MR api used here to the new MR api. * This will be replaced by the new Spark based Linear Algebra bindings. */ public class MatrixMultiplicationJob extends AbstractJob { private static final String OUT_CARD = "output.vector.cardinality"; public static Configuration createMatrixMultiplyJobConf(Path aPath, Path bPath, Path outPath, int outCardinality) { return createMatrixMultiplyJobConf(new Configuration(), aPath, bPath, outPath, outCardinality); } public static Configuration createMatrixMultiplyJobConf(Configuration initialConf, Path aPath, Path bPath, Path outPath, int outCardinality) { JobConf conf = new JobConf(initialConf, MatrixMultiplicationJob.class); conf.setInputFormat(CompositeInputFormat.class); conf.set("mapred.join.expr", CompositeInputFormat.compose("inner", SequenceFileInputFormat.class, aPath, bPath)); conf.setInt(OUT_CARD, outCardinality); conf.setOutputFormat(SequenceFileOutputFormat.class); FileOutputFormat.setOutputPath(conf, outPath); conf.setMapperClass(MatrixMultiplyMapper.class); conf.setCombinerClass(MatrixMultiplicationReducer.class); conf.setReducerClass(MatrixMultiplicationReducer.class); conf.setMapOutputKeyClass(IntWritable.class); conf.setMapOutputValueClass(VectorWritable.class); conf.setOutputKeyClass(IntWritable.class); conf.setOutputValueClass(VectorWritable.class); return conf; } public static void main(String[] args) throws Exception { ToolRunner.run(new MatrixMultiplicationJob(), args); } @Override public int run(String[] strings) throws Exception { addOption("numRowsA", "nra", "Number of rows of the first input matrix", true); addOption("numColsA", "nca", "Number of columns of the first input matrix", true); addOption("numRowsB", "nrb", "Number of rows of the second input matrix", true); addOption("numColsB", "ncb", "Number of columns of the second input matrix", true); addOption("inputPathA", "ia", "Path to the first input matrix", true); addOption("inputPathB", "ib", "Path to the second input matrix", true); addOption("outputPath", "op", "Path to the output matrix", false); Map<String, List<String>> argMap = parseArguments(strings); if (argMap == null) { return -1; } DistributedRowMatrix a = new DistributedRowMatrix(new Path(getOption("inputPathA")), new Path(getOption("tempDir")), Integer.parseInt(getOption("numRowsA")), Integer.parseInt(getOption("numColsA"))); DistributedRowMatrix b = new DistributedRowMatrix(new Path(getOption("inputPathB")), new Path(getOption("tempDir")), Integer.parseInt(getOption("numRowsB")), Integer.parseInt(getOption("numColsB"))); a.setConf(new Configuration(getConf())); b.setConf(new Configuration(getConf())); if (hasOption("outputPath")) { a.times(b, new Path(getOption("outputPath"))); } else { a.times(b); } return 0; } public static class MatrixMultiplyMapper extends MapReduceBase implements Mapper<IntWritable, TupleWritable, IntWritable, VectorWritable> { private int outCardinality; private final IntWritable row = new IntWritable(); @Override public void configure(JobConf conf) { outCardinality = conf.getInt(OUT_CARD, Integer.MAX_VALUE); } @Override public void map(IntWritable index, TupleWritable v, OutputCollector<IntWritable, VectorWritable> out, Reporter reporter) throws IOException { boolean firstIsOutFrag = ((VectorWritable) v.get(0)).get().size() == outCardinality; Vector outFrag = firstIsOutFrag ? ((VectorWritable) v.get(0)).get() : ((VectorWritable) v.get(1)).get(); Vector multiplier = firstIsOutFrag ? ((VectorWritable) v.get(1)).get() : ((VectorWritable) v.get(0)).get(); VectorWritable outVector = new VectorWritable(); for (Vector.Element e : multiplier.nonZeroes()) { row.set(e.index()); outVector.set(outFrag.times(e.get())); out.collect(row, outVector); } } } public static class MatrixMultiplicationReducer extends MapReduceBase implements Reducer<IntWritable, VectorWritable, IntWritable, VectorWritable> { @Override public void reduce(IntWritable rowNum, Iterator<VectorWritable> it, OutputCollector<IntWritable, VectorWritable> out, Reporter reporter) throws IOException { if (!it.hasNext()) { return; } Vector accumulator = new RandomAccessSparseVector(it.next().get()); while (it.hasNext()) { Vector row = it.next().get(); accumulator.assign(row, Functions.PLUS); } out.collect(rowNum, new VectorWritable(new SequentialAccessSparseVector(accumulator))); } } }