org.sf.xrime.algorithms.partitions.connected.bi.SpanningTreeRootChoose.java Source code

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Here is the source code for org.sf.xrime.algorithms.partitions.connected.bi.SpanningTreeRootChoose.java

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/*
 * Copyright (C) IBM Corp. 2009.
 * 
 * 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 org.sf.xrime.algorithms.partitions.connected.bi;

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.GzipCodec;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
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.sf.xrime.ProcessorExecutionException;
import org.sf.xrime.algorithms.GraphAlgorithm;
import org.sf.xrime.algorithms.utils.GraphAlgorithmMapReduceBase;
import org.sf.xrime.model.vertex.LabeledAdjSetVertex;

/**
 * As the first steps of this BCC (bi-connected component) algorithm, we need to generate a
 * spanning tree of the input graph first. To this end, we need to choose the root of this
 * spanning tree first. This algorithm is used to choose the root. Currently, we choose the
 * vertex with largest degree as the root, which is used as a heuristics to minimize the depth
 * of the resulting spanning tree.
 * @author xue
 */
public class SpanningTreeRootChoose extends GraphAlgorithm {
    /**
     * A label name.
     */
    public static final String IN_OUT_DEGREE = "in_out_degree";
    /**
     * The key used to accumulate map output.
     */
    public static final String SPANNING_TREE_ROOT = "spanning_tree_root";

    /**
     * Default constructor.
     */
    public SpanningTreeRootChoose() {
        super();
    }

    /**
     * Emit the degree of each vertex.
     * @author xue
     */
    public static class MapClass extends GraphAlgorithmMapReduceBase
            implements Mapper<Text, LabeledAdjSetVertex, Text, LabeledAdjSetVertex> {
        @Override
        public void map(Text key, LabeledAdjSetVertex value, OutputCollector<Text, LabeledAdjSetVertex> output,
                Reporter reporter) throws IOException {
            int in_out_degree = (value.getOpposites() == null) ? 0 : value.getOpposites().size();
            // Generate the output value.
            LabeledAdjSetVertex result = new LabeledAdjSetVertex();
            result.setId(key.toString());
            result.setLabel(IN_OUT_DEGREE, new IntWritable(in_out_degree));
            output.collect(new Text(SPANNING_TREE_ROOT), result);
        }
    }

    /**
     * Emit the id of the vertex with largest in + out degree.
     * @author xue
     */
    public static class ReduceClass extends GraphAlgorithmMapReduceBase
            implements Reducer<Text, LabeledAdjSetVertex, Text, Text> {

        @Override
        public void reduce(Text key, Iterator<LabeledAdjSetVertex> values, OutputCollector<Text, Text> output,
                Reporter reporter) throws IOException {
            int largest_value = 0;
            String root_vertex_id = null;
            // Loop over all degree values and pick the largest one.
            while (values.hasNext()) {
                LabeledAdjSetVertex curr = values.next();
                int curr_val = ((IntWritable) (curr.getLabel(IN_OUT_DEGREE))).get();
                if (curr_val > largest_value) {
                    largest_value = curr_val;
                    root_vertex_id = curr.getId();
                }
            }
            // Found the pivot vertex.
            output.collect(new Text(SPANNING_TREE_ROOT), new Text(root_vertex_id));
        }
    }

    @Override
    public void execute() throws ProcessorExecutionException {
        JobConf conf = new JobConf(context, SpanningTreeRootChoose.class);
        conf.setJobName("SpanningTreeRootChoose");

        // This is necessary.
        conf.setMapOutputKeyClass(Text.class);
        conf.setMapOutputValueClass(LabeledAdjSetVertex.class);
        conf.setOutputKeyClass(Text.class);
        conf.setOutputValueClass(Text.class);
        conf.setMapperClass(MapClass.class);
        // Since k2,v2 is different from k3,v3. No combiner is permitted.
        conf.setReducerClass(ReduceClass.class);
        // The format of input data is generated with WritableSerialization.
        conf.setInputFormat(SequenceFileInputFormat.class);
        try {
            FileInputFormat.setInputPaths(conf, getSource().getPath());
            FileOutputFormat.setOutputPath(conf, getDestination().getPath());
        } catch (IllegalAccessException e1) {
            throw new ProcessorExecutionException(e1);
        }
        conf.setNumMapTasks(getMapperNum());
        // Only one reducer is permitted, or the largest value will be wrong.
        conf.setNumReduceTasks(1);
        conf.setCompressMapOutput(true);
        conf.setMapOutputCompressorClass(GzipCodec.class);

        try {
            this.runningJob = JobClient.runJob(conf);
        } catch (IOException e) {
            throw new ProcessorExecutionException(e);
        }
    }
}