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Giraph examples (for learning and testing)
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/*
 * 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.giraph.examples;

import org.apache.giraph.aggregators.DoubleSumAggregator;
import org.apache.giraph.aggregators.LongSumAggregator;
import org.apache.giraph.master.DefaultMasterCompute;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.log4j.Logger;

/**
 * Master compute associated with {@link RandomWalkComputation}. It handles
 * dangling nodes.
 */
public class RandomWalkVertexMasterCompute extends DefaultMasterCompute {

  /** threshold for the L1 norm of the state vector difference  */
  static final double CONVERGENCE_THRESHOLD = 0.00001;

  /** logger */
  private static final Logger LOG =
      Logger.getLogger(RandomWalkVertexMasterCompute.class);

  @Override
  public void compute() {
    double danglingContribution =
        this.getAggregatedValue(
            RandomWalkComputation.CUMULATIVE_DANGLING_PROBABILITY).get();
    double cumulativeProbability =
        this.getAggregatedValue(
            RandomWalkComputation.CUMULATIVE_PROBABILITY).get();
    double l1NormOfStateDiff =
        this.getAggregatedValue(
            RandomWalkComputation.L1_NORM_OF_PROBABILITY_DIFFERENCE).get();
    long numDanglingVertices =
        this.getAggregatedValue(
            RandomWalkComputation.NUM_DANGLING_VERTICES).get();

    LOG.info("[Superstep " + getSuperstep() + "] Dangling contribution = " +
        danglingContribution + ", number of dangling vertices = " +
        numDanglingVertices + ", cumulative probability = " +
        cumulativeProbability + ", L1 Norm of state vector difference = " +
        l1NormOfStateDiff);

    // Convergence check: halt once the L1 norm of the difference between the
    // state vectors fall below the threshold
    if (getSuperstep() > 1 && l1NormOfStateDiff < CONVERGENCE_THRESHOLD) {
      haltComputation();
    }
  }

  @Override
  public void initialize() throws InstantiationException,
      IllegalAccessException {
    registerAggregator(RandomWalkComputation.NUM_DANGLING_VERTICES,
        LongSumAggregator.class);
    registerAggregator(RandomWalkComputation.CUMULATIVE_DANGLING_PROBABILITY,
        DoubleSumAggregator.class);
    registerAggregator(RandomWalkComputation.CUMULATIVE_PROBABILITY,
        DoubleSumAggregator.class);
    registerAggregator(RandomWalkComputation.L1_NORM_OF_PROBABILITY_DIFFERENCE,
        DoubleSumAggregator.class);
  }
}