org.gradoop.examples.sna.SNABenchmark1.java Source code

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Here is the source code for org.gradoop.examples.sna.SNABenchmark1.java

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/**
 * Copyright  2014 - 2018 Leipzig University (Database Research Group)
 *
 * 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.gradoop.examples.sna;

import com.google.common.base.Preconditions;
import org.apache.flink.api.common.ProgramDescription;
import org.gradoop.examples.AbstractRunner;
import org.gradoop.flink.model.api.epgm.LogicalGraph;
import org.gradoop.flink.model.impl.operators.aggregation.functions.count.EdgeCount;
import org.gradoop.flink.model.impl.operators.aggregation.functions.count.VertexCount;

import java.util.Arrays;

/**
 * The benchmark program executes the following workflow:
 *
 * 1) Extract subgraph with:
 *    - vertex predicate: must be of type 'Person'
 *    - edge predicate: must be of type 'knows'
 * 2) Group the subgraph using the vertex attributes 'city' and 'gender' and
 *    - count the number of vertices represented by each super vertex
 *    - count the number of edges represented by each super edge
 * 3) Aggregate the grouped graph:
 *    - add the total vertex count as new graph property
 *    - add the total edge count as new graph property
 */
public class SNABenchmark1 extends AbstractRunner implements ProgramDescription {

    /**
     * Runs the example program.
     *
     * Need a (possibly HDFS) input directory that contains
     *  - nodes.json
     *  - edges.json
     *  - graphs.json
     *
     * Needs a (possibly HDFS) output directory to write the resulting graph to.
     *
     * @param args args[0] = input dir, args[1] output dir
     * @throws Exception
     */
    @SuppressWarnings({ "unchecked", "Duplicates" })
    public static void main(String[] args) throws Exception {
        Preconditions.checkArgument(args.length == 2, "input dir and output dir required");
        String inputDir = args[0];
        String outputDir = args[1];

        LogicalGraph epgmDatabase = readLogicalGraph(inputDir);

        LogicalGraph result = execute(epgmDatabase);

        writeLogicalGraph(result, outputDir);
    }

    /**
     * The actual computation.
     *
     * @param socialNetwork social network graph
     * @return summarized, aggregated graph
     */
    private static LogicalGraph execute(LogicalGraph socialNetwork) {
        return socialNetwork
                .subgraph(vertex -> vertex.getLabel().equals("Person"), edge -> edge.getLabel().equals("knows"))
                .groupBy(Arrays.asList("gender", "city")).aggregate(new VertexCount()).aggregate(new EdgeCount());
    }

    @Override
    public String getDescription() {
        return SNABenchmark1.class.getName();
    }
}