107 lines
4.5 KiB
Java
107 lines
4.5 KiB
Java
package eu.dnetlib;
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import eu.dnetlib.graph.GraphProcessor;
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import eu.dnetlib.pace.config.DedupConfig;
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import eu.dnetlib.pace.model.MapDocument;
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import eu.dnetlib.pace.util.BlockProcessor;
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import eu.dnetlib.pace.utils.PaceUtils;
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import eu.dnetlib.reporter.SparkReporter;
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import org.apache.spark.api.java.JavaPairRDD;
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import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.api.java.JavaSparkContext;
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import org.apache.spark.graphx.Edge;
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import org.apache.spark.rdd.RDD;
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import org.apache.spark.sql.SparkSession;
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import org.apache.spark.util.LongAccumulator;
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import scala.Tuple2;
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import java.net.URL;
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import java.util.*;
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import java.util.stream.Collectors;
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public class SparkLocalTest {
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public static void main(String[] args) {
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double startTime = System.currentTimeMillis();
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final SparkSession spark = SparkSession
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.builder()
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.appName("Deduplication")
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.master("local[*]")
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.getOrCreate();
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final JavaSparkContext context = new JavaSparkContext(spark.sparkContext());
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final URL dataset = SparkLocalTest.class.getResource("/eu/dnetlib/pace/organization.to.fix.json");
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JavaRDD<String> dataRDD = context.textFile(dataset.getPath());
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//read the configuration from the classpath
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final DedupConfig config = DedupConfig.load(Utility.readFromClasspath("/eu/dnetlib/pace/org.curr.conf", SparkLocalTest.class));
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Map<String, LongAccumulator> accumulators = Utility.constructAccumulator(config, context.sc());
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//create vertexes of the graph: <ID, MapDocument>
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JavaPairRDD<String, MapDocument> mapDocs = dataRDD.mapToPair(it -> {
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MapDocument mapDocument = PaceUtils.asMapDocument(config, it);
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return new Tuple2<>(mapDocument.getIdentifier(), mapDocument);
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});
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// System.out.println("mapDocs = " + mapDocs.count());
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RDD<Tuple2<Object, MapDocument>> vertexes = mapDocs.mapToPair(t -> new Tuple2<Object, MapDocument>( (long) t._1().hashCode(), t._2())).rdd();
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//create relations between documents
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JavaRDD<Block> blocks = mapDocs.reduceByKey((a, b) -> a) //the reduce is just to be sure that we haven't document with same id
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//Clustering: from <id, doc> to List<groupkey,doc>
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.flatMapToPair(a -> {
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final MapDocument currentDocument = a._2();
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return Utility.getGroupingKeys(config, currentDocument).stream()
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.map(it -> new Tuple2<>(it, currentDocument)).collect(Collectors.toList()).iterator();
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}).groupByKey().map(b -> new Block(b._1(), b._2())).filter(b -> b.getElements().size()>1);
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//create relations by comparing only elements in the same group
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final JavaPairRDD<String, String> relationRDD = blocks.flatMapToPair(it -> {
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final SparkReporter reporter = new SparkReporter(accumulators);
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new BlockProcessor(config).process(it.getKey(), it.getElements(), reporter);
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return reporter.getRelations().iterator();
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});
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final RDD<Edge<String>> edgeRdd = relationRDD.map(it -> new Edge<>(it._1().hashCode(),it._2().hashCode(), "similarTo")).rdd();
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JavaRDD<ConnectedComponent> ccs = GraphProcessor.findCCs(vertexes, edgeRdd, 20).toJavaRDD();
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System.out.println("total time = " + (System.currentTimeMillis()-startTime));
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printStatistics(ccs);
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accumulators.forEach((name, acc) -> System.out.println(name + " -> " + acc.value()));
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}
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public static void printStatistics(JavaRDD<ConnectedComponent> ccs){
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final JavaRDD<ConnectedComponent> connectedComponents = ccs.filter(cc -> cc.getDocs().size()>1);
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final JavaRDD<ConnectedComponent> nonDeduplicated = ccs.filter(cc -> cc.getDocs().size()==1);
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//print deduped
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connectedComponents.foreach(cc -> {
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System.out.println(cc);
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});
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connectedComponents.foreach(cc -> {
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cc.getDocs().stream().forEach(d -> {
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System.out.println(d.getFieldMap().get("legalname") + " | " + d.getFieldMap().get("legalshortname"));
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});
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});
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//print nondeduped
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nonDeduplicated.foreach(cc -> {
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System.out.println(cc);
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});
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System.out.println("Non duplicates: " + nonDeduplicated.count());
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System.out.println("Duplicates: " + connectedComponents.flatMap(cc -> cc.getDocs().iterator()).count());
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System.out.println("Connected Components: " + connectedComponents.count());
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}
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} |