90 lines
4.2 KiB
Java
90 lines
4.2 KiB
Java
//package eu.dnetlib;
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//
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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.utils.PaceUtils;
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//import eu.dnetlib.reporter.SparkBlockProcessor;
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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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//
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//import java.io.IOException;
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//import java.util.Map;
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//import java.util.stream.Collectors;
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//
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//public class SparkTest {
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//
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// public static void main(String[] args) throws IOException {
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//
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// final String inputSpacePath = args[0];
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// final String dedupConfigPath = args[1];
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// final String groupsPath = args[2] + "_groups";
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// final String outputPath = args[2] + "_output";
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//
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// final SparkSession spark = SparkSession
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// .builder()
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// .appName("Deduplication")
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// .master("yarn")
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// .getOrCreate();
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//
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// final JavaSparkContext context = new JavaSparkContext(spark.sparkContext());
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//
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// final JavaRDD<String> dataRDD = Utility.loadDataFromHDFS(inputSpacePath, context);
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//
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// final DedupConfig config = Utility.loadConfigFromHDFS(dedupConfigPath);
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//
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// Map<String, LongAccumulator> accumulators = Utility.constructAccumulator(config, context.sc());
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//
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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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// RDD<Tuple2<Object, MapDocument>> vertexes = mapDocs.mapToPair(t -> new Tuple2<Object, MapDocument>( (long) t._1().hashCode(), t._2())).rdd();
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//
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// //group documents basing on clustering
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// JavaPairRDD<String, Iterable<MapDocument>> 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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//
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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();
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//
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// Utility.deleteIfExists(groupsPath);
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// blocks.map(group -> new DocumentsBlock(group._1(), group._2())).saveAsTextFile(groupsPath);
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//
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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 SparkBlockProcessor(config).process(it._1(), it._2(), reporter, accumulators);
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// return reporter.getRelations().iterator();
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// });
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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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//
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// JavaRDD<ConnectedComponent> ccs = GraphProcessor.findCCs(vertexes, edgeRdd, 20).toJavaRDD();
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//
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// //save connected components on textfile
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// Utility.deleteIfExists(outputPath);
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// ccs.saveAsTextFile(outputPath);
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//
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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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//
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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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// accumulators.forEach((name, acc) -> System.out.println(name + " -> " + acc.value()));
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//
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// }
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//
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//} |