212 lines
9.0 KiB
Java
212 lines
9.0 KiB
Java
package eu.dnetlib;
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import com.google.common.hash.Hashing;
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import eu.dnetlib.graph.JavaGraphProcessor;
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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.BlockProcessorForTesting;
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import eu.dnetlib.pace.util.MapDocumentUtil;
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import eu.dnetlib.pace.utils.Utility;
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import eu.dnetlib.reporter.SparkReporter;
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import eu.dnetlib.support.Block;
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import eu.dnetlib.support.ConnectedComponent;
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import eu.dnetlib.support.Relation;
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import org.apache.commons.logging.Log;
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import org.apache.commons.logging.LogFactory;
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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.api.java.function.MapFunction;
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import org.apache.spark.api.java.function.PairFunction;
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import org.apache.spark.graphx.Edge;
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import org.apache.spark.sql.Dataset;
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import org.apache.spark.sql.Encoders;
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import org.apache.spark.sql.SaveMode;
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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.Serializable;
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import scala.Tuple2;
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import java.nio.charset.Charset;
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import java.util.*;
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import java.util.stream.Collectors;
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import java.util.stream.StreamSupport;
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public class Deduper implements Serializable {
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private static final Log log = LogFactory.getLog(Deduper.class);
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public static JavaPairRDD<String, Block> createSortedBlocks(
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JavaPairRDD<String, MapDocument> mapDocs, DedupConfig config) {
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final String of = config.getWf().getOrderField();
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final int maxQueueSize = config.getWf().getGroupMaxSize();
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return mapDocs
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// the reduce is just to be sure that we haven't document with same id
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.reduceByKey((a, b) -> a)
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.map(Tuple2::_2)
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// Clustering: from <id, doc> to List<groupkey,doc>
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.flatMap(
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a -> Utility
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.getGroupingKeys(config, a)
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.stream()
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.map(it -> Block.from(it, a))
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.collect(Collectors.toList())
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.iterator())
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.mapToPair(block -> new Tuple2<>(block.getKey(), block))
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.reduceByKey((b1, b2) -> Block.from(b1, b2, of, maxQueueSize));
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}
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public static Iterator<Tuple2<String, String>> ccToMergeRel(Tuple2<String, List<String>> cc, DedupConfig dedupConf) {
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return cc._2()
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.stream()
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.flatMap(
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id -> {
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List<Tuple2<String, String>> tmp = new ArrayList<>();
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tmp.add(new Tuple2<>(cc._1(), id));
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return tmp.stream();
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})
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.iterator();
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}
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public static long hash(final String id) {
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return Hashing.murmur3_128().hashString(id, Charset.defaultCharset()).asLong();
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}
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public static ConnectedComponent entityMerger(String key, Iterator<String> values) {
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ConnectedComponent cc = new ConnectedComponent();
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cc.setCcId(key);
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cc.setDocs(StreamSupport.stream(Spliterators.spliteratorUnknownSize(values, Spliterator.ORDERED), false)
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.collect(Collectors.toCollection(HashSet::new)));
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return cc;
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}
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public static JavaRDD<Relation> computeRelations(
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JavaSparkContext context, JavaPairRDD<String, Block> blocks, DedupConfig config, boolean useTree, boolean noMatch) {
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Map<String, LongAccumulator> accumulators = Utility.constructAccumulator(config, context.sc());
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return blocks
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.flatMapToPair(
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it -> {
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final SparkReporter reporter = new SparkReporter(accumulators);
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new BlockProcessorForTesting(config)
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.processSortedBlock(it._1(), it._2().getDocuments(), reporter, useTree, noMatch);
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return reporter.getRelations().iterator();
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})
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.mapToPair(it -> new Tuple2<>(it._1() + it._2(), new Relation(it._1(), it._2(), "simRel")))
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.reduceByKey((a, b) -> a)
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.map(Tuple2::_2);
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}
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public static void createSimRels(DedupConfig dedupConf, SparkSession spark, String entitiesPath, String simRelsPath, boolean useTree, boolean noMatch){
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JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
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JavaPairRDD<String, MapDocument> mapDocuments = sc
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.textFile(entitiesPath)
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.mapToPair(
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(PairFunction<String, String, MapDocument>) s -> {
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MapDocument d = MapDocumentUtil.asMapDocumentWithJPath(dedupConf, s);
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return new Tuple2<>(d.getIdentifier(), d);
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});
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// create blocks for deduplication
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JavaPairRDD<String, Block> blocks = Deduper.createSortedBlocks(mapDocuments, dedupConf);
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// create relations by comparing only elements in the same group
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JavaRDD<Relation> relations = Deduper.computeRelations(sc, blocks, dedupConf, useTree, noMatch);
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// save the simrel in the workingdir
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spark
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.createDataset(relations.rdd(), Encoders.bean(Relation.class))
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.write()
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.mode(SaveMode.Overwrite)
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.save(simRelsPath);
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}
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public static void createMergeRels(DedupConfig dedupConf, String entitiesPath, String mergeRelsPath, String simRelsPath, SparkSession spark){
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final int maxIterations = dedupConf.getWf().getMaxIterations();
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JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
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final JavaPairRDD<Object, String> vertexes = sc
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.textFile(entitiesPath)
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.map(s -> MapDocumentUtil.getJPathString(dedupConf.getWf().getIdPath(), s))
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.mapToPair((PairFunction<String, Object, String>) s -> new Tuple2<>(hash(s), s));
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final JavaRDD<Edge<String>> edgeRdd = spark
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.read()
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.load(simRelsPath)
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.as(Encoders.bean(Relation.class))
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.javaRDD()
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.map(Relation::toEdgeRdd);
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JavaPairRDD<String, List<String>> ccs = JavaGraphProcessor
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.findCCs(vertexes, edgeRdd, dedupConf.getWf().getMaxIterations());
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JavaRDD<Relation> mergeRel = ccs
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.filter(cc -> cc._2().size() > 1)
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.flatMap(cc -> Deduper.ccToMergeRel(cc, dedupConf))
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.map(it -> new Relation(it._1(), it._2(), "mergeRel"));
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final Dataset<Relation> mergeRels = spark
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.createDataset(
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mergeRel.rdd(),
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Encoders.bean(Relation.class));
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mergeRels.write().mode(SaveMode.Overwrite).parquet(mergeRelsPath);
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}
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public static void createDedupEntity(DedupConfig dedupConf, String simRelsPath, String mergeRelsPath, String entitiesPath, SparkSession spark, String dedupEntityPath){
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JavaPairRDD<String, String> entities = spark
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.read()
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.textFile(entitiesPath)
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.map((MapFunction<String, Tuple2<String, String>>) it ->
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new Tuple2<>(MapDocumentUtil.getJPathString(dedupConf.getWf().getIdPath(), it), it),
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Encoders.tuple(Encoders.STRING(), Encoders.STRING()))
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.toJavaRDD()
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.mapToPair(t -> t);
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// <source_raw_id, relation(source, target)>
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JavaPairRDD<String, Relation> simRels = spark
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.read()
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.load(simRelsPath)
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.as(Encoders.bean(Relation.class))
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.toJavaRDD()
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.mapToPair(r-> new Tuple2<>(r.getSource(), r));
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// <raw_id, relation(dedup_id, raw_id)>
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JavaPairRDD<String, Relation> mergeRels = spark
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.read()
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.load(mergeRelsPath)
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.as(Encoders.bean(Relation.class))
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.toJavaRDD()
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.mapToPair(r -> new Tuple2<>(r.getTarget(), r));
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JavaRDD<ConnectedComponent> dedupEntities = mergeRels.join(entities)
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.mapToPair(t -> new Tuple2<>(t._2()._1().getSource(), t._2()._2()))
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.groupByKey()
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.map(t-> entityMerger(t._1(), t._2().iterator()));
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JavaPairRDD<String, Iterable<Relation>> simRelsWithDedupId = simRels
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.join(mergeRels)
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.mapToPair(x -> new Tuple2<>(x._2()._2().getSource(), x._2()._1()))
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.groupByKey();
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JavaRDD<ConnectedComponent> groupEntity = mergeRels.join(entities)
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.mapToPair(t -> new Tuple2<>(t._2()._1().getSource(), t._2()._2()))
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.groupByKey()
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.join(simRelsWithDedupId)
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.map(x -> new ConnectedComponent(
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x._1(),
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x._2()._1(),
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x._2()._2())
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);
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groupEntity.saveAsTextFile(dedupEntityPath);
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}
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} |