forked from D-Net/dnet-hadoop
163 lines
5.7 KiB
Java
163 lines
5.7 KiB
Java
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package eu.dnetlib.dhp.oa.dedup;
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import static org.apache.spark.sql.functions.col;
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import org.apache.commons.io.IOUtils;
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import org.apache.spark.SparkConf;
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import org.apache.spark.api.java.function.MapFunction;
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import org.apache.spark.api.java.function.ReduceFunction;
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import org.apache.spark.sql.*;
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import org.apache.spark.sql.catalyst.encoders.RowEncoder;
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import org.apache.spark.sql.types.StructType;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import eu.dnetlib.dhp.application.ArgumentApplicationParser;
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import eu.dnetlib.dhp.common.HdfsSupport;
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import eu.dnetlib.dhp.schema.common.EntityType;
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import eu.dnetlib.dhp.schema.common.ModelConstants;
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import eu.dnetlib.dhp.schema.common.ModelSupport;
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import eu.dnetlib.dhp.schema.oaf.DataInfo;
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import eu.dnetlib.dhp.schema.oaf.Relation;
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import eu.dnetlib.dhp.schema.oaf.utils.MergeUtils;
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import eu.dnetlib.dhp.utils.ISLookupClientFactory;
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import eu.dnetlib.enabling.is.lookup.rmi.ISLookUpService;
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import scala.Tuple2;
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import scala.Tuple3;
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public class SparkPropagateRelation extends AbstractSparkAction {
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private static final Logger log = LoggerFactory.getLogger(SparkPropagateRelation.class);
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private static Encoder<Relation> REL_BEAN_ENC = Encoders.bean(Relation.class);
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private static Encoder<Relation> REL_KRYO_ENC = Encoders.kryo(Relation.class);
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public SparkPropagateRelation(ArgumentApplicationParser parser, SparkSession spark) {
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super(parser, spark);
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}
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public static void main(String[] args) throws Exception {
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ArgumentApplicationParser parser = new ArgumentApplicationParser(
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IOUtils
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.toString(
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SparkPropagateRelation.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/oa/dedup/propagateRelation_parameters.json")));
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parser.parseArgument(args);
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SparkConf conf = new SparkConf();
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conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
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conf.registerKryoClasses(ModelSupport.getOafModelClasses());
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new SparkPropagateRelation(parser, getSparkSession(conf))
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.run(ISLookupClientFactory.getLookUpService(parser.get("isLookUpUrl")));
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}
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@Override
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public void run(ISLookUpService isLookUpService) {
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final String graphBasePath = parser.get("graphBasePath");
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final String workingPath = parser.get("workingPath");
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final String graphOutputPath = parser.get("graphOutputPath");
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log.info("graphBasePath: '{}'", graphBasePath);
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log.info("workingPath: '{}'", workingPath);
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log.info("graphOutputPath: '{}'", graphOutputPath);
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Dataset<Relation> mergeRels = spark
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.read()
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.load(DedupUtility.createMergeRelPath(workingPath, "*", "*"))
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.as(REL_BEAN_ENC);
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// <mergedObjectID, dedupID>
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Dataset<Row> idsToMerge = mergeRels
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.where(col("relClass").equalTo(ModelConstants.MERGES))
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.select(col("source").as("dedupID"), col("target").as("mergedObjectID"))
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.distinct();
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Dataset<Row> allRels = spark
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.read()
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.schema(REL_BEAN_ENC.schema())
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.json(graphBasePath + "/relation");
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Dataset<Relation> dedupedRels = allRels
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.joinWith(idsToMerge, allRels.col("source").equalTo(idsToMerge.col("mergedObjectID")), "left_outer")
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.joinWith(idsToMerge, col("_1.target").equalTo(idsToMerge.col("mergedObjectID")), "left_outer")
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.select("_1._1", "_1._2.dedupID", "_2.dedupID")
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.as(Encoders.tuple(REL_BEAN_ENC, Encoders.STRING(), Encoders.STRING()))
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.map((MapFunction<Tuple3<Relation, String, String>, Relation>) t -> {
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Relation rel = t._1();
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String newSource = t._2();
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String newTarget = t._3();
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if (rel.getDataInfo() == null) {
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rel.setDataInfo(new DataInfo());
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}
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if (newSource != null || newTarget != null) {
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rel.getDataInfo().setDeletedbyinference(false);
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if (newSource != null)
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rel.setSource(newSource);
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if (newTarget != null)
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rel.setTarget(newTarget);
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}
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return rel;
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}, REL_BEAN_ENC);
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// ids of records that are both not deletedbyinference and not invisible
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Dataset<Row> ids = validIds(spark, graphBasePath);
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// filter relations that point to valid records, can force them to be visible
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Dataset<Relation> cleanedRels = dedupedRels
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.join(ids, col("source").equalTo(ids.col("id")), "leftsemi")
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.join(ids, col("target").equalTo(ids.col("id")), "leftsemi")
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.as(REL_BEAN_ENC)
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.map((MapFunction<Relation, Relation>) r -> {
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r.getDataInfo().setInvisible(false);
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return r;
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}, REL_KRYO_ENC);
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Dataset<Relation> distinctRels = cleanedRels
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.groupByKey(
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(MapFunction<Relation, String>) r -> String
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.join(" ", r.getSource(), r.getTarget(), r.getRelType(), r.getSubRelType(), r.getRelClass()),
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Encoders.STRING())
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.reduceGroups((ReduceFunction<Relation>) MergeUtils::mergeRelation)
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.map((MapFunction<Tuple2<String, Relation>, Relation>) Tuple2::_2, REL_BEAN_ENC);
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final String outputRelationPath = graphOutputPath + "/relation";
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removeOutputDir(spark, outputRelationPath);
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save(
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distinctRels
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.union(mergeRels)
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.filter("source != target AND dataInfo.deletedbyinference != true AND dataInfo.invisible != true"),
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outputRelationPath,
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SaveMode.Overwrite);
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}
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static Dataset<Row> validIds(SparkSession spark, String graphBasePath) {
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StructType idsSchema = StructType
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.fromDDL("`id` STRING, `dataInfo` STRUCT<`deletedbyinference`:BOOLEAN,`invisible`:BOOLEAN>");
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Dataset<Row> allIds = spark.emptyDataset(RowEncoder.apply(idsSchema));
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for (EntityType entityType : ModelSupport.entityTypes.keySet()) {
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String entityPath = graphBasePath + '/' + entityType.name();
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if (HdfsSupport.exists(entityPath, spark.sparkContext().hadoopConfiguration())) {
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allIds = allIds.union(spark.read().schema(idsSchema).json(entityPath));
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}
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}
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return allIds
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.filter("dataInfo.deletedbyinference != true AND dataInfo.invisible != true")
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.select("id")
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.distinct();
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}
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}
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