forked from D-Net/dnet-hadoop
Rewrite SparkPropagateRelation exploiting Dataframe API
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parent
9c8b41475a
commit
0d7b2bf83d
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@ -1,19 +1,6 @@
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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 java.util.Objects;
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import org.apache.commons.io.IOUtils;
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import org.apache.commons.lang3.StringUtils;
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import org.apache.spark.SparkConf;
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import org.apache.spark.api.java.function.FilterFunction;
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import org.apache.spark.api.java.function.MapFunction;
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import org.apache.spark.sql.*;
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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.schema.common.ModelConstants;
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import eu.dnetlib.dhp.schema.common.ModelSupport;
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@ -21,16 +8,29 @@ 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.utils.ISLookupClientFactory;
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import eu.dnetlib.enabling.is.lookup.rmi.ISLookUpService;
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import org.apache.commons.io.IOUtils;
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import org.apache.commons.lang3.StringUtils;
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import org.apache.spark.SparkConf;
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import org.apache.spark.api.java.function.FilterFunction;
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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.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import scala.Tuple2;
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import scala.Tuple3;
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import java.util.Objects;
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import static org.apache.spark.sql.functions.col;
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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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enum FieldType {
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SOURCE, TARGET
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}
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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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@ -71,120 +71,42 @@ public class SparkPropagateRelation extends AbstractSparkAction {
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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(Encoders.bean(Relation.class));
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.as(REL_BEAN_ENC);
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// <mergedObjectID, dedupID>
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Dataset<Tuple2<String, String>> mergedIds = mergeRels
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Dataset<Row> mergedIds = mergeRels
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.where(col("relClass").equalTo(ModelConstants.MERGES))
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.select(col("source"), col("target"))
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.select(col("source").as("dedupID"), col("target").as("mergedObjectID"))
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.distinct()
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.map(
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(MapFunction<Row, Tuple2<String, String>>) r -> new Tuple2<>(r.getString(1), r.getString(0)),
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Encoders.tuple(Encoders.STRING(), Encoders.STRING()))
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.cache();
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final String relationPath = DedupUtility.createEntityPath(graphBasePath, "relation");
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final String inputRelationPath = DedupUtility.createEntityPath(graphBasePath, "relation");
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Dataset<Relation> rels = spark.read().textFile(relationPath).map(patchRelFn(), Encoders.bean(Relation.class));
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Dataset<Relation> rels = spark.read().schema(REL_BEAN_ENC.schema()).json(inputRelationPath)
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.as(REL_BEAN_ENC)
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// .map((MapFunction<Relation, Relation>) rel -> {
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// if (rel.getDataInfo() == null) {
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// rel.setDataInfo(new DataInfo());
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// }
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// return rel;
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// }, REL_BEAN_ENC)
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;
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Dataset<Relation> newRels = createNewRels(rels, mergedIds, getFixRelFn());
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Dataset<Tuple3<Relation, String, String>> dedupedRels = rels
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.joinWith(mergedIds, rels.col("source").equalTo(mergedIds.col("mergedObjectID")), "left_outer")
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.joinWith(mergedIds, col("_1.target").equalTo(mergedIds.col("mergedObjectID")), "left_outer")
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.filter("_1._2 IS NOT NULL OR _2 IS NOT NULL")
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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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.cache();
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Dataset<Relation> updated = processDataset(
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processDataset(rels, mergedIds, FieldType.SOURCE, getDeletedFn()),
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mergedIds,
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FieldType.TARGET,
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getDeletedFn());
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mergedIds.unpersist();
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save(
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distinctRelations(
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newRels
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.union(updated)
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.union(mergeRels)
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.map((MapFunction<Relation, Relation>) r -> r, Encoders.kryo(Relation.class)))
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.filter((FilterFunction<Relation>) r -> !Objects.equals(r.getSource(), r.getTarget())),
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outputRelationPath, SaveMode.Overwrite);
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}
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private Dataset<Relation> distinctRelations(Dataset<Relation> rels) {
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return rels
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.filter(getRelationFilterFunction())
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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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.agg(new RelationAggregator().toColumn())
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.map((MapFunction<Tuple2<String, Relation>, Relation>) Tuple2::_2, Encoders.bean(Relation.class));
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}
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// redirect the relations to the dedupID
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private static Dataset<Relation> createNewRels(
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Dataset<Relation> rels, // all the relations to be redirected
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Dataset<Tuple2<String, String>> mergedIds, // merge rels: <mergedObjectID, dedupID>
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MapFunction<Tuple2<Tuple2<Tuple3<String, Relation, String>, Tuple2<String, String>>, Tuple2<String, String>>, Relation> mapRel) {
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// <sourceID, relation, targetID>
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Dataset<Tuple3<String, Relation, String>> mapped = rels
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.map(
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(MapFunction<Relation, Tuple3<String, Relation, String>>) r -> new Tuple3<>(getId(r, FieldType.SOURCE),
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r, getId(r, FieldType.TARGET)),
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Encoders.tuple(Encoders.STRING(), Encoders.kryo(Relation.class), Encoders.STRING()));
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// < <sourceID, relation, target>, <sourceID, dedupID> >
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Dataset<Tuple2<Tuple3<String, Relation, String>, Tuple2<String, String>>> relSource = mapped
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.joinWith(mergedIds, mapped.col("_1").equalTo(mergedIds.col("_1")), "left_outer");
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// < <<sourceID, relation, targetID>, <sourceID, dedupID>>, <targetID, dedupID> >
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Dataset<Tuple2<Tuple2<Tuple3<String, Relation, String>, Tuple2<String, String>>, Tuple2<String, String>>> relSourceTarget = relSource
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.joinWith(mergedIds, relSource.col("_1._3").equalTo(mergedIds.col("_1")), "left_outer");
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return relSourceTarget
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.filter(
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(FilterFunction<Tuple2<Tuple2<Tuple3<String, Relation, String>, Tuple2<String, String>>, Tuple2<String, String>>>) r -> r
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._1()
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._1() != null || r._2() != null)
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.map(mapRel, Encoders.bean(Relation.class))
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.distinct();
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}
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private static Dataset<Relation> processDataset(
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Dataset<Relation> rels,
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Dataset<Tuple2<String, String>> mergedIds,
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FieldType type,
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MapFunction<Tuple2<Tuple2<String, Relation>, Tuple2<String, String>>, Relation> mapFn) {
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final Dataset<Tuple2<String, Relation>> mapped = rels
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.map(
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(MapFunction<Relation, Tuple2<String, Relation>>) r -> new Tuple2<>(getId(r, type), r),
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Encoders.tuple(Encoders.STRING(), Encoders.kryo(Relation.class)));
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return mapped
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.joinWith(mergedIds, mapped.col("_1").equalTo(mergedIds.col("_1")), "left_outer")
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.map(mapFn, Encoders.bean(Relation.class));
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}
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private FilterFunction<Relation> getRelationFilterFunction() {
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return r -> StringUtils.isNotBlank(r.getSource()) ||
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StringUtils.isNotBlank(r.getTarget()) ||
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StringUtils.isNotBlank(r.getRelType()) ||
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StringUtils.isNotBlank(r.getSubRelType()) ||
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StringUtils.isNotBlank(r.getRelClass());
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}
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private static String getId(Relation r, FieldType type) {
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switch (type) {
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case SOURCE:
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return r.getSource();
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case TARGET:
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return r.getTarget();
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default:
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throw new IllegalArgumentException("");
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}
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}
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private static MapFunction<Tuple2<Tuple2<Tuple3<String, Relation, String>, Tuple2<String, String>>, Tuple2<String, String>>, Relation> getFixRelFn() {
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return value -> {
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Relation r = value._1()._1()._2();
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String newSource = value._1()._2() != null ? value._1()._2()._2() : null;
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String newTarget = value._2() != null ? value._2()._2() : null;
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Dataset<Relation> newRels = dedupedRels
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.map((MapFunction<Tuple3<Relation, String, String>, Relation>) t -> {
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Relation r = t._1();
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String newSource = t._2();
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String newTarget = t._3();
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if (r.getDataInfo() == null) {
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r.setDataInfo(new DataInfo());
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@ -198,21 +120,51 @@ public class SparkPropagateRelation extends AbstractSparkAction {
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r.setTarget(newTarget);
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return r;
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};
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}
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}, REL_BEAN_ENC)
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.distinct();
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private static MapFunction<Tuple2<Tuple2<String, Relation>, Tuple2<String, String>>, Relation> getDeletedFn() {
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return value -> {
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if (value._2() != null) {
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Relation r = value._1()._2();
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Dataset<Relation> updated = dedupedRels
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.map((MapFunction<Tuple3<Relation, String, String>, Relation>) t -> {
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Relation r = t._1();
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if (r.getDataInfo() == null) {
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r.setDataInfo(new DataInfo());
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}
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r.getDataInfo().setDeletedbyinference(true);
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return r;
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}
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return value._1()._2();
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};
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}, REL_BEAN_ENC);
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save(
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distinctRelations(
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newRels
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.union(updated)
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.union(mergeRels)
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.map((MapFunction<Relation, Relation>) r -> r, REL_KRYO_ENC)
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)
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.filter((FilterFunction<Relation>) r -> !Objects.equals(r.getSource(), r.getTarget())),
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outputRelationPath, SaveMode.Overwrite);
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}
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private Dataset<Relation> distinctRelations(Dataset<Relation> rels) {
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return rels
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.filter(getRelationFilterFunction())
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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>) (b, a) -> {
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b.mergeFrom(a);
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return b;
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}
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)
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.map((MapFunction<Tuple2<String, Relation>, Relation>) Tuple2::_2, REL_BEAN_ENC);
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}
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private FilterFunction<Relation> getRelationFilterFunction() {
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return r -> StringUtils.isNotBlank(r.getSource()) ||
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StringUtils.isNotBlank(r.getTarget()) ||
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StringUtils.isNotBlank(r.getRelType()) ||
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StringUtils.isNotBlank(r.getSubRelType()) ||
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StringUtils.isNotBlank(r.getRelClass());
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}
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}
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