forked from D-Net/dnet-hadoop
258 lines
12 KiB
Java
258 lines
12 KiB
Java
package eu.dnetlib.dhp.graph;
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import com.fasterxml.jackson.annotation.JsonInclude;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import com.google.common.collect.Iterables;
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import com.google.common.collect.Lists;
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import com.jayway.jsonpath.DocumentContext;
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import com.jayway.jsonpath.JsonPath;
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import eu.dnetlib.dhp.graph.model.*;
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import eu.dnetlib.dhp.graph.utils.ContextMapper;
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import eu.dnetlib.dhp.graph.utils.GraphMappingUtils;
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import eu.dnetlib.dhp.graph.utils.XmlRecordFactory;
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import eu.dnetlib.dhp.schema.oaf.*;
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import org.apache.hadoop.io.Text;
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import org.apache.hadoop.io.compress.GzipCodec;
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import org.apache.hadoop.mapred.SequenceFileOutputFormat;
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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.PairFunction;
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import org.apache.spark.sql.SparkSession;
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import scala.Tuple2;
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import java.io.IOException;
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import java.io.Serializable;
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import java.util.HashSet;
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import java.util.List;
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import java.util.stream.Collectors;
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import static eu.dnetlib.dhp.graph.utils.GraphMappingUtils.asRelatedEntity;
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/**
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* Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects.
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* The operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization,
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* and all the possible relationships (similarity links produced by the Dedup process are excluded).
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*
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* The operation is implemented creating the union between the entity types (E), joined by the relationships (R), and again
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* by E, finally grouped by E.id;
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*
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* Different manipulations of the E and R sets are introduced to reduce the complexity of the operation
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* 1) treat the object payload as string, extracting only the necessary information beforehand using json path,
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* it seems that deserializing it with jackson's object mapper has higher memory footprint.
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*
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* 2) only consider rels that are not virtually deleted ($.dataInfo.deletedbyinference == false)
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* 3) we only need a subset of fields from the related entities, so we introduce a distinction between E_source = S
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* and E_target = T. Objects in T are heavily pruned by all the unnecessary information
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*
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* 4) perform the join as (((T join R) union S) groupby S.id) yield S -> [ <T, R> ]
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*/
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public class GraphJoiner implements Serializable {
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public static final int MAX_RELS = 100;
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public static final String schemaLocation = "https://www.openaire.eu/schema/1.0/oaf-1.0.xsd";
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private SparkSession spark;
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private ContextMapper contextMapper;
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private String inputPath;
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private String outPath;
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public GraphJoiner(SparkSession spark, ContextMapper contextMapper, String inputPath, String outPath) {
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this.spark = spark;
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this.contextMapper = contextMapper;
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this.inputPath = inputPath;
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this.outPath = outPath;
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}
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public GraphJoiner adjacencyLists() {
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final JavaSparkContext sc = new JavaSparkContext(getSpark().sparkContext());
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// read each entity
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JavaPairRDD<String, TypedRow> datasource = readPathEntity(sc, getInputPath(), "datasource");
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JavaPairRDD<String, TypedRow> organization = readPathEntity(sc, getInputPath(), "organization");
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JavaPairRDD<String, TypedRow> project = readPathEntity(sc, getInputPath(), "project");
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JavaPairRDD<String, TypedRow> dataset = readPathEntity(sc, getInputPath(), "dataset");
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JavaPairRDD<String, TypedRow> otherresearchproduct = readPathEntity(sc, getInputPath(), "otherresearchproduct");
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JavaPairRDD<String, TypedRow> software = readPathEntity(sc, getInputPath(), "software");
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JavaPairRDD<String, TypedRow> publication = readPathEntity(sc, getInputPath(), "publication");
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// create the union between all the entities
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final String entitiesPath = getOutPath() + "/entities";
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datasource
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.union(organization)
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.union(project)
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.union(dataset)
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.union(otherresearchproduct)
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.union(software)
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.union(publication)
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.map(e -> new EntityRelEntity().setSource(e._2()))
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.map(GraphMappingUtils::serialize)
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.saveAsTextFile(entitiesPath, GzipCodec.class);
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JavaPairRDD<String, EntityRelEntity> entities = sc.textFile(entitiesPath)
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.map(t -> new ObjectMapper().readValue(t, EntityRelEntity.class))
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.mapToPair(t -> new Tuple2<>(t.getSource().getSourceId(), t));
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// reads the relationships
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final JavaPairRDD<String, EntityRelEntity> relation = readPathRelation(sc, getInputPath())
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.filter(r -> !r.getDeleted()) //only consider those that are not virtually deleted
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.map(p -> new EntityRelEntity().setRelation(p))
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.mapToPair(p -> new Tuple2<>(p.getRelation().getSourceId(), p))
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.groupByKey()
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.map(p -> Iterables.limit(p._2(), MAX_RELS))
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.flatMap(p -> p.iterator())
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.mapToPair(p -> new Tuple2<>(p.getRelation().getTargetId(), p));
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//final String bySource = getOutPath() + "/1_join_by_target";
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JavaPairRDD<String, EntityRelEntity> bySource = relation
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.join(entities
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.filter(e -> !e._2().getSource().getDeleted())
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.mapToPair(e -> new Tuple2<>(e._1(), asRelatedEntity(e._2()))))
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.map(s -> new EntityRelEntity()
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.setRelation(s._2()._1().getRelation())
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.setTarget(s._2()._2().getSource()))
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.mapToPair(t -> new Tuple2<>(t.getRelation().getSourceId(), t));
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final XmlRecordFactory recordFactory = new XmlRecordFactory(contextMapper, false, schemaLocation, new HashSet<>());
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entities
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.union(bySource)
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.groupByKey() // by source id
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.map(l -> toJoinedEntity(l))
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.mapToPair(je -> new Tuple2<>(
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new Text(je.getEntity().getId()),
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new Text(recordFactory.build(je))))
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.saveAsHadoopFile(getOutPath() + "/xml", Text.class, Text.class, SequenceFileOutputFormat.class, GzipCodec.class);
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return this;
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}
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public GraphJoiner asXML() {
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final JavaSparkContext sc = new JavaSparkContext(getSpark().sparkContext());
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final XmlRecordFactory recordFactory = new XmlRecordFactory(contextMapper, true, "", new HashSet<>());
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final ObjectMapper mapper = new ObjectMapper();
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final String joinedEntitiesPath = getOutPath() + "/1_joined_entities";
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sc.textFile(joinedEntitiesPath)
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.map(s -> mapper.readValue(s, JoinedEntity.class))
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.mapToPair(je -> new Tuple2<>(new Text(je.getEntity().getId()), new Text(recordFactory.build(je))))
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.saveAsHadoopFile(getOutPath() + "/2_xml", Text.class, Text.class, SequenceFileOutputFormat.class, GzipCodec.class);
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return this;
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}
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public SparkSession getSpark() {
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return spark;
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}
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public String getInputPath() {
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return inputPath;
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}
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public String getOutPath() {
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return outPath;
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}
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// HELPERS
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private OafEntity parseOaf(final String json, final String type) {
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final ObjectMapper o = new ObjectMapper();
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try {
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switch (GraphMappingUtils.EntityType.valueOf(type)) {
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case publication:
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return o.readValue(json, Publication.class);
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case dataset:
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return o.readValue(json, Dataset.class);
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case otherresearchproduct:
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return o.readValue(json, OtherResearchProduct.class);
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case software:
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return o.readValue(json, Software.class);
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case datasource:
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return o.readValue(json, Datasource.class);
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case organization:
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return o.readValue(json, Organization.class);
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case project:
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return o.readValue(json, Project.class);
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default:
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throw new IllegalArgumentException("invalid type: " + type);
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}
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} catch (IOException e) {
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throw new IllegalArgumentException(e);
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}
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}
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private JoinedEntity toJoinedEntity(Tuple2<String, Iterable<EntityRelEntity>> p) {
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final ObjectMapper o = new ObjectMapper();
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final JoinedEntity j = new JoinedEntity();
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final Links links2 = new Links();
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for(EntityRelEntity rel : p._2()) {
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if (rel.hasMainEntity() & j.getEntity() == null) {
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j.setType(rel.getSource().getType());
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j.setEntity(parseOaf(rel.getSource().getOaf(), rel.getSource().getType()));
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}
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if (rel.hasRelatedEntity()) {
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try {
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links2.add(
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new eu.dnetlib.dhp.graph.model.Tuple2()
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.setRelation(o.readValue(rel.getRelation().getOaf(), Relation.class))
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.setRelatedEntity(o.readValue(rel.getTarget().getOaf(), RelatedEntity.class)));
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} catch (IOException e) {
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throw new IllegalArgumentException(e);
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}
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}
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}
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j.setLinks(links2);
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if (j.getEntity() == null) {
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throw new IllegalStateException("missing main entity on '" + p._1() + "'");
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}
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return j;
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}
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/**
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* Reads a set of eu.dnetlib.dhp.schema.oaf.OafEntity objects from a sequence file <className, entity json serialization>,
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* extracts necessary information using json path, wraps the oaf object in a eu.dnetlib.dhp.graph.model.TypedRow
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* @param sc
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* @param inputPath
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* @param type
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* @return the JavaPairRDD<String, TypedRow> indexed by entity identifier
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*/
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private JavaPairRDD<String, TypedRow> readPathEntity(final JavaSparkContext sc, final String inputPath, final String type) {
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return sc.sequenceFile(inputPath + "/" + type, Text.class, Text.class)
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.mapToPair((PairFunction<Tuple2<Text, Text>, String, TypedRow>) item -> {
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final String s = item._2().toString();
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final DocumentContext json = JsonPath.parse(s);
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final String id = json.read("$.id");
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return new Tuple2<>(id, new TypedRow()
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.setSourceId(id)
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.setDeleted(json.read("$.dataInfo.deletedbyinference"))
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.setType(type)
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.setOaf(s));
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});
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}
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/**
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* Reads a set of eu.dnetlib.dhp.schema.oaf.Relation objects from a sequence file <className, relation json serialization>,
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* extracts necessary information using json path, wraps the oaf object in a eu.dnetlib.dhp.graph.model.TypedRow
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* @param sc
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* @param inputPath
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* @return the JavaRDD<TypedRow> containing all the relationships
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*/
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private JavaRDD<TypedRow> readPathRelation(final JavaSparkContext sc, final String inputPath) {
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return sc.sequenceFile(inputPath + "/relation", Text.class, Text.class)
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.map(item -> {
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final String s = item._2().toString();
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final DocumentContext json = JsonPath.parse(s);
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return new TypedRow()
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.setSourceId(json.read("$.source"))
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.setTargetId(json.read("$.target"))
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.setDeleted(json.read("$.dataInfo.deletedbyinference"))
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.setType("relation")
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.setOaf(s);
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});
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}
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}
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