184 lines
7.2 KiB
Java
184 lines
7.2 KiB
Java
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package eu.dnetlib.dhp.oa.provision;
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import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
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import java.util.HashSet;
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import java.util.Optional;
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import java.util.Set;
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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.JavaRDD;
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import org.apache.spark.api.java.JavaSparkContext;
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import org.apache.spark.rdd.RDD;
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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.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import com.google.common.base.Splitter;
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import com.google.common.collect.Iterables;
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import com.google.common.collect.Sets;
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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.oa.provision.model.SortableRelationKey;
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import eu.dnetlib.dhp.oa.provision.utils.RelationPartitioner;
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import eu.dnetlib.dhp.schema.oaf.Relation;
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import scala.Tuple2;
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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. The
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* operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
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* all the possible relationships (similarity links produced by the Dedup process are excluded).
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* <p>
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* The operation is implemented by sequentially joining one entity type at time (E) with the relationships (R), and
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* again by E, finally grouped by E.id;
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* <p>
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* The workflow is organized in different parts aimed to to reduce the complexity of the operation 1)
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* PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
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* false), each entity can be linked at most to 100 other objects
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* <p>
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* 2) JoinRelationEntityByTargetJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
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* E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
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* T_i.id) save the tuples (R_i, T_i) (phase 2): create the union of all the entity types E, hash by id read the tuples
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* (R, T), hash by R.source join E.id = (R, T).source, where E becomes the Source Entity S save the tuples (S, R, T)
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* <p>
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* 3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
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* result as JoinedEntity
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* <p>
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* 4) XmlConverterJob: convert the JoinedEntities as XML records
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*/
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public class PrepareRelationsJob {
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private static final Logger log = LoggerFactory.getLogger(PrepareRelationsJob.class);
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private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
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public static final int MAX_RELS = 100;
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public static final int DEFAULT_NUM_PARTITIONS = 3000;
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public static void main(String[] args) throws Exception {
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String jsonConfiguration = IOUtils
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.toString(
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PrepareRelationsJob.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/oa/provision/input_params_prepare_relations.json"));
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration);
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parser.parseArgument(args);
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Boolean isSparkSessionManaged = Optional
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.ofNullable(parser.get("isSparkSessionManaged"))
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.map(Boolean::valueOf)
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.orElse(Boolean.TRUE);
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log.info("isSparkSessionManaged: {}", isSparkSessionManaged);
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String inputRelationsPath = parser.get("inputRelationsPath");
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log.info("inputRelationsPath: {}", inputRelationsPath);
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String outputPath = parser.get("outputPath");
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log.info("outputPath: {}", outputPath);
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int relPartitions = Optional
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.ofNullable(parser.get("relPartitions"))
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.map(Integer::valueOf)
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.orElse(DEFAULT_NUM_PARTITIONS);
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log.info("relPartitions: {}", relPartitions);
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Set<String> relationFilter = Optional
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.ofNullable(parser.get("relationFilter"))
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.map(s -> Sets.newHashSet(Splitter.on(",").split(s)))
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.orElse(new HashSet<>());
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log.info("relationFilter: {}", relationFilter);
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int maxRelations = Optional
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.ofNullable(parser.get("maxRelations"))
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.map(Integer::valueOf)
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.orElse(MAX_RELS);
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log.info("maxRelations: {}", maxRelations);
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SparkConf conf = new SparkConf();
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runWithSparkSession(
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conf,
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isSparkSessionManaged,
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spark -> {
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removeOutputDir(spark, outputPath);
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prepareRelationsRDD(
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spark, inputRelationsPath, outputPath, relationFilter, maxRelations, relPartitions);
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});
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}
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/**
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* RDD based implementation that prepares the graph relations by limiting the number of outgoing links and filtering
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* the relation types according to the given criteria. Moreover, outgoing links kept within the given limit are
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* prioritized according to the weights indicated in eu.dnetlib.dhp.oa.provision.model.SortableRelation.
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*
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* @param spark the spark session
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* @param inputRelationsPath source path for the graph relations
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* @param outputPath output path for the processed relations
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* @param relationFilter set of relation filters applied to the `relClass` field
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* @param maxRelations maximum number of allowed outgoing edges
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* @param relPartitions number of partitions for the output RDD
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*/
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private static void prepareRelationsRDD(
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SparkSession spark, String inputRelationsPath, String outputPath, Set<String> relationFilter, int maxRelations,
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int relPartitions) {
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// group by SOURCE and apply limit
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RDD<Relation> bySource = readPathRelationRDD(spark, inputRelationsPath)
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.filter(rel -> rel.getDataInfo().getDeletedbyinference() == false)
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.filter(rel -> relationFilter.contains(rel.getRelClass()) == false)
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.mapToPair(r -> new Tuple2<>(SortableRelationKey.create(r, r.getSource()), r))
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.repartitionAndSortWithinPartitions(new RelationPartitioner(relPartitions))
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.groupBy(Tuple2::_1)
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.map(Tuple2::_2)
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.map(t -> Iterables.limit(t, maxRelations))
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.flatMap(Iterable::iterator)
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.map(Tuple2::_2)
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.rdd();
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// group by TARGET and apply limit
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RDD<Relation> byTarget = readPathRelationRDD(spark, inputRelationsPath)
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.filter(rel -> rel.getDataInfo().getDeletedbyinference() == false)
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.filter(rel -> relationFilter.contains(rel.getRelClass()) == false)
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.mapToPair(r -> new Tuple2<>(SortableRelationKey.create(r, r.getTarget()), r))
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.repartitionAndSortWithinPartitions(new RelationPartitioner(relPartitions))
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.groupBy(Tuple2::_1)
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.map(Tuple2::_2)
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.map(t -> Iterables.limit(t, maxRelations))
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.flatMap(Iterable::iterator)
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.map(Tuple2::_2)
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.rdd();
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spark
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.createDataset(bySource.union(byTarget), Encoders.bean(Relation.class))
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.repartition(relPartitions)
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.write()
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.mode(SaveMode.Overwrite)
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.parquet(outputPath);
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}
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/**
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* Reads a JavaRDD of eu.dnetlib.dhp.oa.provision.model.SortableRelation objects from a newline delimited json text
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* file,
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*
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* @param spark
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* @param inputPath
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* @return the JavaRDD<SortableRelation> containing all the relationships
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*/
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private static JavaRDD<Relation> readPathRelationRDD(
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SparkSession spark, final String inputPath) {
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JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
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return sc.textFile(inputPath).map(s -> OBJECT_MAPPER.readValue(s, Relation.class));
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}
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private static void removeOutputDir(SparkSession spark, String path) {
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HdfsSupport.remove(path, spark.sparkContext().hadoopConfiguration());
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}
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}
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