dnet-hadoop/dhp-workflows/dhp-graph-provision/src/main/java/eu/dnetlib/dhp/oa/provision/AdjacencyListBuilderJob.java

110 lines
4.1 KiB
Java

package eu.dnetlib.dhp.oa.provision;
import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
import java.util.ArrayList;
import java.util.List;
import java.util.Optional;
import java.util.stream.Collectors;
import org.apache.commons.io.IOUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.api.java.function.MapGroupsFunction;
import org.apache.spark.sql.*;
import org.apache.spark.sql.expressions.Aggregator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import eu.dnetlib.dhp.application.ArgumentApplicationParser;
import eu.dnetlib.dhp.common.HdfsSupport;
import eu.dnetlib.dhp.oa.provision.model.*;
import scala.Tuple2;
import scala.collection.JavaConverters;
import scala.collection.Seq;
/**
* Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects. The
* operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
* all the possible relationships (similarity links produced by the Dedup process are excluded).
* <p>
* The operation is implemented by sequentially joining one entity type at time (E) with the relationships (R), and
* again by E, finally grouped by E.id;
* <p>
* The workflow is organized in different parts aimed to to reduce the complexity of the operation 1)
* PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
* false), each entity can be linked at most to 100 other objects
* <p>
* 2) JoinRelationEntityByTargetJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
* E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
* 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
* (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)
* <p>
* 3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
* result as JoinedEntity
* <p>
* 4) XmlConverterJob: convert the JoinedEntities as XML records
*/
public class AdjacencyListBuilderJob {
private static final Logger log = LoggerFactory.getLogger(AdjacencyListBuilderJob.class);
public static final int MAX_LINKS = 100;
public static void main(String[] args) throws Exception {
final ArgumentApplicationParser parser = new ArgumentApplicationParser(
IOUtils
.toString(
AdjacencyListBuilderJob.class
.getResourceAsStream(
"/eu/dnetlib/dhp/oa/provision/input_params_build_adjacency_lists.json")));
parser.parseArgument(args);
Boolean isSparkSessionManaged = Optional
.ofNullable(parser.get("isSparkSessionManaged"))
.map(Boolean::valueOf)
.orElse(Boolean.TRUE);
log.info("isSparkSessionManaged: {}", isSparkSessionManaged);
String inputPath = parser.get("inputPath");
log.info("inputPath: {}", inputPath);
String outputPath = parser.get("outputPath");
log.info("outputPath: {}", outputPath);
SparkConf conf = new SparkConf();
conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
conf.registerKryoClasses(ProvisionModelSupport.getModelClasses());
runWithSparkSession(
conf,
isSparkSessionManaged,
spark -> {
removeOutputDir(spark, outputPath);
createAdjacencyListsKryo(spark, inputPath, outputPath);
});
}
private static void createAdjacencyListsKryo(
SparkSession spark, String inputPath, String outputPath) {
log.info("Reading joined entities from: {}", inputPath);
final List<String> paths = HdfsSupport
.listFiles(inputPath, spark.sparkContext().hadoopConfiguration());
log.info("Found paths: {}", String.join(",", paths));
}
private static Seq<String> toSeq(List<String> list) {
return JavaConverters.asScalaIteratorConverter(list.iterator()).asScala().toSeq();
}
private static void removeOutputDir(SparkSession spark, String path) {
HdfsSupport.remove(path, spark.sparkContext().hadoopConfiguration());
}
}