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

package eu.dnetlib.dhp.oa.provision;

import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;

import eu.dnetlib.dhp.application.ArgumentApplicationParser;
import eu.dnetlib.dhp.common.HdfsSupport;
import eu.dnetlib.dhp.oa.provision.model.EntityRelEntity;
import eu.dnetlib.dhp.oa.provision.model.JoinedEntity;
import eu.dnetlib.dhp.oa.provision.model.Tuple2;
import eu.dnetlib.dhp.schema.common.ModelSupport;
import java.util.ArrayList;
import java.util.List;
import java.util.Optional;
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.Encoders;
import org.apache.spark.sql.SaveMode;
import org.apache.spark.sql.SparkSession;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * 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(ModelSupport.getOafModelClasses());

    runWithSparkSession(
        conf,
        isSparkSessionManaged,
        spark -> {
          removeOutputDir(spark, outputPath);
          createAdjacencyLists(spark, inputPath, outputPath);
        });
  }

  private static void createAdjacencyLists(
      SparkSession spark, String inputPath, String outputPath) {

    log.info("Reading joined entities from: {}", inputPath);
    spark
        .read()
        .load(inputPath)
        .as(Encoders.bean(EntityRelEntity.class))
        .groupByKey(
            (MapFunction<EntityRelEntity, String>) value -> value.getEntity().getId(),
            Encoders.STRING())
        .mapGroups(
            (MapGroupsFunction<String, EntityRelEntity, JoinedEntity>)
                (key, values) -> {
                  JoinedEntity j = new JoinedEntity();
                  List<Tuple2> links = new ArrayList<>();
                  while (values.hasNext() && links.size() < MAX_LINKS) {
                    EntityRelEntity curr = values.next();
                    if (j.getEntity() == null) {
                      j.setEntity(curr.getEntity());
                    }
                    links.add(new Tuple2(curr.getRelation(), curr.getTarget()));
                  }
                  j.setLinks(links);
                  return j;
                },
            Encoders.bean(JoinedEntity.class))
        .write()
        .mode(SaveMode.Overwrite)
        .parquet(outputPath);
  }

  private static void removeOutputDir(SparkSession spark, String path) {
    HdfsSupport.remove(path, spark.sparkContext().hadoopConfiguration());
  }
}