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

package eu.dnetlib.dhp.oa.provision;

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

import com.fasterxml.jackson.databind.ObjectMapper;
import com.google.common.collect.Iterables;
import com.google.common.collect.Iterators;
import eu.dnetlib.dhp.application.ArgumentApplicationParser;
import eu.dnetlib.dhp.common.HdfsSupport;
import eu.dnetlib.dhp.oa.provision.model.SortableRelation;
import eu.dnetlib.dhp.oa.provision.utils.RelationPartitioner;
import java.util.Optional;
import org.apache.commons.io.IOUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.*;
import org.apache.spark.rdd.RDD;
import org.apache.spark.sql.Dataset;
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;
import scala.Tuple2;

/**
 * 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 PrepareRelationsJob {

    private static final Logger log = LoggerFactory.getLogger(PrepareRelationsJob.class);

    private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();

    public static final int MAX_RELS = 100;

    public static void main(String[] args) throws Exception {
        String jsonConfiguration =
                IOUtils.toString(
                        PrepareRelationsJob.class.getResourceAsStream(
                                "/eu/dnetlib/dhp/oa/provision/input_params_prepare_relations.json"));
        final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration);
        parser.parseArgument(args);

        Boolean isSparkSessionManaged =
                Optional.ofNullable(parser.get("isSparkSessionManaged"))
                        .map(Boolean::valueOf)
                        .orElse(Boolean.TRUE);
        log.info("isSparkSessionManaged: {}", isSparkSessionManaged);

        String inputRelationsPath = parser.get("inputRelationsPath");
        log.info("inputRelationsPath: {}", inputRelationsPath);

        String outputPath = parser.get("outputPath");
        log.info("outputPath: {}", outputPath);

        int numPartitions = Integer.parseInt(parser.get("relPartitions"));
        log.info("relPartitions: {}", numPartitions);

        SparkConf conf = new SparkConf();

        runWithSparkSession(
                conf,
                isSparkSessionManaged,
                spark -> {
                    removeOutputDir(spark, outputPath);
                    prepareRelationsFromPaths(spark, inputRelationsPath, outputPath, numPartitions);
                });
    }

    private static void prepareRelationsFromPaths(
            SparkSession spark, String inputRelationsPath, String outputPath, int numPartitions) {
        readPathRelation(spark, inputRelationsPath)
                .filter(
                        (FilterFunction<SortableRelation>)
                                value -> value.getDataInfo().getDeletedbyinference() == false)
                .groupByKey(
                        (MapFunction<SortableRelation, String>) value -> value.getSource(),
                        Encoders.STRING())
                .flatMapGroups(
                        (FlatMapGroupsFunction<String, SortableRelation, SortableRelation>)
                                (key, values) -> Iterators.limit(values, MAX_RELS),
                        Encoders.bean(SortableRelation.class))
                .repartition(numPartitions)
                .write()
                .mode(SaveMode.Overwrite)
                .parquet(outputPath);
    }

    /**
     * Reads a Dataset of eu.dnetlib.dhp.oa.provision.model.SortableRelation objects from a newline
     * delimited json text file,
     *
     * @param spark
     * @param inputPath
     * @return the Dataset<SortableRelation> containing all the relationships
     */
    private static Dataset<SortableRelation> readPathRelation(
            SparkSession spark, final String inputPath) {
        return spark.read()
                .textFile(inputPath)
                .map(
                        (MapFunction<String, SortableRelation>)
                                value -> OBJECT_MAPPER.readValue(value, SortableRelation.class),
                        Encoders.bean(SortableRelation.class));
    }

    // TODO work in progress
    private static void prepareRelationsRDDFromPaths(
            SparkSession spark, String inputRelationsPath, String outputPath, int numPartitions) {
        JavaRDD<SortableRelation> rels =
                readPathRelationRDD(spark, inputRelationsPath).repartition(numPartitions);

        RDD<SortableRelation> d =
                rels.filter(
                                rel ->
                                        !rel.getDataInfo()
                                                .getDeletedbyinference()) // only consider those
                        // that are not virtually
                        // deleted
                        .mapToPair(
                                (PairFunction<SortableRelation, SortableRelation, SortableRelation>)
                                        rel -> new Tuple2<>(rel, rel))
                        .groupByKey(new RelationPartitioner(rels.getNumPartitions()))
                        .map(p -> Iterables.limit(p._2(), MAX_RELS))
                        .flatMap(p -> p.iterator())
                        .rdd();

        spark.createDataset(d, Encoders.bean(SortableRelation.class))
                .write()
                .mode(SaveMode.Overwrite)
                .parquet(outputPath);
    }

    private static JavaRDD<SortableRelation> readPathRelationRDD(
            SparkSession spark, final String inputPath) {
        JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
        return sc.textFile(inputPath).map(s -> OBJECT_MAPPER.readValue(s, SortableRelation.class));
    }

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