167 lines
7.4 KiB
Java
167 lines
7.4 KiB
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());
|
|
}
|
|
}
|