257 lines
8.9 KiB
Java
257 lines
8.9 KiB
Java
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package eu.dnetlib.dhp.sx.graph;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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import java.util.stream.Collectors;
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import org.apache.commons.io.IOUtils;
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import org.apache.commons.lang3.StringUtils;
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import org.apache.hadoop.fs.FileStatus;
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import org.apache.hadoop.fs.FileSystem;
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import org.apache.hadoop.fs.Path;
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import org.apache.hadoop.io.compress.GzipCodec;
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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.api.java.function.MapFunction;
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import org.apache.spark.api.java.function.PairFunction;
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import org.apache.spark.rdd.RDD;
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import org.apache.spark.sql.Dataset;
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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 com.fasterxml.jackson.databind.DeserializationFeature;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import com.jayway.jsonpath.JsonPath;
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import eu.dnetlib.dhp.application.ArgumentApplicationParser;
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import eu.dnetlib.dhp.schema.oaf.Relation;
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import eu.dnetlib.dhp.schema.scholexplorer.DLIDataset;
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import eu.dnetlib.dhp.schema.scholexplorer.DLIPublication;
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import eu.dnetlib.dhp.schema.scholexplorer.DLIUnknown;
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import eu.dnetlib.dhp.utils.DHPUtils;
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import net.minidev.json.JSONArray;
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import scala.Tuple2;
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/**
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* This job is responsible of the creation of RAW Graph It is applied to the different entities generated from
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* {@link SparkExtractEntitiesJob} In case of dataset, publication and Unknown Entities we group all the entities of the
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* same type by their identifier, and then in the reduce phase we merge all the entities. Merge means: -merge all the
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* metadata -merge the collected From values
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* <p>
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* In case of relation we need to make a different work: -Phase 1: Map reduce jobs Map: Get all Relation and emit a key
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* constructed by (source, relType, Target) and the relation itself Reduce: Merge all relations Looking at the javadoc
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* of {@link SparkSXGeneratePidSimlarity} we take the dataset of pid relation and joining by source and target we
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* replace the wrong identifier in the relation with the correct ones. At the end we replace the new Dataset of Relation
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*/
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public class SparkScholexplorerCreateRawGraphJob {
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static final String IDJSONPATH = "$.id";
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static final String SOURCEJSONPATH = "$.source";
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static final String TARGETJSONPATH = "$.target";
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static final String RELJSONPATH = "$.relType";
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public static void main(String[] args) throws Exception {
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(
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IOUtils
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.toString(
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SparkScholexplorerCreateRawGraphJob.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/sx/graph/argumentparser/merge_entities_scholix_parameters.json")));
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parser.parseArgument(args);
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final SparkSession spark = SparkSession
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.builder()
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.config(
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new SparkConf()
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.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer"))
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.appName(SparkScholexplorerCreateRawGraphJob.class.getSimpleName())
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.master(parser.get("master"))
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.getOrCreate();
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final JavaSparkContext sc = new JavaSparkContext(spark.sparkContext());
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final String inputPath = parser.get("sourcePath");
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final String targetPath = parser.get("targetPath");
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final String entity = parser.get("entity");
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FileSystem fs = FileSystem.get(sc.sc().hadoopConfiguration());
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List<Path> subFolder = Arrays
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.stream(fs.listStatus(new Path(inputPath)))
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.filter(FileStatus::isDirectory)
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.map(FileStatus::getPath)
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.collect(Collectors.toList());
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List<JavaRDD<String>> inputRdd = new ArrayList<>();
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subFolder.forEach(p -> inputRdd.add(sc.textFile(p.toUri().getRawPath())));
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JavaRDD<String> union = sc.emptyRDD();
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for (JavaRDD<String> item : inputRdd) {
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union = union.union(item);
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}
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switch (entity) {
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case "dataset":
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union
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.mapToPair(
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(PairFunction<String, String, DLIDataset>) f -> {
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final String id = getJPathString(IDJSONPATH, f);
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ObjectMapper mapper = new ObjectMapper();
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mapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false);
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return new Tuple2<>(id, mapper.readValue(f, DLIDataset.class));
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})
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.reduceByKey(
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(a, b) -> {
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a.mergeFrom(b);
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return a;
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})
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.map(
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item -> {
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ObjectMapper mapper = new ObjectMapper();
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return mapper.writeValueAsString(item._2());
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})
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.saveAsTextFile(targetPath, GzipCodec.class);
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break;
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case "publication":
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union
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.mapToPair(
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(PairFunction<String, String, DLIPublication>) f -> {
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final String id = getJPathString(IDJSONPATH, f);
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ObjectMapper mapper = new ObjectMapper();
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mapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false);
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return new Tuple2<>(id, mapper.readValue(f, DLIPublication.class));
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})
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.reduceByKey(
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(a, b) -> {
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a.mergeFrom(b);
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return a;
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})
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.map(
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item -> {
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ObjectMapper mapper = new ObjectMapper();
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return mapper.writeValueAsString(item._2());
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})
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.saveAsTextFile(targetPath, GzipCodec.class);
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break;
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case "unknown":
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union
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.mapToPair(
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(PairFunction<String, String, DLIUnknown>) f -> {
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final String id = getJPathString(IDJSONPATH, f);
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ObjectMapper mapper = new ObjectMapper();
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mapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false);
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return new Tuple2<>(id, mapper.readValue(f, DLIUnknown.class));
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})
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.reduceByKey(
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(a, b) -> {
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a.mergeFrom(b);
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return a;
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})
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.map(
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item -> {
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ObjectMapper mapper = new ObjectMapper();
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return mapper.writeValueAsString(item._2());
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})
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.saveAsTextFile(targetPath, GzipCodec.class);
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break;
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case "relation":
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SparkSXGeneratePidSimlarity
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.generateDataFrame(
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spark, sc, inputPath.replace("/relation", ""), targetPath.replace("/relation", ""));
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RDD<Relation> rdd = union
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.mapToPair(
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(PairFunction<String, String, Relation>) f -> {
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final String source = getJPathString(SOURCEJSONPATH, f);
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final String target = getJPathString(TARGETJSONPATH, f);
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final String reltype = getJPathString(RELJSONPATH, f);
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ObjectMapper mapper = new ObjectMapper();
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mapper
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.configure(
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DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false);
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return new Tuple2<>(
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DHPUtils
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.md5(
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String
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.format(
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"%s::%s::%s",
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source.toLowerCase(),
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reltype.toLowerCase(),
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target.toLowerCase())),
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mapper.readValue(f, Relation.class));
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})
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.reduceByKey(
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(a, b) -> {
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a.mergeFrom(b);
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return a;
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})
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.map(Tuple2::_2)
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.rdd();
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spark
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.createDataset(rdd, Encoders.bean(Relation.class))
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.write()
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.mode(SaveMode.Overwrite)
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.save(targetPath);
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Dataset<Relation> rel_ds = spark.read().load(targetPath).as(Encoders.bean(Relation.class));
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System.out.println("LOADING PATH :" + targetPath.replace("/relation", "") + "/pid_simRel");
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Dataset<Relation> sim_ds = spark
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.read()
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.load(targetPath.replace("/relation", "") + "/pid_simRel")
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.as(Encoders.bean(Relation.class));
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Dataset<Relation> ids = sim_ds
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.map(
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(MapFunction<Relation, Relation>) relation -> {
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final String type = StringUtils.substringBefore(relation.getSource(), "|");
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relation
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.setTarget(
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String
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.format(
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"%s|%s",
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type, StringUtils.substringAfter(relation.getTarget(), "::")));
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return relation;
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},
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Encoders.bean(Relation.class));
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final Dataset<Relation> firstJoin = rel_ds
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.joinWith(ids, ids.col("target").equalTo(rel_ds.col("source")), "left_outer")
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.map(
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(MapFunction<Tuple2<Relation, Relation>, Relation>) s -> {
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if (s._2() != null) {
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s._1().setSource(s._2().getSource());
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}
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return s._1();
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},
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Encoders.bean(Relation.class));
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Dataset<Relation> secondJoin = firstJoin
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.joinWith(ids, ids.col("target").equalTo(firstJoin.col("target")), "left_outer")
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.map(
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(MapFunction<Tuple2<Relation, Relation>, Relation>) s -> {
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if (s._2() != null) {
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s._1().setTarget(s._2().getSource());
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}
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return s._1();
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},
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Encoders.bean(Relation.class));
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secondJoin.write().mode(SaveMode.Overwrite).save(targetPath + "_fixed");
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FileSystem fileSystem = FileSystem.get(sc.hadoopConfiguration());
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fileSystem.delete(new Path(targetPath), true);
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fileSystem.rename(new Path(targetPath + "_fixed"), new Path(targetPath));
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}
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}
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public static String getJPathString(final String jsonPath, final String json) {
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try {
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Object o = JsonPath.read(json, jsonPath);
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if (o instanceof String)
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return (String) o;
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if (o instanceof JSONArray && ((JSONArray) o).size() > 0)
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return (String) ((JSONArray) o).get(0);
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return "";
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} catch (Exception e) {
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return "";
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}
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}
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}
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