185 lines
8.0 KiB
Scala
185 lines
8.0 KiB
Scala
package eu.dnetlib.dhp.`export`
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.schema.oaf.{Publication, Relation, Dataset => OafDataset}
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import eu.dnetlib.dhp.schema.scholexplorer.{DLIDataset, DLIPublication, DLIRelation}
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import org.apache.commons.io.IOUtils
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import org.apache.hadoop.io.Text
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import org.apache.hadoop.io.compress.GzipCodec
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import org.apache.hadoop.mapred.SequenceFileOutputFormat
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import org.apache.spark.rdd.RDD
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import org.apache.spark.sql.{Dataset, Encoder, Encoders, SaveMode, SparkSession}
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import org.apache.spark.sql.functions._
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import org.apache.spark.sql.expressions.Window
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import org.apache.spark.{SparkConf, SparkContext}
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import org.codehaus.jackson.map.ObjectMapper
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import scala.collection.mutable.ArrayBuffer
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object SparkExportContentForOpenAire {
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def main(args: Array[String]): Unit = {
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val conf: SparkConf = new SparkConf()
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val parser = new ArgumentApplicationParser(IOUtils.toString(SparkExportContentForOpenAire.getClass.getResourceAsStream("input_export_content_parameters.json")))
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parser.parseArgument(args)
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val spark: SparkSession =
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SparkSession
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.builder()
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.config(conf)
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.appName(SparkExportContentForOpenAire.getClass.getSimpleName)
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.master(parser.get("master")).getOrCreate()
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val sc:SparkContext = spark.sparkContext
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val workingPath = parser.get("workingDirPath")
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implicit val pubEncoder: Encoder[Publication] = Encoders.bean(classOf[Publication])
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implicit val datEncoder: Encoder[OafDataset] = Encoders.bean(classOf[OafDataset])
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implicit val relEncoder: Encoder[Relation] = Encoders.bean(classOf[Relation])
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implicit val dliRelEncoder: Encoder[DLIRelation] = Encoders.bean(classOf[DLIRelation])
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import spark.implicits._
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val relRDD:RDD[Relation] = sc.textFile(s"$workingPath/relation_j")
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.map(s => new ObjectMapper().readValue(s, classOf[DLIRelation]))
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.filter(p => p.getDataInfo.getDeletedbyinference == false)
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.map(DLIToOAF.convertDLIRelation).filter(p=>p!= null)
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spark.createDataset(relRDD).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationDS")
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val datRDD:RDD[OafDataset] = sc.textFile(s"$workingPath/dataset")
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.map(s => new ObjectMapper().readValue(s, classOf[DLIDataset]))
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.filter(p => p.getDataInfo.getDeletedbyinference == false)
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.map(DLIToOAF.convertDLIDatasetTOOAF).filter(p=>p!= null)
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spark.createDataset(datRDD).write.mode(SaveMode.Overwrite).save(s"$workingPath/datasetDS")
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val pubRDD:RDD[Publication] = sc.textFile(s"$workingPath/publication")
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.map(s => new ObjectMapper().readValue(s, classOf[DLIPublication]))
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.filter(p => p.getDataInfo.getDeletedbyinference == false)
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.map(DLIToOAF.convertDLIPublicationToOAF).filter(p=>p!= null)
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spark.createDataset(pubRDD).write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationDS")
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val pubs:Dataset[Publication] = spark.read.load(s"$workingPath/publicationDS").as[Publication]
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val dats :Dataset[OafDataset] = spark.read.load(s"$workingPath/datasetDS").as[OafDataset]
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val relDS1 :Dataset[Relation] = spark.read.load(s"$workingPath/relationDS").as[Relation]
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val pub_id = pubs.select("id").distinct()
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val dat_id = dats.select("id").distinct()
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pub_id.joinWith(relDS1, pub_id("id").equalTo(relDS1("source"))).map(k => k._2).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationDS_f1")
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val relDS2= spark.read.load(s"$workingPath/relationDS_f1").as[Relation]
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relDS2.joinWith(dat_id, relDS2("target").equalTo(dats("id"))).map(k => k._1).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationDS_filtered")
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val r_source = relDS2.select(relDS2("source")).distinct()
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val r_target = relDS2.select(relDS2("target")).distinct()
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val w2 = Window.partitionBy("id").orderBy("lastupdatetimestamp")
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pubs.joinWith(r_source, pubs("id").equalTo(r_source("source")), "inner").map(k => k._1)
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.withColumn("row",row_number.over(w2)).where($"row" === 1).drop("row")
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.write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationDS_filtered")
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dats.joinWith(r_target, dats("id").equalTo(r_target("target")), "inner").map(k => k._1)
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.withColumn("row",row_number.over(w2)).where($"row" === 1).drop("row")
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.write.mode(SaveMode.Overwrite).save(s"$workingPath/datasetAS")
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spark.createDataset(sc.textFile(s"$workingPath/dataset")
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.map(s => new ObjectMapper().readValue(s, classOf[DLIDataset]))
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.map(DLIToOAF.convertDLIDatasetToExternalReference)
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.filter(p => p != null)).as[DLIExternalReference].write.mode(SaveMode.Overwrite).save(s"$workingPath/externalReference")
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val pf = spark.read.load(s"$workingPath/publicationDS_filtered").select("id")
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val relDS3 = spark.read.load(s"$workingPath/relationDS").as[Relation]
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val relationTo = pf.joinWith(relDS3, pf("id").equalTo(relDS3("source")),"inner").map(t =>t._2)
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val extRef = spark.read.load(s"$workingPath/externalReference").as[DLIExternalReference]
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spark.createDataset(relationTo.joinWith(extRef, relationTo("target").equalTo(extRef("id")), "inner").map(d => {
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val r = d._1
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val ext = d._2
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(r.getSource, ext)
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}).rdd.groupByKey.map(f => {
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var dli_ext = ArrayBuffer[DLIExternalReference]()
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f._2.foreach(d => if (dli_ext.size < 100) dli_ext += d )
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(f._1, dli_ext)
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})).write.mode(SaveMode.Overwrite).save(s"$workingPath/externalReference_grouped")
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val pubf :Dataset[Publication] = spark.read.load(s"$workingPath/publicationDS_filtered").as[Publication]
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val groupedERf:Dataset[(String, List[DLIExternalReference])]= spark.read.load(s"$workingPath/externalReference_grouped").as[(String, List[DLIExternalReference])]
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groupedERf.joinWith(pubf,pubf("id").equalTo(groupedERf("_1"))).map(t =>
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{
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val publication = t._2
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if (t._1 != null) {
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val eRefs = t._1._2
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DLIToOAF.insertExternalRefs(publication, eRefs)
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} else
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publication
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}
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).write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationAS")
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spark.createDataset(sc.textFile(s"$workingPath/dataset")
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.map(s => new ObjectMapper().readValue(s, classOf[DLIDataset]))
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.map(DLIToOAF.convertClinicalTrial)
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.filter(p => p != null))
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.write.mode(SaveMode.Overwrite).save(s"$workingPath/clinicalTrials")
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val ct:Dataset[(String,String)] = spark.read.load(s"$workingPath/clinicalTrials").as[(String,String)]
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val relDS= spark.read.load(s"$workingPath/relationDS_f1").as[Relation]
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relDS.joinWith(ct, relDS("target").equalTo(ct("_1")), "inner")
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.map(k =>{
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val currentRel = k._1
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currentRel.setTarget(k._2._2)
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currentRel
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}).write.mode(SaveMode.Overwrite).save(s"$workingPath/clinicalTrialsRels")
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val clRels:Dataset[Relation] = spark.read.load(s"$workingPath/clinicalTrialsRels").as[Relation]
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val rels:Dataset[Relation] = spark.read.load(s"$workingPath/relationDS_filtered").as[Relation]
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rels.union(clRels).flatMap(r => {
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val inverseRel = new Relation
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inverseRel.setSource(r.getTarget)
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inverseRel.setTarget(r.getSource)
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inverseRel.setDataInfo(r.getDataInfo)
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inverseRel.setCollectedfrom(r.getCollectedfrom)
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inverseRel.setRelType(r.getRelType)
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inverseRel.setSubRelType(r.getSubRelType)
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inverseRel.setRelClass(DLIToOAF.rel_inverse(r.getRelClass))
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List(r, inverseRel)
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}).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationAS")
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val fRels:Dataset[(String,String)] = spark.read.load(s"$workingPath/relationAS").as[Relation].map(DLIToOAF.toActionSet)
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val fpubs:Dataset[(String,String)] = spark.read.load(s"$workingPath/publicationAS").as[Publication].map(DLIToOAF.toActionSet)
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val fdats:Dataset[(String,String)] = spark.read.load(s"$workingPath/datasetAS").as[OafDataset].map(DLIToOAF.toActionSet)
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fRels.union(fpubs).union(fdats).rdd.map(s => (new Text(s._1), new Text(s._2))).saveAsHadoopFile(s"$workingPath/rawset", classOf[Text], classOf[Text], classOf[SequenceFileOutputFormat[Text,Text]], classOf[GzipCodec])
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}
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}
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