forked from D-Net/dnet-hadoop
99 lines
3.7 KiB
Scala
99 lines
3.7 KiB
Scala
package eu.dnetlib.doiboost.crossref
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.schema.oaf
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import eu.dnetlib.dhp.schema.oaf.{Oaf, Publication, Relation, Dataset => OafDataset}
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import org.apache.commons.io.IOUtils
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import org.apache.hadoop.io.{IntWritable, Text}
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import org.apache.spark.SparkConf
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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.slf4j.{Logger, LoggerFactory}
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case class Reference(author: String, firstPage: String) {}
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object SparkMapDumpIntoOAF {
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def main(args: Array[String]): Unit = {
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val logger: Logger = LoggerFactory.getLogger(SparkMapDumpIntoOAF.getClass)
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val conf: SparkConf = new SparkConf()
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val parser = new ArgumentApplicationParser(IOUtils.toString(SparkMapDumpIntoOAF.getClass.getResourceAsStream("/eu/dnetlib/dhp/doiboost/convert_map_to_oaf_params.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(SparkMapDumpIntoOAF.getClass.getSimpleName)
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.master(parser.get("master")).getOrCreate()
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implicit val mapEncoderPubs: Encoder[Publication] = Encoders.kryo[Publication]
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implicit val mapEncoderRelatons: Encoder[Relation] = Encoders.kryo[Relation]
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implicit val mapEncoderDatasets: Encoder[oaf.Dataset] = Encoders.kryo[OafDataset]
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val sc = spark.sparkContext
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val targetPath = parser.get("targetPath")
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sc.sequenceFile(parser.get("sourcePath"), classOf[IntWritable], classOf[Text])
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.map(k => k._2.toString).map(CrossrefImporter.decompressBlob)
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.flatMap(k => Crossref2Oaf.convert(k)).saveAsObjectFile(s"${targetPath}/mixObject")
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val inputRDD = sc.objectFile[Oaf](s"${targetPath}/mixObject").filter(p=> p!= null)
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val distinctPubs:RDD[Publication] = inputRDD.filter(k => k != null && k.isInstanceOf[Publication])
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.map(k => k.asInstanceOf[Publication]).map { p: Publication => Tuple2(p.getId, p) }.reduceByKey { case (p1: Publication, p2: Publication) =>
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var r = if (p1 == null) p2 else p1
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if (p1 != null && p2 != null) {
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if (p1.getLastupdatetimestamp != null && p2.getLastupdatetimestamp != null) {
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if (p1.getLastupdatetimestamp < p2.getLastupdatetimestamp)
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r = p2
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else
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r = p1
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} else {
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r = if (p1.getLastupdatetimestamp == null) p2 else p1
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}
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}
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r
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}.map(_._2)
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val pubs:Dataset[Publication] = spark.createDataset(distinctPubs)
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pubs.write.mode(SaveMode.Overwrite).save(s"${targetPath}/publication")
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val distincDatasets:RDD[OafDataset] = inputRDD.filter(k => k != null && k.isInstanceOf[OafDataset])
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.map(k => k.asInstanceOf[OafDataset]).map(p => Tuple2(p.getId, p)).reduceByKey { case (p1: OafDataset, p2: OafDataset) =>
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var r = if (p1 == null) p2 else p1
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if (p1 != null && p2 != null) {
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if (p1.getLastupdatetimestamp != null && p2.getLastupdatetimestamp != null) {
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if (p1.getLastupdatetimestamp < p2.getLastupdatetimestamp)
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r = p2
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else
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r = p1
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} else {
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r = if (p1.getLastupdatetimestamp == null) p2 else p1
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}
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}
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r
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}.map(_._2)
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spark.createDataset(distincDatasets).write.mode(SaveMode.Overwrite).save(s"${targetPath}/dataset")
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val distinctRels =inputRDD.filter(k => k != null && k.isInstanceOf[Relation])
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.map(k => k.asInstanceOf[Relation]).map(r=> (s"${r.getSource}::${r.getTarget}",r))
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.reduceByKey { case (p1: Relation, p2: Relation) =>
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if (p1 == null) p2 else p1
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}.map(_._2)
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val rels: Dataset[Relation] = spark.createDataset(distinctRels)
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rels.write.mode(SaveMode.Overwrite).save(s"${targetPath}/relations")
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}
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}
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