145 lines
4.1 KiB
Scala
145 lines
4.1 KiB
Scala
package eu.dnetlib.dhp.sx.graph
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.schema.oaf.{Dataset => OafDataset, _}
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import org.apache.commons.io.IOUtils
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import org.apache.spark.SparkConf
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import org.apache.spark.sql._
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import org.slf4j.{Logger, LoggerFactory}
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object SparkCreateInputGraph {
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def main(args: Array[String]): Unit = {
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val log: Logger = LoggerFactory.getLogger(getClass)
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val conf: SparkConf = new SparkConf()
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val parser = new ArgumentApplicationParser(
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IOUtils.toString(
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getClass.getResourceAsStream("/eu/dnetlib/dhp/sx/graph/extract_entities_params.json")
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)
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)
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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(getClass.getSimpleName)
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.master(parser.get("master"))
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.getOrCreate()
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val resultObject = List(
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("publication", classOf[Publication]),
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("dataset", classOf[OafDataset]),
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("software", classOf[Software]),
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("otherResearchProduct", classOf[OtherResearchProduct])
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)
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implicit val oafEncoder: Encoder[Oaf] = Encoders.kryo(classOf[Oaf])
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implicit val publicationEncoder: Encoder[Publication] = Encoders.kryo(classOf[Publication])
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implicit val datasetEncoder: Encoder[OafDataset] = Encoders.kryo(classOf[OafDataset])
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implicit val softwareEncoder: Encoder[Software] = Encoders.kryo(classOf[Software])
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implicit val orpEncoder: Encoder[OtherResearchProduct] =
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Encoders.kryo(classOf[OtherResearchProduct])
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implicit val relEncoder: Encoder[Relation] = Encoders.kryo(classOf[Relation])
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val sourcePath = parser.get("sourcePath")
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log.info(s"sourcePath -> $sourcePath")
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val targetPath = parser.get("targetPath")
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log.info(s"targetPath -> $targetPath")
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val oafDs: Dataset[Oaf] = spark.read.load(s"$sourcePath/*").as[Oaf]
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log.info("Extract Publication")
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oafDs
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.filter(o => o.isInstanceOf[Publication])
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.map(p => p.asInstanceOf[Publication])
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.write
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.mode(SaveMode.Overwrite)
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.save(s"$targetPath/extracted/publication")
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log.info("Extract dataset")
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oafDs
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.filter(o => o.isInstanceOf[OafDataset])
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.map(p => p.asInstanceOf[OafDataset])
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.write
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.mode(SaveMode.Overwrite)
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.save(s"$targetPath/extracted/dataset")
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log.info("Extract software")
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oafDs
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.filter(o => o.isInstanceOf[Software])
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.map(p => p.asInstanceOf[Software])
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.write
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.mode(SaveMode.Overwrite)
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.save(s"$targetPath/extracted/software")
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log.info("Extract otherResearchProduct")
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oafDs
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.filter(o => o.isInstanceOf[OtherResearchProduct])
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.map(p => p.asInstanceOf[OtherResearchProduct])
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.write
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.mode(SaveMode.Overwrite)
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.save(s"$targetPath/extracted/otherResearchProduct")
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log.info("Extract Relation")
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oafDs
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.filter(o => o.isInstanceOf[Relation])
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.map(p => p.asInstanceOf[Relation])
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.write
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.mode(SaveMode.Overwrite)
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.save(s"$targetPath/extracted/relation")
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resultObject.foreach { r =>
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log.info(s"Make ${r._1} unique")
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makeDatasetUnique(
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s"$targetPath/extracted/${r._1}",
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s"$targetPath/preprocess/${r._1}",
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spark,
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r._2
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)
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}
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}
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def extractEntities[T <: Oaf](
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oafDs: Dataset[Oaf],
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targetPath: String,
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clazz: Class[T],
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log: Logger
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): Unit = {
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implicit val resEncoder: Encoder[T] = Encoders.kryo(clazz)
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log.info(s"Extract ${clazz.getSimpleName}")
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oafDs
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.filter(o => o.isInstanceOf[T])
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.map(p => p.asInstanceOf[T])
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.write
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.mode(SaveMode.Overwrite)
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.save(targetPath)
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}
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def makeDatasetUnique[T <: Result](
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sourcePath: String,
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targetPath: String,
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spark: SparkSession,
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clazz: Class[T]
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): Unit = {
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import spark.implicits._
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implicit val resEncoder: Encoder[T] = Encoders.kryo(clazz)
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val ds: Dataset[T] = spark.read.load(sourcePath).as[T]
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ds.groupByKey(_.getId)
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.reduceGroups { (x, y) =>
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x.mergeFrom(y)
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x
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}
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.map(_._2)
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.write
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.mode(SaveMode.Overwrite)
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.save(targetPath)
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}
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}
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