139 lines
4.4 KiB
Scala
139 lines
4.4 KiB
Scala
package eu.dnetlib.dhp.oa.graph.resolution
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import com.fasterxml.jackson.databind.ObjectMapper
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.schema.common.EntityType
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import eu.dnetlib.dhp.schema.oaf.utils.MergeUtils
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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.hadoop.fs.{FileSystem, Path}
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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 SparkResolveEntities {
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val mapper = new ObjectMapper()
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val entities = List(
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EntityType.dataset,
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EntityType.publication,
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EntityType.software,
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EntityType.otherresearchproduct
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)
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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(
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"/eu/dnetlib/dhp/oa/graph/resolution/resolve_entities_params.json"
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)
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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 graphBasePath = parser.get("graphBasePath")
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log.info(s"graphBasePath -> $graphBasePath")
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val workingPath = parser.get("workingPath")
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log.info(s"workingPath -> $workingPath")
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val unresolvedPath = parser.get("unresolvedPath")
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log.info(s"unresolvedPath -> $unresolvedPath")
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val targetPath = parser.get("targetPath")
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log.info(s"targetPath -> $targetPath")
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val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration)
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fs.mkdirs(new Path(workingPath))
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resolveEntities(spark, workingPath, unresolvedPath)
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generateResolvedEntities(spark, workingPath, graphBasePath, targetPath)
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}
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def resolveEntities(spark: SparkSession, workingPath: String, unresolvedPath: String) = {
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implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result])
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import spark.implicits._
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val rPid: Dataset[(String, String)] =
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spark.read.load(s"$workingPath/relationResolvedPid").as[(String, String)]
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val up: Dataset[(String, Result)] = spark.read
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.text(unresolvedPath)
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.as[String]
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.map(s => mapper.readValue(s, classOf[Result]))
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.map(r => (r.getId, r))(Encoders.tuple(Encoders.STRING, resEncoder))
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rPid
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.joinWith(up, rPid("_2").equalTo(up("_1")), "inner")
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.map { r =>
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val result = r._2._2
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val dnetId = r._1._1
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result.setId(dnetId)
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result
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}
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.write
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.mode(SaveMode.Overwrite)
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.save(s"$workingPath/resolvedEntities")
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}
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def deserializeObject(input: String, entity: EntityType): Result = {
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entity match {
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case EntityType.publication => mapper.readValue(input, classOf[Publication])
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case EntityType.dataset => mapper.readValue(input, classOf[OafDataset])
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case EntityType.software => mapper.readValue(input, classOf[Software])
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case EntityType.otherresearchproduct => mapper.readValue(input, classOf[OtherResearchProduct])
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}
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}
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def generateResolvedEntities(
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spark: SparkSession,
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workingPath: String,
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graphBasePath: String,
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targetPath: String
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) = {
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implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result])
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import spark.implicits._
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val re: Dataset[(String, Result)] = spark.read
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.load(s"$workingPath/resolvedEntities")
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.as[Result]
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.map(r => (r.getId, r))(Encoders.tuple(Encoders.STRING, resEncoder))
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entities.foreach { e =>
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{
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val currentEntityDataset: Dataset[(String, Result)] = spark.read
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.text(s"$graphBasePath/$e")
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.as[String]
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.map(s => deserializeObject(s, e))
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.map(r => (r.getId, r))(Encoders.tuple(Encoders.STRING, resEncoder))
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currentEntityDataset
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.joinWith(re, currentEntityDataset("_1").equalTo(re("_1")), "left")
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.map(k => {
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val a = k._1
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val b = k._2
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if (b == null)
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a._2
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else
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MergeUtils.mergeResult(a._2, b._2)
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})
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.map(r => mapper.writeValueAsString(r))(Encoders.STRING)
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.write
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.text(s"$targetPath/$e")
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}
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}
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}
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}
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