forked from D-Net/dnet-hadoop
Hosted By Map - modification in the code to prepare the info needed to apply the HostedByMap. There is no need to join datasources with the hbm: all the information needed is in the hosted by map already
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@ -3,7 +3,8 @@ package eu.dnetlib.dhp.oa.graph.hostedbymap
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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.oa.graph.hostedbymap.model.{DatasourceInfo, EntityInfo}
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import eu.dnetlib.dhp.schema.oaf.{Datasource, Journal, Publication}
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import eu.dnetlib.dhp.schema.oaf.{Journal, Publication}
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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.{Dataset, Encoder, Encoders, SaveMode, SparkSession}
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@ -12,11 +13,13 @@ import org.json4s.DefaultFormats
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import org.json4s.jackson.JsonMethods.parse
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import org.slf4j.{Logger, LoggerFactory}
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object SparkPrepareHostedByInfoToApply {
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implicit val mapEncoderDSInfo: Encoder[DatasourceInfo] = Encoders.kryo[DatasourceInfo]
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implicit val mapEncoderPInfo: Encoder[EntityInfo] = Encoders.kryo[EntityInfo]
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implicit val mapEncoderDSInfo: Encoder[DatasourceInfo] = Encoders.bean(classOf[DatasourceInfo])
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implicit val mapEncoderPInfo: Encoder[EntityInfo] = Encoders.bean(classOf[EntityInfo])
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def getList(id: String, j: Journal, name: String ) : List[EntityInfo] = {
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var lst:List[EntityInfo] = List()
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@ -47,25 +50,12 @@ object SparkPrepareHostedByInfoToApply {
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}
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def prepareDatasourceInfo(spark:SparkSession, datasourcePath:String) : Dataset[DatasourceInfo] = {
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implicit val mapEncoderDats: Encoder[Datasource] = Encoders.bean(classOf[Datasource])
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val mapper = new ObjectMapper()
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val dd : Dataset[Datasource] = spark.read.textFile(datasourcePath)
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.map(r => mapper.readValue(r, classOf[Datasource]))
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dd.filter(d => d.getJournal != null ).map(d => DatasourceInfo.newInstance(d.getId, d.getOfficialname.getValue,
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d.getJournal.getIssnPrinted, d.getJournal.getIssnOnline, d.getJournal.getIssnLinking))
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}
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def toHostedByItem(input:String): HostedByItemType = {
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def toEntityInfo(input:String): EntityInfo = {
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implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
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lazy val json: json4s.JValue = parse(input)
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val c :Map[String,HostedByItemType] = json.extract[Map[String, HostedByItemType]]
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c.values.head
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toEntityItem(c.keys.head, c.values.head)
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}
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def explodeJournalInfo(input: DatasourceInfo): List[EntityInfo] = {
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@ -73,10 +63,49 @@ object SparkPrepareHostedByInfoToApply {
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if (input.getEissn != null && !input.getEissn.equals("")){
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lst = EntityInfo.newInstance(input.getId, input.getEissn, input.getOfficialname, input.getOpenAccess) :: lst
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}
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if (input.getLissn != null && !input.getLissn.equals("")){
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lst = EntityInfo.newInstance(input.getId, input.getLissn, input.getOfficialname, input.getOpenAccess) :: lst
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}
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if (input.getIssn != null && !input.getIssn.equals("")){
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lst = EntityInfo.newInstance(input.getId, input.getIssn, input.getOfficialname, input.getOpenAccess) :: lst
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}
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lst
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}
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def joinDsandHBM(left:Dataset[DatasourceInfo], right:Dataset[HostedByItemType]): Dataset[EntityInfo] = {
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left.joinWith(right,
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left.col("id").equalTo(right.col("id")), "left")
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.map(t2 => {
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val dsi : DatasourceInfo = t2._1
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if(t2._2 != null){
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val hbi : HostedByItemType = t2._2
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dsi.setOpenAccess(hbi.openAccess)
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}
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dsi
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}).flatMap(explodeJournalInfo)
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}
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def toEntityItem(journal_id: String , hbi: HostedByItemType): EntityInfo = {
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EntityInfo.newInstance(hbi.id, journal_id, hbi.officialname, hbi.openAccess)
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}
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def joinResHBM(res: Dataset[EntityInfo], hbm: Dataset[EntityInfo]): Dataset[EntityInfo] = {
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Aggregators.resultToSingleId(res.joinWith(hbm, res.col("journal_id").equalTo(hbm.col("journal_id")), "left")
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.map(t2 => {
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val res: EntityInfo = t2._1
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if(t2._2 != null ){
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val ds = t2._2
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res.setHb_id(ds.getId)
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res.setOpenaccess(ds.getOpenaccess)
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res.setName(ds.getName)
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}
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res
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}))
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}
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def main(args: Array[String]): Unit = {
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@ -105,43 +134,20 @@ object SparkPrepareHostedByInfoToApply {
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import spark.implicits._
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//STEP1: leggere le DS e creare le entries {dsid, dsofficialname, issn, eissn, lissn, openaccess}
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val datasourceInfoDataset: Dataset[DatasourceInfo] = prepareDatasourceInfo(spark, "$graphPath/datasource")
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//STEP2: leggere la hostedbymap e raggruppare per datasource id
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val hostedByDataset = Aggregators.hostedByToSingleDSId(spark.createDataset(spark.sparkContext.textFile(hostedByMapPath).map(toHostedByItem)))
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//STEP1: leggere la hostedbymap e trasformarla in entity info
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val hostedByInfo:Dataset[EntityInfo] = spark.createDataset(spark.sparkContext.textFile(hostedByMapPath)).map(toEntityInfo)
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//STEP3: eseguire una join fra le datasource e la hostedby map (left) per settare se la datasource e' open access o no
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//ed esplodere l'info della datasource per ogni journal id diverso da nullo
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val join : Dataset[EntityInfo] = datasourceInfoDataset.joinWith(hostedByDataset,
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datasourceInfoDataset.col("id").equalTo(hostedByDataset.col("id"), "left"))
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.map(t2 => {
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val dsi : DatasourceInfo = t2._1
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if(t2._2 != null){
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dsi.setOpenAccess(t2._2.openAccess)
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}
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dsi
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}).flatMap(explodeJournalInfo)
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//STEP4: creare la mappa publication id issn, eissn, lissn esplosa
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//STEP2: creare la mappa publication id issn, eissn, lissn esplosa
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val resultInfoDataset:Dataset[EntityInfo] = prepareResultInfo(spark, "$graphPath/publication")
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//STEP5: join di join con resultInfo sul journal_id dal result con left
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// e riduzione di tutti i result con lo stesso id in una unica entry
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Aggregators.resultToSingleId(resultInfoDataset.joinWith(join, resultInfoDataset.col("journal_id").equalTo(join.col("journal_id")), "left")
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.map(t2 => {
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val res: EntityInfo = t2._1
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if(t2._2 != null ){
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val ds = t2._2
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res.setHb_id(ds.getId)
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res.setOpenaccess(ds.getOpenaccess)
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res.setName(ds.getName)
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}
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res
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})).write.mode(SaveMode.Overwrite).option("compression", "gzip").json(outputPath)
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//STEP3: join resultInfo con hostedByInfo sul journal_id dal result con left
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// e riduzione di tutti i result con lo stesso id in una unica entry con aggiunto l'id della datasource
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joinResHBM(resultInfoDataset, hostedByInfo)
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.write.mode(SaveMode.Overwrite).option("compression", "gzip").json(outputPath)
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}
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}
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