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

dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/hostedbymap/SparkPrepareHostedByInfoToApply.scala

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