serialise records in the OAF-store-graph mdstores in json format. Read them again in the graph construction phase using a tolerant parser to support backward compatible changes in the evolution of the schema

dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/collection/CollectionUtils.scala

@@ -1,7 +1,9 @@
 package eu.dnetlib.dhp.collection
 
+import com.fasterxml.jackson.databind.ObjectMapper
 import eu.dnetlib.dhp.schema.common.ModelSupport
 import eu.dnetlib.dhp.schema.oaf.{Oaf, OafEntity, Relation}
+import org.apache.spark.sql.{Dataset, Encoder, Encoders, SaveMode}
 
 object CollectionUtils {
 
@@ -46,4 +48,15 @@ object CollectionUtils {
     List()
   }
 
+  def saveDataset(d: Dataset[Oaf], targetPath: String):Unit = {
+    implicit val resultEncoder: Encoder[Oaf] = Encoders.kryo(classOf[Oaf])
+    val mapper = new ObjectMapper
+
+    d
+      .flatMap(i => CollectionUtils.fixRelations(i))
+      .filter(i => i != null)
+      .map(r => mapper.writeValueAsString(r))(Encoders.STRING)
+      .write.mode(SaveMode.Overwrite).save(targetPath)
+  }
+
 }

dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/datacite/GenerateDataciteDatasetSpark.scala

@@ -2,14 +2,14 @@ package eu.dnetlib.dhp.datacite
 
 import com.fasterxml.jackson.databind.ObjectMapper
 import eu.dnetlib.dhp.application.AbstractScalaApplication
-import eu.dnetlib.dhp.collection.CollectionUtils.fixRelations
+import eu.dnetlib.dhp.collection.CollectionUtils
 import eu.dnetlib.dhp.common.Constants.{MDSTORE_DATA_PATH, MDSTORE_SIZE_PATH}
 import eu.dnetlib.dhp.common.vocabulary.VocabularyGroup
 import eu.dnetlib.dhp.schema.mdstore.{MDStoreVersion, MetadataRecord}
 import eu.dnetlib.dhp.schema.oaf.Oaf
 import eu.dnetlib.dhp.utils.DHPUtils.writeHdfsFile
 import eu.dnetlib.dhp.utils.ISLookupClientFactory
-import org.apache.spark.sql.{Encoder, Encoders, SaveMode, SparkSession}
+import org.apache.spark.sql.{Encoder, Encoders, SparkSession}
 import org.slf4j.{Logger, LoggerFactory}
 
 
@@ -73,12 +73,12 @@ class GenerateDataciteDatasetSpark (propertyPath:String, args:Array[String], log
     implicit val mrEncoder: Encoder[MetadataRecord] = Encoders.kryo[MetadataRecord]
 
     implicit val resEncoder: Encoder[Oaf] = Encoders.kryo[Oaf]
-    spark.read.load(sourcePath).as[DataciteType]
-      .filter(d => d.isActive)
-      .flatMap(d => DataciteToOAFTransformation.generateOAF(d.json, d.timestamp, d.timestamp, vocabularies, exportLinks))
-      .filter(d => d != null)
-      .flatMap(i => fixRelations(i)).filter(i => i != null)
-      .write.mode(SaveMode.Overwrite).save(targetPath)
+    CollectionUtils.saveDataset(
+      spark.read.load(sourcePath).as[DataciteType]
+        .filter(d => d.isActive)
+        .flatMap(d => DataciteToOAFTransformation.generateOAF(d.json, d.timestamp, d.timestamp, vocabularies, exportLinks))
+        .filter(d => d != null),
+      targetPath)
   }
 
 }

dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/sx/bio/SparkTransformBioDatabaseToOAF.scala

@@ -1,12 +1,12 @@
 package eu.dnetlib.dhp.sx.bio
 
 import eu.dnetlib.dhp.application.ArgumentApplicationParser
-import eu.dnetlib.dhp.schema.oaf.Oaf
-import BioDBToOAF.ScholixResolved
 import eu.dnetlib.dhp.collection.CollectionUtils
+import eu.dnetlib.dhp.schema.oaf.Oaf
+import eu.dnetlib.dhp.sx.bio.BioDBToOAF.ScholixResolved
 import org.apache.commons.io.IOUtils
 import org.apache.spark.SparkConf
-import org.apache.spark.sql.{Encoder, Encoders, SaveMode, SparkSession}
+import org.apache.spark.sql.{Encoder, Encoders, SparkSession}
 import org.slf4j.{Logger, LoggerFactory}
 
 object SparkTransformBioDatabaseToOAF {
@@ -36,13 +36,13 @@ object SparkTransformBioDatabaseToOAF {
     import spark.implicits._
     database.toUpperCase() match {
       case "UNIPROT" =>
-        spark.createDataset(sc.textFile(dbPath).flatMap(i => BioDBToOAF.uniprotToOAF(i))).flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null).write.mode(SaveMode.Overwrite).save(targetPath)
+        CollectionUtils.saveDataset(spark.createDataset(sc.textFile(dbPath).flatMap(i => BioDBToOAF.uniprotToOAF(i))), targetPath)
       case "PDB" =>
-        spark.createDataset(sc.textFile(dbPath).flatMap(i => BioDBToOAF.pdbTOOaf(i))).flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null).write.mode(SaveMode.Overwrite).save(targetPath)
+        CollectionUtils.saveDataset(spark.createDataset(sc.textFile(dbPath).flatMap(i => BioDBToOAF.pdbTOOaf(i))), targetPath)
       case "SCHOLIX" =>
-        spark.read.load(dbPath).as[ScholixResolved].map(i => BioDBToOAF.scholixResolvedToOAF(i)).flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null).write.mode(SaveMode.Overwrite).save(targetPath)
+        CollectionUtils.saveDataset(spark.read.load(dbPath).as[ScholixResolved].map(i => BioDBToOAF.scholixResolvedToOAF(i)), targetPath)
       case "CROSSREF_LINKS" =>
-        spark.createDataset(sc.textFile(dbPath).map(i => BioDBToOAF.crossrefLinksToOaf(i))).flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null).write.mode(SaveMode.Overwrite).save(targetPath)
+        CollectionUtils.saveDataset(spark.createDataset(sc.textFile(dbPath).map(i => BioDBToOAF.crossrefLinksToOaf(i))), targetPath)
     }
   }
 

dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/sx/bio/ebi/SparkCreateBaselineDataFrame.scala

@@ -1,9 +1,10 @@
 package eu.dnetlib.dhp.sx.bio.ebi
 
 import eu.dnetlib.dhp.application.ArgumentApplicationParser
+import eu.dnetlib.dhp.collection.CollectionUtils
 import eu.dnetlib.dhp.common.vocabulary.VocabularyGroup
-import eu.dnetlib.dhp.schema.oaf.Result
-import eu.dnetlib.dhp.sx.bio.pubmed.{PMArticle, PMAuthor, PMJournal, PMParser, PubMedToOaf}
+import eu.dnetlib.dhp.schema.oaf.{Oaf, Result}
+import eu.dnetlib.dhp.sx.bio.pubmed._
 import eu.dnetlib.dhp.utils.ISLookupClientFactory
 import org.apache.commons.io.IOUtils
 import org.apache.hadoop.conf.Configuration
@@ -177,7 +178,7 @@ object SparkCreateBaselineDataFrame {
     implicit val PMEncoder: Encoder[PMArticle] = Encoders.kryo(classOf[PMArticle])
     implicit val PMJEncoder: Encoder[PMJournal] = Encoders.kryo(classOf[PMJournal])
     implicit val PMAEncoder: Encoder[PMAuthor] = Encoders.kryo(classOf[PMAuthor])
-    implicit val resultEncoder: Encoder[Result] = Encoders.kryo(classOf[Result])
+    implicit val resultEncoder: Encoder[Oaf] = Encoders.kryo(classOf[Oaf])
 
     if (!"true".equalsIgnoreCase(skipUpdate)) {
       downloadBaseLineUpdate(s"$workingPath/baseline", hdfsServerUri)
@@ -192,9 +193,10 @@ object SparkCreateBaselineDataFrame {
     }
 
     val exported_dataset = spark.read.load(s"$workingPath/baseline_dataset").as[PMArticle]
-    exported_dataset
-      .map(a => PubMedToOaf.convert(a, vocabularies)).as[Result]
-      .filter(p => p != null)
-      .write.mode(SaveMode.Overwrite).save(targetPath)
+    CollectionUtils.saveDataset(exported_dataset
+      .map(a => PubMedToOaf.convert(a, vocabularies)).as[Oaf]
+      .filter(p => p != null),
+      targetPath)
+
   }
 }

dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/sx/bio/ebi/SparkEBILinksToOaf.scala

@@ -1,11 +1,10 @@
 package eu.dnetlib.dhp.sx.bio.ebi
 
 import eu.dnetlib.dhp.application.ArgumentApplicationParser
+import eu.dnetlib.dhp.collection.CollectionUtils
 import eu.dnetlib.dhp.schema.oaf.Oaf
 import eu.dnetlib.dhp.sx.bio.BioDBToOAF
 import eu.dnetlib.dhp.sx.bio.BioDBToOAF.EBILinkItem
-import BioDBToOAF.EBILinkItem
-import eu.dnetlib.dhp.collection.CollectionUtils
 import org.apache.commons.io.IOUtils
 import org.apache.spark.SparkConf
 import org.apache.spark.sql._
@@ -35,10 +34,9 @@ object SparkEBILinksToOaf {
 
     val ebLinks: Dataset[EBILinkItem] = spark.read.load(sourcePath).as[EBILinkItem].filter(l => l.links != null && l.links.startsWith("{"))
 
-    ebLinks.flatMap(j => BioDBToOAF.parse_ebi_links(j.links))
+    CollectionUtils.saveDataset(ebLinks.flatMap(j => BioDBToOAF.parse_ebi_links(j.links))
       .filter(p => BioDBToOAF.EBITargetLinksFilter(p))
-      .flatMap(p => BioDBToOAF.convertEBILinksToOaf(p))
-      .flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null)
-      .write.mode(SaveMode.Overwrite).save(targetPath)
+      .flatMap(p => BioDBToOAF.convertEBILinksToOaf(p)),
+      targetPath)
   }
 }

dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/sx/bio/pubmed/PubMedToOaf.scala

@@ -111,7 +111,7 @@ object PubMedToOaf {
    * @param vocabularies the vocabularies
    * @return The OAF instance if the mapping did not fail
    */
-  def convert(article: PMArticle, vocabularies: VocabularyGroup): Result = {
+  def convert(article: PMArticle, vocabularies: VocabularyGroup): Oaf = {
 
     if (article.getPublicationTypes == null)
       return null

dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/raw/CopyHdfsOafSparkApplication.scala

@@ -1,18 +1,15 @@
 package eu.dnetlib.dhp.oa.graph.raw
 
-import com.fasterxml.jackson.databind.ObjectMapper
+import com.fasterxml.jackson.databind.{DeserializationFeature, ObjectMapper}
 import eu.dnetlib.dhp.application.ArgumentApplicationParser
 import eu.dnetlib.dhp.common.HdfsSupport
 import eu.dnetlib.dhp.schema.common.ModelSupport
-import eu.dnetlib.dhp.schema.mdstore.MDStoreWithInfo
 import eu.dnetlib.dhp.schema.oaf.Oaf
 import eu.dnetlib.dhp.utils.DHPUtils
-import org.apache.commons.io.IOUtils
-import org.apache.commons.lang3.StringUtils
-import org.apache.http.client.methods.HttpGet
-import org.apache.http.impl.client.HttpClients
 import org.apache.spark.sql.{Encoder, Encoders, SaveMode, SparkSession}
 import org.apache.spark.{SparkConf, SparkContext}
+import org.json4s.DefaultFormats
+import org.json4s.jackson.JsonMethods.parse
 import org.slf4j.LoggerFactory
 
 import scala.collection.JavaConverters._
@@ -51,18 +48,21 @@ object CopyHdfsOafSparkApplication {
     log.info("hdfsPath: {}", hdfsPath)
 
     implicit val oafEncoder: Encoder[Oaf] = Encoders.kryo[Oaf]
+    import spark.implicits._
 
     val paths = DHPUtils.mdstorePaths(mdstoreManagerUrl, mdFormat, mdLayout, mdInterpretation, true).asScala
 
     val validPaths: List[String] = paths.filter(p => HdfsSupport.exists(p, sc.hadoopConfiguration)).toList
 
     if (validPaths.nonEmpty) {
-      val oaf = spark.read.load(validPaths: _*).as[Oaf]
-      val mapper = new ObjectMapper()
-      val l =ModelSupport.oafTypes.entrySet.asScala.map(e => e.getKey).toList
+      val oaf = spark.read.load(validPaths: _*).as[String]
+      val mapper = new ObjectMapper().configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false)
+      val l = ModelSupport.oafTypes.entrySet.asScala.toList
       l.foreach(
         e =>
-          oaf.filter(o => o.getClass.getSimpleName.equalsIgnoreCase(e))
+          oaf
+            .filter(o => isOafType(o, e.getKey))
+            .map(j => mapper.readValue(j, e.getValue).asInstanceOf[Oaf])
             .map(s => mapper.writeValueAsString(s))(Encoders.STRING)
             .write
             .option("compression", "gzip")
@@ -71,4 +71,20 @@ object CopyHdfsOafSparkApplication {
       )
     }
   }
+
+  def isOafType(input: String, oafType: String): Boolean = {
+    implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
+    lazy val json: org.json4s.JValue = parse(input)
+    if (oafType == "relation") {
+      val hasSource = (json \ "source").extractOrElse[String](null)
+      val hasTarget = (json \ "target").extractOrElse[String](null)
+
+      hasSource != null && hasTarget != null
+    } else {
+      val hasId = (json \ "id").extractOrElse[String](null)
+      val resultType = (json \ "resulttype" \ "classid").extractOrElse[String](null)
+      hasId != null && oafType.equalsIgnoreCase(resultType)
+    }
+
+  }
 }