start implementing MAG mapping
This commit is contained in:
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1e06bbaee8
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@ -378,13 +378,13 @@ case object Crossref2Oaf {
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val page = (json \ "page").extractOrElse[String](null)
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if (page != null) {
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val pp = page.split("-")
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journal.setSp(pp.head)
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if (pp.nonEmpty)
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journal.setSp(pp.head)
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if (pp.size > 1)
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journal.setEp(pp(1))
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}
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publication.setJournal(journal)
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}
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}
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@ -28,9 +28,9 @@ object SparkMapDumpIntoOAF {
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.appName(SparkMapDumpIntoOAF.getClass.getSimpleName)
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.master(parser.get("master")).getOrCreate()
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implicit val mapEncoderPubs: Encoder[Publication] = Encoders.kryo(classOf[Publication])
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implicit val mapEncoderRelatons: Encoder[Relation] = Encoders.kryo(classOf[Relation])
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implicit val mapEncoderDatasets: Encoder[oaf.Dataset] = Encoders.kryo(classOf[eu.dnetlib.dhp.schema.oaf.Dataset])
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implicit val mapEncoderPubs: Encoder[Publication] = Encoders.bean(classOf[Publication])
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implicit val mapEncoderRelatons: Encoder[Relation] = Encoders.bean(classOf[Relation])
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implicit val mapEncoderDatasets: Encoder[oaf.Dataset] = Encoders.bean(classOf[eu.dnetlib.dhp.schema.oaf.Dataset])
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val sc = spark.sparkContext
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val targetPath = parser.get("targetPath")
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@ -42,17 +42,7 @@ object SparkMapDumpIntoOAF {
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val inputRDD = sc.objectFile[Oaf](s"${targetPath}/mixObject").filter(p=> p!= null)
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val total = inputRDD.count()
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val totalPub = inputRDD.filter(p => p.isInstanceOf[Publication]).count()
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val totalDat = inputRDD.filter(p => p.isInstanceOf[eu.dnetlib.dhp.schema.oaf.Dataset]).count()
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val totalRel = inputRDD.filter(p => p.isInstanceOf[eu.dnetlib.dhp.schema.oaf.Relation]).count()
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logger.info(s"Created $total")
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logger.info(s"totalPub $totalPub")
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logger.info(s"totalDat $totalDat")
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logger.info(s"totalRel $totalRel")
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val pubs: Dataset[Publication] = spark.createDataset(inputRDD.filter(k => k != null && k.isInstanceOf[Publication])
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.map(k => k.asInstanceOf[Publication]))
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pubs.write.mode(SaveMode.Overwrite).save(s"${targetPath}/publication")
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@ -64,10 +54,6 @@ object SparkMapDumpIntoOAF {
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val rels: Dataset[Relation] = spark.createDataset(inputRDD.filter(k => k != null && k.isInstanceOf[Relation])
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.map(k => k.asInstanceOf[Relation]))
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rels.write.mode(SaveMode.Overwrite).save(s"${targetPath}/relations")
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}
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@ -0,0 +1,92 @@
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package eu.dnetlib.doiboost.mag
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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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.{SaveMode, SparkSession}
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import org.apache.spark.sql.types._
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import org.slf4j.{Logger, LoggerFactory}
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import org.apache.spark.sql.functions._
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object SparkImportMagIntoDataset {
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val datatypedict = Map(
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"int" -> IntegerType,
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"uint" -> IntegerType,
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"long" -> LongType,
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"ulong" -> LongType,
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"float" -> FloatType,
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"string" -> StringType,
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"DateTime" -> DateType
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)
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val stream = Map(
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"Affiliations" -> Tuple2("mag/Affiliations.txt", Seq("AffiliationId:long", "Rank:uint", "NormalizedName:string", "DisplayName:string", "GridId:string", "OfficialPage:string", "WikiPage:string", "PaperCount:long", "CitationCount:long", "Latitude:float?", "Longitude:float?", "CreatedDate:DateTime")),
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"Authors" -> Tuple2("mag/Authors.txt", Seq("AuthorId:long", "Rank:uint", "NormalizedName:string", "DisplayName:string", "LastKnownAffiliationId:long?", "PaperCount:long", "CitationCount:long", "CreatedDate:DateTime")),
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"ConferenceInstances" -> Tuple2("mag/ConferenceInstances.txt", Seq("ConferenceInstanceId:long", "NormalizedName:string", "DisplayName:string", "ConferenceSeriesId:long", "Location:string", "OfficialUrl:string", "StartDate:DateTime?", "EndDate:DateTime?", "AbstractRegistrationDate:DateTime?", "SubmissionDeadlineDate:DateTime?", "NotificationDueDate:DateTime?", "FinalVersionDueDate:DateTime?", "PaperCount:long", "CitationCount:long", "Latitude:float?", "Longitude:float?", "CreatedDate:DateTime")),
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"ConferenceSeries" -> Tuple2("mag/ConferenceSeries.txt", Seq("ConferenceSeriesId:long", "Rank:uint", "NormalizedName:string", "DisplayName:string", "PaperCount:long", "CitationCount:long", "CreatedDate:DateTime")),
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"EntityRelatedEntities" -> Tuple2("advanced/EntityRelatedEntities.txt", Seq("EntityId:long", "EntityType:string", "RelatedEntityId:long", "RelatedEntityType:string", "RelatedType:int", "Score:float")),
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"FieldOfStudyChildren" -> Tuple2("advanced/FieldOfStudyChildren.txt", Seq("FieldOfStudyId:long", "ChildFieldOfStudyId:long")),
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"FieldOfStudyExtendedAttributes" -> Tuple2("advanced/FieldOfStudyExtendedAttributes.txt", Seq("FieldOfStudyId:long", "AttributeType:int", "AttributeValue:string")),
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"FieldsOfStudy" -> Tuple2("advanced/FieldsOfStudy.txt", Seq("FieldOfStudyId:long", "Rank:uint", "NormalizedName:string", "DisplayName:string", "MainType:string", "Level:int", "PaperCount:long", "CitationCount:long", "CreatedDate:DateTime")),
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"Journals" -> Tuple2("mag/Journals.txt", Seq("JournalId:long", "Rank:uint", "NormalizedName:string", "DisplayName:string", "Issn:string", "Publisher:string", "Webpage:string", "PaperCount:long", "CitationCount:long", "CreatedDate:DateTime")),
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"PaperAbstractsInvertedIndex" -> Tuple2("nlp/PaperAbstractsInvertedIndex.txt.*", Seq("PaperId:long", "IndexedAbstract:string")),
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"PaperAuthorAffiliations" -> Tuple2("mag/PaperAuthorAffiliations.txt", Seq("PaperId:long", "AuthorId:long", "AffiliationId:long?", "AuthorSequenceNumber:uint", "OriginalAuthor:string", "OriginalAffiliation:string")),
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"PaperCitationContexts" -> Tuple2("nlp/PaperCitationContexts.txt", Seq("PaperId:long", "PaperReferenceId:long", "CitationContext:string")),
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"PaperExtendedAttributes" -> Tuple2("mag/PaperExtendedAttributes.txt", Seq("PaperId:long", "AttributeType:int", "AttributeValue:string")),
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"PaperFieldsOfStudy" -> Tuple2("advanced/PaperFieldsOfStudy.txt", Seq("PaperId:long", "FieldOfStudyId:long", "Score:float")),
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"PaperRecommendations" -> Tuple2("advanced/PaperRecommendations.txt", Seq("PaperId:long", "RecommendedPaperId:long", "Score:float")),
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"PaperReferences" -> Tuple2("mag/PaperReferences.txt", Seq("PaperId:long", "PaperReferenceId:long")),
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"PaperResources" -> Tuple2("mag/PaperResources.txt", Seq("PaperId:long", "ResourceType:int", "ResourceUrl:string", "SourceUrl:string", "RelationshipType:int")),
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"PaperUrls" -> Tuple2("mag/PaperUrls.txt", Seq("PaperId:long", "SourceType:int?", "SourceUrl:string", "LanguageCode:string")),
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"Papers" -> Tuple2("mag/Papers.txt", Seq("PaperId:long", "Rank:uint", "Doi:string", "DocType:string", "PaperTitle:string", "OriginalTitle:string", "BookTitle:string", "Year:int?", "Date:DateTime?", "Publisher:string", "JournalId:long?", "ConferenceSeriesId:long?", "ConferenceInstanceId:long?", "Volume:string", "Issue:string", "FirstPage:string", "LastPage:string", "ReferenceCount:long", "CitationCount:long", "EstimatedCitation:long", "OriginalVenue:string", "FamilyId:long?", "CreatedDate:DateTime")),
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"RelatedFieldOfStudy" -> Tuple2("advanced/RelatedFieldOfStudy.txt", Seq("FieldOfStudyId1:long", "Type1:string", "FieldOfStudyId2:long", "Type2:string", "Rank:float"))
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)
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def getSchema(streamName: String): StructType = {
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var schema = new StructType()
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val d: Seq[String] = stream(streamName)._2
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d.foreach { case t =>
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val currentType = t.split(":")
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val fieldName: String = currentType.head
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var fieldType: String = currentType.last
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val nullable: Boolean = fieldType.endsWith("?")
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if (nullable)
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fieldType = fieldType.replace("?", "")
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schema = schema.add(StructField(fieldName, datatypedict(fieldType), nullable))
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}
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schema
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}
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def main(args: Array[String]): Unit = {
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val logger: Logger = LoggerFactory.getLogger(getClass)
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val conf: SparkConf = new SparkConf()
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val parser = new ArgumentApplicationParser(IOUtils.toString(getClass.getResourceAsStream("/eu/dnetlib/dhp/doiboost/convert_mag_to_oaf_params.json")))
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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")).getOrCreate()
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stream.foreach { case (k, v) =>
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val s: StructType = getSchema(k)
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val df = spark.read
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.option("header", "false")
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.option("charset", "UTF8")
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.option("delimiter", "\t")
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.schema(s)
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.csv(s"${parser.get("sourcePath")}/${v._1}")
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logger.info(s"Converting $k")
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df.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/$k")
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}
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}
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}
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@ -1,18 +0,0 @@
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<configuration>
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<property>
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<name>jobTracker</name>
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<value>yarnRM</value>
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</property>
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<property>
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<name>nameNode</name>
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<value>hdfs://nameservice1</value>
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</property>
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<property>
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<name>oozie.action.sharelib.for.java</name>
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<value>spark2</value>
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</property>
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<property>
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<name>oozie.launcher.mapreduce.user.classpath.first</name>
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<value>true</value>
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</property>
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</configuration>
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@ -1,39 +0,0 @@
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<workflow-app name="import Crossref from index into HDFS" xmlns="uri:oozie:workflow:0.5">
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<parameters>
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<property>
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<name>workingPath</name>
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<description>the working dir base path</description>
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</property>
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</parameters>
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<start to="ResetWorkingPath"/>
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<kill name="Kill">
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<message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
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</kill>
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<action name="ResetWorkingPath">
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<fs>
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<delete path='${workingPath}'/>
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<mkdir path='${workingPath}/input/crossref'/>
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</fs>
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<ok to="ImportCrossRef"/>
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<error to="Kill"/>
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</action>
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<action name="ImportCrossRef">
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<java>
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<job-tracker>${jobTracker}</job-tracker>
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<name-node>${nameNode}</name-node>
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<main-class>eu.dnetlib.doiboost.crossref.CrossrefImporter</main-class>
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<arg>-t</arg><arg>${workingPath}/input/crossref/index_dump</arg>
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<arg>-n</arg><arg>${nameNode}</arg>
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</java>
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<ok to="End"/>
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<error to="Kill"/>
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</action>
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<end name="End"/>
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</workflow-app>
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@ -0,0 +1,6 @@
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[
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{"paramName":"s", "paramLongName":"sourcePath", "paramDescription": "the base path of MAG input", "paramRequired": true},
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{"paramName":"t", "paramLongName":"targetPath", "paramDescription": "the working dir path", "paramRequired": true},
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{"paramName":"m", "paramLongName":"master", "paramDescription": "the master name", "paramRequired": true}
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]
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@ -0,0 +1,38 @@
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<configuration>
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<property>
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<name>jobTracker</name>
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<value>yarnRM</value>
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</property>
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<property>
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<name>nameNode</name>
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<value>hdfs://nameservice1</value>
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</property>
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<property>
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<name>oozie.use.system.libpath</name>
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<value>true</value>
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</property>
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<property>
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<name>oozie.action.sharelib.for.spark</name>
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<value>spark2</value>
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</property>
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<property>
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<name>hive_metastore_uris</name>
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<value>thrift://iis-cdh5-test-m3.ocean.icm.edu.pl:9083</value>
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</property>
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<property>
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<name>spark2YarnHistoryServerAddress</name>
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<value>http://iis-cdh5-test-gw.ocean.icm.edu.pl:18089</value>
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</property>
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<property>
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<name>spark2EventLogDir</name>
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<value>/user/spark/spark2ApplicationHistory</value>
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</property>
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<property>
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<name>spark2ExtraListeners</name>
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<value>"com.cloudera.spark.lineage.NavigatorAppListener"</value>
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</property>
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<property>
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<name>spark2SqlQueryExecutionListeners</name>
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<value>"com.cloudera.spark.lineage.NavigatorQueryListener"</value>
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</property>
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</configuration>
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@ -0,0 +1,75 @@
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<workflow-app name="import Crossref from index into HDFS" xmlns="uri:oozie:workflow:0.5">
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<parameters>
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<property>
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<name>workingPath</name>
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<description>the working dir base path</description>
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</property>
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<property>
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<name>sparkDriverMemory</name>
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<description>memory for driver process</description>
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</property>
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<property>
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<name>sparkExecutorMemory</name>
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<description>memory for individual executor</description>
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</property>
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<property>
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<name>sparkExecutorCores</name>
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<description>number of cores used by single executor</description>
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</property>
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</parameters>
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<start to="ExtractCrossrefToOAF"/>
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<kill name="Kill">
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<message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
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</kill>
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<action name="ResetWorkingPath">
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<fs>
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<delete path='${workingPath}'/>
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<mkdir path='${workingPath}/input/crossref'/>
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</fs>
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<ok to="ImportCrossRef"/>
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<error to="Kill"/>
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</action>
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<action name="ImportCrossRef">
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<java>
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<job-tracker>${jobTracker}</job-tracker>
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<name-node>${nameNode}</name-node>
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<main-class>eu.dnetlib.doiboost.crossref.CrossrefImporter</main-class>
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<arg>-t</arg><arg>${workingPath}/input/crossref/index_dump</arg>
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<arg>-n</arg><arg>${nameNode}</arg>
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</java>
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<ok to="End"/>
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<error to="Kill"/>
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</action>
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<action name="ExtractCrossrefToOAF">
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<spark xmlns="uri:oozie:spark-action:0.2">
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<master>yarn-cluster</master>
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<mode>cluster</mode>
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<name>ExtractCrossrefToOAF</name>
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<class>eu.dnetlib.doiboost.crossref.SparkMapDumpIntoOAF</class>
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<jar>dhp-doiboost-${projectVersion}.jar</jar>
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<spark-opts>
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--executor-memory=${sparkExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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--driver-memory=${sparkDriverMemory}
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${sparkExtraOPT}
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</spark-opts>
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<arg>--sourcePath</arg><arg>${workingPath}/index_dump</arg>
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<arg>--targetPath</arg><arg>${workingPath}</arg>
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<arg>--master</arg><arg>yarn-cluster</arg>
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</spark>
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<ok to="End"/>
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<error to="Kill"/>
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</action>
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<end name="End"/>
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</workflow-app>
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@ -0,0 +1,38 @@
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<configuration>
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<property>
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<name>jobTracker</name>
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<value>yarnRM</value>
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</property>
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<property>
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<name>nameNode</name>
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<value>hdfs://nameservice1</value>
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</property>
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<property>
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<name>oozie.use.system.libpath</name>
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<value>true</value>
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</property>
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<property>
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<name>oozie.action.sharelib.for.spark</name>
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<value>spark2</value>
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</property>
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<property>
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<name>hive_metastore_uris</name>
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<value>thrift://iis-cdh5-test-m3.ocean.icm.edu.pl:9083</value>
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</property>
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<property>
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<name>spark2YarnHistoryServerAddress</name>
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<value>http://iis-cdh5-test-gw.ocean.icm.edu.pl:18089</value>
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</property>
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<property>
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<name>spark2EventLogDir</name>
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<value>/user/spark/spark2ApplicationHistory</value>
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</property>
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<property>
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<name>spark2ExtraListeners</name>
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<value>"com.cloudera.spark.lineage.NavigatorAppListener"</value>
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</property>
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<property>
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<name>spark2SqlQueryExecutionListeners</name>
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<value>"com.cloudera.spark.lineage.NavigatorQueryListener"</value>
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</property>
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</configuration>
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@ -0,0 +1,63 @@
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<workflow-app name="import MAG into HDFS" xmlns="uri:oozie:workflow:0.5">
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<parameters>
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<property>
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<name>sourcePath</name>
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<description>the working dir base path</description>
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</property>
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<property>
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<name>targetPath</name>
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<description>the working dir base path</description>
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</property>
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<property>
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<name>sparkDriverMemory</name>
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<description>memory for driver process</description>
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</property>
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<property>
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<name>sparkExecutorMemory</name>
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<description>memory for individual executor</description>
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</property>
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<property>
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<name>sparkExecutorCores</name>
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<description>number of cores used by single executor</description>
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</property>
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</parameters>
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<start to="ResetWorkingPath"/>
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<kill name="Kill">
|
||||
<message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
|
||||
</kill>
|
||||
|
||||
<action name="ResetWorkingPath">
|
||||
<fs>
|
||||
<delete path='${targetPath}'/>
|
||||
<mkdir path='${targetPath}'/>
|
||||
</fs>
|
||||
<ok to="ConvertMagToDataset"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
|
||||
<action name="ConvertMagToDataset">
|
||||
<spark xmlns="uri:oozie:spark-action:0.2">
|
||||
<master>yarn-cluster</master>
|
||||
<mode>cluster</mode>
|
||||
<name>Convert Mag to Dataset</name>
|
||||
<class>eu.dnetlib.doiboost.mag.SparkImportMagIntoDataset</class>
|
||||
<jar>dhp-doiboost-${projectVersion}.jar</jar>
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
--driver-memory=${sparkDriverMemory}
|
||||
${sparkExtraOPT}
|
||||
</spark-opts>
|
||||
<arg>--sourcePath</arg><arg>${sourcePath}</arg>
|
||||
<arg>--targetPath</arg><arg>${targetPath}</arg>
|
||||
<arg>--master</arg><arg>yarn-cluster</arg>
|
||||
</spark>
|
||||
<ok to="End"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
|
||||
<end name="End"/>
|
||||
</workflow-app>
|
|
@ -2,7 +2,9 @@ package eu.dnetlib.doiboost
|
|||
|
||||
import com.fasterxml.jackson.databind.SerializationFeature
|
||||
import eu.dnetlib.dhp.schema.oaf.{Dataset, KeyValue, Oaf, Publication, Relation, Result}
|
||||
import eu.dnetlib.dhp.utils.DHPUtils
|
||||
import eu.dnetlib.doiboost.crossref.{Crossref2Oaf, SparkMapDumpIntoOAF}
|
||||
import eu.dnetlib.doiboost.mag.SparkImportMagIntoDataset
|
||||
import org.apache.spark.{SparkConf, sql}
|
||||
import org.apache.spark.sql.{Encoder, Encoders, SparkSession}
|
||||
import org.codehaus.jackson.map.ObjectMapper
|
||||
|
@ -13,6 +15,7 @@ import org.junit.jupiter.api.Assertions._
|
|||
import org.slf4j.{Logger, LoggerFactory}
|
||||
|
||||
import scala.collection.JavaConverters._
|
||||
import scala.util.matching.Regex
|
||||
|
||||
|
||||
class CrossrefMappingTest {
|
||||
|
@ -22,30 +25,8 @@ class CrossrefMappingTest {
|
|||
|
||||
|
||||
|
||||
//@Test
|
||||
def testRelSpark() :Unit = {
|
||||
val conf: SparkConf = new SparkConf()
|
||||
conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
|
||||
|
||||
val spark: SparkSession =
|
||||
SparkSession
|
||||
.builder()
|
||||
.config(conf)
|
||||
.appName(SparkMapDumpIntoOAF.getClass.getSimpleName)
|
||||
.master("local[*]").getOrCreate()
|
||||
|
||||
import spark.implicits._
|
||||
implicit val mapEncoderRelations: Encoder[Relation] = Encoders.kryo(classOf[Relation])
|
||||
implicit val mapEncoderPublication: Encoder[Publication] = Encoders.kryo(classOf[Publication])
|
||||
implicit val mapEncoderTupleJoinPubs: Encoder[(String, Publication)] = Encoders.tuple(Encoders.STRING, mapEncoderPublication)
|
||||
implicit val mapEncoderTupleJoinRels: Encoder[(String, Relation)] = Encoders.tuple(Encoders.STRING, mapEncoderRelations)
|
||||
|
||||
val relations:sql.Dataset[Relation] = spark.read.load("/data/doiboost/relations").as[Relation]
|
||||
val publications :sql.Dataset[Publication] = spark.read.load("/data/doiboost/publication").as[Publication]
|
||||
val ds1 = publications.map(p => Tuple2(p.getId, p))
|
||||
val ds2 = relations.map(p => Tuple2(p.getSource, p))
|
||||
val total =ds1.joinWith(ds2, ds1.col("_1")===ds2.col("_1")).count()
|
||||
println(s"total : $total")
|
||||
def testMAGCSV() :Unit = {
|
||||
SparkImportMagIntoDataset.main(null)
|
||||
}
|
||||
|
||||
|
||||
|
@ -86,6 +67,45 @@ class CrossrefMappingTest {
|
|||
|
||||
})
|
||||
|
||||
}
|
||||
|
||||
def extractECAward(award: String): String = {
|
||||
val awardECRegex: Regex = "[0-9]{4,9}".r
|
||||
if (awardECRegex.findAllIn(award).hasNext)
|
||||
return awardECRegex.findAllIn(award).max
|
||||
null
|
||||
}
|
||||
|
||||
|
||||
@Test
|
||||
def extractECTest(): Unit = {
|
||||
val s = "FP7/2007-2013"
|
||||
val awardExtracted = extractECAward(s)
|
||||
println(awardExtracted)
|
||||
|
||||
println(DHPUtils.md5(awardExtracted))
|
||||
|
||||
|
||||
}
|
||||
|
||||
@Test
|
||||
def testJournalRelation(): Unit = {
|
||||
val json = Source.fromInputStream(getClass.getResourceAsStream("awardTest.json")).mkString
|
||||
assertNotNull(json)
|
||||
|
||||
assertFalse(json.isEmpty)
|
||||
|
||||
val resultList: List[Oaf] = Crossref2Oaf.convert(json)
|
||||
|
||||
assertTrue(resultList.nonEmpty)
|
||||
val rels:List[Relation] = resultList.filter(p => p.isInstanceOf[Relation]).map(r=> r.asInstanceOf[Relation])
|
||||
|
||||
|
||||
assertEquals(rels.size, 4)
|
||||
rels.foreach(s => logger.info(s.getTarget))
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
|
|
@ -0,0 +1,53 @@
|
|||
package eu.dnetlib.doiboost.mag
|
||||
|
||||
import org.apache.spark.SparkConf
|
||||
import org.apache.spark.api.java.function.ReduceFunction
|
||||
import org.apache.spark.rdd.RDD
|
||||
import org.apache.spark.sql.{Dataset, Encoders, SaveMode, SparkSession}
|
||||
import org.codehaus.jackson.map.ObjectMapper
|
||||
import org.junit.jupiter.api.Test
|
||||
import org.slf4j.{Logger, LoggerFactory}
|
||||
import org.apache.spark.sql.functions._
|
||||
|
||||
|
||||
class MAGMappingTest {
|
||||
|
||||
val logger: Logger = LoggerFactory.getLogger(getClass)
|
||||
val mapper = new ObjectMapper()
|
||||
|
||||
|
||||
//@Test
|
||||
def testMAGCSV(): Unit = {
|
||||
|
||||
val conf: SparkConf = new SparkConf()
|
||||
val spark: SparkSession =
|
||||
SparkSession
|
||||
.builder()
|
||||
.config(conf)
|
||||
.appName(getClass.getSimpleName)
|
||||
.master("local[*]").getOrCreate()
|
||||
|
||||
|
||||
import spark.implicits._
|
||||
val d: Dataset[Papers] = spark.read.load("/data/doiboost/mag/datasets/Papers").as[Papers]
|
||||
logger.info(s"Total number of element: ${d.where(col("Doi").isNotNull).count()}")
|
||||
implicit val mapEncoder = org.apache.spark.sql.Encoders.kryo[Papers]
|
||||
val result: RDD[Papers] = d.where(col("Doi").isNotNull).rdd.map { p: Papers => Tuple2(p.Doi, p) }.reduceByKey {case (p1:Papers, p2:Papers) =>
|
||||
var r = if (p1==null) p2 else p1
|
||||
if (p1!=null && p2!=null ) if (p1.CreatedDate.before(p2.CreatedDate))
|
||||
r = p1
|
||||
else
|
||||
r = p2
|
||||
r
|
||||
}.map(_._2)
|
||||
|
||||
|
||||
val distinctPaper:Dataset[Papers] = spark.createDataset(result)
|
||||
distinctPaper.write.mode(SaveMode.Overwrite).save("/data/doiboost/mag/datasets/Papers_d")
|
||||
logger.info(s"Total number of element: ${result.count()}")
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
}
|
|
@ -0,0 +1,192 @@
|
|||
{
|
||||
"DOI": "10.1016/j.infbeh.2016.11.001",
|
||||
"issued": {
|
||||
"date-parts": [
|
||||
[
|
||||
2017,
|
||||
8
|
||||
]
|
||||
]
|
||||
},
|
||||
"update-policy": "http://dx.doi.org/10.1016/elsevier_cm_policy",
|
||||
"prefix": "10.1016",
|
||||
"subject": [
|
||||
"Developmental and Educational Psychology"
|
||||
],
|
||||
"author": [
|
||||
{
|
||||
"affiliation": [],
|
||||
"given": "Dora",
|
||||
"family": "Kampis",
|
||||
"sequence": "first"
|
||||
},
|
||||
{
|
||||
"affiliation": [],
|
||||
"given": "D\u00f3ra",
|
||||
"family": "Fogd",
|
||||
"sequence": "additional"
|
||||
},
|
||||
{
|
||||
"affiliation": [],
|
||||
"given": "\u00c1gnes Melinda",
|
||||
"family": "Kov\u00e1cs",
|
||||
"sequence": "additional"
|
||||
}
|
||||
],
|
||||
"reference-count": 109,
|
||||
"ISSN": [
|
||||
"0163-6383"
|
||||
],
|
||||
"assertion": [
|
||||
{
|
||||
"name": "publisher",
|
||||
"value": "Elsevier",
|
||||
"label": "This article is maintained by"
|
||||
},
|
||||
{
|
||||
"name": "articletitle",
|
||||
"value": "Nonverbal components of Theory of Mind in typical and atypical development",
|
||||
"label": "Article Title"
|
||||
},
|
||||
{
|
||||
"name": "journaltitle",
|
||||
"value": "Infant Behavior and Development",
|
||||
"label": "Journal Title"
|
||||
},
|
||||
{
|
||||
"name": "articlelink",
|
||||
"value": "https://doi.org/10.1016/j.infbeh.2016.11.001",
|
||||
"label": "CrossRef DOI link to publisher maintained version"
|
||||
},
|
||||
{
|
||||
"name": "content_type",
|
||||
"value": "article",
|
||||
"label": "Content Type"
|
||||
},
|
||||
{
|
||||
"name": "copyright",
|
||||
"value": "\u00a9 2016 Elsevier Inc. All rights reserved.",
|
||||
"label": "Copyright"
|
||||
}
|
||||
],
|
||||
"member": "78",
|
||||
"source": "Crossref",
|
||||
"score": 1.0,
|
||||
"deposited": {
|
||||
"timestamp": 1565383284000,
|
||||
"date-parts": [
|
||||
[
|
||||
2019,
|
||||
8,
|
||||
9
|
||||
]
|
||||
],
|
||||
"date-time": "2019-08-09T20:41:24Z"
|
||||
},
|
||||
"indexed": {
|
||||
"timestamp": 1565385055278,
|
||||
"date-parts": [
|
||||
[
|
||||
2019,
|
||||
8,
|
||||
9
|
||||
]
|
||||
],
|
||||
"date-time": "2019-08-09T21:10:55Z"
|
||||
},
|
||||
"type": "journal-article",
|
||||
"URL": "http://dx.doi.org/10.1016/j.infbeh.2016.11.001",
|
||||
"is-referenced-by-count": 1,
|
||||
"volume": "48",
|
||||
"issn-type": [
|
||||
{
|
||||
"type": "print",
|
||||
"value": "0163-6383"
|
||||
}
|
||||
],
|
||||
"link": [
|
||||
{
|
||||
"URL": "https://api.elsevier.com/content/article/PII:S0163638315300059?httpAccept=text/xml",
|
||||
"intended-application": "text-mining",
|
||||
"content-version": "vor",
|
||||
"content-type": "text/xml"
|
||||
},
|
||||
{
|
||||
"URL": "https://api.elsevier.com/content/article/PII:S0163638315300059?httpAccept=text/plain",
|
||||
"intended-application": "text-mining",
|
||||
"content-version": "vor",
|
||||
"content-type": "text/plain"
|
||||
}
|
||||
],
|
||||
"published-print": {
|
||||
"date-parts": [
|
||||
[
|
||||
2017,
|
||||
8
|
||||
]
|
||||
]
|
||||
},
|
||||
"references-count": 109,
|
||||
"short-container-title": [
|
||||
"Infant Behavior and Development"
|
||||
],
|
||||
"publisher": "Elsevier BV",
|
||||
"content-domain": {
|
||||
"domain": [
|
||||
"elsevier.com",
|
||||
"sciencedirect.com"
|
||||
],
|
||||
"crossmark-restriction": true
|
||||
},
|
||||
"license": [
|
||||
{
|
||||
"URL": "https://www.elsevier.com/tdm/userlicense/1.0/",
|
||||
"start": {
|
||||
"timestamp": 1501545600000,
|
||||
"date-parts": [
|
||||
[
|
||||
2017,
|
||||
8,
|
||||
1
|
||||
]
|
||||
],
|
||||
"date-time": "2017-08-01T00:00:00Z"
|
||||
},
|
||||
"content-version": "tdm",
|
||||
"delay-in-days": 0
|
||||
}
|
||||
],
|
||||
"language": "en",
|
||||
"created": {
|
||||
"timestamp": 1479142046000,
|
||||
"date-parts": [
|
||||
[
|
||||
2016,
|
||||
11,
|
||||
14
|
||||
]
|
||||
],
|
||||
"date-time": "2016-11-14T16:47:26Z"
|
||||
},
|
||||
"title": [
|
||||
"Nonverbal components of Theory of Mind in typical and atypical development"
|
||||
],
|
||||
"alternative-id": [
|
||||
"S0163638315300059"
|
||||
],
|
||||
"container-title": [
|
||||
"Infant Behavior and Development"
|
||||
],
|
||||
"funder": [
|
||||
{
|
||||
"doi-asserted-by": "publisher",
|
||||
"DOI": "10.13039/501100000781",
|
||||
"name": "European Research Council",
|
||||
"award": [
|
||||
"284236-REPCOLLAB",
|
||||
"FP7/2007-2013"
|
||||
]
|
||||
}
|
||||
],
|
||||
"page": "54-62"
|
||||
}
|
Loading…
Reference in New Issue