forked from D-Net/dnet-hadoop
94 lines
6.7 KiB
Scala
94 lines
6.7 KiB
Scala
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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object SparkImportMagIntoDataset {
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val datatypedict = Map(
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"bool" -> BooleanType,
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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", "PaperFamilyCount:long", "CitationCount:long", "Iso3166Code:string", "Latitude:float?", "Longitude:float?", "CreatedDate:DateTime")),
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"AuthorExtendedAttributes" -> Tuple2("mag/AuthorExtendedAttributes.txt", Seq("AuthorId:long", "AttributeType:int", "AttributeValue:string")),
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"Authors" -> Tuple2("mag/Authors.txt", Seq("AuthorId:long", "Rank:uint", "NormalizedName:string", "DisplayName:string", "LastKnownAffiliationId:long?", "PaperCount:long", "PaperFamilyCount: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", "PaperFamilyCount: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", "PaperFamilyCount: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", "PaperFamilyCount: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", "PaperFamilyCount: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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"PaperMeSH" -> Tuple2("advanced/PaperMeSH.txt", Seq("PaperId:long", "DescriptorUI:string", "DescriptorName:string", "QualifierUI:string", "QualifierName:string", "IsMajorTopic:bool")),
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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?", "OnlineDate: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?", "FamilyRank:uint?", "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/mag/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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} |