forked from D-Net/dnet-hadoop
added first part of refactoring of the code generating MAG,
make it more readable using spark sql queries
This commit is contained in:
Sandro La Bruzzo
parent
9d94648f3b
commit
ad0e9aa80c
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package eu.dnetlib.dhp.collection.mag
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import org.apache.spark.sql.types._
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import org.apache.spark.sql.{Dataset, Row, SparkSession}
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import org.json4s
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import org.json4s.DefaultFormats
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import org.json4s.jackson.JsonMethods.parse
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object MagUtility extends Serializable {
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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(
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"mag/Affiliations.txt",
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Seq(
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"AffiliationId:long",
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"Rank:uint",
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"NormalizedName:string",
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"DisplayName:string",
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"GridId:string",
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"OfficialPage:string",
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"WikiPage:string",
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"PaperCount:long",
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"PaperFamilyCount:long",
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"CitationCount:long",
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"Iso3166Code:string",
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"Latitude:float?",
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"Longitude:float?",
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"CreatedDate:DateTime"
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)
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),
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"AuthorExtendedAttributes" -> Tuple2(
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"mag/AuthorExtendedAttributes.txt",
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Seq("AuthorId:long", "AttributeType:int", "AttributeValue:string")
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),
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"Authors" -> Tuple2(
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"mag/Authors.txt",
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Seq(
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"AuthorId:long",
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"Rank:uint",
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"NormalizedName:string",
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"DisplayName:string",
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"LastKnownAffiliationId:long?",
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"PaperCount:long",
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"PaperFamilyCount:long",
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"CitationCount:long",
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"CreatedDate:DateTime"
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)
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),
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"ConferenceInstances" -> Tuple2(
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"mag/ConferenceInstances.txt",
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Seq(
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"ConferenceInstanceId:long",
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"NormalizedName:string",
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"DisplayName:string",
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"ConferenceSeriesId:long",
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"Location:string",
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"OfficialUrl:string",
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"StartDate:DateTime?",
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"EndDate:DateTime?",
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"AbstractRegistrationDate:DateTime?",
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"SubmissionDeadlineDate:DateTime?",
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"NotificationDueDate:DateTime?",
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"FinalVersionDueDate:DateTime?",
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"PaperCount:long",
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"PaperFamilyCount:long",
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"CitationCount:long",
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"Latitude:float?",
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"Longitude:float?",
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"CreatedDate:DateTime"
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)
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),
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"ConferenceSeries" -> Tuple2(
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"mag/ConferenceSeries.txt",
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Seq(
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"ConferenceSeriesId:long",
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"Rank:uint",
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"NormalizedName:string",
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"DisplayName:string",
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"PaperCount:long",
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"PaperFamilyCount:long",
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"CitationCount:long",
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"CreatedDate:DateTime"
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)
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),
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"EntityRelatedEntities" -> Tuple2(
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"advanced/EntityRelatedEntities.txt",
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Seq(
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"EntityId:long",
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"EntityType:string",
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"RelatedEntityId:long",
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"RelatedEntityType:string",
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"RelatedType:int",
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"Score:float"
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)
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),
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"FieldOfStudyChildren" -> Tuple2(
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"advanced/FieldOfStudyChildren.txt",
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Seq("FieldOfStudyId:long", "ChildFieldOfStudyId:long")
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),
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"FieldOfStudyExtendedAttributes" -> Tuple2(
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"advanced/FieldOfStudyExtendedAttributes.txt",
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Seq("FieldOfStudyId:long", "AttributeType:int", "AttributeValue:string")
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),
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"FieldsOfStudy" -> Tuple2(
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"advanced/FieldsOfStudy.txt",
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Seq(
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"FieldOfStudyId:long",
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"Rank:uint",
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"NormalizedName:string",
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"DisplayName:string",
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"MainType:string",
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"Level:int",
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"PaperCount:long",
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"PaperFamilyCount:long",
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"CitationCount:long",
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"CreatedDate:DateTime"
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)
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),
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"Journals" -> Tuple2(
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"mag/Journals.txt",
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Seq(
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"JournalId:long",
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"Rank:uint",
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"NormalizedName:string",
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"DisplayName:string",
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"Issn:string",
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"Publisher:string",
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"Webpage:string",
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"PaperCount:long",
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"PaperFamilyCount:long",
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"CitationCount:long",
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"CreatedDate:DateTime"
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)
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),
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"PaperAbstractsInvertedIndex" -> Tuple2(
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"nlp/PaperAbstractsInvertedIndex.txt.*",
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Seq("PaperId:long", "IndexedAbstract:string")
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),
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"PaperAuthorAffiliations" -> Tuple2(
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"mag/PaperAuthorAffiliations.txt",
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Seq(
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"PaperId:long",
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"AuthorId:long",
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"AffiliationId:long?",
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"AuthorSequenceNumber:uint",
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"OriginalAuthor:string",
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"OriginalAffiliation:string"
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)
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),
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"PaperCitationContexts" -> Tuple2(
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"nlp/PaperCitationContexts.txt",
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Seq("PaperId:long", "PaperReferenceId:long", "CitationContext:string")
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),
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"PaperExtendedAttributes" -> Tuple2(
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"mag/PaperExtendedAttributes.txt",
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Seq("PaperId:long", "AttributeType:int", "AttributeValue:string")
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),
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"PaperFieldsOfStudy" -> Tuple2(
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"advanced/PaperFieldsOfStudy.txt",
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Seq("PaperId:long", "FieldOfStudyId:long", "Score:float")
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),
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"PaperMeSH" -> Tuple2(
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"advanced/PaperMeSH.txt",
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Seq(
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"PaperId:long",
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"DescriptorUI:string",
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"DescriptorName:string",
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"QualifierUI:string",
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"QualifierName:string",
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"IsMajorTopic:bool"
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)
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),
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"PaperRecommendations" -> Tuple2(
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"advanced/PaperRecommendations.txt",
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Seq("PaperId:long", "RecommendedPaperId:long", "Score:float")
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),
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"PaperReferences" -> Tuple2(
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"mag/PaperReferences.txt",
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Seq("PaperId:long", "PaperReferenceId:long")
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),
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"PaperResources" -> Tuple2(
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"mag/PaperResources.txt",
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Seq(
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"PaperId:long",
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"ResourceType:int",
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"ResourceUrl:string",
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"SourceUrl:string",
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"RelationshipType:int"
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)
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),
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"PaperUrls" -> Tuple2(
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"mag/PaperUrls.txt",
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Seq("PaperId:long", "SourceType:int?", "SourceUrl:string", "LanguageCode:string")
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),
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"Papers" -> Tuple2(
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"mag/Papers.txt",
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Seq(
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"PaperId:long",
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"Rank:uint",
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"Doi:string",
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"DocType:string",
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"PaperTitle:string",
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"OriginalTitle:string",
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"BookTitle:string",
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"Year:int?",
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"Date:DateTime?",
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"OnlineDate:DateTime?",
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"Publisher:string",
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"JournalId:long?",
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"ConferenceSeriesId:long?",
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"ConferenceInstanceId:long?",
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"Volume:string",
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"Issue:string",
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"FirstPage:string",
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"LastPage:string",
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"ReferenceCount:long",
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"CitationCount:long",
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"EstimatedCitation:long",
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"OriginalVenue:string",
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"FamilyId:long?",
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"FamilyRank:uint?",
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"DocSubTypes:string",
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"CreatedDate:DateTime"
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)
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),
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"RelatedFieldOfStudy" -> Tuple2(
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"advanced/RelatedFieldOfStudy.txt",
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Seq(
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"FieldOfStudyId1:long",
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"Type1:string",
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"FieldOfStudyId2:long",
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"Type2:string",
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"Rank:float"
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)
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)
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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 loadMagEntity(spark:SparkSession, entity:String, basePath:String):Dataset[Row] = {
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if (stream.contains(entity)) {
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val s =getSchema(entity)
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val pt = stream(entity)._1
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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"$basePath/$pt")
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} else
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null
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}
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def convertInvertedIndexString(json_input: String): String = {
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implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
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lazy val json: json4s.JValue = parse(json_input)
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val idl = (json \ "IndexLength").extract[Int]
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if (idl > 0) {
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val res = Array.ofDim[String](idl)
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val iid = (json \ "InvertedIndex").extract[Map[String, List[Int]]]
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for { (k: String, v: List[Int]) <- iid } {
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v.foreach(item => res(item) = k)
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}
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(0 until idl).foreach(i => {
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if (res(i) == null)
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res(i) = ""
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})
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return res.mkString(" ")
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}
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""
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}
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}
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@ -0,0 +1,77 @@
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package eu.dnetlib.dhp.collection.mag
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import eu.dnetlib.dhp.application.AbstractScalaApplication
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import org.apache.spark.sql.{Dataset, Row, SparkSession}
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import org.apache.spark.sql.functions._
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import org.apache.spark.sql.types.{StringType, StructField, StructType}
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import org.slf4j.Logger
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class SparkCreateMagDataset (propertyPath: String, args: Array[String], log: Logger)
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extends AbstractScalaApplication(propertyPath, args, log: Logger) {
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/** Here all the spark applications runs this method
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* where the whole logic of the spark node is defined
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*/
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override def run(): Unit = {
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}
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private def loadAndFilterPapers(spark:SparkSession, crossrefPath:String, magBasePath:String, workingPath): Unit = {
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import spark.implicits._
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val schema:StructType= StructType(StructField("DOI", StringType)::Nil)
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log.info("Phase 1 intersect MAG Paper containing doi also present in crossref")
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//Filter all the MAG Papers that intersect with a Crossref DOI
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val crId= spark.read.schema(schema).json(crossrefPath).withColumn("crId", lower(col("DOI"))).distinct.select("crId")
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val magPapers = MagUtility.loadMagEntity(spark, "Papers", magBasePath).withColumn("Doi", lower(col("Doi"))).where(col("Doi").isNotNull)
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val intersectedPapers:Dataset[Row] =magPapers.join(crId, magPapers("Doi").equalTo(crId("crId")), "leftsemi").dropDuplicates("Doi")
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intersectedPapers.cache()
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intersectedPapers.count()
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log.info("Create current abstract")
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//Abstract is an inverted list, we define a function that convert in string the abstract and recreate
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// a table(PaperId, Abstract)
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val paperAbstract = MagUtility.loadMagEntity(spark, "PaperAbstractsInvertedIndex", magBasePath)
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.map(s => (s.getLong(0),MagUtility.convertInvertedIndexString(s.getString(1))))
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.withColumnRenamed("_1","PaperId")
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.withColumnRenamed("_2","Abstract")
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//We define Step0 as the result of the Join between PaperIntersection and the PaperAbstract
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val step0 =intersectedPapers
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.join(paperAbstract, intersectedPapers("PaperId") === paperAbstract("PaperId"), "left")
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.select(intersectedPapers("*"),paperAbstract("Abstract")).cache()
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step0.count()
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intersectedPapers.unpersist()
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// We have three table Author, Affiliation, and PaperAuthorAffiliation, in the
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//next step we create a table containing
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val authors = MagUtility.loadMagEntity(spark, "Authors", magBasePath)
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val affiliations= MagUtility.loadMagEntity(spark, "Affiliations", magBasePath)
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val paaf= MagUtility.loadMagEntity(spark, "PaperAuthorAffiliations", magBasePath)
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val paperAuthorAffiliations =paaf.join(step0,paaf("PaperId") === step0("PaperId"),"leftsemi")
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val j1 = paperAuthorAffiliations.join(authors,paperAuthorAffiliations("AuthorId") === authors("AuthorId"), "inner")
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.select(col("PaperId"), col("AffiliationId"),col("AuthorSequenceNumber"), authors("DisplayName").alias("AuthorName"), authors("AuthorId"))
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val paperAuthorAffiliationNormalized = j1.join(affiliations, j1("AffiliationId")=== affiliations("AffiliationId"), "left")
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.select(j1("*"), affiliations("DisplayName").alias("AffiliationName"), affiliations("GridId"))
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.groupBy("PaperId")
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.agg(collect_list(struct("AffiliationId","AuthorSequenceNumber","AffiliationName","AuthorName","AuthorId","GridId")).alias("authors"))
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val step1 =step0.join(paperAuthorAffiliationNormalized, step0("PaperId")=== paperAuthorAffiliationNormalized("PaperId"), "left").cache()
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step1.count()
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step0.unpersist()
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}
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}
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