forked from D-Net/dnet-hadoop
Hosted By Map - first attempt for the creation of intermedia information to be used to applu the hosted by map on the graph entities
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@ -1,5 +1,6 @@
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package eu.dnetlib.dhp.oa.graph.hostedbymap
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import eu.dnetlib.dhp.oa.graph.hostedbymap.model.EntityInfo
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import org.apache.spark.sql.{Dataset, Encoder, Encoders, TypedColumn}
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import org.apache.spark.sql.expressions.Aggregator
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@ -25,43 +26,10 @@ object Aggregators {
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}
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def createHostedByItemTypes(df: Dataset[HostedByItemType]): Dataset[HostedByItemType] = {
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val transformedData : Dataset[HostedByItemType] = df
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.groupByKey(_.id)(Encoders.STRING)
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.agg(Aggregators.hostedByAggregator)
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.map{
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case (id:String , res:HostedByItemType) => res
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}(Encoders.product[HostedByItemType])
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transformedData
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}
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val hostedByAggregator: TypedColumn[HostedByItemType, HostedByItemType] = new Aggregator[HostedByItemType, HostedByItemType, HostedByItemType] {
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override def zero: HostedByItemType = HostedByItemType("","","","","",false)
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override def reduce(b: HostedByItemType, a:HostedByItemType): HostedByItemType = {
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return merge(b, a)
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}
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override def merge(b1: HostedByItemType, b2: HostedByItemType): HostedByItemType = {
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if (b1 == null){
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return b2
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}
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if(b2 == null){
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return b1
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}
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HostedByItemType(getId(b1.id, b2.id), getId(b1.officialname, b2.officialname), getId(b1.issn, b2.issn), getId(b1.eissn, b2.eissn), getId(b1.lissn, b2.lissn), b1.openAccess || b2.openAccess)
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}
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override def finish(reduction: HostedByItemType): HostedByItemType = reduction
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override def bufferEncoder: Encoder[HostedByItemType] = Encoders.product[HostedByItemType]
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override def outputEncoder: Encoder[HostedByItemType] = Encoders.product[HostedByItemType]
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}.toColumn
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def explodeHostedByItemType(df: Dataset[(String, HostedByItemType)]): Dataset[(String, HostedByItemType)] = {
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val transformedData : Dataset[(String, HostedByItemType)] = df
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.groupByKey(_._1)(Encoders.STRING)
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.agg(Aggregators.hostedByAggregator1)
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.agg(Aggregators.hostedByAggregator)
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.map{
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case (id:String , res:(String, HostedByItemType)) => res
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}(Encoders.tuple(Encoders.STRING, Encoders.product[HostedByItemType]))
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@ -69,7 +37,7 @@ object Aggregators {
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transformedData
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}
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val hostedByAggregator1: TypedColumn[(String, HostedByItemType), (String, HostedByItemType)] = new Aggregator[(String, HostedByItemType), (String, HostedByItemType), (String, HostedByItemType)] {
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val hostedByAggregator: TypedColumn[(String, HostedByItemType), (String, HostedByItemType)] = new Aggregator[(String, HostedByItemType), (String, HostedByItemType), (String, HostedByItemType)] {
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override def zero: (String, HostedByItemType) = ("", HostedByItemType("","","","","",false))
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override def reduce(b: (String, HostedByItemType), a:(String,HostedByItemType)): (String, HostedByItemType) = {
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return merge(b, a)
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@ -94,4 +62,79 @@ object Aggregators {
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override def outputEncoder: Encoder[(String,HostedByItemType)] = Encoders.tuple(Encoders.STRING,Encoders.product[HostedByItemType])
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}.toColumn
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def hostedByToSingleDSId(df: Dataset[ HostedByItemType]): Dataset[ HostedByItemType] = {
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val transformedData : Dataset[HostedByItemType] = df
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.groupByKey(_.id)(Encoders.STRING)
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.agg(Aggregators.hostedByToDSAggregator)
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.map{
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case (id:String , res: HostedByItemType) => res
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}(Encoders.product[HostedByItemType])
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transformedData
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}
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def hostedByToDSAggregator: TypedColumn[HostedByItemType, HostedByItemType] = new Aggregator[HostedByItemType, HostedByItemType, HostedByItemType] {
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override def zero: HostedByItemType = HostedByItemType("","","","","",false)
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override def reduce(b: HostedByItemType, a:HostedByItemType): HostedByItemType = {
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return merge(b, a)
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}
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override def merge(b1: HostedByItemType, b2: HostedByItemType): HostedByItemType = {
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if (b1 == null){
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return b2
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}
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if(b2 == null){
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return b1
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}
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if(!b1.id.equals("")){
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return HostedByItemType(b1.id, b1.officialname, b1.issn, b1.eissn, b1.lissn, b1.openAccess || b2.openAccess)
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}
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return HostedByItemType(b2.id, b2.officialname, b2.issn, b2.eissn, b2.lissn, b1.openAccess || b2.openAccess)
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}
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override def finish(reduction: HostedByItemType): HostedByItemType = reduction
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override def bufferEncoder: Encoder[HostedByItemType] = Encoders.product[HostedByItemType]
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override def outputEncoder: Encoder[HostedByItemType] = Encoders.product[HostedByItemType]
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}.toColumn
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def resultToSingleIdAggregator: TypedColumn[EntityInfo, EntityInfo] = new Aggregator[EntityInfo, EntityInfo, EntityInfo]{
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override def zero: EntityInfo = EntityInfo.newInstance("","","")
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override def reduce(b: EntityInfo, a:EntityInfo): EntityInfo = {
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return merge(b, a)
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}
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override def merge(b1: EntityInfo, b2: EntityInfo): EntityInfo = {
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if (b1 == null){
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return b2
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}
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if(b2 == null){
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return b1
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}
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if(!b1.getHb_id.equals("")){
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b1.setOpenaccess(b1.getOpenaccess || b2.getOpenaccess)
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}
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b2.setOpenaccess(b1.getOpenaccess || b2.getOpenaccess)
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b2
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}
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override def finish(reduction: EntityInfo): EntityInfo = reduction
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override def bufferEncoder: Encoder[EntityInfo] = Encoders.bean(classOf[EntityInfo])
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override def outputEncoder: Encoder[EntityInfo] = Encoders.bean(classOf[EntityInfo])
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}.toColumn
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def resultToSingleId(df:Dataset[EntityInfo]): Dataset[EntityInfo] = {
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val transformedData : Dataset[EntityInfo] = df
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.groupByKey(_.getId)(Encoders.STRING)
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.agg(Aggregators.resultToSingleIdAggregator)
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.map{
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case (id:String , res: EntityInfo) => res
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}(Encoders.bean(classOf[EntityInfo]))
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transformedData
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}
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}
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@ -0,0 +1,147 @@
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package eu.dnetlib.dhp.oa.graph.hostedbymap
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import com.fasterxml.jackson.databind.ObjectMapper
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.oa.graph.hostedbymap.model.{DatasourceInfo, EntityInfo}
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import eu.dnetlib.dhp.schema.oaf.{Datasource, Journal, Publication}
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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.{Dataset, Encoder, Encoders, SaveMode, 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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import org.slf4j.{Logger, LoggerFactory}
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object SparkPrepareHostedByInfoToApply {
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implicit val mapEncoderDSInfo: Encoder[DatasourceInfo] = Encoders.kryo[DatasourceInfo]
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implicit val mapEncoderPInfo: Encoder[EntityInfo] = Encoders.kryo[EntityInfo]
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def getList(id: String, j: Journal, name: String ) : List[EntityInfo] = {
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var lst:List[EntityInfo] = List()
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if (j.getIssnLinking != null && !j.getIssnLinking.equals("")){
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lst = EntityInfo.newInstance(id, j.getIssnLinking, name) :: lst
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}
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if (j.getIssnOnline != null && !j.getIssnOnline.equals("")){
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lst = EntityInfo.newInstance(id, j.getIssnOnline, name) :: lst
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}
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if (j.getIssnPrinted != null && !j.getIssnPrinted.equals("")){
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lst = EntityInfo.newInstance(id, j.getIssnPrinted, name) :: lst
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}
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lst
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}
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def prepareResultInfo(spark:SparkSession, publicationPath:String) : Dataset[EntityInfo] = {
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implicit val mapEncoderPubs: Encoder[Publication] = Encoders.bean(classOf[Publication])
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val mapper = new ObjectMapper()
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val dd : Dataset[Publication] = spark.read.textFile(publicationPath)
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.map(r => mapper.readValue(r, classOf[Publication]))
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dd.filter(p => p.getJournal != null ).flatMap(p => getList(p.getId, p.getJournal, ""))
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}
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def prepareDatasourceInfo(spark:SparkSession, datasourcePath:String) : Dataset[DatasourceInfo] = {
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implicit val mapEncoderDats: Encoder[Datasource] = Encoders.bean(classOf[Datasource])
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val mapper = new ObjectMapper()
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val dd : Dataset[Datasource] = spark.read.textFile(datasourcePath)
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.map(r => mapper.readValue(r, classOf[Datasource]))
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dd.filter(d => d.getJournal != null ).map(d => DatasourceInfo.newInstance(d.getId, d.getOfficialname.getValue,
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d.getJournal.getIssnPrinted, d.getJournal.getIssnOnline, d.getJournal.getIssnLinking))
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}
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def toHostedByItem(input:String): HostedByItemType = {
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implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
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lazy val json: json4s.JValue = parse(input)
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val c :Map[String,HostedByItemType] = json.extract[Map[String, HostedByItemType]]
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c.values.head
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}
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def explodeJournalInfo(input: DatasourceInfo): List[EntityInfo] = {
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var lst : List[EntityInfo] = List()
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if (input.getEissn != null && !input.getEissn.equals("")){
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lst = EntityInfo.newInstance(input.getId, input.getEissn, input.getOfficialname, input.getOpenAccess) :: lst
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}
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lst
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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/oa/graph/hostedbymap/hostedby_prepare_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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val graphPath = parser.get("graphPath")
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val outputPath = parser.get("outputPath")
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val hostedByMapPath = parser.get("hostedByMapPath")
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implicit val formats = DefaultFormats
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logger.info("Getting the Datasources")
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import spark.implicits._
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//STEP1: leggere le DS e creare le entries {dsid, dsofficialname, issn, eissn, lissn, openaccess}
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val datasourceInfoDataset: Dataset[DatasourceInfo] = prepareDatasourceInfo(spark, "$graphPath/datasource")
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//STEP2: leggere la hostedbymap e raggruppare per datasource id
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val hostedByDataset = Aggregators.hostedByToSingleDSId(spark.createDataset(spark.sparkContext.textFile(hostedByMapPath).map(toHostedByItem)))
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//STEP3: eseguire una join fra le datasource e la hostedby map (left) per settare se la datasource e' open access o no
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//ed esplodere l'info della datasource per ogni journal id diverso da nullo
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val join : Dataset[EntityInfo] = datasourceInfoDataset.joinWith(hostedByDataset,
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datasourceInfoDataset.col("id").equalTo(hostedByDataset.col("id"), "left"))
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.map(t2 => {
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val dsi : DatasourceInfo = t2._1
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if(t2._2 != null){
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dsi.setOpenAccess(t2._2.openAccess)
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}
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dsi
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}).flatMap(explodeJournalInfo)
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//STEP4: creare la mappa publication id issn, eissn, lissn esplosa
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val resultInfoDataset:Dataset[EntityInfo] = prepareResultInfo(spark, "$graphPath/publication")
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//STEP5: join di join con resultInfo sul journal_id dal result con left
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// e riduzione di tutti i result con lo stesso id in una unica entry
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Aggregators.resultToSingleId(resultInfoDataset.joinWith(join, resultInfoDataset.col("journal_id").equalTo(join.col("journal_id")), "left")
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.map(t2 => {
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val res: EntityInfo = t2._1
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if(t2._2 != null ){
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val ds = t2._2
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res.setHb_id(ds.getId)
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res.setOpenaccess(ds.getOpenaccess)
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res.setName(ds.getName)
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}
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res
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})).write.mode(SaveMode.Overwrite).option("compression", "gzip").json(outputPath)
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}
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}
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