123 lines
4.2 KiB
Scala
123 lines
4.2 KiB
Scala
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.EntityInfo
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import eu.dnetlib.dhp.schema.oaf.{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._
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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 mapEncoderPInfo: Encoder[EntityInfo] = Encoders.bean(classOf[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 toEntityInfo(input: String): EntityInfo = {
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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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toEntityItem(c.keys.head, c.values.head)
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}
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def toEntityItem(journal_id: String, hbi: HostedByItemType): EntityInfo = {
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EntityInfo.newInstance(hbi.id, journal_id, hbi.officialname, hbi.openAccess)
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}
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def joinResHBM(res: Dataset[EntityInfo], hbm: Dataset[EntityInfo]): Dataset[EntityInfo] = {
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Aggregators.resultToSingleId(res.joinWith(hbm, res.col("journalId").equalTo(hbm.col("journalId")), "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.setHostedById(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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}))
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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("preparedInfoPath")
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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: read the hostedbymap and transform it in EntityInfo
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val hostedByInfo: Dataset[EntityInfo] = spark.createDataset(spark.sparkContext.textFile(hostedByMapPath)).map(toEntityInfo)
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//STEP2: create association (publication, issn), (publication, eissn), (publication, lissn)
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val resultInfoDataset: Dataset[EntityInfo] = prepareResultInfo(spark, graphPath + "/publication")
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//STEP3: left join resultInfo with hostedByInfo on journal_id. Reduction of all the results with the same id in just
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//one entry (one result could be associated to issn and eissn and so possivly matching more than once against the map)
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//to this entry we add the id of the datasource for the next step
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joinResHBM(resultInfoDataset, hostedByInfo)
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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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