95 lines
3.8 KiB
Scala
95 lines
3.8 KiB
Scala
package eu.dnetlib.dhp.datacite
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import com.fasterxml.jackson.databind.ObjectMapper
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import eu.dnetlib.dhp.application.AbstractScalaApplication
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import eu.dnetlib.dhp.collection.CollectionUtils.fixRelations
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import eu.dnetlib.dhp.common.Constants.{MDSTORE_DATA_PATH, MDSTORE_SIZE_PATH}
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import eu.dnetlib.dhp.common.vocabulary.VocabularyGroup
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import eu.dnetlib.dhp.schema.mdstore.{MDStoreVersion, MetadataRecord}
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import eu.dnetlib.dhp.schema.oaf.Oaf
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import eu.dnetlib.dhp.utils.DHPUtils.writeHdfsFile
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import eu.dnetlib.dhp.utils.ISLookupClientFactory
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import org.apache.spark.sql.{Encoder, Encoders, SaveMode, SparkSession}
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import org.slf4j.{Logger, LoggerFactory}
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class GenerateDataciteDatasetSpark (propertyPath:String, args:Array[String], log:Logger) extends AbstractScalaApplication(propertyPath, args, log:Logger) {
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/**
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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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val sourcePath = parser.get("sourcePath")
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log.info(s"SourcePath is '$sourcePath'")
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val exportLinks = "true".equalsIgnoreCase(parser.get("exportLinks"))
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log.info(s"exportLinks is '$exportLinks'")
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val isLookupUrl: String = parser.get("isLookupUrl")
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log.info("isLookupUrl: {}", isLookupUrl)
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val isLookupService = ISLookupClientFactory.getLookUpService(isLookupUrl)
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val vocabularies = VocabularyGroup.loadVocsFromIS(isLookupService)
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require(vocabularies != null)
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val mdstoreOutputVersion = parser.get("mdstoreOutputVersion")
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log.info(s"mdstoreOutputVersion is '$mdstoreOutputVersion'")
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val mapper = new ObjectMapper()
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val cleanedMdStoreVersion = mapper.readValue(mdstoreOutputVersion, classOf[MDStoreVersion])
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val outputBasePath = cleanedMdStoreVersion.getHdfsPath
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log.info(s"outputBasePath is '$outputBasePath'")
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val targetPath = s"$outputBasePath/$MDSTORE_DATA_PATH"
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log.info(s"targetPath is '$targetPath'")
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generateDataciteDataset(sourcePath, exportLinks, vocabularies, targetPath, spark)
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reportTotalSize(targetPath, outputBasePath)
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}
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/**
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* For working with MDStore we need to store in a file on hdfs the size of
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* the current dataset
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* @param targetPath
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* @param outputBasePath
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*/
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def reportTotalSize( targetPath: String, outputBasePath: String ):Unit = {
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val total_items = spark.read.load(targetPath).count()
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writeHdfsFile(spark.sparkContext.hadoopConfiguration, s"$total_items", outputBasePath + MDSTORE_SIZE_PATH)
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}
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/**
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* Generate the transformed and cleaned OAF Dataset from the native one
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* @param sourcePath sourcePath of the native Dataset in format JSON/Datacite
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* @param exportLinks If true it generates unresolved links
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* @param vocabularies vocabularies for cleaning
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* @param targetPath the targetPath of the result Dataset
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*/
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def generateDataciteDataset(sourcePath: String, exportLinks: Boolean, vocabularies: VocabularyGroup, targetPath: String, spark:SparkSession):Unit = {
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require(spark!= null)
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import spark.implicits._
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implicit val mrEncoder: Encoder[MetadataRecord] = Encoders.kryo[MetadataRecord]
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implicit val resEncoder: Encoder[Oaf] = Encoders.kryo[Oaf]
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spark.read.load(sourcePath).as[DataciteType]
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.filter(d => d.isActive)
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.flatMap(d => DataciteToOAFTransformation.generateOAF(d.json, d.timestamp, d.timestamp, vocabularies, exportLinks))
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.filter(d => d != null)
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.flatMap(i => fixRelations(i)).filter(i => i != null)
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.write.mode(SaveMode.Overwrite).save(targetPath)
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}
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}
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object GenerateDataciteDatasetSpark {
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val log: Logger = LoggerFactory.getLogger(GenerateDataciteDatasetSpark.getClass)
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def main(args: Array[String]): Unit = {
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new GenerateDataciteDatasetSpark("/eu/dnetlib/dhp/datacite/generate_dataset_params.json", args, log).initialize().run()
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}
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}
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