forked from D-Net/dnet-hadoop
96 lines
3.3 KiB
Scala
96 lines
3.3 KiB
Scala
package eu.dnetlib.dhp.bioschema
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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
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import eu.dnetlib.dhp.common.Constants.{MDSTORE_DATA_PATH, MDSTORE_SIZE_PATH}
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import eu.dnetlib.dhp.schema.mdstore.MDStoreVersion
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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 org.apache.spark.sql.{Encoder, Encoders, SparkSession}
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import org.slf4j.{Logger, LoggerFactory}
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class GenerateBioschemaDatasetSpark(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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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 datasourceKey = parser.get("datasourceKey").toLowerCase
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log.info(s"datasourceKey is '$datasourceKey'")
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// val mdstoreOutputVersion = parser.get("mdstoreOutputVersion")
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// log.info(s"mdstoreOutputVersion is '$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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val targetPath = parser.get("targetPath")
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generateBioschemaDataset(sourcePath, exportLinks, targetPath, datasourceKey, spark)
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// reportTotalSize(targetPath, outputBasePath)
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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.text(targetPath).count()
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writeHdfsFile(
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spark.sparkContext.hadoopConfiguration,
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s"$total_items",
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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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*
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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 targetPath the targetPath of the result Dataset
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*/
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def generateBioschemaDataset(
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sourcePath: String,
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exportLinks: Boolean,
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targetPath: String,
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datasourceKey: String,
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spark: SparkSession
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): Unit = {
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require(spark != null)
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implicit val resultEncoder: Encoder[Oaf] = Encoders.kryo(classOf[Oaf])
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CollectionUtils.saveDataset(
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spark.createDataset(
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spark.sparkContext
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.textFile(sourcePath)
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.flatMap(i => BioschemaToOAFTransformation.generateOAF(i, exportLinks, datasourceKey))
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),
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targetPath
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)
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}
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}
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object GenerateBioschemaDatasetSpark {
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val log: Logger = LoggerFactory.getLogger(GenerateBioschemaDatasetSpark.getClass)
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def main(args: Array[String]): Unit = {
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new GenerateBioschemaDatasetSpark(
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"/eu/dnetlib/dhp/bioschema/generate_dataset_params.json",
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args,
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log
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).initialize().run()
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}
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} |