OAF-store-graph mdstores: save them in text format
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@ -48,18 +48,16 @@ object CollectionUtils {
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List()
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}
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def saveDataset(d: Dataset[Oaf], targetPath: String):Unit = {
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def saveDataset(dataset: Dataset[Oaf], targetPath: String): Unit = {
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implicit val resultEncoder: Encoder[Oaf] = Encoders.kryo(classOf[Oaf])
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val mapper = new ObjectMapper
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d
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dataset
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.flatMap(i => CollectionUtils.fixRelations(i))
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.filter(i => i != null)
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.map(r => mapper.writeValueAsString(r))(Encoders.STRING)
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.write
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.text(targetPath)
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.json(targetPath)
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}
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}
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@ -38,7 +38,7 @@ class GenerateDataciteDatasetSpark (propertyPath:String, args:Array[String], log
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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/$MDSTORE_DATA_PATH"
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val targetPath = s"$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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@ -54,7 +54,7 @@ class GenerateDataciteDatasetSpark (propertyPath:String, args:Array[String], log
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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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val total_items = spark.read.text(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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