forked from D-Net/dnet-hadoop
Merge branch 'stable_ids' of https://code-repo.d4science.org/D-Net/dnet-hadoop into stable_ids
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commit
fcd13f5350
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package eu.dnetlib.sx.pangaea
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import org.apache.spark.sql.expressions.Aggregator
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import org.apache.spark.sql.{Encoder, Encoders}
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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 java.text.SimpleDateFormat
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import java.util.Date
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case class PangaeaDataModel(datestamp:String, identifier:String, xml:String) {}
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object PangaeaUtils {
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def toDataset(input:String):PangaeaDataModel = {
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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 d = new Date()
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val s:String = s"${new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSS")format(d)}Z"
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val ds = (json \ "internal-datestamp").extractOrElse[String](s)
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val identifier= (json \ "metadatalink").extractOrElse[String]()
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val xml= (json \ "xml").extract[String]
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PangaeaDataModel(ds, identifier,xml)
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}
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def getDatasetAggregator(): Aggregator[(String, PangaeaDataModel), PangaeaDataModel, PangaeaDataModel] = new Aggregator[(String, PangaeaDataModel), PangaeaDataModel, PangaeaDataModel]{
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override def zero: PangaeaDataModel = null
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override def reduce(b: PangaeaDataModel, a: (String, PangaeaDataModel)): PangaeaDataModel = {
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if (b == null)
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a._2
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else {
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if (a == null)
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b
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else {
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val ts1 = b.datestamp
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val ts2 = a._2.datestamp
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if (ts1 > ts2)
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b
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else
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a._2
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}
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}
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}
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override def merge(b1: PangaeaDataModel, b2: PangaeaDataModel): PangaeaDataModel = {
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if (b1 == null)
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b2
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else {
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if (b2 == null)
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b1
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else {
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val ts1 = b1.datestamp
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val ts2 = b2.datestamp
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if (ts1 > ts2)
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b1
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else
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b2
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}
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}
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}
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override def finish(reduction: PangaeaDataModel): PangaeaDataModel = reduction
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override def bufferEncoder: Encoder[PangaeaDataModel] = Encoders.kryo[PangaeaDataModel]
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override def outputEncoder: Encoder[PangaeaDataModel] = Encoders.kryo[PangaeaDataModel]
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}
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}
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package eu.dnetlib.sx.pangaea
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.sx.ebi.SparkCreateEBIDataFrame
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import org.apache.spark.rdd.RDD
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import org.apache.spark.{SparkConf, SparkContext}
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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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import scala.collection.JavaConverters._
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import scala.io.Source
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object SparkGeneratePanagaeaDataset {
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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(Source.fromInputStream(getClass.getResourceAsStream("/eu/dnetlib/dhp/sx/pangaea/pangaea_to_dataset.json")).mkString)
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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(SparkCreateEBIDataFrame.getClass.getSimpleName)
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.master(parser.get("master")).getOrCreate()
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parser.getObjectMap.asScala.foreach(s => logger.info(s"${s._1} -> ${s._2}"))
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logger.info("Converting sequential file into Dataset")
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val sc:SparkContext = spark.sparkContext
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val workingPath:String = parser.get("workingPath")
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implicit val pangaeaEncoders: Encoder[PangaeaDataModel] = Encoders.kryo[PangaeaDataModel]
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val inputRDD:RDD[PangaeaDataModel] = sc.textFile(s"$workingPath/update").map(s => PangaeaUtils.toDataset(s))
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spark.createDataset(inputRDD).as[PangaeaDataModel]
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.map(s => (s.identifier,s))(Encoders.tuple(Encoders.STRING, pangaeaEncoders))
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.groupByKey(_._1)(Encoders.STRING)
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.agg(PangaeaUtils.getDatasetAggregator().toColumn)
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.map(s => s._2)
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.write.mode(SaveMode.Overwrite).save(s"$workingPath/dataset_updated")
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}
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}
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<configuration>
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<property>
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<name>jobTracker</name>
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<value>yarnRM</value>
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</property>
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<property>
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<name>nameNode</name>
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<value>hdfs://nameservice1</value>
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</property>
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<property>
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<name>oozie.use.system.libpath</name>
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<value>true</value>
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</property>
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<property>
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<name>oozie.action.sharelib.for.spark</name>
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<value>spark2</value>
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</property>
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</configuration>
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<workflow-app name="Transform_Pangaea_Workflow" xmlns="uri:oozie:workflow:0.5">
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<parameters>
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<property>
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<name>pangaeaWorkingPath</name>
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<description>the Pangaea Working Path</description>
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</property>
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</parameters>
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<start to="ConvertDataset"/>
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<kill name="Kill">
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<message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
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</kill>
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<action name="ConvertDataset">
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<spark xmlns="uri:oozie:spark-action:0.2">
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<master>yarn</master>
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<mode>cluster</mode>
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<name>Convert Pangaea to Dataset</name>
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<class>eu.dnetlib.sx.pangaea.SparkGeneratePanagaeaDataset</class>
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<jar>dhp-graph-mapper-${projectVersion}.jar</jar>
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<spark-opts>
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--executor-memory=${sparkExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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--driver-memory=${sparkDriverMemory}
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--conf spark.extraListeners=${spark2ExtraListeners}
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--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
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</spark-opts>
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<arg>--workingPath</arg><arg>${pangaeaWorkingPath}</arg>
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<arg>--master</arg><arg>yarn</arg>
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</spark>
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<ok to="End"/>
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<error to="Kill"/>
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</action>
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<end name="End"/>
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</workflow-app>
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[
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{"paramName":"mt", "paramLongName":"master", "paramDescription": "should be local or yarn", "paramRequired": true},
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{"paramName":"w", "paramLongName":"workingPath", "paramDescription": "the path of the sequencial file to read", "paramRequired": true}
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]
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package eu.dnetlib.dhp.sx.pangaea
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import org.junit.jupiter.api.Test
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import java.util.TimeZone
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import java.text.SimpleDateFormat
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import java.util.Date
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class PangaeaTransformTest {
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@Test
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def test_dateStamp() :Unit ={
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val d = new Date()
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val s:String = s"${new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSS")format(d)}Z"
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println(s)
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}
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}
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