forked from D-Net/dnet-hadoop
Merge branch 'master' into stable_ids
This commit is contained in:
commit
3e6c8bca39
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@ -14,6 +14,8 @@ import scala.collection.JavaConverters._
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import scala.collection.mutable
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import scala.util.matching.Regex
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case class CrossrefDT(doi: String, json:String) {}
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case class mappingAffiliation(name: String) {}
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case class mappingAuthor(given: Option[String], family: String, ORCID: Option[String], affiliation: Option[mappingAffiliation]) {}
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@ -0,0 +1,93 @@
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package eu.dnetlib.doiboost.crossref
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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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.expressions.Aggregator
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import org.apache.spark.sql.{Dataset, Encoder, Encoders, SaveMode, SparkSession}
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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 CrossrefDataset {
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def extractTimestamp(input:String): Long = {
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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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(json\"indexed"\"timestamp").extractOrElse[Long](0)
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}
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def main(args: Array[String]): Unit = {
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val logger: Logger = LoggerFactory.getLogger(SparkMapDumpIntoOAF.getClass)
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val conf: SparkConf = new SparkConf()
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val parser = new ArgumentApplicationParser(IOUtils.toString(CrossrefDataset.getClass.getResourceAsStream("/eu/dnetlib/dhp/doiboost/crossref_to_dataset_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(SparkMapDumpIntoOAF.getClass.getSimpleName)
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.master(parser.get("master")).getOrCreate()
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import spark.implicits._
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val crossrefAggregator = new Aggregator[CrossrefDT, CrossrefDT, CrossrefDT] with Serializable {
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override def zero: CrossrefDT = null
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override def reduce(b: CrossrefDT, a: CrossrefDT): CrossrefDT = {
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if (b == null)
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return a
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if (a == null)
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return b
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val tb = extractTimestamp(b.json)
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val ta = extractTimestamp(a.json)
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if(ta >tb) {
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return a
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}
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b
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}
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override def merge(a: CrossrefDT, b: CrossrefDT): CrossrefDT = {
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if (b == null)
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return a
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if (a == null)
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return b
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val tb = extractTimestamp(b.json)
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val ta = extractTimestamp(a.json)
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if(ta >tb) {
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return a
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}
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b
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}
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override def bufferEncoder: Encoder[CrossrefDT] = implicitly[Encoder[CrossrefDT]]
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override def outputEncoder: Encoder[CrossrefDT] = implicitly[Encoder[CrossrefDT]]
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override def finish(reduction: CrossrefDT): CrossrefDT = reduction
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}
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val sourcePath:String = parser.get("sourcePath")
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val targetPath:String = parser.get("targetPath")
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val ds:Dataset[CrossrefDT] = spark.read.load(sourcePath).as[CrossrefDT]
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ds.groupByKey(_.doi)
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.agg(crossrefAggregator.toColumn)
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.map(s=>s._2)
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.write.mode(SaveMode.Overwrite).save(targetPath)
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}
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}
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@ -46,11 +46,11 @@
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<job-tracker>${jobTracker}</job-tracker>
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<name-node>${nameNode}</name-node>
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<main-class>eu.dnetlib.doiboost.crossref.CrossrefImporter</main-class>
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<arg>-t</arg><arg>${workingPath}/input/crossref/index_dump</arg>
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<arg>-t</arg><arg>${workingPath}/input/crossref/index_dump_1</arg>
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<arg>-n</arg><arg>${nameNode}</arg>
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<arg>-ts</arg><arg>${timestamp}</arg>
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</java>
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<ok to="ExtractCrossrefToOAF"/>
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<ok to="End"/>
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<error to="Kill"/>
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</action>
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@ -68,7 +68,7 @@
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--driver-memory=${sparkDriverMemory}
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${sparkExtraOPT}
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</spark-opts>
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<arg>--sourcePath</arg><arg>${workingPath}/input/crossref/index_dump,${workingPath}/crossref/index_dump</arg>
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<arg>--sourcePath</arg><arg>${workingPath}/input/crossref/index_dump,${workingPath}/input/crossref/index_dump_1,${workingPath}/crossref/index_dump</arg>
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<arg>--targetPath</arg><arg>${workingPath}/input/crossref</arg>
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<arg>--master</arg><arg>yarn-cluster</arg>
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</spark>
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@ -76,5 +76,28 @@
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<error to="Kill"/>
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</action>
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<action name="GenerateDataset">
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<spark xmlns="uri:oozie:spark-action:0.2">
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<master>yarn-cluster</master>
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<mode>cluster</mode>
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<name>ExtractCrossrefToOAF</name>
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<class>eu.dnetlib.doiboost.crossref.CrossrefDataset</class>
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<jar>dhp-doiboost-${projectVersion}.jar</jar>
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<spark-opts>
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--executor-memory=${sparkExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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--driver-memory=${sparkDriverMemory}
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${sparkExtraOPT}
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</spark-opts>
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<arg>--sourcePath</arg><arg>/data/doiboost/crossref/cr_dataset</arg>
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<arg>--targetPath</arg><arg>/data/doiboost/crossref/crossrefDataset</arg>
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<arg>--master</arg><arg>yarn-cluster</arg>
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</spark>
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<ok to="End"/>
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<error to="Kill"/>
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</action>
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<end name="End"/>
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</workflow-app>
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@ -0,0 +1,6 @@
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[
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{"paramName":"s", "paramLongName":"sourcePath", "paramDescription": "the path of the sequencial file to read", "paramRequired": true},
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{"paramName":"t", "paramLongName":"targetPath", "paramDescription": "the working dir path", "paramRequired": true},
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{"paramName":"m", "paramLongName":"master", "paramDescription": "the master name", "paramRequired": true}
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]
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@ -89,7 +89,7 @@
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</spark-opts>
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<arg>--dbPublicationPath</arg><arg>${workingDirPath}/doiBoostPublicationFiltered</arg>
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<arg>--dbDatasetPath</arg><arg>${workingDirPath}/crossrefDataset</arg>
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<arg>--crossRefRelation</arg><arg>/data/doiboost/input/crossref/relations</arg>
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<arg>--crossRefRelation</arg><arg>${workingDirPath}/crossrefRelation</arg>
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<arg>--dbaffiliationRelationPath</arg><arg>${workingDirPath}/doiBoostPublicationAffiliation</arg>
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<arg>-do</arg><arg>${workingDirPath}/doiBoostOrganization</arg>
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<arg>--targetPath</arg><arg>${workingDirPath}/actionDataSet</arg>
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@ -1,54 +1,45 @@
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package eu.dnetlib.dhp.doiboost
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import eu.dnetlib.dhp.schema.oaf.{Publication, Relation, StructuredProperty, Dataset => OafDataset}
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import eu.dnetlib.dhp.schema.oaf.Project
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import org.apache.spark.SparkContext
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import org.apache.spark.sql.functions.{col, sum}
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import org.apache.hadoop.io.Text
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import org.apache.spark.rdd.RDD
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import org.apache.spark.sql.{Dataset, Encoder, Encoders, SparkSession}
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import org.codehaus.jackson.map.ObjectMapper
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import org.json4s.DefaultFormats
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import org.json4s
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import org.json4s.DefaultFormats
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import org.json4s.JsonAST._
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import org.json4s.jackson.JsonMethods._
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import scala.::
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import scala.collection.JavaConverters._
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class QueryTest {
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def extract_payload(input:String) :String = {
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def extractLicense(p:Publication):Tuple2[String,String] = {
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val tmp = p.getInstance().asScala.map(i => i.getLicense.getValue).distinct.mkString(",")
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(p.getId,tmp)
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}
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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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def hasDOI(publication: Publication, doi:String):Boolean = {
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compact(render((json \ "payload")))
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val s = publication.getOriginalId.asScala.filter(i => i.equalsIgnoreCase(doi))
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s.nonEmpty
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}
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def hasNullHostedBy(publication: Publication):Boolean = {
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publication.getInstance().asScala.exists(i => i.getHostedby == null || i.getHostedby.getValue == null)
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}
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def myQuery(spark:SparkSession, sc:SparkContext): Unit = {
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implicit val mapEncoderPub: Encoder[Project] = Encoders.kryo[Project]
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// val ds:Dataset[Project] = spark.createDataset(sc.sequenceFile("", classOf[Text], classOf[Text])
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// .map(_._2.toString)
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// .map(s => new ObjectMapper().readValue(s, classOf[Project])))
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//
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// ds.write.saveAsTable()
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def myQuery(spark:SparkSession): Unit = {
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implicit val mapEncoderPub: Encoder[Publication] = Encoders.kryo[Publication]
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implicit val mapEncoderDat: Encoder[OafDataset] = Encoders.kryo[OafDataset]
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implicit val mapEncoderRel: Encoder[Relation] = Encoders.kryo[Relation]
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val doiboostPubs:Dataset[Publication] = spark.read.load("/data/doiboost/process/doiBoostPublicationFiltered").as[Publication]
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val relFunder: Dataset[Relation] = spark.read.format("org.apache.spark.sql.parquet").load("/data/doiboost/process/crossrefRelation").as[Relation]
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doiboostPubs.filter(p => p.getDateofacceptance != null && p.getDateofacceptance.getValue!= null && p.getDateofacceptance.getValue.length > 0 )
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doiboostPubs.filter(p=>hasDOI(p, "10.1016/j.is.2020.101522")).collect()(0).getDescription.get(0).getValue
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doiboostPubs.filter(p=> hasNullHostedBy(p)).count()
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doiboostPubs.map(p=> (p.getId, p.getBestaccessright.getClassname))(Encoders.tuple(Encoders.STRING,Encoders.STRING))
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}
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}
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|
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@ -19,8 +19,6 @@ class CrossrefMappingTest {
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@Test
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def testFunderRelationshipsMapping(): Unit = {
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val template = Source.fromInputStream(getClass.getResourceAsStream("article_funder_template.json")).mkString
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@ -84,6 +84,12 @@
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<version>${project.version}</version>
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</dependency>
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<dependency>
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<groupId>eu.dnetlib.dhp</groupId>
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<artifactId>dhp-dedup-openaire</artifactId>
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<version>${project.version}</version>
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</dependency>
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<dependency>
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<groupId>com.jayway.jsonpath</groupId>
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<artifactId>json-path</artifactId>
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|
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@ -1,4 +1,5 @@
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package eu.dnetlib.dhp.sx.ebi
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import eu.dnetlib.dhp.oa.dedup.AuthorMerger
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import eu.dnetlib.dhp.schema.oaf.{Publication, Relation, Dataset => OafDataset}
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import eu.dnetlib.dhp.schema.scholexplorer.{DLIDataset, DLIPublication, DLIUnknown}
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import org.apache.spark.sql.{Encoder, Encoders}
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@ -14,6 +15,7 @@ object EBIAggregator {
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override def reduce(b: OafDataset, a: (String, OafDataset)): OafDataset = {
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b.mergeFrom(a._2)
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b.setAuthor(AuthorMerger.mergeAuthor(a._2.getAuthor, b.getAuthor))
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if (b.getId == null)
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b.setId(a._2.getId)
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b
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@ -22,6 +24,7 @@ object EBIAggregator {
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override def merge(wx: OafDataset, wy: OafDataset): OafDataset = {
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wx.mergeFrom(wy)
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wx.setAuthor(AuthorMerger.mergeAuthor(wy.getAuthor, wx.getAuthor))
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if(wx.getId == null && wy.getId.nonEmpty)
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wx.setId(wy.getId)
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wx
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@ -35,8 +38,6 @@ object EBIAggregator {
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Encoders.kryo(classOf[OafDataset])
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}
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|
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|
||||
|
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def getDLIUnknownAggregator(): Aggregator[(String, DLIUnknown), DLIUnknown, DLIUnknown] = new Aggregator[(String, DLIUnknown), DLIUnknown, DLIUnknown]{
|
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|
||||
override def zero: DLIUnknown = new DLIUnknown()
|
||||
|
|
@ -69,6 +70,7 @@ object EBIAggregator {
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|
||||
override def reduce(b: DLIDataset, a: (String, DLIDataset)): DLIDataset = {
|
||||
b.mergeFrom(a._2)
|
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b.setAuthor(AuthorMerger.mergeAuthor(a._2.getAuthor, b.getAuthor))
|
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if (b.getId == null)
|
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b.setId(a._2.getId)
|
||||
b
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||||
|
|
@ -76,6 +78,7 @@ object EBIAggregator {
|
|||
|
||||
override def merge(wx: DLIDataset, wy: DLIDataset): DLIDataset = {
|
||||
wx.mergeFrom(wy)
|
||||
wx.setAuthor(AuthorMerger.mergeAuthor(wy.getAuthor, wx.getAuthor))
|
||||
if(wx.getId == null && wy.getId.nonEmpty)
|
||||
wx.setId(wy.getId)
|
||||
wx
|
||||
|
|
@ -96,6 +99,8 @@ object EBIAggregator {
|
|||
|
||||
override def reduce(b: DLIPublication, a: (String, DLIPublication)): DLIPublication = {
|
||||
b.mergeFrom(a._2)
|
||||
b.setAuthor(AuthorMerger.mergeAuthor(a._2.getAuthor, b.getAuthor))
|
||||
|
||||
if (b.getId == null)
|
||||
b.setId(a._2.getId)
|
||||
b
|
||||
|
|
@ -104,6 +109,7 @@ object EBIAggregator {
|
|||
|
||||
override def merge(wx: DLIPublication, wy: DLIPublication): DLIPublication = {
|
||||
wx.mergeFrom(wy)
|
||||
wx.setAuthor(AuthorMerger.mergeAuthor(wy.getAuthor, wx.getAuthor))
|
||||
if(wx.getId == null && wy.getId.nonEmpty)
|
||||
wx.setId(wy.getId)
|
||||
wx
|
||||
|
|
@ -124,6 +130,7 @@ object EBIAggregator {
|
|||
|
||||
override def reduce(b: Publication, a: (String, Publication)): Publication = {
|
||||
b.mergeFrom(a._2)
|
||||
b.setAuthor(AuthorMerger.mergeAuthor(a._2.getAuthor, b.getAuthor))
|
||||
if (b.getId == null)
|
||||
b.setId(a._2.getId)
|
||||
b
|
||||
|
|
@ -132,6 +139,7 @@ object EBIAggregator {
|
|||
|
||||
override def merge(wx: Publication, wy: Publication): Publication = {
|
||||
wx.mergeFrom(wy)
|
||||
wx.setAuthor(AuthorMerger.mergeAuthor(wy.getAuthor, wx.getAuthor))
|
||||
if(wx.getId == null && wy.getId.nonEmpty)
|
||||
wx.setId(wy.getId)
|
||||
wx
|
||||
|
|
@ -145,7 +153,6 @@ object EBIAggregator {
|
|||
Encoders.kryo(classOf[Publication])
|
||||
}
|
||||
|
||||
|
||||
def getRelationAggregator(): Aggregator[(String, Relation), Relation, Relation] = new Aggregator[(String, Relation), Relation, Relation]{
|
||||
|
||||
override def zero: Relation = new Relation()
|
||||
|
|
@ -166,10 +173,4 @@ object EBIAggregator {
|
|||
override def outputEncoder: Encoder[Relation] =
|
||||
Encoders.kryo(classOf[Relation])
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
DROP VIEW IF EXISTS ${hiveDbName}.result;
|
||||
|
||||
CREATE VIEW IF NOT EXISTS ${hiveDbName}.result as
|
||||
select id, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.publication p
|
||||
select id, originalid, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.publication p
|
||||
union all
|
||||
select id, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.dataset d
|
||||
select id, originalid, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.dataset d
|
||||
union all
|
||||
select id, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.software s
|
||||
select id, originalid, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.software s
|
||||
union all
|
||||
select id, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.otherresearchproduct o;
|
||||
select id, originalid, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.otherresearchproduct o;
|
||||
|
|
|
|||
|
|
@ -2,4 +2,5 @@
|
|||
package eu.dnetlib.dhp.sx.graph;
|
||||
|
||||
public class SparkScholexplorerGraphImporterTest {
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,10 @@
|
|||
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|
||||
{"collectedfrom":[{"key":"dli_________::crossref","value":"Crossref","dataInfo":null}],"dataInfo":{"invisible":false,"inferred":null,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":null,"provenanceaction":null},"lastupdatetimestamp":null,"id":"50|1307198540d2264d839dfd8c9a19f4a7","originalId":["1307198540d2264d839dfd8c9a19f4a7"],"pid":[{"value":"10.3390/w11050916","qualifier":{"classid":"doi","classname":"doi","schemeid":"dnet:pid_types","schemename":"dnet:pid_types"},"dataInfo":null}],"dateofcollection":"2020-08-14T14:25:55.176Z","dateoftransformation":null,"extraInfo":null,"oaiprovenance":null,"measures":null,"author":[{"fullname":"Cao Qing","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Hao Zhenchun","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Yuan Feifei","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Berndtsson Ronny","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Xu Shijie","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Gao Huibin","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Hao Jie","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null}],"resulttype":{"classid":"publication","classname":"publication","schemeid":"publication","schemename":"publication"},"language":null,"country":null,"subject":[],"title":[{"value":"On the Predictability of Daily Rainfall during Rainy Season over the Huaihe River Basin","qualifier":{"classid":"main title","classname":null,"schemeid":"dnet:dataCite_title","schemename":"dnet:dataCite_title"},"dataInfo":null}],"relevantdate":[{"value":"2019-05-02T07:15:22Z","qualifier":{"classid":"date","classname":"date","schemeid":"dnet::date","schemename":"dnet::date"},"dataInfo":null}],"description":[{"value":null,"dataInfo":null}],"dateofacceptance":null,"publisher":{"value":"MDPI AG","dataInfo":null},"embargoenddate":null,"source":null,"fulltext":null,"format":null,"contributor":null,"resourcetype":null,"coverage":null,"bestaccessright":null,"context":null,"externalReference":null,"instance":[],"journal":null,"originalObjIdentifier":"dli_resolver::1307198540d2264d839dfd8c9a19f4a7","dlicollectedfrom":[{"id":"dli_________::crossref","name":"Crossref","completionStatus":"complete","collectionMode":"resolved"}],"completionStatus":"complete"}
|
||||
{"collectedfrom":[{"key":"dli_________::crossref","value":"Crossref","dataInfo":null}],"dataInfo":{"invisible":false,"inferred":null,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":null,"provenanceaction":null},"lastupdatetimestamp":null,"id":"50|1307198540d2264d839dfd8c9a19f4a7","originalId":["1307198540d2264d839dfd8c9a19f4a7"],"pid":[{"value":"10.3390/w11050916","qualifier":{"classid":"doi","classname":"doi","schemeid":"dnet:pid_types","schemename":"dnet:pid_types"},"dataInfo":null}],"dateofcollection":"2020-08-09T11:35:23.526Z","dateoftransformation":null,"extraInfo":null,"oaiprovenance":null,"measures":null,"author":[{"fullname":"Cao Qing","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Hao Zhenchun","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Yuan Feifei","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Berndtsson Ronny","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Xu Shijie","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Gao Huibin","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Hao Jie","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null}],"resulttype":{"classid":"publication","classname":"publication","schemeid":"publication","schemename":"publication"},"language":null,"country":null,"subject":[],"title":[{"value":"On the Predictability of Daily Rainfall during Rainy Season over the Huaihe River Basin","qualifier":{"classid":"main title","classname":null,"schemeid":"dnet:dataCite_title","schemename":"dnet:dataCite_title"},"dataInfo":null}],"relevantdate":[{"value":"2019-05-02T07:15:22Z","qualifier":{"classid":"date","classname":"date","schemeid":"dnet::date","schemename":"dnet::date"},"dataInfo":null}],"description":[{"value":null,"dataInfo":null}],"dateofacceptance":null,"publisher":{"value":"MDPI AG","dataInfo":null},"embargoenddate":null,"source":null,"fulltext":null,"format":null,"contributor":null,"resourcetype":null,"coverage":null,"bestaccessright":null,"context":null,"externalReference":null,"instance":[],"journal":null,"originalObjIdentifier":"dli_resolver::1307198540d2264d839dfd8c9a19f4a7","dlicollectedfrom":[{"id":"dli_________::crossref","name":"Crossref","completionStatus":"complete","collectionMode":"resolved"}],"completionStatus":"complete"}
|
||||
|
|
@ -32,10 +32,10 @@ object SparkExportContentForOpenAire {
|
|||
.master(parser.get("master")).getOrCreate()
|
||||
|
||||
|
||||
val sc:SparkContext = spark.sparkContext
|
||||
|
||||
val workingPath = parser.get("workingDirPath")
|
||||
|
||||
implicit val dliPubEncoder: Encoder[DLIPublication] = Encoders.kryo(classOf[DLIPublication])
|
||||
implicit val dliDatEncoder: Encoder[DLIDataset] = Encoders.kryo(classOf[DLIDataset])
|
||||
implicit val pubEncoder: Encoder[Publication] = Encoders.bean(classOf[Publication])
|
||||
implicit val datEncoder: Encoder[OafDataset] = Encoders.bean(classOf[OafDataset])
|
||||
implicit val relEncoder: Encoder[Relation] = Encoders.bean(classOf[Relation])
|
||||
|
|
@ -43,40 +43,41 @@ object SparkExportContentForOpenAire {
|
|||
import spark.implicits._
|
||||
|
||||
|
||||
val relRDD:RDD[Relation] = sc.textFile(s"$workingPath/relation_j")
|
||||
.map(s => new ObjectMapper().readValue(s, classOf[Relation]))
|
||||
.filter(p => p.getDataInfo.getDeletedbyinference == false)
|
||||
spark.createDataset(relRDD).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationDS")
|
||||
val dsRel = spark.read.load(s"$workingPath/relation_b").as[Relation]
|
||||
dsRel.filter(r => r.getDataInfo==null || r.getDataInfo.getDeletedbyinference ==false).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/relationDS")
|
||||
|
||||
val datRDD:RDD[OafDataset] = sc.textFile(s"$workingPath/dataset")
|
||||
.map(s => new ObjectMapper().readValue(s, classOf[DLIDataset]))
|
||||
|
||||
val dsPubs = spark.read.load(s"$workingPath/publication").as[DLIPublication]
|
||||
dsPubs
|
||||
.filter(p=>p.getDataInfo.getDeletedbyinference == false)
|
||||
.map(DLIToOAF.convertDLIPublicationToOAF)
|
||||
.filter(p=>p!= null)
|
||||
.write.mode(SaveMode.Overwrite).save(s"$workingPath/export/publicationDS")
|
||||
|
||||
|
||||
val dsDataset = spark.read.load(s"$workingPath/dataset").as[DLIDataset]
|
||||
dsDataset
|
||||
.filter(p => p.getDataInfo.getDeletedbyinference == false)
|
||||
.map(DLIToOAF.convertDLIDatasetTOOAF).filter(p=>p!= null)
|
||||
spark.createDataset(datRDD).write.mode(SaveMode.Overwrite).save(s"$workingPath/datasetDS")
|
||||
|
||||
|
||||
val pubRDD:RDD[Publication] = sc.textFile(s"$workingPath/publication")
|
||||
.map(s => new ObjectMapper().readValue(s, classOf[DLIPublication]))
|
||||
.filter(p => p.getDataInfo.getDeletedbyinference == false)
|
||||
.map(DLIToOAF.convertDLIPublicationToOAF).filter(p=>p!= null)
|
||||
spark.createDataset(pubRDD).write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationDS")
|
||||
.write.mode(SaveMode.Overwrite).save(s"$workingPath/export/datasetDS")
|
||||
|
||||
|
||||
|
||||
val pubs:Dataset[Publication] = spark.read.load(s"$workingPath/publicationDS").as[Publication]
|
||||
val dats :Dataset[OafDataset] = spark.read.load(s"$workingPath/datasetDS").as[OafDataset]
|
||||
val relDS1 :Dataset[Relation] = spark.read.load(s"$workingPath/relationDS").as[Relation]
|
||||
|
||||
val pubs:Dataset[Publication] = spark.read.load(s"$workingPath/export/publicationDS").as[Publication]
|
||||
val dats :Dataset[OafDataset] = spark.read.load(s"$workingPath/export/datasetDS").as[OafDataset]
|
||||
val relDS1 :Dataset[Relation] = spark.read.load(s"$workingPath/export/relationDS").as[Relation]
|
||||
|
||||
|
||||
val pub_id = pubs.select("id").distinct()
|
||||
val dat_id = dats.select("id").distinct()
|
||||
|
||||
|
||||
pub_id.joinWith(relDS1, pub_id("id").equalTo(relDS1("source"))).map(k => k._2).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationDS_f1")
|
||||
pub_id.joinWith(relDS1, pub_id("id").equalTo(relDS1("source"))).map(k => k._2).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/relationDS_f1")
|
||||
|
||||
val relDS2= spark.read.load(s"$workingPath/relationDS_f1").as[Relation]
|
||||
val relDS2= spark.read.load(s"$workingPath/export/relationDS_f1").as[Relation]
|
||||
|
||||
relDS2.joinWith(dat_id, relDS2("target").equalTo(dats("id"))).map(k => k._1).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationDS_filtered")
|
||||
relDS2.joinWith(dat_id, relDS2("target").equalTo(dats("id"))).map(k => k._1).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/relationDS_filtered")
|
||||
|
||||
|
||||
val r_source = relDS2.select(relDS2("source")).distinct()
|
||||
|
|
@ -87,22 +88,20 @@ object SparkExportContentForOpenAire {
|
|||
|
||||
pubs.joinWith(r_source, pubs("id").equalTo(r_source("source")), "inner").map(k => k._1)
|
||||
.withColumn("row",row_number.over(w2)).where($"row" === 1).drop("row")
|
||||
.write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationDS_filtered")
|
||||
.write.mode(SaveMode.Overwrite).save(s"$workingPath/export/publicationDS_filtered")
|
||||
|
||||
dats.joinWith(r_target, dats("id").equalTo(r_target("target")), "inner").map(k => k._1)
|
||||
.withColumn("row",row_number.over(w2)).where($"row" === 1).drop("row")
|
||||
.write.mode(SaveMode.Overwrite).save(s"$workingPath/datasetAS")
|
||||
.write.mode(SaveMode.Overwrite).save(s"$workingPath/export/datasetAS")
|
||||
|
||||
spark.createDataset(sc.textFile(s"$workingPath/dataset")
|
||||
.map(s => new ObjectMapper().readValue(s, classOf[DLIDataset]))
|
||||
.map(DLIToOAF.convertDLIDatasetToExternalReference)
|
||||
.filter(p => p != null)).as[DLIExternalReference].write.mode(SaveMode.Overwrite).save(s"$workingPath/externalReference")
|
||||
|
||||
val pf = spark.read.load(s"$workingPath/publicationDS_filtered").select("id")
|
||||
val relDS3 = spark.read.load(s"$workingPath/relationDS").as[Relation]
|
||||
dsDataset.map(DLIToOAF.convertDLIDatasetToExternalReference).filter(p => p != null).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/externalReference")
|
||||
|
||||
val pf = spark.read.load(s"$workingPath/export/publicationDS_filtered").select("id")
|
||||
val relDS3 = spark.read.load(s"$workingPath/export/relationDS").as[Relation]
|
||||
val relationTo = pf.joinWith(relDS3, pf("id").equalTo(relDS3("source")),"inner").map(t =>t._2)
|
||||
|
||||
val extRef = spark.read.load(s"$workingPath/externalReference").as[DLIExternalReference]
|
||||
val extRef = spark.read.load(s"$workingPath/export/externalReference").as[DLIExternalReference]
|
||||
|
||||
spark.createDataset(relationTo.joinWith(extRef, relationTo("target").equalTo(extRef("id")), "inner").map(d => {
|
||||
val r = d._1
|
||||
|
|
@ -112,11 +111,11 @@ object SparkExportContentForOpenAire {
|
|||
var dli_ext = ArrayBuffer[DLIExternalReference]()
|
||||
f._2.foreach(d => if (dli_ext.size < 100) dli_ext += d )
|
||||
(f._1, dli_ext)
|
||||
})).write.mode(SaveMode.Overwrite).save(s"$workingPath/externalReference_grouped")
|
||||
})).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/externalReference_grouped")
|
||||
|
||||
val pubf :Dataset[Publication] = spark.read.load(s"$workingPath/publicationDS_filtered").as[Publication]
|
||||
val pubf :Dataset[Publication] = spark.read.load(s"$workingPath/export/publicationDS_filtered").as[Publication]
|
||||
|
||||
val groupedERf:Dataset[(String, List[DLIExternalReference])]= spark.read.load(s"$workingPath/externalReference_grouped").as[(String, List[DLIExternalReference])]
|
||||
val groupedERf:Dataset[(String, List[DLIExternalReference])]= spark.read.load(s"$workingPath/export/externalReference_grouped").as[(String, List[DLIExternalReference])]
|
||||
|
||||
groupedERf.joinWith(pubf,pubf("id").equalTo(groupedERf("_1"))).map(t =>
|
||||
{
|
||||
|
|
@ -128,29 +127,28 @@ object SparkExportContentForOpenAire {
|
|||
} else
|
||||
publication
|
||||
}
|
||||
).write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationAS")
|
||||
).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/publicationAS")
|
||||
|
||||
|
||||
spark.createDataset(sc.textFile(s"$workingPath/dataset")
|
||||
.map(s => new ObjectMapper().readValue(s, classOf[DLIDataset]))
|
||||
dsDataset
|
||||
.map(DLIToOAF.convertClinicalTrial)
|
||||
.filter(p => p != null))
|
||||
.write.mode(SaveMode.Overwrite).save(s"$workingPath/clinicalTrials")
|
||||
.filter(p => p != null)
|
||||
.write.mode(SaveMode.Overwrite).save(s"$workingPath/export/clinicalTrials")
|
||||
|
||||
val ct:Dataset[(String,String)] = spark.read.load(s"$workingPath/clinicalTrials").as[(String,String)]
|
||||
val ct:Dataset[(String,String)] = spark.read.load(s"$workingPath/export/clinicalTrials").as[(String,String)]
|
||||
|
||||
val relDS= spark.read.load(s"$workingPath/relationDS_f1").as[Relation]
|
||||
val relDS= spark.read.load(s"$workingPath/export/relationDS_f1").as[Relation]
|
||||
|
||||
relDS.joinWith(ct, relDS("target").equalTo(ct("_1")), "inner")
|
||||
.map(k =>{
|
||||
val currentRel = k._1
|
||||
currentRel.setTarget(k._2._2)
|
||||
currentRel
|
||||
}).write.mode(SaveMode.Overwrite).save(s"$workingPath/clinicalTrialsRels")
|
||||
}).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/clinicalTrialsRels")
|
||||
|
||||
|
||||
val clRels:Dataset[Relation] = spark.read.load(s"$workingPath/clinicalTrialsRels").as[Relation]
|
||||
val rels:Dataset[Relation] = spark.read.load(s"$workingPath/relationDS_filtered").as[Relation]
|
||||
val clRels:Dataset[Relation] = spark.read.load(s"$workingPath/export/clinicalTrialsRels").as[Relation]
|
||||
val rels:Dataset[Relation] = spark.read.load(s"$workingPath/export/relationDS_filtered").as[Relation]
|
||||
|
||||
rels.union(clRels).flatMap(r => {
|
||||
val inverseRel = new Relation
|
||||
|
|
@ -162,18 +160,18 @@ object SparkExportContentForOpenAire {
|
|||
inverseRel.setSubRelType(r.getSubRelType)
|
||||
inverseRel.setRelClass(DLIToOAF.rel_inverse(r.getRelClass))
|
||||
List(r, inverseRel)
|
||||
}).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationAS")
|
||||
}).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/relationAS")
|
||||
|
||||
|
||||
|
||||
spark.read.load(s"$workingPath/publicationAS").as[Publication].map(DLIToOAF.fixInstance).write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationAS_fixed")
|
||||
spark.read.load(s"$workingPath/datasetAS").as[OafDataset].map(DLIToOAF.fixInstanceDataset).write.mode(SaveMode.Overwrite).save(s"$workingPath/datasetAS_fixed")
|
||||
spark.read.load(s"$workingPath/export/publicationAS").as[Publication].map(DLIToOAF.fixInstance).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/publicationAS_fixed")
|
||||
spark.read.load(s"$workingPath/export/datasetAS").as[OafDataset].map(DLIToOAF.fixInstanceDataset).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/datasetAS_fixed")
|
||||
|
||||
val fRels:Dataset[(String,String)] = spark.read.load(s"$workingPath/relationAS").as[Relation].map(DLIToOAF.toActionSet)
|
||||
val fpubs:Dataset[(String,String)] = spark.read.load(s"$workingPath/publicationAS_fixed").as[Publication].map(DLIToOAF.toActionSet)
|
||||
val fdats:Dataset[(String,String)] = spark.read.load(s"$workingPath/datasetAS_fixed").as[OafDataset].map(DLIToOAF.toActionSet)
|
||||
val fRels:Dataset[(String,String)] = spark.read.load(s"$workingPath/export/relationAS").as[Relation].map(DLIToOAF.toActionSet)
|
||||
val fpubs:Dataset[(String,String)] = spark.read.load(s"$workingPath/export/publicationAS_fixed").as[Publication].map(DLIToOAF.toActionSet)
|
||||
val fdats:Dataset[(String,String)] = spark.read.load(s"$workingPath/export/datasetAS_fixed").as[OafDataset].map(DLIToOAF.toActionSet)
|
||||
|
||||
fRels.union(fpubs).union(fdats).rdd.map(s => (new Text(s._1), new Text(s._2))).saveAsHadoopFile(s"$workingPath/rawset", classOf[Text], classOf[Text], classOf[SequenceFileOutputFormat[Text,Text]], classOf[GzipCodec])
|
||||
fRels.union(fpubs).union(fdats).rdd.map(s => (new Text(s._1), new Text(s._2))).saveAsHadoopFile(s"$workingPath/export/rawset", classOf[Text], classOf[Text], classOf[SequenceFileOutputFormat[Text,Text]], classOf[GzipCodec])
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue