forked from D-Net/dnet-hadoop
added oozie workflow
This commit is contained in:
Sandro La Bruzzo
parent
f417515e43
commit
3b837d38ce
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@ -0,0 +1,23 @@
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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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<property>
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<name>oozie.launcher.mapreduce.user.classpath.first</name>
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<value>true</value>
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</property>
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</configuration>
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@ -0,0 +1,95 @@
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<workflow-app name="generate_MAG_Datasource" xmlns="uri:oozie:workflow:0.5">
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<parameters>
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<property>
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<name>crossrefPath</name>
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<description>the path of the native Crossref DUMP</description>
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</property>
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<property>
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<name>magBasePath</name>
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<description>The base path of MAG DUMP CSV Tables</description>
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</property>
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<property>
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<name>workingPath</name>
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<description>The working path</description>
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</property>
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<property>
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<name>resume_from</name>
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<description>start Node</description>
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</property>
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</parameters>
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<start to="resume_from"/>
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<kill name="Kill">
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<message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
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</kill>
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<decision name="resume_from">
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<switch>
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<case to="generateTable">${wf:conf('resumeFrom') eq 'generateTable'}</case>
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<default to="generateOAF"/> <!-- first action to be done when downloadDump is to be performed -->
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</switch>
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</decision>
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<action name="generateTables">
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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>Generate ORCID Tables</name>
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<class>eu.dnetlib.dhp.collection.mag.SparkCreateMagDenormalizedTable</class>
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<jar>dhp-aggregation-${projectVersion}.jar</jar>
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<spark-opts>
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--executor-memory=${sparkExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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--driver-memory=${sparkDriverMemory}
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--conf spark.executor.memoryOverhead=2g
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--conf spark.sql.shuffle.partitions=3000
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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}${spark2EventLogDir}
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</spark-opts>
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<arg>--crossrefPath</arg><arg>${crossrefPath}</arg>
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<arg>--magBasePath</arg><arg>${magBasePath}</arg>
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<arg>--workingPath</arg><arg>${workingPath}</arg>
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<arg>--master</arg><arg>yarn</arg>
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</spark>
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<ok to="generateOAF"/>
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<error to="Kill"/>
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</action>
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<action name="generateOAF">
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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>Generate ORCID Tables</name>
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<class>eu.dnetlib.dhp.collection.mag.SparkCreateMagDenormalizedTable</class>
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<jar>dhp-aggregation-${projectVersion}.jar</jar>
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<spark-opts>
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--executor-memory=${sparkExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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--driver-memory=${sparkDriverMemory}
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--conf spark.executor.memoryOverhead=2g
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--conf spark.sql.shuffle.partitions=3000
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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}${spark2EventLogDir}
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</spark-opts>
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<arg>--crossrefPath</arg><arg>${crossrefPath}</arg>
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<arg>--magBasePath</arg><arg>${magBasePath}</arg>
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<arg>--workingPath</arg><arg>${workingPath}</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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@ -6,10 +6,67 @@ 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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case class MAGPaper(
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paperId: Option[Long],
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rank: Option[Int],
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doi: Option[String],
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docType: Option[String],
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paperTitle: Option[String],
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originalTitle: Option[String],
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bookTitle: Option[String],
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year: Option[Int],
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date: Option[String],
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onlineDate: Option[String],
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publisher: Option[String],
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// Journal or Conference information (one will be populated)
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journalId: Option[Long],
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journalName: Option[String],
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journalIssn: Option[String],
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journalPublisher: Option[String],
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journalWebpage: Option[String],
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conferenceSeriesId: Option[Long],
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conferenceInstanceId: Option[Long],
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conferenceName: Option[String],
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conferenceLocation: Option[String],
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conferenceStartDate: Option[String],
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conferenceEndDate: Option[String],
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volume: Option[String],
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issue: Option[String],
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firstPage: Option[String],
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lastPage: Option[String],
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referenceCount: Option[Long],
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citationCount: Option[Long],
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estimatedCitation: Option[Long],
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originalVenue: Option[String],
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familyId: Option[Long],
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familyRank: Option[Int],
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docSubTypes: Option[String],
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createdDate: Option[String],
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abstractText: Option[String],
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// List of authors
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authors: Option[List[MAGAuthor]],
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// List of Fields of Study
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fos: Option[List[MAGFieldOfStudy]]
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)
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case class MAGAuthor(
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AffiliationId: Option[Long],
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AuthorSequenceNumber: Option[Int],
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AffiliationName: Option[String],
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AuthorName: Option[String],
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AuthorId: Option[Long],
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GridId: Option[String]
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)
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case class MAGFieldOfStudy(
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FieldOfStudyId: Option[Long],
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DisplayName: Option[String],
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MainType: Option[String],
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Score: Option[Double]
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)
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object MagUtility extends Serializable {
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val datatypedict = Map(
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"bool" -> BooleanType,
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"int" -> IntegerType,
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@ -251,7 +308,8 @@ object MagUtility extends Serializable {
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def getSchema(streamName: String): StructType = {
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var schema = new StructType()
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val d: Seq[String] = stream(streamName)._2
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d.foreach { case t =>
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d.foreach {
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case t =>
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val currentType = t.split(":")
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val fieldName: String = currentType.head
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var fieldType: String = currentType.last
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@ -263,7 +321,6 @@ object MagUtility extends Serializable {
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schema
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}
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def loadMagEntity(spark: SparkSession, entity: String, basePath: String): Dataset[Row] = {
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if (stream.contains(entity)) {
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val s = getSchema(entity)
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@ -278,6 +335,7 @@ object MagUtility extends Serializable {
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null
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}
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def convertInvertedIndexString(json_input: String): String = {
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implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
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lazy val json: json4s.JValue = parse(json_input)
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@ -1,128 +0,0 @@
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package eu.dnetlib.dhp.collection.mag
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import eu.dnetlib.dhp.application.AbstractScalaApplication
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import org.apache.spark.sql.{Dataset, Row, SaveMode, SparkSession}
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import org.apache.spark.sql.functions._
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import org.apache.spark.sql.types.{StringType, StructField, StructType}
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import org.slf4j.Logger
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class SparkCreateMagDataset (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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}
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private def loadAndFilterPapers(spark:SparkSession, crossrefPath:String, magBasePath:String, workingPath:String): Unit = {
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import spark.implicits._
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val schema:StructType= StructType(StructField("DOI", StringType)::Nil)
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//Filter all the MAG Papers that intersect with a Crossref DOI
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val crId= spark.read.schema(schema).json(crossrefPath).withColumn("crId", lower(col("DOI"))).distinct.select("crId")
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val magPapers = MagUtility.loadMagEntity(spark, "Papers", magBasePath)
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.withColumn("Doi", lower(col("Doi")))
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.where(col("Doi").isNotNull)
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val intersectedPapers:Dataset[Row] =magPapers.join(crId, magPapers("Doi").equalTo(crId("crId")), "leftsemi").dropDuplicates("Doi")
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intersectedPapers.cache()
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intersectedPapers.count()
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//log.info("Create current abstract")
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//Abstract is an inverted list, we define a function that convert in string the abstract and recreate
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// a table(PaperId, Abstract)
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val paperAbstract = MagUtility.loadMagEntity(spark, "PaperAbstractsInvertedIndex", magBasePath)
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.map(s => (s.getLong(0),MagUtility.convertInvertedIndexString(s.getString(1))))
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.withColumnRenamed("_1","PaperId")
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.withColumnRenamed("_2","Abstract")
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//We define Step0 as the result of the Join between PaperIntersection and the PaperAbstract
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val step0 =intersectedPapers
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.join(paperAbstract, intersectedPapers("PaperId") === paperAbstract("PaperId"), "left")
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.select(intersectedPapers("*"),paperAbstract("Abstract")).cache()
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step0.count()
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intersectedPapers.unpersist()
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// We have three table Author, Affiliation, and PaperAuthorAffiliation, in the
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//next step we create a table containing
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val authors = MagUtility.loadMagEntity(spark, "Authors", magBasePath)
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val affiliations= MagUtility.loadMagEntity(spark, "Affiliations", magBasePath)
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val paaf= MagUtility.loadMagEntity(spark, "PaperAuthorAffiliations", magBasePath)
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val paperAuthorAffiliations =paaf.join(step0,paaf("PaperId") === step0("PaperId"),"leftsemi")
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val j1 = paperAuthorAffiliations.join(authors,paperAuthorAffiliations("AuthorId") === authors("AuthorId"), "inner")
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.select(col("PaperId"), col("AffiliationId"),col("AuthorSequenceNumber"), authors("DisplayName").alias("AuthorName"), authors("AuthorId"))
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val paperAuthorAffiliationNormalized = j1.join(affiliations, j1("AffiliationId")=== affiliations("AffiliationId"), "left")
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.select(j1("*"), affiliations("DisplayName").alias("AffiliationName"), affiliations("GridId"))
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.groupBy("PaperId")
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.agg(collect_list(struct("AffiliationId","AuthorSequenceNumber","AffiliationName","AuthorName","AuthorId","GridId")).alias("authors"))
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val step1 =step0.join(paperAuthorAffiliationNormalized, step0("PaperId")=== paperAuthorAffiliationNormalized("PaperId"), "left")
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.select(step0("*"),paperAuthorAffiliationNormalized("authors"))
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.cache()
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step1.count()
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step0.unpersist()
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val conference = MagUtility.loadMagEntity(spark, "ConferenceInstances", magBasePath).select(
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$"ConferenceInstanceId",
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$"DisplayName".as("conferenceName"),
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$"Location".as("conferenceLocation"),
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$"StartDate".as("conferenceStartDate"),
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$"EndDate".as("conferenceEndDate")
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)
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val step2 =step1.join(conference, step1("ConferenceInstanceId")=== conference("ConferenceInstanceId"),"left").select(
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step1("*"), conference("conferenceName"),
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conference("conferenceLocation"),
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conference("conferenceStartDate"),
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conference("conferenceEndDate")).cache()
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step2.count()
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step1.unpersist()
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val fos = MagUtility.loadMagEntity(spark, "FieldsOfStudy", magBasePath)
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.select($"FieldOfStudyId".alias("fos"), $"DisplayName", $"MainType")
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val paperFieldsOfStudy = MagUtility.loadMagEntity(spark, "PaperFieldsOfStudy", magBasePath)
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.select($"FieldOfStudyId", $"Score", $"PaperId")
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val paperFoS = paperFieldsOfStudy.join(broadcast(fos),fos("fos")===paperFieldsOfStudy("FieldOfStudyId")).groupBy("PaperId")
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.agg(collect_set(struct("FieldOfStudyId","DisplayName","MainType","Score")).as("FoS"))
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val step3=step2.join(paperFoS, step2("PaperId")===paperFoS("PaperId"), "left")
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.select(step2("*"), paperFoS("FoS")).cache()
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step3.count()
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step2.unpersist()
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val journals= MagUtility.loadMagEntity(spark, "Journals", magBasePath)
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.select(
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$"JournalId",
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$"DisplayName".as("journalName"),
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$"Issn".as("journalIssn"),
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$"Publisher".as("journalPublisher"),
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$"Webpage".as("journalWebpage")
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)
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step3.join(journals, step3("JournalId")===journals("JournalId"), "left").
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select(step3("*"),
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journals("journalName"),
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journals("journalIssn"),
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journals("journalPublisher"),
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journals("journalWebpage")
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).write.mode("OverWrite")
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.save(s"$workingPath/generatedMAG")
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step3.unpersist()
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}
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}
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@ -0,0 +1,222 @@
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package eu.dnetlib.dhp.collection.mag
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import eu.dnetlib.dhp.application.AbstractScalaApplication
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import org.apache.spark.sql.functions._
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import org.apache.spark.sql.types.{StringType, StructField, StructType}
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import org.apache.spark.sql.{Dataset, Row, SparkSession}
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import org.slf4j.{Logger, LoggerFactory}
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class SparkCreateMagDenormalizedTable(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 crossrefPath: String = parser.get("crossrefPath")
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log.info("found parameters crossrefPath: {}", crossrefPath)
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val magBasePath: String = parser.get("magBasePath")
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log.info("found parameters magBasePath: {}", magBasePath)
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val workingPath: String = parser.get("workingPath")
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log.info("found parameters workingPath: {}", workingPath)
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generatedDenormalizedMAGTable(spark, crossrefPath, magBasePath, workingPath)
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}
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private def generatedDenormalizedMAGTable(
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spark: SparkSession,
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crossrefPath: String,
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magBasePath: String,
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workingPath: String
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): Unit = {
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import spark.implicits._
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val schema: StructType = StructType(StructField("DOI", StringType) :: Nil)
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//Filter all the MAG Papers that intersect with a Crossref DOI
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val crId =
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spark.read.schema(schema).json(crossrefPath).withColumn("crId", lower(col("DOI"))).distinct.select("crId")
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val magPapers = MagUtility
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.loadMagEntity(spark, "Papers", magBasePath)
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.withColumn("Doi", lower(col("Doi")))
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.where(col("Doi").isNotNull)
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val intersectedPapers: Dataset[Row] =
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magPapers.join(crId, magPapers("Doi").equalTo(crId("crId")), "leftsemi").dropDuplicates("Doi")
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intersectedPapers.cache()
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intersectedPapers.count()
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//log.info("Create current abstract")
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//Abstract is an inverted list, we define a function that convert in string the abstract and recreate
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// a table(PaperId, Abstract)
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val paperAbstract = MagUtility
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.loadMagEntity(spark, "PaperAbstractsInvertedIndex", magBasePath)
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.map(s => (s.getLong(0), MagUtility.convertInvertedIndexString(s.getString(1))))
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.withColumnRenamed("_1", "PaperId")
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.withColumnRenamed("_2", "Abstract")
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//We define Step0 as the result of the Join between PaperIntersection and the PaperAbstract
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val step0 = intersectedPapers
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.join(paperAbstract, intersectedPapers("PaperId") === paperAbstract("PaperId"), "left")
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.select(intersectedPapers("*"), paperAbstract("Abstract"))
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.cache()
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step0.count()
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intersectedPapers.unpersist()
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// We have three table Author, Affiliation, and PaperAuthorAffiliation, in the
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//next step we create a table containing
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val authors = MagUtility.loadMagEntity(spark, "Authors", magBasePath)
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val affiliations = MagUtility.loadMagEntity(spark, "Affiliations", magBasePath)
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val paaf = MagUtility.loadMagEntity(spark, "PaperAuthorAffiliations", magBasePath)
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val paperAuthorAffiliations = paaf.join(step0, paaf("PaperId") === step0("PaperId"), "leftsemi")
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val j1 = paperAuthorAffiliations
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.join(authors, paperAuthorAffiliations("AuthorId") === authors("AuthorId"), "inner")
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.select(
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col("PaperId"),
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col("AffiliationId"),
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col("AuthorSequenceNumber"),
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authors("DisplayName").alias("AuthorName"),
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authors("AuthorId")
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)
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val paperAuthorAffiliationNormalized = j1
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.join(affiliations, j1("AffiliationId") === affiliations("AffiliationId"), "left")
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.select(j1("*"), affiliations("DisplayName").alias("AffiliationName"), affiliations("GridId"))
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.groupBy("PaperId")
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.agg(
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collect_list(
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struct("AffiliationId", "AuthorSequenceNumber", "AffiliationName", "AuthorName", "AuthorId", "GridId")
|
||||
).alias("authors")
|
||||
)
|
||||
val step1 = step0
|
||||
.join(paperAuthorAffiliationNormalized, step0("PaperId") === paperAuthorAffiliationNormalized("PaperId"), "left")
|
||||
.select(step0("*"), paperAuthorAffiliationNormalized("authors"))
|
||||
.cache()
|
||||
step1.count()
|
||||
|
||||
step0.unpersist()
|
||||
|
||||
val conference = MagUtility
|
||||
.loadMagEntity(spark, "ConferenceInstances", magBasePath)
|
||||
.select(
|
||||
$"ConferenceInstanceId",
|
||||
$"DisplayName".as("conferenceName"),
|
||||
$"Location".as("conferenceLocation"),
|
||||
$"StartDate".as("conferenceStartDate"),
|
||||
$"EndDate".as("conferenceEndDate")
|
||||
)
|
||||
|
||||
val step2 = step1
|
||||
.join(conference, step1("ConferenceInstanceId") === conference("ConferenceInstanceId"), "left")
|
||||
.select(
|
||||
step1("*"),
|
||||
conference("conferenceName"),
|
||||
conference("conferenceLocation"),
|
||||
conference("conferenceStartDate"),
|
||||
conference("conferenceEndDate")
|
||||
)
|
||||
.cache()
|
||||
step2.count()
|
||||
step1.unpersist()
|
||||
|
||||
val fos = MagUtility
|
||||
.loadMagEntity(spark, "FieldsOfStudy", magBasePath)
|
||||
.select($"FieldOfStudyId".alias("fos"), $"DisplayName", $"MainType")
|
||||
|
||||
val paperFieldsOfStudy = MagUtility
|
||||
.loadMagEntity(spark, "PaperFieldsOfStudy", magBasePath)
|
||||
.select($"FieldOfStudyId", $"Score", $"PaperId")
|
||||
|
||||
val paperFoS = paperFieldsOfStudy
|
||||
.join(broadcast(fos), fos("fos") === paperFieldsOfStudy("FieldOfStudyId"))
|
||||
.groupBy("PaperId")
|
||||
.agg(collect_set(struct("FieldOfStudyId", "DisplayName", "MainType", "Score")).as("FoS"))
|
||||
|
||||
val step3 = step2
|
||||
.join(paperFoS, step2("PaperId") === paperFoS("PaperId"), "left")
|
||||
.select(step2("*"), paperFoS("FoS"))
|
||||
.cache()
|
||||
step3.count()
|
||||
|
||||
step2.unpersist()
|
||||
|
||||
val journals = MagUtility
|
||||
.loadMagEntity(spark, "Journals", magBasePath)
|
||||
.select(
|
||||
$"JournalId",
|
||||
$"DisplayName".as("journalName"),
|
||||
$"Issn".as("journalIssn"),
|
||||
$"Publisher".as("journalPublisher"),
|
||||
$"Webpage".as("journalWebpage")
|
||||
)
|
||||
step3
|
||||
.join(journals, step3("JournalId") === journals("JournalId"), "left")
|
||||
.select(
|
||||
step3("*"),
|
||||
journals("journalName"),
|
||||
journals("journalIssn"),
|
||||
journals("journalPublisher"),
|
||||
journals("journalWebpage")
|
||||
)
|
||||
.select(
|
||||
$"PaperId".as("paperId"),
|
||||
$"Rank".as("rank"),
|
||||
$"Doi".as("doi"),
|
||||
$"DocType".as("docType"),
|
||||
$"PaperTitle".as("paperTitle"),
|
||||
$"OriginalTitle".as("originalTitle"),
|
||||
$"BookTitle".as("bookTitle"),
|
||||
$"Year".as("year"),
|
||||
$"Date".as("date"),
|
||||
$"OnlineDate".as("onlineDate"),
|
||||
$"Publisher".as("publisher"),
|
||||
$"JournalId".as("journalId"),
|
||||
$"ConferenceSeriesId".as("conferenceSeriesId"),
|
||||
$"ConferenceInstanceId".as("conferenceInstanceId"),
|
||||
$"Volume".as("volume"),
|
||||
$"Issue".as("issue"),
|
||||
$"FirstPage".as("firstPage"),
|
||||
$"LastPage".as("lastPage"),
|
||||
$"ReferenceCount".as("referenceCount"),
|
||||
$"CitationCount".as("citationCount"),
|
||||
$"EstimatedCitation".as("estimatedCitation"),
|
||||
$"OriginalVenue".as("originalVenue"),
|
||||
$"FamilyId".as("familyId"),
|
||||
$"FamilyRank".as("familyRank"),
|
||||
$"DocSubTypes".as("docSubTypes"),
|
||||
$"CreatedDate".as("createdDate"),
|
||||
$"Abstract".as("abstractText"),
|
||||
$"authors".as("authors"),
|
||||
$"conferenceName".as("conferenceName"),
|
||||
$"conferenceLocation".as("conferenceLocation"),
|
||||
$"conferenceStartDate".as("conferenceStartDate"),
|
||||
$"conferenceEndDate".as("conferenceEndDate"),
|
||||
$"FoS".as("fos"),
|
||||
$"journalName".as("journalName"),
|
||||
$"journalIssn".as("journalIssn"),
|
||||
$"journalPublisher".as("journalPublisher"),
|
||||
$"journalWebpage".as("journalWebpage")
|
||||
)
|
||||
.write
|
||||
.mode("OverWrite")
|
||||
.save(s"$workingPath/mag")
|
||||
step3.unpersist()
|
||||
}
|
||||
}
|
||||
|
||||
object SparkCreateMagDenormalizedTable {
|
||||
|
||||
val log: Logger = LoggerFactory.getLogger(SparkCreateMagDenormalizedTable.getClass)
|
||||
|
||||
def main(args: Array[String]): Unit = {
|
||||
new SparkCreateMagDenormalizedTable(
|
||||
"/eu/dnetlib/dhp/collection/mag/create_MAG_denormalized_table_properites.json",
|
||||
args,
|
||||
log
|
||||
)
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue