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added oozie workflow

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Sandro La Bruzzo 2024-03-05 11:44:59 +01:00
parent f417515e43
commit 3b837d38ce
6 changed files with 410 additions and 140 deletions

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@ -0,0 +1,23 @@
<configuration>
<property>
<name>jobTracker</name>
<value>yarnRM</value>
</property>
<property>
<name>nameNode</name>
<value>hdfs://nameservice1</value>
</property>
<property>
<name>oozie.use.system.libpath</name>
<value>true</value>
</property>
<property>
<name>oozie.action.sharelib.for.spark</name>
<value>spark2</value>
</property>
<property>
<name>oozie.launcher.mapreduce.user.classpath.first</name>
<value>true</value>
</property>
</configuration>

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@ -0,0 +1,95 @@
<workflow-app name="generate_MAG_Datasource" xmlns="uri:oozie:workflow:0.5">
<parameters>
<property>
<name>crossrefPath</name>
<description>the path of the native Crossref DUMP</description>
</property>
<property>
<name>magBasePath</name>
<description>The base path of MAG DUMP CSV Tables</description>
</property>
<property>
<name>workingPath</name>
<description>The working path</description>
</property>
<property>
<name>resume_from</name>
<description>start Node</description>
</property>
</parameters>
<start to="resume_from"/>
<kill name="Kill">
<message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
</kill>
<decision name="resume_from">
<switch>
<case to="generateTable">${wf:conf('resumeFrom') eq 'generateTable'}</case>
<default to="generateOAF"/> <!-- first action to be done when downloadDump is to be performed -->
</switch>
</decision>
<action name="generateTables">
<spark xmlns="uri:oozie:spark-action:0.2">
<master>yarn</master>
<mode>cluster</mode>
<name>Generate ORCID Tables</name>
<class>eu.dnetlib.dhp.collection.mag.SparkCreateMagDenormalizedTable</class>
<jar>dhp-aggregation-${projectVersion}.jar</jar>
<spark-opts>
--executor-memory=${sparkExecutorMemory}
--executor-cores=${sparkExecutorCores}
--driver-memory=${sparkDriverMemory}
--conf spark.executor.memoryOverhead=2g
--conf spark.sql.shuffle.partitions=3000
--conf spark.extraListeners=${spark2ExtraListeners}
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
</spark-opts>
<arg>--crossrefPath</arg><arg>${crossrefPath}</arg>
<arg>--magBasePath</arg><arg>${magBasePath}</arg>
<arg>--workingPath</arg><arg>${workingPath}</arg>
<arg>--master</arg><arg>yarn</arg>
</spark>
<ok to="generateOAF"/>
<error to="Kill"/>
</action>
<action name="generateOAF">
<spark xmlns="uri:oozie:spark-action:0.2">
<master>yarn</master>
<mode>cluster</mode>
<name>Generate ORCID Tables</name>
<class>eu.dnetlib.dhp.collection.mag.SparkCreateMagDenormalizedTable</class>
<jar>dhp-aggregation-${projectVersion}.jar</jar>
<spark-opts>
--executor-memory=${sparkExecutorMemory}
--executor-cores=${sparkExecutorCores}
--driver-memory=${sparkDriverMemory}
--conf spark.executor.memoryOverhead=2g
--conf spark.sql.shuffle.partitions=3000
--conf spark.extraListeners=${spark2ExtraListeners}
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
</spark-opts>
<arg>--crossrefPath</arg><arg>${crossrefPath}</arg>
<arg>--magBasePath</arg><arg>${magBasePath}</arg>
<arg>--workingPath</arg><arg>${workingPath}</arg>
<arg>--master</arg><arg>yarn</arg>
</spark>
<ok to="End"/>
<error to="Kill"/>
</action>
<end name="End"/>
</workflow-app>

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@ -6,10 +6,67 @@ import org.json4s
import org.json4s.DefaultFormats import org.json4s.DefaultFormats
import org.json4s.jackson.JsonMethods.parse import org.json4s.jackson.JsonMethods.parse
case class MAGPaper(
paperId: Option[Long],
rank: Option[Int],
doi: Option[String],
docType: Option[String],
paperTitle: Option[String],
originalTitle: Option[String],
bookTitle: Option[String],
year: Option[Int],
date: Option[String],
onlineDate: Option[String],
publisher: Option[String],
// Journal or Conference information (one will be populated)
journalId: Option[Long],
journalName: Option[String],
journalIssn: Option[String],
journalPublisher: Option[String],
journalWebpage: Option[String],
conferenceSeriesId: Option[Long],
conferenceInstanceId: Option[Long],
conferenceName: Option[String],
conferenceLocation: Option[String],
conferenceStartDate: Option[String],
conferenceEndDate: Option[String],
volume: Option[String],
issue: Option[String],
firstPage: Option[String],
lastPage: Option[String],
referenceCount: Option[Long],
citationCount: Option[Long],
estimatedCitation: Option[Long],
originalVenue: Option[String],
familyId: Option[Long],
familyRank: Option[Int],
docSubTypes: Option[String],
createdDate: Option[String],
abstractText: Option[String],
// List of authors
authors: Option[List[MAGAuthor]],
// List of Fields of Study
fos: Option[List[MAGFieldOfStudy]]
)
case class MAGAuthor(
AffiliationId: Option[Long],
AuthorSequenceNumber: Option[Int],
AffiliationName: Option[String],
AuthorName: Option[String],
AuthorId: Option[Long],
GridId: Option[String]
)
case class MAGFieldOfStudy(
FieldOfStudyId: Option[Long],
DisplayName: Option[String],
MainType: Option[String],
Score: Option[Double]
)
object MagUtility extends Serializable { object MagUtility extends Serializable {
val datatypedict = Map( val datatypedict = Map(
"bool" -> BooleanType, "bool" -> BooleanType,
"int" -> IntegerType, "int" -> IntegerType,
@ -251,7 +308,8 @@ object MagUtility extends Serializable {
def getSchema(streamName: String): StructType = { def getSchema(streamName: String): StructType = {
var schema = new StructType() var schema = new StructType()
val d: Seq[String] = stream(streamName)._2 val d: Seq[String] = stream(streamName)._2
d.foreach { case t => d.foreach {
case t =>
val currentType = t.split(":") val currentType = t.split(":")
val fieldName: String = currentType.head val fieldName: String = currentType.head
var fieldType: String = currentType.last var fieldType: String = currentType.last
@ -263,7 +321,6 @@ object MagUtility extends Serializable {
schema schema
} }
def loadMagEntity(spark: SparkSession, entity: String, basePath: String): Dataset[Row] = { def loadMagEntity(spark: SparkSession, entity: String, basePath: String): Dataset[Row] = {
if (stream.contains(entity)) { if (stream.contains(entity)) {
val s = getSchema(entity) val s = getSchema(entity)
@ -278,6 +335,7 @@ object MagUtility extends Serializable {
null null
} }
def convertInvertedIndexString(json_input: String): String = { def convertInvertedIndexString(json_input: String): String = {
implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
lazy val json: json4s.JValue = parse(json_input) lazy val json: json4s.JValue = parse(json_input)

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@ -1,128 +0,0 @@
package eu.dnetlib.dhp.collection.mag
import eu.dnetlib.dhp.application.AbstractScalaApplication
import org.apache.spark.sql.{Dataset, Row, SaveMode, SparkSession}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.slf4j.Logger
class SparkCreateMagDataset (propertyPath: String, args: Array[String], log: Logger)
extends AbstractScalaApplication(propertyPath, args, log: Logger) {
/** Here all the spark applications runs this method
* where the whole logic of the spark node is defined
*/
override def run(): Unit = {
}
private def loadAndFilterPapers(spark:SparkSession, crossrefPath:String, magBasePath:String, workingPath:String): Unit = {
import spark.implicits._
val schema:StructType= StructType(StructField("DOI", StringType)::Nil)
//Filter all the MAG Papers that intersect with a Crossref DOI
val crId= spark.read.schema(schema).json(crossrefPath).withColumn("crId", lower(col("DOI"))).distinct.select("crId")
val magPapers = MagUtility.loadMagEntity(spark, "Papers", magBasePath)
.withColumn("Doi", lower(col("Doi")))
.where(col("Doi").isNotNull)
val intersectedPapers:Dataset[Row] =magPapers.join(crId, magPapers("Doi").equalTo(crId("crId")), "leftsemi").dropDuplicates("Doi")
intersectedPapers.cache()
intersectedPapers.count()
//log.info("Create current abstract")
//Abstract is an inverted list, we define a function that convert in string the abstract and recreate
// a table(PaperId, Abstract)
val paperAbstract = MagUtility.loadMagEntity(spark, "PaperAbstractsInvertedIndex", magBasePath)
.map(s => (s.getLong(0),MagUtility.convertInvertedIndexString(s.getString(1))))
.withColumnRenamed("_1","PaperId")
.withColumnRenamed("_2","Abstract")
//We define Step0 as the result of the Join between PaperIntersection and the PaperAbstract
val step0 =intersectedPapers
.join(paperAbstract, intersectedPapers("PaperId") === paperAbstract("PaperId"), "left")
.select(intersectedPapers("*"),paperAbstract("Abstract")).cache()
step0.count()
intersectedPapers.unpersist()
// We have three table Author, Affiliation, and PaperAuthorAffiliation, in the
//next step we create a table containing
val authors = MagUtility.loadMagEntity(spark, "Authors", magBasePath)
val affiliations= MagUtility.loadMagEntity(spark, "Affiliations", magBasePath)
val paaf= MagUtility.loadMagEntity(spark, "PaperAuthorAffiliations", magBasePath)
val paperAuthorAffiliations =paaf.join(step0,paaf("PaperId") === step0("PaperId"),"leftsemi")
val j1 = paperAuthorAffiliations.join(authors,paperAuthorAffiliations("AuthorId") === authors("AuthorId"), "inner")
.select(col("PaperId"), col("AffiliationId"),col("AuthorSequenceNumber"), authors("DisplayName").alias("AuthorName"), authors("AuthorId"))
val paperAuthorAffiliationNormalized = j1.join(affiliations, j1("AffiliationId")=== affiliations("AffiliationId"), "left")
.select(j1("*"), affiliations("DisplayName").alias("AffiliationName"), affiliations("GridId"))
.groupBy("PaperId")
.agg(collect_list(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"),
$"Webpage".as("journalWebpage")
)
step3.join(journals, step3("JournalId")===journals("JournalId"), "left").
select(step3("*"),
journals("journalName"),
journals("journalIssn"),
journals("journalPublisher"),
journals("journalWebpage")
).write.mode("OverWrite")
.save(s"$workingPath/generatedMAG")
step3.unpersist()
}
}

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@ -0,0 +1,222 @@
package eu.dnetlib.dhp.collection.mag
import eu.dnetlib.dhp.application.AbstractScalaApplication
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.sql.{Dataset, Row, SparkSession}
import org.slf4j.{Logger, LoggerFactory}
class SparkCreateMagDenormalizedTable(propertyPath: String, args: Array[String], log: Logger)
extends AbstractScalaApplication(propertyPath, args, log: Logger) {
/** Here all the spark applications runs this method
* where the whole logic of the spark node is defined
*/
override def run(): Unit = {
val crossrefPath: String = parser.get("crossrefPath")
log.info("found parameters crossrefPath: {}", crossrefPath)
val magBasePath: String = parser.get("magBasePath")
log.info("found parameters magBasePath: {}", magBasePath)
val workingPath: String = parser.get("workingPath")
log.info("found parameters workingPath: {}", workingPath)
generatedDenormalizedMAGTable(spark, crossrefPath, magBasePath, workingPath)
}
private def generatedDenormalizedMAGTable(
spark: SparkSession,
crossrefPath: String,
magBasePath: String,
workingPath: String
): Unit = {
import spark.implicits._
val schema: StructType = StructType(StructField("DOI", StringType) :: Nil)
//Filter all the MAG Papers that intersect with a Crossref DOI
val crId =
spark.read.schema(schema).json(crossrefPath).withColumn("crId", lower(col("DOI"))).distinct.select("crId")
val magPapers = MagUtility
.loadMagEntity(spark, "Papers", magBasePath)
.withColumn("Doi", lower(col("Doi")))
.where(col("Doi").isNotNull)
val intersectedPapers: Dataset[Row] =
magPapers.join(crId, magPapers("Doi").equalTo(crId("crId")), "leftsemi").dropDuplicates("Doi")
intersectedPapers.cache()
intersectedPapers.count()
//log.info("Create current abstract")
//Abstract is an inverted list, we define a function that convert in string the abstract and recreate
// a table(PaperId, Abstract)
val paperAbstract = MagUtility
.loadMagEntity(spark, "PaperAbstractsInvertedIndex", magBasePath)
.map(s => (s.getLong(0), MagUtility.convertInvertedIndexString(s.getString(1))))
.withColumnRenamed("_1", "PaperId")
.withColumnRenamed("_2", "Abstract")
//We define Step0 as the result of the Join between PaperIntersection and the PaperAbstract
val step0 = intersectedPapers
.join(paperAbstract, intersectedPapers("PaperId") === paperAbstract("PaperId"), "left")
.select(intersectedPapers("*"), paperAbstract("Abstract"))
.cache()
step0.count()
intersectedPapers.unpersist()
// We have three table Author, Affiliation, and PaperAuthorAffiliation, in the
//next step we create a table containing
val authors = MagUtility.loadMagEntity(spark, "Authors", magBasePath)
val affiliations = MagUtility.loadMagEntity(spark, "Affiliations", magBasePath)
val paaf = MagUtility.loadMagEntity(spark, "PaperAuthorAffiliations", magBasePath)
val paperAuthorAffiliations = paaf.join(step0, paaf("PaperId") === step0("PaperId"), "leftsemi")
val j1 = paperAuthorAffiliations
.join(authors, paperAuthorAffiliations("AuthorId") === authors("AuthorId"), "inner")
.select(
col("PaperId"),
col("AffiliationId"),
col("AuthorSequenceNumber"),
authors("DisplayName").alias("AuthorName"),
authors("AuthorId")
)
val paperAuthorAffiliationNormalized = j1
.join(affiliations, j1("AffiliationId") === affiliations("AffiliationId"), "left")
.select(j1("*"), affiliations("DisplayName").alias("AffiliationName"), affiliations("GridId"))
.groupBy("PaperId")
.agg(
collect_list(
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
)
}
}