serialise records in the OAF-store-graph mdstores in json format. Read them again in the graph construction phase using a tolerant parser to support backward compatible changes in the evolution of the schema
This commit is contained in:
parent
f0b523cfa7
commit
9458ee7938
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@ -1,7 +1,9 @@
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package eu.dnetlib.dhp.collection
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import com.fasterxml.jackson.databind.ObjectMapper
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import eu.dnetlib.dhp.schema.common.ModelSupport
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import eu.dnetlib.dhp.schema.oaf.{Oaf, OafEntity, Relation}
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import org.apache.spark.sql.{Dataset, Encoder, Encoders, SaveMode}
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object CollectionUtils {
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@ -46,4 +48,15 @@ object CollectionUtils {
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List()
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}
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def saveDataset(d: Dataset[Oaf], targetPath: String):Unit = {
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implicit val resultEncoder: Encoder[Oaf] = Encoders.kryo(classOf[Oaf])
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val mapper = new ObjectMapper
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d
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.flatMap(i => CollectionUtils.fixRelations(i))
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.filter(i => i != null)
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.map(r => mapper.writeValueAsString(r))(Encoders.STRING)
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.write.mode(SaveMode.Overwrite).save(targetPath)
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}
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}
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@ -2,14 +2,14 @@ package eu.dnetlib.dhp.datacite
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import com.fasterxml.jackson.databind.ObjectMapper
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import eu.dnetlib.dhp.application.AbstractScalaApplication
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import eu.dnetlib.dhp.collection.CollectionUtils.fixRelations
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import eu.dnetlib.dhp.collection.CollectionUtils
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import eu.dnetlib.dhp.common.Constants.{MDSTORE_DATA_PATH, MDSTORE_SIZE_PATH}
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import eu.dnetlib.dhp.common.vocabulary.VocabularyGroup
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import eu.dnetlib.dhp.schema.mdstore.{MDStoreVersion, MetadataRecord}
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import eu.dnetlib.dhp.schema.oaf.Oaf
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import eu.dnetlib.dhp.utils.DHPUtils.writeHdfsFile
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import eu.dnetlib.dhp.utils.ISLookupClientFactory
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import org.apache.spark.sql.{Encoder, Encoders, SaveMode, SparkSession}
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import org.apache.spark.sql.{Encoder, Encoders, SparkSession}
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import org.slf4j.{Logger, LoggerFactory}
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@ -73,12 +73,12 @@ class GenerateDataciteDatasetSpark (propertyPath:String, args:Array[String], log
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implicit val mrEncoder: Encoder[MetadataRecord] = Encoders.kryo[MetadataRecord]
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implicit val resEncoder: Encoder[Oaf] = Encoders.kryo[Oaf]
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CollectionUtils.saveDataset(
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spark.read.load(sourcePath).as[DataciteType]
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.filter(d => d.isActive)
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.flatMap(d => DataciteToOAFTransformation.generateOAF(d.json, d.timestamp, d.timestamp, vocabularies, exportLinks))
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.filter(d => d != null)
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.flatMap(i => fixRelations(i)).filter(i => i != null)
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.write.mode(SaveMode.Overwrite).save(targetPath)
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.filter(d => d != null),
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targetPath)
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}
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}
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@ -1,12 +1,12 @@
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package eu.dnetlib.dhp.sx.bio
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.schema.oaf.Oaf
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import BioDBToOAF.ScholixResolved
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import eu.dnetlib.dhp.collection.CollectionUtils
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import eu.dnetlib.dhp.schema.oaf.Oaf
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import eu.dnetlib.dhp.sx.bio.BioDBToOAF.ScholixResolved
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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.{Encoder, Encoders, SaveMode, SparkSession}
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import org.apache.spark.sql.{Encoder, Encoders, SparkSession}
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import org.slf4j.{Logger, LoggerFactory}
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object SparkTransformBioDatabaseToOAF {
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@ -36,13 +36,13 @@ object SparkTransformBioDatabaseToOAF {
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import spark.implicits._
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database.toUpperCase() match {
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case "UNIPROT" =>
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spark.createDataset(sc.textFile(dbPath).flatMap(i => BioDBToOAF.uniprotToOAF(i))).flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null).write.mode(SaveMode.Overwrite).save(targetPath)
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CollectionUtils.saveDataset(spark.createDataset(sc.textFile(dbPath).flatMap(i => BioDBToOAF.uniprotToOAF(i))), targetPath)
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case "PDB" =>
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spark.createDataset(sc.textFile(dbPath).flatMap(i => BioDBToOAF.pdbTOOaf(i))).flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null).write.mode(SaveMode.Overwrite).save(targetPath)
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CollectionUtils.saveDataset(spark.createDataset(sc.textFile(dbPath).flatMap(i => BioDBToOAF.pdbTOOaf(i))), targetPath)
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case "SCHOLIX" =>
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spark.read.load(dbPath).as[ScholixResolved].map(i => BioDBToOAF.scholixResolvedToOAF(i)).flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null).write.mode(SaveMode.Overwrite).save(targetPath)
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CollectionUtils.saveDataset(spark.read.load(dbPath).as[ScholixResolved].map(i => BioDBToOAF.scholixResolvedToOAF(i)), targetPath)
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case "CROSSREF_LINKS" =>
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spark.createDataset(sc.textFile(dbPath).map(i => BioDBToOAF.crossrefLinksToOaf(i))).flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null).write.mode(SaveMode.Overwrite).save(targetPath)
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CollectionUtils.saveDataset(spark.createDataset(sc.textFile(dbPath).map(i => BioDBToOAF.crossrefLinksToOaf(i))), targetPath)
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}
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}
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@ -1,9 +1,10 @@
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package eu.dnetlib.dhp.sx.bio.ebi
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.collection.CollectionUtils
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import eu.dnetlib.dhp.common.vocabulary.VocabularyGroup
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import eu.dnetlib.dhp.schema.oaf.Result
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import eu.dnetlib.dhp.sx.bio.pubmed.{PMArticle, PMAuthor, PMJournal, PMParser, PubMedToOaf}
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import eu.dnetlib.dhp.schema.oaf.{Oaf, Result}
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import eu.dnetlib.dhp.sx.bio.pubmed._
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import eu.dnetlib.dhp.utils.ISLookupClientFactory
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import org.apache.commons.io.IOUtils
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import org.apache.hadoop.conf.Configuration
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@ -177,7 +178,7 @@ object SparkCreateBaselineDataFrame {
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implicit val PMEncoder: Encoder[PMArticle] = Encoders.kryo(classOf[PMArticle])
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implicit val PMJEncoder: Encoder[PMJournal] = Encoders.kryo(classOf[PMJournal])
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implicit val PMAEncoder: Encoder[PMAuthor] = Encoders.kryo(classOf[PMAuthor])
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implicit val resultEncoder: Encoder[Result] = Encoders.kryo(classOf[Result])
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implicit val resultEncoder: Encoder[Oaf] = Encoders.kryo(classOf[Oaf])
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if (!"true".equalsIgnoreCase(skipUpdate)) {
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downloadBaseLineUpdate(s"$workingPath/baseline", hdfsServerUri)
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@ -192,9 +193,10 @@ object SparkCreateBaselineDataFrame {
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}
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val exported_dataset = spark.read.load(s"$workingPath/baseline_dataset").as[PMArticle]
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exported_dataset
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.map(a => PubMedToOaf.convert(a, vocabularies)).as[Result]
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.filter(p => p != null)
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.write.mode(SaveMode.Overwrite).save(targetPath)
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CollectionUtils.saveDataset(exported_dataset
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.map(a => PubMedToOaf.convert(a, vocabularies)).as[Oaf]
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.filter(p => p != null),
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targetPath)
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}
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}
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@ -1,11 +1,10 @@
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package eu.dnetlib.dhp.sx.bio.ebi
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.collection.CollectionUtils
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import eu.dnetlib.dhp.schema.oaf.Oaf
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import eu.dnetlib.dhp.sx.bio.BioDBToOAF
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import eu.dnetlib.dhp.sx.bio.BioDBToOAF.EBILinkItem
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import BioDBToOAF.EBILinkItem
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import eu.dnetlib.dhp.collection.CollectionUtils
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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._
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@ -35,10 +34,9 @@ object SparkEBILinksToOaf {
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val ebLinks: Dataset[EBILinkItem] = spark.read.load(sourcePath).as[EBILinkItem].filter(l => l.links != null && l.links.startsWith("{"))
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ebLinks.flatMap(j => BioDBToOAF.parse_ebi_links(j.links))
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CollectionUtils.saveDataset(ebLinks.flatMap(j => BioDBToOAF.parse_ebi_links(j.links))
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.filter(p => BioDBToOAF.EBITargetLinksFilter(p))
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.flatMap(p => BioDBToOAF.convertEBILinksToOaf(p))
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.flatMap(i=> CollectionUtils.fixRelations(i)).filter(i => i != null)
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.write.mode(SaveMode.Overwrite).save(targetPath)
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.flatMap(p => BioDBToOAF.convertEBILinksToOaf(p)),
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targetPath)
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}
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}
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@ -111,7 +111,7 @@ object PubMedToOaf {
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* @param vocabularies the vocabularies
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* @return The OAF instance if the mapping did not fail
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*/
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def convert(article: PMArticle, vocabularies: VocabularyGroup): Result = {
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def convert(article: PMArticle, vocabularies: VocabularyGroup): Oaf = {
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if (article.getPublicationTypes == null)
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return null
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@ -1,18 +1,15 @@
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package eu.dnetlib.dhp.oa.graph.raw
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import com.fasterxml.jackson.databind.ObjectMapper
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import com.fasterxml.jackson.databind.{DeserializationFeature, ObjectMapper}
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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import eu.dnetlib.dhp.common.HdfsSupport
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import eu.dnetlib.dhp.schema.common.ModelSupport
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import eu.dnetlib.dhp.schema.mdstore.MDStoreWithInfo
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import eu.dnetlib.dhp.schema.oaf.Oaf
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import eu.dnetlib.dhp.utils.DHPUtils
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import org.apache.commons.io.IOUtils
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import org.apache.commons.lang3.StringUtils
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import org.apache.http.client.methods.HttpGet
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import org.apache.http.impl.client.HttpClients
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import org.apache.spark.sql.{Encoder, Encoders, SaveMode, SparkSession}
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import org.apache.spark.{SparkConf, SparkContext}
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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.LoggerFactory
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import scala.collection.JavaConverters._
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@ -51,18 +48,21 @@ object CopyHdfsOafSparkApplication {
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log.info("hdfsPath: {}", hdfsPath)
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implicit val oafEncoder: Encoder[Oaf] = Encoders.kryo[Oaf]
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import spark.implicits._
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val paths = DHPUtils.mdstorePaths(mdstoreManagerUrl, mdFormat, mdLayout, mdInterpretation, true).asScala
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val validPaths: List[String] = paths.filter(p => HdfsSupport.exists(p, sc.hadoopConfiguration)).toList
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if (validPaths.nonEmpty) {
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val oaf = spark.read.load(validPaths: _*).as[Oaf]
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val mapper = new ObjectMapper()
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val l =ModelSupport.oafTypes.entrySet.asScala.map(e => e.getKey).toList
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val oaf = spark.read.load(validPaths: _*).as[String]
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val mapper = new ObjectMapper().configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false)
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val l = ModelSupport.oafTypes.entrySet.asScala.toList
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l.foreach(
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e =>
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oaf.filter(o => o.getClass.getSimpleName.equalsIgnoreCase(e))
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oaf
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.filter(o => isOafType(o, e.getKey))
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.map(j => mapper.readValue(j, e.getValue).asInstanceOf[Oaf])
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.map(s => mapper.writeValueAsString(s))(Encoders.STRING)
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.write
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.option("compression", "gzip")
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@ -71,4 +71,20 @@ object CopyHdfsOafSparkApplication {
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)
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}
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}
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def isOafType(input: String, oafType: String): Boolean = {
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implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
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lazy val json: org.json4s.JValue = parse(input)
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if (oafType == "relation") {
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val hasSource = (json \ "source").extractOrElse[String](null)
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val hasTarget = (json \ "target").extractOrElse[String](null)
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hasSource != null && hasTarget != null
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} else {
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val hasId = (json \ "id").extractOrElse[String](null)
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val resultType = (json \ "resulttype" \ "classid").extractOrElse[String](null)
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hasId != null && oafType.equalsIgnoreCase(resultType)
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}
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}
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}
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