forked from D-Net/dnet-hadoop
207 lines
7.0 KiB
Scala
207 lines
7.0 KiB
Scala
package eu.dnetlib.dhp.sx.graph.ebi
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import eu.dnetlib.dhp.application.ArgumentApplicationParser
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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.graph.bio.pubmed.{PMArticle, PMAuthor, PMJournal, PMParser, PubMedToOaf}
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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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import org.apache.hadoop.fs.{FSDataOutputStream, FileSystem, Path}
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import org.apache.http.client.config.RequestConfig
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import org.apache.http.client.methods.HttpGet
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import org.apache.http.impl.client.HttpClientBuilder
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import org.apache.spark.SparkConf
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import org.apache.spark.rdd.RDD
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import org.apache.spark.sql.expressions.Aggregator
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import org.apache.spark.sql._
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import org.slf4j.{Logger, LoggerFactory}
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import java.io.InputStream
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import scala.io.Source
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import scala.xml.pull.XMLEventReader
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object SparkCreateBaselineDataFrame {
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def requestBaseLineUpdatePage(maxFile:String):List[(String,String)] = {
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val data =requestPage("https://ftp.ncbi.nlm.nih.gov/pubmed/updatefiles/")
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val result =data.lines.filter(l => l.startsWith("<a href=")).map{l =>
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val end = l.lastIndexOf("\">")
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val start = l.indexOf("<a href=\"")
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if (start>= 0 && end >start)
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l.substring(start+9, (end-start))
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else
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""
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}.filter(s =>s.endsWith(".gz") ).filter(s => s > maxFile).map(s => (s,s"https://ftp.ncbi.nlm.nih.gov/pubmed/updatefiles/$s")).toList
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result
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}
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def downloadBaselinePart(url:String):InputStream = {
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val r = new HttpGet(url)
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val timeout = 60; // seconds
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val config = RequestConfig.custom()
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.setConnectTimeout(timeout * 1000)
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.setConnectionRequestTimeout(timeout * 1000)
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.setSocketTimeout(timeout * 1000).build()
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val client = HttpClientBuilder.create().setDefaultRequestConfig(config).build()
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val response = client.execute(r)
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println(s"get response with status${response.getStatusLine.getStatusCode}")
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response.getEntity.getContent
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}
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def requestPage(url:String):String = {
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val r = new HttpGet(url)
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val timeout = 60; // seconds
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val config = RequestConfig.custom()
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.setConnectTimeout(timeout * 1000)
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.setConnectionRequestTimeout(timeout * 1000)
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.setSocketTimeout(timeout * 1000).build()
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val client = HttpClientBuilder.create().setDefaultRequestConfig(config).build()
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try {
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var tries = 4
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while (tries > 0) {
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println(s"requesting ${r.getURI}")
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try {
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val response = client.execute(r)
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println(s"get response with status${response.getStatusLine.getStatusCode}")
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if (response.getStatusLine.getStatusCode > 400) {
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tries -= 1
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}
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else
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return IOUtils.toString(response.getEntity.getContent)
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} catch {
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case e: Throwable =>
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println(s"Error on requesting ${r.getURI}")
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e.printStackTrace()
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tries -= 1
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}
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}
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""
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} finally {
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if (client != null)
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client.close()
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}
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}
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def downloadBaseLineUpdate(baselinePath:String, hdfsServerUri:String ):Unit = {
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val conf = new Configuration
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conf.set("fs.defaultFS", hdfsServerUri)
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val fs = FileSystem.get(conf)
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val p = new Path(baselinePath)
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val files = fs.listFiles(p,false)
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var max_file = ""
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while (files.hasNext) {
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val c = files.next()
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val data = c.getPath.toString
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val fileName = data.substring(data.lastIndexOf("/")+1)
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if (fileName> max_file)
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max_file = fileName
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}
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val files_to_download = requestBaseLineUpdatePage(max_file)
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files_to_download.foreach { u =>
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val hdfsWritePath: Path = new Path(s"$baselinePath/${u._1}")
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val fsDataOutputStream: FSDataOutputStream = fs.create(hdfsWritePath, true)
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val i = downloadBaselinePart(u._2)
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val buffer = Array.fill[Byte](1024)(0)
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while(i.read(buffer)>0) {
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fsDataOutputStream.write(buffer)
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}
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i.close()
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println(s"Downloaded ${u._2} into $baselinePath/${u._1}")
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fsDataOutputStream.close()
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}
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}
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val pmArticleAggregator: Aggregator[(String, PMArticle), PMArticle, PMArticle] = new Aggregator[(String, PMArticle), PMArticle, PMArticle] with Serializable {
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override def zero: PMArticle = new PMArticle
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override def reduce(b: PMArticle, a: (String, PMArticle)): PMArticle = {
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if (b != null && b.getPmid!= null) b else a._2
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}
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override def merge(b1: PMArticle, b2: PMArticle): PMArticle = {
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if (b1 != null && b1.getPmid!= null) b1 else b2
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}
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override def finish(reduction: PMArticle): PMArticle = reduction
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override def bufferEncoder: Encoder[PMArticle] = Encoders.kryo[PMArticle]
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override def outputEncoder: Encoder[PMArticle] = Encoders.kryo[PMArticle]
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}
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def main(args: Array[String]): Unit = {
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val conf: SparkConf = new SparkConf()
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val log: Logger = LoggerFactory.getLogger(getClass)
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val parser = new ArgumentApplicationParser(IOUtils.toString(SparkEBILinksToOaf.getClass.getResourceAsStream("/eu/dnetlib/dhp/sx/graph/ebi/baseline_to_oaf_params.json")))
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parser.parseArgument(args)
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val isLookupUrl: String = parser.get("isLookupUrl")
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log.info("isLookupUrl: {}", isLookupUrl)
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val workingPath = parser.get("workingPath")
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log.info("workingPath: {}", workingPath)
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val targetPath = parser.get("targetPath")
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log.info("targetPath: {}", targetPath)
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val hdfsServerUri = parser.get("hdfsServerUri")
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log.info("hdfsServerUri: {}", targetPath)
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val isLookupService = ISLookupClientFactory.getLookUpService(isLookupUrl)
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val vocabularies = VocabularyGroup.loadVocsFromIS(isLookupService)
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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(SparkEBILinksToOaf.getClass.getSimpleName)
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.master(parser.get("master")).getOrCreate()
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import spark.implicits._
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val sc = spark.sparkContext
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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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downloadBaseLineUpdate(s"$workingPath/baseline", hdfsServerUri)
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val k: RDD[(String, String)] = sc.wholeTextFiles(s"$workingPath/baseline",2000)
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val ds:Dataset[PMArticle] = spark.createDataset(k.filter(i => i._1.endsWith(".gz")).flatMap(i =>{
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val xml = new XMLEventReader(Source.fromBytes(i._2.getBytes()))
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new PMParser(xml)
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} ))
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ds.map(p => (p.getPmid,p))(Encoders.tuple(Encoders.STRING, PMEncoder)).groupByKey(_._1)
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.agg(pmArticleAggregator.toColumn)
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.map(p => p._2).write.mode(SaveMode.Overwrite).save(s"$workingPath/baseline_dataset")
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val exported_dataset = spark.read.load(s"$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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}
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}
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