105 lines
4.0 KiB
Java
105 lines
4.0 KiB
Java
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package eu.dnetlib.dhp.transformation;
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import java.io.ByteArrayInputStream;
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import java.util.HashMap;
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import java.util.Map;
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import java.util.Objects;
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import org.apache.commons.cli.*;
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import org.apache.commons.io.IOUtils;
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import org.apache.spark.sql.Dataset;
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import org.apache.spark.sql.Encoder;
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import org.apache.spark.sql.Encoders;
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import org.apache.spark.sql.SparkSession;
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import org.apache.spark.util.LongAccumulator;
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import org.dom4j.Document;
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import org.dom4j.DocumentException;
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import org.dom4j.Node;
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import org.dom4j.io.SAXReader;
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import eu.dnetlib.dhp.application.ArgumentApplicationParser;
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import eu.dnetlib.dhp.model.mdstore.MetadataRecord;
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import eu.dnetlib.dhp.transformation.vocabulary.Vocabulary;
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import eu.dnetlib.dhp.transformation.vocabulary.VocabularyHelper;
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import eu.dnetlib.dhp.utils.DHPUtils;
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import eu.dnetlib.message.Message;
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import eu.dnetlib.message.MessageManager;
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import eu.dnetlib.message.MessageType;
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public class TransformSparkJobNode {
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public static void main(String[] args) throws Exception {
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(
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IOUtils
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.toString(
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TransformSparkJobNode.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/transformation/transformation_input_parameters.json")));
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parser.parseArgument(args);
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final String inputPath = parser.get("input");
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final String outputPath = parser.get("output");
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final String workflowId = parser.get("workflowId");
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final String trasformationRule = extractXSLTFromTR(
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Objects.requireNonNull(DHPUtils.decompressString(parser.get("transformationRule"))));
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final String master = parser.get("master");
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final String rabbitUser = parser.get("rabbitUser");
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final String rabbitPassword = parser.get("rabbitPassword");
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final String rabbitHost = parser.get("rabbitHost");
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final String rabbitReportQueue = parser.get("rabbitReportQueue");
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final long dateOfCollection = new Long(parser.get("dateOfCollection"));
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final boolean test = parser.get("isTest") == null ? false : Boolean.valueOf(parser.get("isTest"));
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final SparkSession spark = SparkSession
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.builder()
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.appName("TransformStoreSparkJob")
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.master(master)
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.getOrCreate();
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final Encoder<MetadataRecord> encoder = Encoders.bean(MetadataRecord.class);
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final Dataset<MetadataRecord> mdstoreInput = spark.read().format("parquet").load(inputPath).as(encoder);
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final LongAccumulator totalItems = spark.sparkContext().longAccumulator("TotalItems");
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final LongAccumulator errorItems = spark.sparkContext().longAccumulator("errorItems");
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final LongAccumulator transformedItems = spark.sparkContext().longAccumulator("transformedItems");
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final Map<String, Vocabulary> vocabularies = new HashMap<>();
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vocabularies.put("dnet:languages", VocabularyHelper.getVocabularyFromAPI("dnet:languages"));
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final TransformFunction transformFunction = new TransformFunction(
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totalItems,
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errorItems,
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transformedItems,
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trasformationRule,
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dateOfCollection,
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vocabularies);
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mdstoreInput.map(transformFunction, encoder).write().format("parquet").save(outputPath);
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if (rabbitHost != null) {
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System.out.println("SEND FINAL REPORT");
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final Map<String, String> reportMap = new HashMap<>();
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reportMap.put("inputItem", "" + totalItems.value());
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reportMap.put("invalidRecords", "" + errorItems.value());
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reportMap.put("mdStoreSize", "" + transformedItems.value());
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System.out.println(new Message(workflowId, "Transform", MessageType.REPORT, reportMap));
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if (!test) {
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final MessageManager manager = new MessageManager(rabbitHost, rabbitUser, rabbitPassword, false, false,
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null);
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manager
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.sendMessage(
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new Message(workflowId, "Transform", MessageType.REPORT, reportMap),
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rabbitReportQueue,
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true,
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false);
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manager.close();
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}
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}
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}
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private static String extractXSLTFromTR(final String tr) throws DocumentException {
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SAXReader reader = new SAXReader();
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Document document = reader.read(new ByteArrayInputStream(tr.getBytes()));
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Node node = document.selectSingleNode("//CODE/*[local-name()='stylesheet']");
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return node.asXML();
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}
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}
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