forked from D-Net/dnet-hadoop
167 lines
6.6 KiB
Java
167 lines
6.6 KiB
Java
package eu.dnetlib.dhp.transformation;
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import eu.dnetlib.dhp.model.mdstore.MetadataRecord;
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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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import org.apache.commons.cli.*;
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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 java.io.ByteArrayInputStream;
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import java.util.HashMap;
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import java.util.Map;
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public class TransformSparkJobNode {
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public static void main(String[] args) throws Exception {
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for (int i = 0; i < args.length; i++) {
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System.out.println(args[i]);
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}
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Options options = new Options();
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options.addOption(Option.builder("mt")
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.longOpt("master")
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.required(true)
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.desc("should be local or yarn")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("d")
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.longOpt("dateOfCollection")
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.required(true)
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.desc("the date of collection")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("i")
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.longOpt("input")
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.required(true)
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.desc("input path of the sequence file")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("o")
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.longOpt("output")
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.required(true)
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.desc("output path of the mdstore")
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.hasArg()
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.build());
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options.addOption(Option.builder("w")
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.longOpt("workflowId")
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.required(true)
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.desc("the identifier of the dnet Workflow")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("tr")
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.longOpt("transformationRule")
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.required(true)
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.desc("the transformation Rule to apply to the input MDStore")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("ru")
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.longOpt("rabbitUser")
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.required(false)
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.desc("the user to connect with RabbitMq for messaging")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("rp")
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.longOpt("rabbitPassWord")
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.required(false)
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.desc("the password to connect with RabbitMq for messaging")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("rh")
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.longOpt("rabbitHost")
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.required(false)
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.desc("the host of the RabbitMq server")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("ro")
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.longOpt("rabbitOngoingQueue")
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.required(false)
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.desc("the name of the ongoing queue")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("rr")
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.longOpt("rabbitReportQueue")
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.required(false)
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.desc("the name of the report queue")
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.hasArg() // This option has an argument.
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.build());
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final CommandLineParser parser = new DefaultParser();
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final CommandLine cmd = parser.parse( options, args);
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final String inputPath = cmd.getOptionValue("i");
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final String outputPath = cmd.getOptionValue("o");
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final String workflowId = cmd.getOptionValue("w");
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final String trasformationRule = extractXSLTFromTR(DHPUtils.decompressString(cmd.getOptionValue("tr")));
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final String master = cmd.getOptionValue("mt");
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final String rabbitUser = cmd.getOptionValue("ru");
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final String rabbitPassword = cmd.getOptionValue("rp");
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final String rabbitHost = cmd.getOptionValue("rh");
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final String rabbitReportQueue = cmd.getOptionValue("rr");
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final long dateOfCollection = new Long(cmd.getOptionValue("d"));
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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 TransformFunction transformFunction = new TransformFunction(totalItems, errorItems, transformedItems, trasformationRule, dateOfCollection) ;
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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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final MessageManager manager = new MessageManager(rabbitHost, rabbitUser, rabbitPassword, false, false, null);
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System.out.println(new Message(workflowId, "Transform", MessageType.REPORT, reportMap));
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manager.sendMessage(new Message(workflowId, "Transform", MessageType.REPORT, reportMap), rabbitReportQueue, true, false);
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manager.close();
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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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} |