forked from D-Net/dnet-hadoop
140 lines
5.5 KiB
Java
140 lines
5.5 KiB
Java
package eu.dnetlib.dhp.collection;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import eu.dnetlib.dhp.model.mdstore.MetadataRecord;
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import eu.dnetlib.dhp.model.mdstore.Provenance;
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import org.apache.commons.cli.*;
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import org.apache.commons.lang3.StringUtils;
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import org.apache.hadoop.io.IntWritable;
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import org.apache.hadoop.io.Text;
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import org.apache.spark.api.java.JavaPairRDD;
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import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.api.java.JavaSparkContext;
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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.Node;
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import org.dom4j.io.SAXReader;
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import java.io.ByteArrayInputStream;
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import java.nio.charset.StandardCharsets;
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import java.util.Objects;
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public class GenerateNativeStoreSparkJob {
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public static MetadataRecord parseRecord (final String input, final String xpath, final String encoding, final Provenance provenance, final Long dateOfCollection, final LongAccumulator totalItems, final LongAccumulator invalidRecords) {
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if(totalItems != null)
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totalItems.add(1);
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try {
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SAXReader reader = new SAXReader();
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Document document = reader.read(new ByteArrayInputStream(input.getBytes(StandardCharsets.UTF_8)));
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Node node = document.selectSingleNode(xpath);
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final String originalIdentifier = node.getText();
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if (StringUtils.isBlank(originalIdentifier)) {
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if (invalidRecords!= null)
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invalidRecords.add(1);
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return null;
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}
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return new MetadataRecord(originalIdentifier, encoding, provenance, input, dateOfCollection);
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} catch (Throwable e) {
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if (invalidRecords!= null)
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invalidRecords.add(1);
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e.printStackTrace();
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return null;
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}
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}
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public static void main(String[] args) throws Exception {
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Options options = new Options();
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options.addOption(Option.builder("e")
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.longOpt("encoding")
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.required(true)
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.desc("the encoding type should be xml or json")
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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("p")
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.longOpt("provenance")
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.required(true)
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.desc("the json Provenance information")
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.hasArg() // This option has an argument.
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.build());
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options.addOption(Option.builder("x")
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.longOpt("xpath")
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.required(true)
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.desc("xpath of the identifier")
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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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CommandLineParser parser = new DefaultParser();
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CommandLine cmd = parser.parse( options, args);
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final String encoding = cmd.getOptionValue("e");
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final long dateOfCollection = new Long(cmd.getOptionValue("d"));
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final String jsonProvenance = cmd.getOptionValue("p");
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final ObjectMapper jsonMapper = new ObjectMapper();
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final Provenance provenance = jsonMapper.readValue(jsonProvenance, Provenance.class);
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final String xpath = cmd.getOptionValue("x");
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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 SparkSession spark = SparkSession
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.builder()
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.appName("GenerateNativeStoreSparkJob")
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.master("yarn")
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.getOrCreate();
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final JavaSparkContext sc = new JavaSparkContext(spark.sparkContext());
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final JavaPairRDD<IntWritable, Text> inputRDD = sc.sequenceFile(inputPath, IntWritable.class, Text.class);
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final LongAccumulator totalItems = sc.sc().longAccumulator("TotalItems");
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final LongAccumulator invalidRecords = sc.sc().longAccumulator("InvalidRecords");
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final JavaRDD<MetadataRecord> mappeRDD = inputRDD.map(item -> parseRecord(item._2().toString(), xpath, encoding, provenance, dateOfCollection, totalItems, invalidRecords))
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.filter(Objects::nonNull).distinct();
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final Encoder<MetadataRecord> encoder = Encoders.bean(MetadataRecord.class);
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final Dataset<MetadataRecord> mdstore = spark.createDataset(mappeRDD.rdd(), encoder);
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final LongAccumulator mdStoreRecords = sc.sc().longAccumulator("MDStoreRecords");
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mdStoreRecords.add(mdstore.count());
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System.out.println("totalItems.value() = " + totalItems.value());
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System.out.println("invalidRecords = " + invalidRecords.value());
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System.out.println("mdstoreRecords.value() = " + mdStoreRecords.value());
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mdstore.write().format("parquet").save(outputPath);
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}
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}
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