forked from D-Net/dnet-hadoop
120 lines
3.5 KiB
Java
120 lines
3.5 KiB
Java
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package eu.dnetlib.dhp.ircdl_extention;
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import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Optional;
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import java.util.stream.Collectors;
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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.api.java.function.FilterFunction;
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import org.apache.spark.api.java.function.FlatMapFunction;
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import org.apache.spark.sql.Dataset;
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import org.apache.spark.sql.Encoders;
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import org.apache.spark.sql.SaveMode;
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import org.apache.spark.sql.SparkSession;
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import eu.dnetlib.dhp.application.ArgumentApplicationParser;
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import eu.dnetlib.dhp.ircdl_extention.model.KeyValue;
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import eu.dnetlib.dhp.ircdl_extention.model.Result;
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import eu.dnetlib.dhp.schema.common.ModelConstants;
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public class PrepareResultSpark {
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public static void main(String[] args) throws Exception {
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String jsonConfiguration = IOUtils
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.toString(
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PrepareResultSpark.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/ircdl_extention/prepare_result_parameters.json"));
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration);
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parser.parseArgument(args);
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final String resultClassName = parser.get("resultClass");
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Class<? extends eu.dnetlib.dhp.schema.oaf.Result> resultClazz = (Class<? extends eu.dnetlib.dhp.schema.oaf.Result>) Class
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.forName(resultClassName);
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Boolean isSparkSessionManaged = Optional
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.ofNullable(parser.get("isSparkSessionManaged"))
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.map(Boolean::valueOf)
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.orElse(Boolean.TRUE);
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final String inputPath = parser.get("inputPath");
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final String outputPath = parser.get("outputPath");
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SparkConf conf = new SparkConf();
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runWithSparkSession(
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conf,
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isSparkSessionManaged,
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spark -> {
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Utils.removeOutputDir(spark, outputPath);
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mapToResult(spark, inputPath, resultClazz, outputPath);
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});
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}
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private static <R extends eu.dnetlib.dhp.schema.oaf.Result> void mapToResult(SparkSession spark,
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String input_path,
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Class<R> resultClazz, String output_path) {
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Dataset<R> publicationDataset = Utils.readPath(spark, input_path, resultClazz);
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Dataset<Result> result = publicationDataset.filter((FilterFunction<R>) p -> {
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if (p.getAuthor() == null)
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return false;
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if (p.getAuthor().size() == 0)
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return false;
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return true;
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})
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.flatMap((FlatMapFunction<R, Result>) p -> {
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List<Result> reslist = new ArrayList<>();
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p.getAuthor().forEach(a -> {
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a.getPid().forEach(apid -> {
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if (apid.getQualifier().getClassid().equals(ModelConstants.ORCID)
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|| apid.getQualifier().getClassid().equals(ModelConstants.ORCID_PENDING)) {
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Result r = new Result();
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r.setDeletedbyinference(p.getDataInfo().getDeletedbyinference());
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r.setId(p.getId());
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r
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.setCf(
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p
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.getCollectedfrom()
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.stream()
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.map(cf -> KeyValue.newInstance(cf.getKey(), cf.getValue()))
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.collect(Collectors.toList()));
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r
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.setPid(
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p
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.getPid()
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.stream()
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.map(
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pid -> KeyValue
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.newInstance(pid.getQualifier().getClassid(), pid.getValue()))
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.collect(Collectors.toList()));
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r.setName(a.getName());
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r.setSurname(a.getSurname());
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r.setFullname(a.getFullname());
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r.setOid(apid.getValue());
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reslist.add(r);
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}
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});
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});
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return reslist.iterator();
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}, Encoders.bean(Result.class));
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result
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.write()
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.option("compressio", "gzip")
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.mode(SaveMode.Overwrite)
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.json(output_path);
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}
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}
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