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code formatting
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@ -1,11 +1,11 @@
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package eu.dnetlib.dhp.oa.graph.merge;
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package eu.dnetlib.dhp.oa.graph.merge;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
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import eu.dnetlib.dhp.application.ArgumentApplicationParser;
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import eu.dnetlib.dhp.common.HdfsSupport;
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import java.util.Objects;
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import eu.dnetlib.dhp.oa.graph.clean.CleanGraphSparkJob;
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import java.util.Optional;
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import eu.dnetlib.dhp.schema.common.ModelSupport;
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import eu.dnetlib.dhp.schema.oaf.*;
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import org.apache.commons.io.IOUtils;
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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.SparkConf;
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import org.apache.spark.api.java.function.FilterFunction;
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import org.apache.spark.api.java.function.FilterFunction;
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@ -16,13 +16,16 @@ import org.apache.spark.sql.SaveMode;
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import org.apache.spark.sql.SparkSession;
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import org.apache.spark.sql.SparkSession;
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import org.slf4j.Logger;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.slf4j.LoggerFactory;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import eu.dnetlib.dhp.application.ArgumentApplicationParser;
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import eu.dnetlib.dhp.common.HdfsSupport;
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import eu.dnetlib.dhp.oa.graph.clean.CleanGraphSparkJob;
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import eu.dnetlib.dhp.schema.common.ModelSupport;
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import eu.dnetlib.dhp.schema.oaf.*;
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import scala.Tuple2;
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import scala.Tuple2;
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import java.util.Objects;
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import java.util.Optional;
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import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
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/**
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/**
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* Combines the content from two aggregator graph tables of the same type, entities (or relationships) with the same ids
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* Combines the content from two aggregator graph tables of the same type, entities (or relationships) with the same ids
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* are picked preferring those from the BETA aggregator rather then from PROD. The identity of a relationship is defined
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* are picked preferring those from the BETA aggregator rather then from PROD. The identity of a relationship is defined
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@ -88,7 +91,8 @@ public class MergeGraphSparkJob {
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Dataset<Tuple2<String, B>> beta = readTableFromPath(spark, betaInputPath, b_clazz);
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Dataset<Tuple2<String, B>> beta = readTableFromPath(spark, betaInputPath, b_clazz);
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Dataset<Tuple2<String, P>> prod = readTableFromPath(spark, prodInputPath, p_clazz);
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Dataset<Tuple2<String, P>> prod = readTableFromPath(spark, prodInputPath, p_clazz);
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prod.joinWith(beta, prod.col("_1").equalTo(beta.col("_1")), "full_outer")
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prod
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.joinWith(beta, prod.col("_1").equalTo(beta.col("_1")), "full_outer")
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.map((MapFunction<Tuple2<Tuple2<String, P>, Tuple2<String, B>>, P>) value -> {
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.map((MapFunction<Tuple2<Tuple2<String, P>, Tuple2<String, B>>, P>) value -> {
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Optional<P> p = Optional.ofNullable(value._1()).map(Tuple2::_2);
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Optional<P> p = Optional.ofNullable(value._1()).map(Tuple2::_2);
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Optional<B> b = Optional.ofNullable(value._2()).map(Tuple2::_2);
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Optional<B> b = Optional.ofNullable(value._2()).map(Tuple2::_2);
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