288 lines
8.7 KiB
Java
288 lines
8.7 KiB
Java
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package eu.dnetlib.dhp.oa.graph.merge;
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import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
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import java.util.*;
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import java.util.stream.Collectors;
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import java.util.stream.Stream;
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import eu.dnetlib.dhp.schema.oaf.utils.MergeUtils;
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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.MapFunction;
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import org.apache.spark.sql.*;
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import org.apache.spark.sql.Dataset;
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import org.apache.spark.sql.expressions.Aggregator;
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import org.slf4j.Logger;
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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.schema.common.ModelConstants;
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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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/**
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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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* by eu.dnetlib.dhp.schema.common.ModelSupport#idFn()
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*/
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public class MergeGraphTableSparkJob {
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private static final Logger log = LoggerFactory.getLogger(MergeGraphTableSparkJob.class);
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private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
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private static final String PRIORITY_DEFAULT = "BETA"; // BETA | PROD
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private static final Datasource DATASOURCE = new Datasource();
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static {
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Qualifier compatibility = new Qualifier();
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compatibility.setClassid(ModelConstants.UNKNOWN);
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DATASOURCE.setOpenairecompatibility(compatibility);
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}
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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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Objects
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.requireNonNull(
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MergeGraphTableSparkJob.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/oa/graph/merge_graphs_parameters.json")));
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration);
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parser.parseArgument(args);
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String priority = Optional
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.ofNullable(parser.get("priority"))
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.orElse(PRIORITY_DEFAULT);
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log.info("priority: {}", priority);
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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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log.info("isSparkSessionManaged: {}", isSparkSessionManaged);
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String betaInputPath = parser.get("betaInputPath");
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log.info("betaInputPath: {}", betaInputPath);
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String prodInputPath = parser.get("prodInputPath");
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log.info("prodInputPath: {}", prodInputPath);
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String outputPath = parser.get("outputPath");
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log.info("outputPath: {}", outputPath);
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String graphTableClassName = parser.get("graphTableClassName");
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log.info("graphTableClassName: {}", graphTableClassName);
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Class<? extends OafEntity> entityClazz = (Class<? extends OafEntity>) Class.forName(graphTableClassName);
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SparkConf conf = new SparkConf();
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conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
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conf.registerKryoClasses(ModelSupport.getOafModelClasses());
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runWithSparkSession(
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conf,
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isSparkSessionManaged,
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spark -> {
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removeOutputDir(spark, outputPath);
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mergeGraphTable(spark, priority, betaInputPath, prodInputPath, entityClazz, entityClazz, outputPath);
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});
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}
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private static <P extends Oaf, B extends Oaf> void mergeGraphTable(
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SparkSession spark,
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String priority,
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String betaInputPath,
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String prodInputPath,
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Class<P> p_clazz,
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Class<B> b_clazz,
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String outputPath) {
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Dataset<Tuple2<String, B>> beta = readTableAndGroupById(spark, betaInputPath, b_clazz);
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Dataset<Tuple2<String, P>> prod = readTableAndGroupById(spark, prodInputPath, p_clazz);
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prod
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.joinWith(beta, prod.col("value").equalTo(beta.col("value")), "full_outer")
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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<B> b = Optional.ofNullable(value._2()).map(Tuple2::_2);
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if (p.orElse((P) b.orElse((B) DATASOURCE)) instanceof Datasource) {
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return mergeDatasource(p, b);
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}
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switch (priority) {
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default:
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case "BETA":
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return mergeWithPriorityToBETA(p, b);
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case "PROD":
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return mergeWithPriorityToPROD(p, b);
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}
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}, Encoders.kryo(p_clazz))
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.filter((FilterFunction<P>) Objects::nonNull)
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.map((MapFunction<P, String>) OBJECT_MAPPER::writeValueAsString, Encoders.STRING())
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.write()
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.text(outputPath);
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}
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/**
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* Datasources involved in the merge operation doesn't obey to the infra precedence policy, but relies on a custom
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* behaviour that, given two datasources from beta and prod returns the one from prod with the highest
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* compatibility among the two. Furthermore, the procedure merges the collectedfrom, originalId, and pid lists.
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*
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* @param p datasource from PROD
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* @param b datasource from BETA
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* @param <P> Datasource class type from PROD
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* @param <B> Datasource class type from BETA
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* @return the datasource from PROD with the highest compatibility level.
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*/
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protected static <P extends Oaf, B extends Oaf> P mergeDatasource(Optional<P> p, Optional<B> b) {
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if (p.isPresent() & !b.isPresent()) {
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return p.get();
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}
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if (b.isPresent() & !p.isPresent()) {
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return (P) b.get();
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}
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if (!b.isPresent() & !p.isPresent()) {
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return null; // unlikely, at least one should be produced by the join operation
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}
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Datasource dp = (Datasource) p.get();
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Datasource db = (Datasource) b.get();
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List<Qualifier> list = Arrays.asList(dp.getOpenairecompatibility(), db.getOpenairecompatibility());
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dp.setOpenairecompatibility(Collections.min(list, new DatasourceCompatibilityComparator()));
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dp
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.setCollectedfrom(
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Stream
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.concat(
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Optional
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.ofNullable(dp.getCollectedfrom())
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.map(Collection::stream)
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.orElse(Stream.empty()),
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Optional
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.ofNullable(db.getCollectedfrom())
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.map(Collection::stream)
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.orElse(Stream.empty()))
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.distinct() // relies on KeyValue.equals
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.collect(Collectors.toList()));
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dp.setOriginalId(mergeLists(dp.getOriginalId(), db.getOriginalId()));
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dp.setPid(mergeLists(dp.getPid(), db.getPid()));
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return (P) dp;
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}
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private static final <T> List<T> mergeLists(final List<T>... lists) {
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return Arrays
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.stream(lists)
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.filter(Objects::nonNull)
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.flatMap(List::stream)
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.filter(Objects::nonNull)
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.distinct()
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.collect(Collectors.toList());
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}
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private static <P extends Oaf, B extends Oaf> P mergeWithPriorityToPROD(Optional<P> p, Optional<B> b) {
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if (b.isPresent() & !p.isPresent()) {
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return (P) b.get();
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}
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if (p.isPresent()) {
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return p.get();
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}
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return null;
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}
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private static <P extends Oaf, B extends Oaf> P mergeWithPriorityToBETA(Optional<P> p, Optional<B> b) {
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if (p.isPresent() & !b.isPresent()) {
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return p.get();
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}
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if (b.isPresent()) {
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return (P) b.get();
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}
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return null;
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}
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private static <T extends Oaf> Dataset<Tuple2<String, T>> readTableAndGroupById(
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SparkSession spark, String inputEntityPath, Class<T> clazz) {
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final TypedColumn<T, T> aggregator = new GroupingAggregator(clazz).toColumn();
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log.info("Reading Graph table from: {}", inputEntityPath);
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return spark
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.read()
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.textFile(inputEntityPath)
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.map((MapFunction<String, T>) value -> OBJECT_MAPPER.readValue(value, clazz), Encoders.kryo(clazz))
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.groupByKey((MapFunction<T, String>) oaf -> ModelSupport.idFn().apply(oaf), Encoders.STRING())
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.agg(aggregator);
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}
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public static class GroupingAggregator<T extends Oaf> extends Aggregator<T, T, T> {
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private Class<T> clazz;
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public GroupingAggregator(Class<T> clazz) {
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this.clazz = clazz;
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}
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@Override
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public T zero() {
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return null;
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}
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@Override
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public T reduce(T b, T a) {
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return mergeAndGet(b, a);
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}
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private T mergeAndGet(T b, T a) {
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if (Objects.nonNull(a) && Objects.nonNull(b)) {
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if (ModelSupport.isSubClass(a, OafEntity.class) && ModelSupport.isSubClass(b, OafEntity.class)) {
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return (T) MergeUtils.merge(b, a);
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}
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if (a instanceof Relation && b instanceof Relation) {
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((Relation) a).mergeFrom(b);
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return (T) MergeUtils.mergeRelation((Relation)a, (Relation) b);
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}
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}
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return Objects.isNull(a) ? b : a;
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}
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@Override
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public T merge(T b, T a) {
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return mergeAndGet(b, a);
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}
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@Override
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public T finish(T j) {
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return j;
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}
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@Override
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public Encoder<T> bufferEncoder() {
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return Encoders.kryo(clazz);
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}
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@Override
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public Encoder<T> outputEncoder() {
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return Encoders.kryo(clazz);
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}
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}
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private static void removeOutputDir(SparkSession spark, String path) {
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HdfsSupport.remove(path, spark.sparkContext().hadoopConfiguration());
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}
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}
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