forked from D-Net/dnet-hadoop
experimenting with pruning of relations
This commit is contained in:
parent
67e1d222b6
commit
ff4d6214f1
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@ -59,200 +59,204 @@ import scala.Tuple2;
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*/
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public class PrepareRelationsJob {
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private static final Logger log = LoggerFactory.getLogger(PrepareRelationsJob.class);
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private static final Logger log = LoggerFactory.getLogger(PrepareRelationsJob.class);
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private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
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private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
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public static final int MAX_RELS = 100;
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public static final int MAX_RELS = 100;
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public static final int DEFAULT_NUM_PARTITIONS = 3000;
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public static final int DEFAULT_NUM_PARTITIONS = 3000;
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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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PrepareRelationsJob.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/oa/provision/input_params_prepare_relations.json"));
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration);
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parser.parseArgument(args);
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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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PrepareRelationsJob.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/oa/provision/input_params_prepare_relations.json"));
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration);
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parser.parseArgument(args);
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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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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 inputRelationsPath = parser.get("inputRelationsPath");
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log.info("inputRelationsPath: {}", inputRelationsPath);
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String inputRelationsPath = parser.get("inputRelationsPath");
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log.info("inputRelationsPath: {}", inputRelationsPath);
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String outputPath = parser.get("outputPath");
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log.info("outputPath: {}", outputPath);
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String outputPath = parser.get("outputPath");
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log.info("outputPath: {}", outputPath);
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int relPartitions = Optional
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.ofNullable(parser.get("relPartitions"))
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.map(Integer::valueOf)
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.orElse(DEFAULT_NUM_PARTITIONS);
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log.info("relPartitions: {}", relPartitions);
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int relPartitions = Optional
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.ofNullable(parser.get("relPartitions"))
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.map(Integer::valueOf)
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.orElse(DEFAULT_NUM_PARTITIONS);
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log.info("relPartitions: {}", relPartitions);
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Set<String> relationFilter = Optional
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.ofNullable(parser.get("relationFilter"))
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.map(s -> Sets.newHashSet(Splitter.on(",").split(s)))
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.orElse(new HashSet<>());
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log.info("relationFilter: {}", relationFilter);
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Set<String> relationFilter = Optional
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.ofNullable(parser.get("relationFilter"))
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.map(s -> Sets.newHashSet(Splitter.on(",").split(s)))
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.orElse(new HashSet<>());
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log.info("relationFilter: {}", relationFilter);
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int maxRelations = Optional
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.ofNullable(parser.get("maxRelations"))
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.map(Integer::valueOf)
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.orElse(MAX_RELS);
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log.info("maxRelations: {}", maxRelations);
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int maxRelations = Optional
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.ofNullable(parser.get("maxRelations"))
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.map(Integer::valueOf)
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.orElse(MAX_RELS);
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log.info("maxRelations: {}", maxRelations);
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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(ProvisionModelSupport.getModelClasses());
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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(ProvisionModelSupport.getModelClasses());
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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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prepareRelationsRDD(
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spark, inputRelationsPath, outputPath, relationFilter, maxRelations, relPartitions);
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});
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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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prepareRelationsRDD(
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spark, inputRelationsPath, outputPath, relationFilter, maxRelations, relPartitions);
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});
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}
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/**
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* RDD based implementation that prepares the graph relations by limiting the number of outgoing links and filtering
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* the relation types according to the given criteria. Moreover, outgoing links kept within the given limit are
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* prioritized according to the weights indicated in eu.dnetlib.dhp.oa.provision.model.SortableRelation.
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*
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* @param spark the spark session
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* @param inputRelationsPath source path for the graph relations
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* @param outputPath output path for the processed relations
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* @param relationFilter set of relation filters applied to the `relClass` field
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* @param maxRelations maximum number of allowed outgoing edges
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* @param relPartitions number of partitions for the output RDD
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*/
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private static void prepareRelationsRDD(SparkSession spark, String inputRelationsPath, String outputPath,
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Set<String> relationFilter, int maxRelations, int relPartitions) {
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JavaRDD<Relation> rels = readPathRelationRDD(spark, inputRelationsPath);
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JavaRDD<Relation> pruned = pruneRels(
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pruneRels(rels, relationFilter, maxRelations, relPartitions, (Function<Relation, String>) r -> r.getSource()),
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relationFilter, maxRelations, relPartitions, (Function<Relation, String>) r -> r.getTarget());
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spark
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.createDataset(pruned.rdd(), Encoders.bean(Relation.class))
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.repartition(relPartitions)
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.write()
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.mode(SaveMode.Overwrite)
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.parquet(outputPath);
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}
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private static JavaRDD<Relation> pruneRels(JavaRDD<Relation> rels, Set<String> relationFilter, int maxRelations, int relPartitions, Function<Relation, String> idFn) {
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return rels
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.filter(rel -> rel.getDataInfo().getDeletedbyinference() == false)
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.filter(rel -> relationFilter.contains(rel.getRelClass()) == false)
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.mapToPair(r -> new Tuple2<>(SortableRelationKey.create(r, idFn.call(r)), r))
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.repartitionAndSortWithinPartitions(new RelationPartitioner(relPartitions))
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.groupBy(Tuple2::_1)
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.map(Tuple2::_2)
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.map(t -> Iterables.limit(t, maxRelations))
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.flatMap(Iterable::iterator).map(Tuple2::_2);
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}
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/**
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* RDD based implementation that prepares the graph relations by limiting the number of outgoing links and filtering
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* the relation types according to the given criteria. Moreover, outgoing links kept within the given limit are
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* prioritized according to the weights indicated in eu.dnetlib.dhp.oa.provision.model.SortableRelation.
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*
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* @param spark the spark session
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* @param inputRelationsPath source path for the graph relations
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* @param outputPath output path for the processed relations
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* @param relationFilter set of relation filters applied to the `relClass` field
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* @param maxRelations maximum number of allowed outgoing edges
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* @param relPartitions number of partitions for the output RDD
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*/
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private static void prepareRelationsRDD(SparkSession spark, String inputRelationsPath, String outputPath,
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Set<String> relationFilter, int maxRelations, int relPartitions) {
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// experimental
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private static void prepareRelationsDataset(
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SparkSession spark, String inputRelationsPath, String outputPath, Set<String> relationFilter, int maxRelations,
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int relPartitions) {
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spark
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.read()
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.textFile(inputRelationsPath)
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.repartition(relPartitions)
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.map(
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(MapFunction<String, Relation>) s -> OBJECT_MAPPER.readValue(s, Relation.class),
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Encoders.kryo(Relation.class))
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.filter((FilterFunction<Relation>) rel -> rel.getDataInfo().getDeletedbyinference() == false)
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.filter((FilterFunction<Relation>) rel -> relationFilter.contains(rel.getRelClass()) == false)
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.groupByKey(
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(MapFunction<Relation, String>) Relation::getSource,
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Encoders.STRING())
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.agg(new RelationAggregator(maxRelations).toColumn())
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.flatMap(
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(FlatMapFunction<Tuple2<String, RelationList>, Relation>) t -> Iterables
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.limit(t._2().getRelations(), maxRelations)
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.iterator(),
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Encoders.bean(Relation.class))
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.repartition(relPartitions)
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.write()
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.mode(SaveMode.Overwrite)
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.parquet(outputPath);
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}
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// group by SOURCE and apply limit
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RDD<Relation> bySource = readPathRelationRDD(spark, inputRelationsPath)
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.filter(rel -> rel.getDataInfo().getDeletedbyinference() == false)
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.filter(rel -> relationFilter.contains(rel.getRelClass()) == false)
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.mapToPair(r -> new Tuple2<>(SortableRelationKey.create(r, r.getSource()), r))
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.repartitionAndSortWithinPartitions(new RelationPartitioner(relPartitions))
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.groupBy(Tuple2::_1)
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.map(Tuple2::_2)
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.map(t -> Iterables.limit(t, maxRelations))
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.flatMap(Iterable::iterator)
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.map(Tuple2::_2)
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.rdd();
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public static class RelationAggregator
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extends Aggregator<Relation, RelationList, RelationList> {
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spark
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.createDataset(bySource, Encoders.bean(Relation.class))
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.repartition(relPartitions)
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.write()
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.mode(SaveMode.Overwrite)
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.parquet(outputPath);
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}
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private int maxRelations;
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// experimental
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private static void prepareRelationsDataset(
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SparkSession spark, String inputRelationsPath, String outputPath, Set<String> relationFilter, int maxRelations,
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int relPartitions) {
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spark
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.read()
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.textFile(inputRelationsPath)
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.repartition(relPartitions)
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.map(
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(MapFunction<String, Relation>) s -> OBJECT_MAPPER.readValue(s, Relation.class),
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Encoders.kryo(Relation.class))
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.filter((FilterFunction<Relation>) rel -> rel.getDataInfo().getDeletedbyinference() == false)
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.filter((FilterFunction<Relation>) rel -> relationFilter.contains(rel.getRelClass()) == false)
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.groupByKey(
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(MapFunction<Relation, String>) Relation::getSource,
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Encoders.STRING())
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.agg(new RelationAggregator(maxRelations).toColumn())
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.flatMap(
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(FlatMapFunction<Tuple2<String, RelationList>, Relation>) t -> Iterables
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.limit(t._2().getRelations(), maxRelations)
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.iterator(),
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Encoders.bean(Relation.class))
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.repartition(relPartitions)
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.write()
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.mode(SaveMode.Overwrite)
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.parquet(outputPath);
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}
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public RelationAggregator(int maxRelations) {
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this.maxRelations = maxRelations;
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}
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public static class RelationAggregator
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extends Aggregator<Relation, RelationList, RelationList> {
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@Override
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public RelationList zero() {
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return new RelationList();
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}
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private int maxRelations;
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@Override
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public RelationList reduce(RelationList b, Relation a) {
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b.getRelations().add(a);
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return getSortableRelationList(b);
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}
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public RelationAggregator(int maxRelations) {
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this.maxRelations = maxRelations;
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}
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@Override
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public RelationList merge(RelationList b1, RelationList b2) {
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b1.getRelations().addAll(b2.getRelations());
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return getSortableRelationList(b1);
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}
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@Override
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public RelationList zero() {
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return new RelationList();
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}
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@Override
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public RelationList finish(RelationList r) {
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return getSortableRelationList(r);
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}
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@Override
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public RelationList reduce(RelationList b, Relation a) {
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b.getRelations().add(a);
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return getSortableRelationList(b);
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}
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private RelationList getSortableRelationList(RelationList b1) {
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RelationList sr = new RelationList();
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sr
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.setRelations(
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b1
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.getRelations()
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.stream()
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.limit(maxRelations)
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.collect(Collectors.toCollection(() -> new PriorityQueue<>(new RelationComparator()))));
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return sr;
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}
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@Override
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public RelationList merge(RelationList b1, RelationList b2) {
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b1.getRelations().addAll(b2.getRelations());
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return getSortableRelationList(b1);
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}
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@Override
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public Encoder<RelationList> bufferEncoder() {
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return Encoders.kryo(RelationList.class);
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}
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@Override
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public RelationList finish(RelationList r) {
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return getSortableRelationList(r);
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}
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@Override
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public Encoder<RelationList> outputEncoder() {
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return Encoders.kryo(RelationList.class);
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}
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}
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private RelationList getSortableRelationList(RelationList b1) {
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RelationList sr = new RelationList();
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sr
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.setRelations(
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b1
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.getRelations()
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.stream()
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.limit(maxRelations)
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.collect(Collectors.toCollection(() -> new PriorityQueue<>(new RelationComparator()))));
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return sr;
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}
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/**
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* Reads a JavaRDD of eu.dnetlib.dhp.oa.provision.model.SortableRelation objects from a newline delimited json text
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* file,
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*
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* @param spark
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* @param inputPath
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* @return the JavaRDD<SortableRelation> containing all the relationships
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*/
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private static JavaRDD<Relation> readPathRelationRDD(
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SparkSession spark, final String inputPath) {
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JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
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return sc.textFile(inputPath).map(s -> OBJECT_MAPPER.readValue(s, Relation.class));
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}
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@Override
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public Encoder<RelationList> bufferEncoder() {
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return Encoders.kryo(RelationList.class);
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}
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@Override
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public Encoder<RelationList> outputEncoder() {
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return Encoders.kryo(RelationList.class);
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}
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}
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/**
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* Reads a JavaRDD of eu.dnetlib.dhp.oa.provision.model.SortableRelation objects from a newline delimited json text
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* file,
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*
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* @param spark
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* @param inputPath
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* @return the JavaRDD<SortableRelation> containing all the relationships
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*/
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private static JavaRDD<Relation> readPathRelationRDD(
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SparkSession spark, final String inputPath) {
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JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
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return sc.textFile(inputPath).map(s -> OBJECT_MAPPER.readValue(s, Relation.class));
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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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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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