forked from D-Net/dnet-hadoop
allow to set different to relations cut points by source and by target; adjusted weight assigned to relationship types
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@ -100,11 +100,17 @@ public class PrepareRelationsJob {
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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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int sourceMaxRelations = Optional
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.ofNullable(parser.get("sourceMaxRelations"))
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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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log.info("sourceMaxRelations: {}", sourceMaxRelations);
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int targetMaxRelations = Optional
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.ofNullable(parser.get("targetMaxRelations"))
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.map(Integer::valueOf)
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.orElse(MAX_RELS);
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log.info("targetMaxRelations: {}", targetMaxRelations);
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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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@ -116,7 +122,8 @@ public class PrepareRelationsJob {
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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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spark, inputRelationsPath, outputPath, relationFilter, sourceMaxRelations, targetMaxRelations,
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relPartitions);
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});
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}
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@ -129,17 +136,22 @@ public class PrepareRelationsJob {
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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 sourceMaxRelations maximum number of allowed outgoing edges grouping by relation.source
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* @param targetMaxRelations maximum number of allowed outgoing edges grouping by relation.target
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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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Set<String> relationFilter, int sourceMaxRelations, int targetMaxRelations, int relPartitions) {
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JavaRDD<Relation> rels = readPathRelationRDD(spark, inputRelationsPath);
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JavaRDD<Relation> rels = 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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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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pruneRels(
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rels,
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sourceMaxRelations, relPartitions, (Function<Relation, String>) r -> r.getSource()),
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targetMaxRelations, 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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@ -148,16 +160,16 @@ public class PrepareRelationsJob {
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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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private static JavaRDD<Relation> pruneRels(JavaRDD<Relation> rels, int maxRelations,
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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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.flatMap(Iterable::iterator)
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.map(Tuple2::_2);
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}
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// experimental
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@ -16,18 +16,18 @@ public class SortableRelationKey implements Comparable<SortableRelationKey>, Ser
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private static final Map<String, Integer> weights = Maps.newHashMap();
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static {
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weights.put("outcome", 0);
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weights.put("supplement", 1);
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weights.put("review", 2);
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weights.put("citation", 3);
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weights.put("affiliation", 4);
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weights.put("relationship", 5);
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weights.put("publicationDataset", 6);
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weights.put("similarity", 7);
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weights.put("participation", 0);
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weights.put("provision", 8);
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weights.put("participation", 9);
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weights.put("dedup", 10);
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weights.put("outcome", 1);
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weights.put("affiliation", 2);
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weights.put("dedup", 3);
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weights.put("publicationDataset", 4);
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weights.put("citation", 5);
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weights.put("supplement", 6);
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weights.put("review", 7);
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weights.put("relationship", 8);
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weights.put("provision", 9);
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weights.put("similarity", 10);
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}
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private static final long serialVersionUID = 3232323;
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@ -30,9 +30,16 @@
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"paramRequired": false
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},
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{
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"paramName": "mr",
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"paramLongName": "maxRelations",
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"paramDescription": "maximum number of relations allowed for a each entity",
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"paramName": "smr",
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"paramLongName": "sourceMaxRelations",
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"paramDescription": "maximum number of relations allowed for a each entity grouping by source",
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"paramRequired": false
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},
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{
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"paramName": "tmr",
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"paramLongName": "targetMaxRelations",
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"paramDescription": "maximum number of relations allowed for a each entity grouping by target",
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"paramRequired": false
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}
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]
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