public class CoGroupedRDD<K> extends RDD<scala.Tuple2<K,scala.collection.Iterable<?>[]>>
param: rdds parent RDDs. param: part partitioner used to partition the shuffle output
Constructor and Description |
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CoGroupedRDD(scala.collection.Seq<RDD<? extends scala.Product2<K,?>>> rdds,
Partitioner part,
scala.reflect.ClassTag<K> evidence$1) |
Modifier and Type | Method and Description |
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void |
clearDependencies()
Clears the dependencies of this RDD.
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scala.collection.Iterator<scala.Tuple2<K,scala.collection.Iterable<?>[]>> |
compute(Partition s,
TaskContext context)
:: DeveloperApi ::
Implemented by subclasses to compute a given partition.
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scala.collection.Seq<Dependency<?>> |
getDependencies()
Implemented by subclasses to return how this RDD depends on parent RDDs.
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Partition[] |
getPartitions()
Implemented by subclasses to return the set of partitions in this RDD.
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scala.Some<Partitioner> |
partitioner()
Optionally overridden by subclasses to specify how they are partitioned.
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scala.collection.Seq<RDD<? extends scala.Product2<K,?>>> |
rdds() |
CoGroupedRDD<K> |
setSerializer(Serializer serializer)
Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer)
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aggregate, cache, cartesian, checkpoint, coalesce, collect, collect, context, count, countApprox, countApproxDistinct, countApproxDistinct, countByValue, countByValueApprox, dependencies, distinct, distinct, doubleRDDToDoubleRDDFunctions, filter, first, flatMap, fold, foreach, foreachPartition, getCheckpointFile, getNumPartitions, getStorageLevel, glom, groupBy, groupBy, groupBy, id, intersection, intersection, intersection, isCheckpointed, isEmpty, iterator, keyBy, localCheckpoint, map, mapPartitions, mapPartitionsWithIndex, max, min, name, numericRDDToDoubleRDDFunctions, partitions, persist, persist, pipe, pipe, pipe, preferredLocations, randomSplit, rddToAsyncRDDActions, rddToOrderedRDDFunctions, rddToPairRDDFunctions, rddToSequenceFileRDDFunctions, reduce, repartition, sample, saveAsObjectFile, saveAsTextFile, saveAsTextFile, setName, sortBy, sparkContext, subtract, subtract, subtract, take, takeOrdered, takeSample, toDebugString, toJavaRDD, toLocalIterator, top, toString, treeAggregate, treeReduce, union, unpersist, zip, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipWithIndex, zipWithUniqueId
public CoGroupedRDD(scala.collection.Seq<RDD<? extends scala.Product2<K,?>>> rdds, Partitioner part, scala.reflect.ClassTag<K> evidence$1)
public CoGroupedRDD<K> setSerializer(Serializer serializer)
public scala.collection.Seq<Dependency<?>> getDependencies()
RDD
public Partition[] getPartitions()
RDD
The partitions in this array must satisfy the following property:
rdd.partitions.zipWithIndex.forall { case (partition, index) => partition.index == index }
public scala.Some<Partitioner> partitioner()
RDD
partitioner
in class RDD<scala.Tuple2<K,scala.collection.Iterable<?>[]>>
public scala.collection.Iterator<scala.Tuple2<K,scala.collection.Iterable<?>[]>> compute(Partition s, TaskContext context)
RDD
public void clearDependencies()
RDD
UnionRDD
for an example.