public class EdgeRDDImpl<ED,VD> extends EdgeRDD<ED>
| Modifier and Type | Method and Description |
|---|---|
EdgeRDDImpl<ED,VD> |
cache()
Persists the edge partitions using
targetStorageLevel, which defaults to MEMORY_ONLY. |
void |
checkpoint()
Mark this RDD for checkpointing.
|
Edge<ED>[] |
collect()
Return an array that contains all of the elements in this RDD.
|
long |
count()
The number of edges in the RDD.
|
EdgeRDDImpl<ED,VD> |
filter(scala.Function1<EdgeTriplet<VD,ED>,Object> epred,
scala.Function2<Object,VD,Object> vpred) |
scala.Option<String> |
getCheckpointFile()
Gets the name of the directory to which this RDD was checkpointed.
|
StorageLevel |
getStorageLevel()
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
|
<ED2,ED3> EdgeRDDImpl<ED3,VD> |
innerJoin(EdgeRDD<ED2> other,
scala.Function4<Object,Object,ED,ED2,ED3> f,
scala.reflect.ClassTag<ED2> evidence$4,
scala.reflect.ClassTag<ED3> evidence$5)
Inner joins this EdgeRDD with another EdgeRDD, assuming both are partitioned using the same
PartitionStrategy. |
boolean |
isCheckpointed()
Return whether this RDD is checkpointed and materialized, either reliably or locally.
|
<ED2,VD2> EdgeRDDImpl<ED2,VD2> |
mapEdgePartitions(scala.Function2<Object,org.apache.spark.graphx.impl.EdgePartition<ED,VD>,org.apache.spark.graphx.impl.EdgePartition<ED2,VD2>> f,
scala.reflect.ClassTag<ED2> evidence$6,
scala.reflect.ClassTag<VD2> evidence$7) |
<ED2> EdgeRDDImpl<ED2,VD> |
mapValues(scala.Function1<Edge<ED>,ED2> f,
scala.reflect.ClassTag<ED2> evidence$3)
Map the values in an edge partitioning preserving the structure but changing the values.
|
scala.Option<Partitioner> |
partitioner()
If
partitionsRDD already has a partitioner, use it. |
RDD<scala.Tuple2<Object,org.apache.spark.graphx.impl.EdgePartition<ED,VD>>> |
partitionsRDD() |
EdgeRDDImpl<ED,VD> |
persist(StorageLevel newLevel)
Persists the edge partitions at the specified storage level, ignoring any existing target
storage level.
|
EdgeRDDImpl<ED,VD> |
reverse()
Reverse all the edges in this RDD.
|
EdgeRDDImpl<ED,VD> |
setName(String _name)
Assign a name to this RDD
|
StorageLevel |
targetStorageLevel() |
EdgeRDDImpl<ED,VD> |
unpersist(boolean blocking)
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
|
aggregate, barrier, cartesian, coalesce, collect, context, countApprox, countApproxDistinct, countApproxDistinct, countByValue, countByValueApprox, dependencies, distinct, distinct, doubleRDDToDoubleRDDFunctions, filter, first, flatMap, fold, foreach, foreachPartition, getNumPartitions, glom, groupBy, groupBy, groupBy, id, intersection, intersection, intersection, isEmpty, iterator, keyBy, localCheckpoint, map, mapPartitions, mapPartitionsWithIndex, max, min, name, numericRDDToDoubleRDDFunctions, partitions, persist, pipe, pipe, pipe, preferredLocations, randomSplit, rddToAsyncRDDActions, rddToOrderedRDDFunctions, rddToPairRDDFunctions, rddToSequenceFileRDDFunctions, reduce, repartition, sample, saveAsObjectFile, saveAsTextFile, saveAsTextFile, sortBy, sparkContext, subtract, subtract, subtract, take, takeOrdered, takeSample, toDebugString, toJavaRDD, toLocalIterator, top, toString, treeAggregate, treeReduce, union, zip, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipWithIndex, zipWithUniqueId$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitializepublic RDD<scala.Tuple2<Object,org.apache.spark.graphx.impl.EdgePartition<ED,VD>>> partitionsRDD()
public StorageLevel targetStorageLevel()
public EdgeRDDImpl<ED,VD> setName(String _name)
RDDpublic scala.Option<Partitioner> partitioner()
partitionsRDD already has a partitioner, use it. Otherwise assume that the
PartitionIDs in partitionsRDD correspond to the actual partitions and create a new
partitioner that allows co-partitioning with partitionsRDD.partitioner in class RDD<Edge<ED>>public Edge<ED>[] collect()
RDDpublic EdgeRDDImpl<ED,VD> persist(StorageLevel newLevel)
public EdgeRDDImpl<ED,VD> unpersist(boolean blocking)
RDDpublic EdgeRDDImpl<ED,VD> cache()
targetStorageLevel, which defaults to MEMORY_ONLY.public StorageLevel getStorageLevel()
RDDgetStorageLevel in class RDD<Edge<ED>>public void checkpoint()
RDDSparkContext#setCheckpointDir and all references to its parent
RDDs will be removed. This function must be called before any job has been
executed on this RDD. It is strongly recommended that this RDD is persisted in
memory, otherwise saving it on a file will require recomputation.checkpoint in class RDD<Edge<ED>>public boolean isCheckpointed()
RDDisCheckpointed in class RDD<Edge<ED>>public scala.Option<String> getCheckpointFile()
RDDgetCheckpointFile in class RDD<Edge<ED>>public long count()
public <ED2> EdgeRDDImpl<ED2,VD> mapValues(scala.Function1<Edge<ED>,ED2> f, scala.reflect.ClassTag<ED2> evidence$3)
EdgeRDDpublic EdgeRDDImpl<ED,VD> reverse()
EdgeRDDpublic EdgeRDDImpl<ED,VD> filter(scala.Function1<EdgeTriplet<VD,ED>,Object> epred, scala.Function2<Object,VD,Object> vpred)
public <ED2,ED3> EdgeRDDImpl<ED3,VD> innerJoin(EdgeRDD<ED2> other, scala.Function4<Object,Object,ED,ED2,ED3> f, scala.reflect.ClassTag<ED2> evidence$4, scala.reflect.ClassTag<ED3> evidence$5)
EdgeRDDPartitionStrategy.
innerJoin in class EdgeRDD<ED>other - the EdgeRDD to join withf - the join function applied to corresponding values of this and otherevidence$4 - (undocumented)evidence$5 - (undocumented)this and other,
with values supplied by fpublic <ED2,VD2> EdgeRDDImpl<ED2,VD2> mapEdgePartitions(scala.Function2<Object,org.apache.spark.graphx.impl.EdgePartition<ED,VD>,org.apache.spark.graphx.impl.EdgePartition<ED2,VD2>> f, scala.reflect.ClassTag<ED2> evidence$6, scala.reflect.ClassTag<VD2> evidence$7)