org.apache.spark.rdd

Class ParallelCollectionRDD<T>

Object

org.apache.spark.rdd.RDD<T>

org.apache.spark.rdd.ParallelCollectionRDD<T>

All Implemented Interfaces:

java.io.Serializable, Logging

public class ParallelCollectionRDD<T>
extends RDD<T>

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ParallelCollectionRDD(SparkContext sc, scala.collection.Seq<T> data, int numSlices, scala.collection.Map<Object,scala.collection.Seq<String>> locationPrefs, scala.reflect.ClassTag<T> evidence$2)

Method Summary

Methods

Modifier and Type	Method and Description
InterruptibleIterator<T>	compute(Partition s, TaskContext context) :: DeveloperApi :: Implemented by subclasses to compute a given partition.
Partition[]	getPartitions() Implemented by subclasses to return the set of partitions in this RDD.
scala.collection.Seq<String>	getPreferredLocations(Partition s) Optionally overridden by subclasses to specify placement preferences.
static <T> scala.collection.Seq<scala.collection.Seq<T>>	slice(scala.collection.Seq<T> seq, int numSlices, scala.reflect.ClassTag<T> evidence$3) Slice a collection into numSlices sub-collections.

Methods inherited from class org.apache.spark.rdd.RDD

aggregate, cache, cartesian, checkpoint, checkpointData, coalesce, collect, collect, collectPartitions, computeOrReadCheckpoint, conf, context, count, countApprox, countApproxDistinct, countApproxDistinct, countByValue, countByValueApprox, creationSite, dependencies, distinct, distinct, doCheckpoint, doubleRDDToDoubleRDDFunctions, elementClassTag, filter, filterWith, first, flatMap, flatMapWith, fold, foreach, foreachPartition, foreachWith, getCheckpointFile, getCreationSite, getNarrowAncestors, getStorageLevel, glom, groupBy, groupBy, groupBy, id, intersection, intersection, intersection, isCheckpointed, isEmpty, iterator, keyBy, map, mapPartitions, mapPartitionsWithContext, mapPartitionsWithIndex, mapPartitionsWithSplit, mapWith, markCheckpointed, max, min, name, numericRDDToDoubleRDDFunctions, partitioner, partitions, persist, persist, pipe, pipe, pipe, preferredLocations, randomSplit, rddToAsyncRDDActions, rddToOrderedRDDFunctions, rddToPairRDDFunctions, rddToSequenceFileRDDFunctions, reduce, repartition, retag, retag, sample, saveAsObjectFile, saveAsTextFile, saveAsTextFile, setName, sortBy, sparkContext, subtract, subtract, subtract, take, takeOrdered, takeSample, toArray, toDebugString, toJavaRDD, toLocalIterator, top, toString, treeAggregate, treeReduce, union, unpersist, zip, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipWithIndex, zipWithUniqueId

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

ParallelCollectionRDD

public ParallelCollectionRDD(SparkContext sc,
                     scala.collection.Seq<T> data,
                     int numSlices,
                     scala.collection.Map<Object,scala.collection.Seq<String>> locationPrefs,
                     scala.reflect.ClassTag<T> evidence$2)

Method Detail

slice

public static <T> scala.collection.Seq<scala.collection.Seq<T>> slice(scala.collection.Seq<T> seq,
                                                      int numSlices,
                                                      scala.reflect.ClassTag<T> evidence$3)

Slice a collection into numSlices sub-collections. One extra thing we do here is to treat Range collections specially, encoding the slices as other Ranges to minimize memory cost. This makes it efficient to run Spark over RDDs representing large sets of numbers. And if the collection is an inclusive Range, we use inclusive range for the last slice.

getPartitions

public Partition[] getPartitions()

Description copied from class: RDD

Implemented by subclasses to return the set of partitions in this RDD. This method will only be called once, so it is safe to implement a time-consuming computation in it.

compute

public InterruptibleIterator<T> compute(Partition s,
                               TaskContext context)

Description copied from class: RDD

:: DeveloperApi :: Implemented by subclasses to compute a given partition.

Specified by:

compute in class RDD<T>

getPreferredLocations

public scala.collection.Seq<String> getPreferredLocations(Partition s)

Description copied from class: RDD

Optionally overridden by subclasses to specify placement preferences.