JavaRDDLike

Abstract Value Members

implicit abstract val classTag: ClassTag[T]
abstract def rdd: RDD[T]
abstract def wrapRDD(rdd: RDD[T]): This

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def aggregate[U](zeroValue: U)(seqOp: Function2[U, T, U], combOp: Function2[U, U, U]): U

Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value".
Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an U and one operation for merging two U's, as in scala.TraversableOnce. Both of these functions are allowed to modify and return their first argument instead of creating a new U to avoid memory allocation.
final def asInstanceOf[T0]: T0

Definition Classes
Any
def cartesian[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, U]

Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in this and b is in other.
def checkpoint(): Unit

Mark this RDD for checkpointing.
Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint directory set with SparkContext.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.
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def collect(): List[T]

Return an array that contains all of the elements in this RDD.
Return an array that contains all of the elements in this RDD.

Note
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
def collectAsync(): JavaFutureAction[List[T]]

The asynchronous version of collect, which returns a future for retrieving an array containing all of the elements in this RDD.
The asynchronous version of collect, which returns a future for retrieving an array containing all of the elements in this RDD.

Note
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
def collectPartitions(partitionIds: Array[Int]): Array[List[T]]

Return an array that contains all of the elements in a specific partition of this RDD.
def context: SparkContext

The org.apache.spark.SparkContext that this RDD was created on.
def count(): Long

Return the number of elements in the RDD.
def countApprox(timeout: Long): PartialResult[BoundedDouble]

Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
timeout
maximum time to wait for the job, in milliseconds
def countApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble]

Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
The confidence is the probability that the error bounds of the result will contain the true value. That is, if countApprox were called repeatedly with confidence 0.9, we would expect 90% of the results to contain the true count. The confidence must be in the range [0,1] or an exception will be thrown.
timeout
maximum time to wait for the job, in milliseconds
confidence
the desired statistical confidence in the result
returns
a potentially incomplete result, with error bounds
def countApproxDistinct(relativeSD: Double): Long

Return approximate number of distinct elements in the RDD.
Return approximate number of distinct elements in the RDD.
The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.
relativeSD
Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.
def countAsync(): JavaFutureAction[Long]

The asynchronous version of count, which returns a future for counting the number of elements in this RDD.
def countByValue(): Map[T, Long]

Return the count of each unique value in this RDD as a map of (value, count) pairs.
Return the count of each unique value in this RDD as a map of (value, count) pairs. The final combine step happens locally on the master, equivalent to running a single reduce task.
def countByValueApprox(timeout: Long): PartialResult[Map[T, BoundedDouble]]

Approximate version of countByValue().
Approximate version of countByValue().
timeout
maximum time to wait for the job, in milliseconds
returns
a potentially incomplete result, with error bounds
def countByValueApprox(timeout: Long, confidence: Double): PartialResult[Map[T, BoundedDouble]]

Approximate version of countByValue().
Approximate version of countByValue().
The confidence is the probability that the error bounds of the result will contain the true value. That is, if countApprox were called repeatedly with confidence 0.9, we would expect 90% of the results to contain the true count. The confidence must be in the range [0,1] or an exception will be thrown.
timeout
maximum time to wait for the job, in milliseconds
confidence
the desired statistical confidence in the result
returns
a potentially incomplete result, with error bounds
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def first(): T

Return the first element in this RDD.
def flatMap[U](f: FlatMapFunction[T, U]): JavaRDD[U]

Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
def flatMapToDouble(f: DoubleFlatMapFunction[T]): JavaDoubleRDD

Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
def flatMapToPair[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairRDD[K2, V2]

Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
def fold(zeroValue: T)(f: Function2[T, T, T]): T

Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value".
Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value". The function op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2.
This behaves somewhat differently from fold operations implemented for non-distributed collections in functional languages like Scala. This fold operation may be applied to partitions individually, and then fold those results into the final result, rather than apply the fold to each element sequentially in some defined ordering. For functions that are not commutative, the result may differ from that of a fold applied to a non-distributed collection.
def foreach(f: VoidFunction[T]): Unit

Applies a function f to all elements of this RDD.
def foreachAsync(f: VoidFunction[T]): JavaFutureAction[Void]

The asynchronous version of the foreach action, which applies a function f to all the elements of this RDD.
def foreachPartition(f: VoidFunction[Iterator[T]]): Unit

Applies a function f to each partition of this RDD.
def foreachPartitionAsync(f: VoidFunction[Iterator[T]]): JavaFutureAction[Void]

The asynchronous version of the foreachPartition action, which applies a function f to each partition of this RDD.
def getCheckpointFile(): Optional[String]

Gets the name of the file to which this RDD was checkpointed
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getNumPartitions: Int

Return the number of partitions in this RDD.
Return the number of partitions in this RDD.

Annotations
@Since( "1.6.0" )
def getStorageLevel: StorageLevel

Get the RDD's current storage level, or StorageLevel.NONE if none is set.
def glom(): JavaRDD[List[T]]

Return an RDD created by coalescing all elements within each partition into an array.
def groupBy[U](f: Function[T, U], numPartitions: Int): JavaPairRDD[U, Iterable[T]]

Return an RDD of grouped elements.
Return an RDD of grouped elements. Each group consists of a key and a sequence of elements mapping to that key.
def groupBy[U](f: Function[T, U]): JavaPairRDD[U, Iterable[T]]

Return an RDD of grouped elements.
Return an RDD of grouped elements. Each group consists of a key and a sequence of elements mapping to that key.
def hashCode(): Int

Definition Classes
AnyRef → Any
def id: Int

A unique ID for this RDD (within its SparkContext).
def isCheckpointed: Boolean

Return whether this RDD has been checkpointed or not
def isEmpty(): Boolean

returns
true if and only if the RDD contains no elements at all. Note that an RDD may be empty even when it has at least 1 partition.
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def iterator(split: Partition, taskContext: TaskContext): Iterator[T]

Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
Internal method to this RDD; will read from cache if applicable, or otherwise compute it. This should not be called by users directly, but is available for implementors of custom subclasses of RDD.
def keyBy[U](f: Function[T, U]): JavaPairRDD[U, T]

Creates tuples of the elements in this RDD by applying f.
def map[R](f: Function[T, R]): JavaRDD[R]

Return a new RDD by applying a function to all elements of this RDD.
def mapPartitions[U](f: FlatMapFunction[Iterator[T], U], preservesPartitioning: Boolean): JavaRDD[U]

Return a new RDD by applying a function to each partition of this RDD.
def mapPartitions[U](f: FlatMapFunction[Iterator[T], U]): JavaRDD[U]

Return a new RDD by applying a function to each partition of this RDD.
def mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[T]], preservesPartitioning: Boolean): JavaDoubleRDD

Return a new RDD by applying a function to each partition of this RDD.
def mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[T]]): JavaDoubleRDD

Return a new RDD by applying a function to each partition of this RDD.
def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[T], K2, V2], preservesPartitioning: Boolean): JavaPairRDD[K2, V2]

Return a new RDD by applying a function to each partition of this RDD.
def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[T], K2, V2]): JavaPairRDD[K2, V2]

Return a new RDD by applying a function to each partition of this RDD.
def mapPartitionsWithIndex[R](f: Function2[Integer, Iterator[T], Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R]

Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
def mapToDouble[R](f: DoubleFunction[T]): JavaDoubleRDD

Return a new RDD by applying a function to all elements of this RDD.
def mapToPair[K2, V2](f: PairFunction[T, K2, V2]): JavaPairRDD[K2, V2]

Return a new RDD by applying a function to all elements of this RDD.
def max(comp: Comparator[T]): T

Returns the maximum element from this RDD as defined by the specified Comparator[T].
Returns the maximum element from this RDD as defined by the specified Comparator[T].
comp
the comparator that defines ordering
returns
the maximum of the RDD
def min(comp: Comparator[T]): T

Returns the minimum element from this RDD as defined by the specified Comparator[T].
Returns the minimum element from this RDD as defined by the specified Comparator[T].
comp
the comparator that defines ordering
returns
the minimum of the RDD
def name(): String
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def partitioner: Optional[Partitioner]

The partitioner of this RDD.
def partitions: List[Partition]

Set of partitions in this RDD.
def pipe(command: List[String], env: Map[String, String], separateWorkingDir: Boolean, bufferSize: Int, encoding: String): JavaRDD[String]

Return an RDD created by piping elements to a forked external process.
def pipe(command: List[String], env: Map[String, String], separateWorkingDir: Boolean, bufferSize: Int): JavaRDD[String]

Return an RDD created by piping elements to a forked external process.
def pipe(command: List[String], env: Map[String, String]): JavaRDD[String]

Return an RDD created by piping elements to a forked external process.
def pipe(command: List[String]): JavaRDD[String]

Return an RDD created by piping elements to a forked external process.
def pipe(command: String): JavaRDD[String]

Return an RDD created by piping elements to a forked external process.
def reduce(f: Function2[T, T, T]): T

Reduces the elements of this RDD using the specified commutative and associative binary operator.
def saveAsObjectFile(path: String): Unit

Save this RDD as a SequenceFile of serialized objects.
def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit

Save this RDD as a compressed text file, using string representations of elements.
def saveAsTextFile(path: String): Unit

Save this RDD as a text file, using string representations of elements.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def take(num: Int): List[T]

Take the first num elements of the RDD.
Take the first num elements of the RDD. This currently scans the partitions *one by one*, so it will be slow if a lot of partitions are required. In that case, use collect() to get the whole RDD instead.

Note
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
def takeAsync(num: Int): JavaFutureAction[List[T]]

The asynchronous version of the take action, which returns a future for retrieving the first num elements of this RDD.
The asynchronous version of the take action, which returns a future for retrieving the first num elements of this RDD.

Note
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
def takeOrdered(num: Int): List[T]

Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.
Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.
num
k, the number of top elements to return
returns
an array of top elements

Note
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
def takeOrdered(num: Int, comp: Comparator[T]): List[T]

Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
num
k, the number of elements to return
comp
the comparator that defines the order
returns
an array of top elements

Note
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
def takeSample(withReplacement: Boolean, num: Int, seed: Long): List[T]
def takeSample(withReplacement: Boolean, num: Int): List[T]
def toDebugString(): String

A description of this RDD and its recursive dependencies for debugging.
def toLocalIterator(): Iterator[T]

Return an iterator that contains all of the elements in this RDD.
Return an iterator that contains all of the elements in this RDD.
The iterator will consume as much memory as the largest partition in this RDD.
def toString(): String

Definition Classes
AnyRef → Any
def top(num: Int): List[T]

Returns the top k (largest) elements from this RDD using the natural ordering for T and maintains the order.
Returns the top k (largest) elements from this RDD using the natural ordering for T and maintains the order.
num
k, the number of top elements to return
returns
an array of top elements

Note
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
def top(num: Int, comp: Comparator[T]): List[T]

Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
num
k, the number of top elements to return
comp
the comparator that defines the order
returns
an array of top elements

Note
this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
def treeAggregate[U](zeroValue: U, seqOp: Function2[U, T, U], combOp: Function2[U, U, U]): U

org.apache.spark.api.java.JavaRDDLike.treeAggregate with suggested depth 2.
def treeAggregate[U](zeroValue: U, seqOp: Function2[U, T, U], combOp: Function2[U, U, U], depth: Int): U

Aggregates the elements of this RDD in a multi-level tree pattern.
Aggregates the elements of this RDD in a multi-level tree pattern.
depth
suggested depth of the tree

See also
org.apache.spark.api.java.JavaRDDLike#aggregate
def treeReduce(f: Function2[T, T, T]): T

org.apache.spark.api.java.JavaRDDLike.treeReduce with suggested depth 2.
def treeReduce(f: Function2[T, T, T], depth: Int): T

Reduces the elements of this RDD in a multi-level tree pattern.
Reduces the elements of this RDD in a multi-level tree pattern.
depth
suggested depth of the tree

See also
org.apache.spark.api.java.JavaRDDLike#reduce
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
def zip[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, U]

Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc.
Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc. Assumes that the two RDDs have the *same number of partitions* and the *same number of elements in each partition* (e.g. one was made through a map on the other).
def zipPartitions[U, V](other: JavaRDDLike[U, _], f: FlatMapFunction2[Iterator[T], Iterator[U], V]): JavaRDD[V]

Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions.
Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions. Assumes that all the RDDs have the *same number of partitions*, but does *not* require them to have the same number of elements in each partition.
def zipWithIndex(): JavaPairRDD[T, Long]

Zips this RDD with its element indices.
Zips this RDD with its element indices. The ordering is first based on the partition index and then the ordering of items within each partition. So the first item in the first partition gets index 0, and the last item in the last partition receives the largest index. This is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type. This method needs to trigger a spark job when this RDD contains more than one partitions.
def zipWithUniqueId(): JavaPairRDD[T, Long]

Zips this RDD with generated unique Long ids.
Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method won't trigger a spark job, which is different from org.apache.spark.rdd.RDD#zipWithIndex.

Related Doc: package java

trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable

Abstract Value Members

implicit abstract val classTag: ClassTag[T]

abstract def rdd: RDD[T]

abstract def wrapRDD(rdd: RDD[T]): This

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def aggregate[U](zeroValue: U)(seqOp: Function2[U, T, U], combOp: Function2[U, U, U]): U

final def asInstanceOf[T0]: T0

def cartesian[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, U]

def checkpoint(): Unit

def clone(): AnyRef

def collect(): List[T]

def collectAsync(): JavaFutureAction[List[T]]

def collectPartitions(partitionIds: Array[Int]): Array[List[T]]

def context: SparkContext

def count(): Long

def countApprox(timeout: Long): PartialResult[BoundedDouble]

def countApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble]

def countApproxDistinct(relativeSD: Double): Long

def countAsync(): JavaFutureAction[Long]

def countByValue(): Map[T, Long]

def countByValueApprox(timeout: Long): PartialResult[Map[T, BoundedDouble]]

def countByValueApprox(timeout: Long, confidence: Double): PartialResult[Map[T, BoundedDouble]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def first(): T

def flatMap[U](f: FlatMapFunction[T, U]): JavaRDD[U]

def flatMapToDouble(f: DoubleFlatMapFunction[T]): JavaDoubleRDD

def flatMapToPair[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairRDD[K2, V2]

def fold(zeroValue: T)(f: Function2[T, T, T]): T

def foreach(f: VoidFunction[T]): Unit

def foreachAsync(f: VoidFunction[T]): JavaFutureAction[Void]

def foreachPartition(f: VoidFunction[Iterator[T]]): Unit

def foreachPartitionAsync(f: VoidFunction[Iterator[T]]): JavaFutureAction[Void]

def getCheckpointFile(): Optional[String]

final def getClass(): Class[_]

def getNumPartitions: Int

def getStorageLevel: StorageLevel

def glom(): JavaRDD[List[T]]

def groupBy[U](f: Function[T, U], numPartitions: Int): JavaPairRDD[U, Iterable[T]]

def groupBy[U](f: Function[T, U]): JavaPairRDD[U, Iterable[T]]

def hashCode(): Int

def id: Int

def isCheckpointed: Boolean

def isEmpty(): Boolean

final def isInstanceOf[T0]: Boolean

def iterator(split: Partition, taskContext: TaskContext): Iterator[T]

def keyBy[U](f: Function[T, U]): JavaPairRDD[U, T]

def map[R](f: Function[T, R]): JavaRDD[R]

def mapPartitions[U](f: FlatMapFunction[Iterator[T], U], preservesPartitioning: Boolean): JavaRDD[U]

def mapPartitions[U](f: FlatMapFunction[Iterator[T], U]): JavaRDD[U]

def mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[T]], preservesPartitioning: Boolean): JavaDoubleRDD

def mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[T]]): JavaDoubleRDD

def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[T], K2, V2], preservesPartitioning: Boolean): JavaPairRDD[K2, V2]

def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[T], K2, V2]): JavaPairRDD[K2, V2]

def mapPartitionsWithIndex[R](f: Function2[Integer, Iterator[T], Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R]

def mapToDouble[R](f: DoubleFunction[T]): JavaDoubleRDD

def mapToPair[K2, V2](f: PairFunction[T, K2, V2]): JavaPairRDD[K2, V2]

def max(comp: Comparator[T]): T

def min(comp: Comparator[T]): T

def name(): String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def partitioner: Optional[Partitioner]

def partitions: List[Partition]

def pipe(command: List[String], env: Map[String, String], separateWorkingDir: Boolean, bufferSize: Int, encoding: String): JavaRDD[String]

def pipe(command: List[String], env: Map[String, String], separateWorkingDir: Boolean, bufferSize: Int): JavaRDD[String]

def pipe(command: List[String], env: Map[String, String]): JavaRDD[String]

def pipe(command: List[String]): JavaRDD[String]

def pipe(command: String): JavaRDD[String]

def reduce(f: Function2[T, T, T]): T

def saveAsObjectFile(path: String): Unit

def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit