Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  def aggregate[U](zeroValue: U)(seqOp: Function2[U, Double, 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.

   Definition Classes

   JavaRDDLike

5.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

6.  def cache(): JavaDoubleRDD

   Persist this RDD with the default storage level (MEMORY_ONLY).

7.  def cartesian[U](other: JavaRDDLike[U, _]): JavaPairRDD[Double, 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.

   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.

   Definition Classes

   JavaRDDLike

8.  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.

   Definition Classes

   JavaRDDLike

9.  val classTag: ClassTag[Double]

   Definition Classes

   JavaDoubleRDD → JavaRDDLike

10.  def clone(): AnyRef

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()

11.  def coalesce(numPartitions: Int, shuffle: Boolean): JavaDoubleRDD

    Return a new RDD that is reduced into numPartitions partitions.

12.  def coalesce(numPartitions: Int): JavaDoubleRDD

    Return a new RDD that is reduced into numPartitions partitions.

13.  def collect(): List[Double]

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

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

    Definition Classes

    JavaRDDLike

    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.

14.  def collectAsync(): JavaFutureAction[List[Double]]

    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.

    Definition Classes

    JavaRDDLike

    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.

15.  def collectPartitions(partitionIds: Array[Int]): Array[List[Double]]

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

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

    Definition Classes

    JavaRDDLike

16.  def context: SparkContext

    The org.apache.spark.SparkContext that this RDD was created on.

    The org.apache.spark.SparkContext that this RDD was created on.

    Definition Classes

    JavaRDDLike

17.  def count(): Long

    Return the number of elements in the RDD.

    Return the number of elements in the RDD.

    Definition Classes

    JavaRDDLike

18.  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

    Definition Classes

    JavaRDDLike

19.  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

    Definition Classes

    JavaRDDLike

20.  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.

    Definition Classes

    JavaRDDLike

21.  def countAsync(): JavaFutureAction[Long]

    The asynchronous version of count, which returns a future for counting the number of elements in this RDD.

    The asynchronous version of count, which returns a future for counting the number of elements in this RDD.

    Definition Classes

    JavaRDDLike

22.  def countByValue(): Map[Double, 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.

    Definition Classes

    JavaRDDLike

23.  def countByValueApprox(timeout: Long): PartialResult[Map[Double, 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

    Definition Classes

    JavaRDDLike

24.  def countByValueApprox(timeout: Long, confidence: Double): PartialResult[Map[Double, 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

    Definition Classes

    JavaRDDLike

25.  def distinct(numPartitions: Int): JavaDoubleRDD

    Return a new RDD containing the distinct elements in this RDD.

26.  def distinct(): JavaDoubleRDD

    Return a new RDD containing the distinct elements in this RDD.

27.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

28.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

29.  def filter(f: Function[Double, Boolean]): JavaDoubleRDD

    Return a new RDD containing only the elements that satisfy a predicate.

30.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

31.  def first(): Double

    Return the first element in this RDD.

    Return the first element in this RDD.

    Definition Classes

    JavaDoubleRDD → JavaRDDLike

32.  def flatMap[U](f: FlatMapFunction[Double, U]): JavaRDD[U]

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Definition Classes

    JavaRDDLike

33.  def flatMapToDouble(f: DoubleFlatMapFunction[Double]): JavaDoubleRDD

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Definition Classes

    JavaRDDLike

34.  def flatMapToPair[K2, V2](f: PairFlatMapFunction[Double, 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.

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Definition Classes

    JavaRDDLike

35.  def fold(zeroValue: Double)(f: Function2[Double, Double, Double]): Double

    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.

    Definition Classes

    JavaRDDLike

36.  def foreach(f: VoidFunction[Double]): Unit

    Applies a function f to all elements of this RDD.

    Applies a function f to all elements of this RDD.

    Definition Classes

    JavaRDDLike

37.  def foreachAsync(f: VoidFunction[Double]): JavaFutureAction[Void]

    The asynchronous version of the foreach action, which applies a function f to all the elements of this RDD.

    The asynchronous version of the foreach action, which applies a function f to all the elements of this RDD.

    Definition Classes

    JavaRDDLike

38.  def foreachPartition(f: VoidFunction[Iterator[Double]]): Unit

    Applies a function f to each partition of this RDD.

    Applies a function f to each partition of this RDD.

    Definition Classes

    JavaRDDLike

39.  def foreachPartitionAsync(f: VoidFunction[Iterator[Double]]): JavaFutureAction[Void]

    The asynchronous version of the foreachPartition action, which applies a function f to each partition of this RDD.

    The asynchronous version of the foreachPartition action, which applies a function f to each partition of this RDD.

    Definition Classes

    JavaRDDLike

40.  def getCheckpointFile(): Optional[String]

    Gets the name of the file to which this RDD was checkpointed

    Gets the name of the file to which this RDD was checkpointed

    Definition Classes

    JavaRDDLike

41.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

42.  def getNumPartitions: Int

    Return the number of partitions in this RDD.

    Return the number of partitions in this RDD.

    Definition Classes

    JavaRDDLike

    Annotations

    @Since( "1.6.0" )

43.  def getStorageLevel: StorageLevel

    Get the RDD's current storage level, or StorageLevel.NONE if none is set.

    Get the RDD's current storage level, or StorageLevel.NONE if none is set.

    Definition Classes

    JavaRDDLike

44.  def glom(): JavaRDD[List[Double]]

    Return an RDD created by coalescing all elements within each partition into an array.

    Return an RDD created by coalescing all elements within each partition into an array.

    Definition Classes

    JavaRDDLike

45.  def groupBy[U](f: Function[Double, U], numPartitions: Int): JavaPairRDD[U, Iterable[Double]]

    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.

    Definition Classes

    JavaRDDLike

46.  def groupBy[U](f: Function[Double, U]): JavaPairRDD[U, Iterable[Double]]

    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.

    Definition Classes

    JavaRDDLike

47.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

48.  def histogram(buckets: Array[Double], evenBuckets: Boolean): Array[Long]
49.  def histogram(buckets: Array[Double]): Array[Long]

    Compute a histogram using the provided buckets.

    Compute a histogram using the provided buckets. The buckets are all open to the left except for the last which is closed e.g. for the array [1,10,20,50] the buckets are [1,10) [10,20) [20,50] e.g 1<=x<10 , 10<=x<20, 20<=x<50 And on the input of 1 and 50 we would have a histogram of 1,0,0

    Note

    If your histogram is evenly spaced (e.g. [0, 10, 20, 30]) this can be switched from an O(log n) insertion to O(1) per element. (where n = # buckets) if you set evenBuckets to true. buckets must be sorted and not contain any duplicates. buckets array must be at least two elements All NaN entries are treated the same. If you have a NaN bucket it must be the maximum value of the last position and all NaN entries will be counted in that bucket.

50.  def histogram(bucketCount: Int): (Array[Double], Array[Long])

    Compute a histogram of the data using bucketCount number of buckets evenly spaced between the minimum and maximum of the RDD.

    Compute a histogram of the data using bucketCount number of buckets evenly spaced between the minimum and maximum of the RDD. For example if the min value is 0 and the max is 100 and there are two buckets the resulting buckets will be [0,50) [50,100]. bucketCount must be at least 1 If the RDD contains infinity, NaN throws an exception If the elements in RDD do not vary (max == min) always returns a single bucket.

51.  def id: Int

    A unique ID for this RDD (within its SparkContext).

    A unique ID for this RDD (within its SparkContext).

    Definition Classes

    JavaRDDLike

52.  def intersection(other: JavaDoubleRDD): JavaDoubleRDD

    Return the intersection of this RDD and another one.

    Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did.

    Note

    This method performs a shuffle internally.

53.  def isCheckpointed: Boolean

    Return whether this RDD has been checkpointed or not

    Return whether this RDD has been checkpointed or not

    Definition Classes

    JavaRDDLike

54.  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.

    Definition Classes

    JavaRDDLike

55.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

56.  def iterator(split: Partition, taskContext: TaskContext): Iterator[Double]

    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.

    Definition Classes

    JavaRDDLike

57.  def keyBy[U](f: Function[Double, U]): JavaPairRDD[U, Double]

    Creates tuples of the elements in this RDD by applying f.

    Creates tuples of the elements in this RDD by applying f.

    Definition Classes

    JavaRDDLike

58.  def map[R](f: Function[Double, R]): JavaRDD[R]

    Return a new RDD by applying a function to all elements of this RDD.

    Return a new RDD by applying a function to all elements of this RDD.

    Definition Classes

    JavaRDDLike

59.  def mapPartitions[U](f: FlatMapFunction[Iterator[Double], U], preservesPartitioning: Boolean): JavaRDD[U]

    Return a new RDD by applying a function to each partition of this RDD.

    Return a new RDD by applying a function to each partition of this RDD.

    Definition Classes

    JavaRDDLike

60.  def mapPartitions[U](f: FlatMapFunction[Iterator[Double], U]): JavaRDD[U]

    Return a new RDD by applying a function to each partition of this RDD.

    Return a new RDD by applying a function to each partition of this RDD.

    Definition Classes

    JavaRDDLike

61.  def mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[Double]], preservesPartitioning: Boolean): JavaDoubleRDD

    Return a new RDD by applying a function to each partition of this RDD.

    Return a new RDD by applying a function to each partition of this RDD.

    Definition Classes

    JavaRDDLike

62.  def mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[Double]]): JavaDoubleRDD

    Return a new RDD by applying a function to each partition of this RDD.

    Return a new RDD by applying a function to each partition of this RDD.

    Definition Classes

    JavaRDDLike

63.  def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[Double], K2, V2], preservesPartitioning: Boolean): JavaPairRDD[K2, V2]

    Return a new RDD by applying a function to each partition of this RDD.

    Return a new RDD by applying a function to each partition of this RDD.

    Definition Classes

    JavaRDDLike

64.  def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[Double], K2, V2]): JavaPairRDD[K2, V2]

    Return a new RDD by applying a function to each partition of this RDD.

    Return a new RDD by applying a function to each partition of this RDD.

    Definition Classes

    JavaRDDLike

65.  def mapPartitionsWithIndex[R](f: Function2[Integer, Iterator[Double], 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.

    Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.

    Definition Classes

    JavaRDDLike

66.  def mapToDouble[R](f: DoubleFunction[Double]): JavaDoubleRDD

    Return a new RDD by applying a function to all elements of this RDD.

    Return a new RDD by applying a function to all elements of this RDD.

    Definition Classes

    JavaRDDLike

67.  def mapToPair[K2, V2](f: PairFunction[Double, K2, V2]): JavaPairRDD[K2, V2]

    Return a new RDD by applying a function to all elements of this RDD.

    Return a new RDD by applying a function to all elements of this RDD.

    Definition Classes

    JavaRDDLike

68.  def max(): Double

    Returns the maximum element from this RDD as defined by the default comparator natural order.

    Returns the maximum element from this RDD as defined by the default comparator natural order.

    returns

    the maximum of the RDD

69.  def max(comp: Comparator[Double]): Double

    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

    Definition Classes

    JavaRDDLike

70.  def mean(): Double

    Compute the mean of this RDD's elements.

71.  def meanApprox(timeout: Long): PartialResult[BoundedDouble]

    Approximate operation to return the mean within a timeout.

72.  def meanApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble]

    Return the approximate mean of the elements in this RDD.

73.  def min(): Double

    Returns the minimum element from this RDD as defined by the default comparator natural order.

    Returns the minimum element from this RDD as defined by the default comparator natural order.

    returns

    the minimum of the RDD

74.  def min(comp: Comparator[Double]): Double

    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

    Definition Classes

    JavaRDDLike

75.  def name(): String

    Definition Classes

    JavaRDDLike

76.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

77.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

78.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

79.  def partitioner: Optional[Partitioner]

    The partitioner of this RDD.

    The partitioner of this RDD.

    Definition Classes

    JavaRDDLike

80.  def partitions: List[Partition]

    Set of partitions in this RDD.

    Set of partitions in this RDD.

    Definition Classes

    JavaRDDLike

81.  def persist(newLevel: StorageLevel): JavaDoubleRDD

    Set this RDD's storage level to persist its values across operations after the first time it is computed.

    Set this RDD's storage level to persist its values across operations after the first time it is computed. Can only be called once on each RDD.

82.  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.

    Return an RDD created by piping elements to a forked external process.

    Definition Classes

    JavaRDDLike

83.  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.

    Return an RDD created by piping elements to a forked external process.

    Definition Classes

    JavaRDDLike

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

    Return an RDD created by piping elements to a forked external process.

    Return an RDD created by piping elements to a forked external process.

    Definition Classes

    JavaRDDLike

85.  def pipe(command: List[String]): JavaRDD[String]

    Return an RDD created by piping elements to a forked external process.

    Return an RDD created by piping elements to a forked external process.

    Definition Classes

    JavaRDDLike

86.  def pipe(command: String): JavaRDD[String]

    Return an RDD created by piping elements to a forked external process.

    Return an RDD created by piping elements to a forked external process.

    Definition Classes

    JavaRDDLike

87.  def popStdev(): Double

    Compute the population standard deviation of this RDD's elements.

    Compute the population standard deviation of this RDD's elements.

    Annotations

    @Since( "2.1.0" )

88.  def popVariance(): Double

    Compute the population variance of this RDD's elements.

    Compute the population variance of this RDD's elements.

    Annotations

    @Since( "2.1.0" )

89.  val rdd: RDD[Double]

    Definition Classes

    JavaDoubleRDD → JavaRDDLike

90.  def reduce(f: Function2[Double, Double, Double]): Double

    Reduces the elements of this RDD using the specified commutative and associative binary operator.

    Reduces the elements of this RDD using the specified commutative and associative binary operator.

    Definition Classes

    JavaRDDLike

91.  def repartition(numPartitions: Int): JavaDoubleRDD

    Return a new RDD that has exactly numPartitions partitions.

    Return a new RDD that has exactly numPartitions partitions.

    Can increase or decrease the level of parallelism in this RDD. Internally, this uses a shuffle to redistribute data.

    If you are decreasing the number of partitions in this RDD, consider using coalesce, which can avoid performing a shuffle.

92.  def sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaDoubleRDD

    Return a sampled subset of this RDD.

93.  def sample(withReplacement: Boolean, fraction: Double): JavaDoubleRDD

    Return a sampled subset of this RDD.

94.  def sampleStdev(): Double

    Compute the sample standard deviation of this RDD's elements (which corrects for bias in estimating the standard deviation by dividing by N-1 instead of N).

95.  def sampleVariance(): Double

    Compute the sample variance of this RDD's elements (which corrects for bias in estimating the standard variance by dividing by N-1 instead of N).

96.  def saveAsObjectFile(path: String): Unit

    Save this RDD as a SequenceFile of serialized objects.

    Save this RDD as a SequenceFile of serialized objects.

    Definition Classes

    JavaRDDLike

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

    Save this RDD as a compressed text file, using string representations of elements.

    Save this RDD as a compressed text file, using string representations of elements.

    Definition Classes

    JavaRDDLike

98.  def saveAsTextFile(path: String): Unit

    Save this RDD as a text file, using string representations of elements.

    Save this RDD as a text file, using string representations of elements.

    Definition Classes

    JavaRDDLike

99.  def setName(name: String): JavaDoubleRDD

    Assign a name to this RDD

100.  val srdd: RDD[Double]
101.  def stats(): StatCounter

     Return a org.apache.spark.util.StatCounter object that captures the mean, variance and count of the RDD's elements in one operation.

102.  def stdev(): Double

     Compute the population standard deviation of this RDD's elements.

103.  def subtract(other: JavaDoubleRDD, p: Partitioner): JavaDoubleRDD

     Return an RDD with the elements from this that are not in other.

104.  def subtract(other: JavaDoubleRDD, numPartitions: Int): JavaDoubleRDD

     Return an RDD with the elements from this that are not in other.

105.  def subtract(other: JavaDoubleRDD): JavaDoubleRDD

     Return an RDD with the elements from this that are not in other.

     Return an RDD with the elements from this that are not in other.

     Uses this partitioner/partition size, because even if other is huge, the resulting RDD will be <= us.

106.  def sum(): Double

     Add up the elements in this RDD.

107.  def sumApprox(timeout: Long): PartialResult[BoundedDouble]

     Approximate operation to return the sum within a timeout.

108.  def sumApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble]

     Approximate operation to return the sum within a timeout.

109.  final def synchronized[T0](arg0: ⇒ T0): T0

     Definition Classes

     AnyRef

110.  def take(num: Int): List[Double]

     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.

     Definition Classes

     JavaRDDLike

     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.

111.  def takeAsync(num: Int): JavaFutureAction[List[Double]]

     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.

     Definition Classes

     JavaRDDLike

     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.

112.  def takeOrdered(num: Int): List[Double]

     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

     Definition Classes

     JavaRDDLike

     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.

113.  def takeOrdered(num: Int, comp: Comparator[Double]): List[Double]

     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

     Definition Classes

     JavaRDDLike

     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.

114.  def takeSample(withReplacement: Boolean, num: Int, seed: Long): List[Double]

     Definition Classes

     JavaRDDLike

115.  def takeSample(withReplacement: Boolean, num: Int): List[Double]

     Definition Classes

     JavaRDDLike

116.  def toDebugString(): String

     A description of this RDD and its recursive dependencies for debugging.

     A description of this RDD and its recursive dependencies for debugging.

     Definition Classes

     JavaRDDLike

117.  def toLocalIterator(): Iterator[Double]

     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.

     Definition Classes

     JavaRDDLike

118.  def toString(): String

     Definition Classes

     AnyRef → Any

119.  def top(num: Int): List[Double]

     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

     Definition Classes

     JavaRDDLike

     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.

120.  def top(num: Int, comp: Comparator[Double]): List[Double]

     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

     Definition Classes

     JavaRDDLike

     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.

121.  def treeAggregate[U](zeroValue: U, seqOp: Function2[U, Double, U], combOp: Function2[U, U, U]): U

     org.apache.spark.api.java.JavaRDDLike.treeAggregate with suggested depth 2.

     org.apache.spark.api.java.JavaRDDLike.treeAggregate with suggested depth 2.

     Definition Classes

     JavaRDDLike

122.  def treeAggregate[U](zeroValue: U, seqOp: Function2[U, Double, 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

     Definition Classes

     JavaRDDLike

     See also

     org.apache.spark.api.java.JavaRDDLike#aggregate

123.  def treeReduce(f: Function2[Double, Double, Double]): Double

     org.apache.spark.api.java.JavaRDDLike.treeReduce with suggested depth 2.

     org.apache.spark.api.java.JavaRDDLike.treeReduce with suggested depth 2.

     Definition Classes

     JavaRDDLike

124.  def treeReduce(f: Function2[Double, Double, Double], depth: Int): Double

     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

     Definition Classes

     JavaRDDLike

     See also

     org.apache.spark.api.java.JavaRDDLike#reduce

125.  def union(other: JavaDoubleRDD): JavaDoubleRDD

     Return the union of this RDD and another one.

     Return the union of this RDD and another one. Any identical elements will appear multiple times (use .distinct() to eliminate them).

126.  def unpersist(blocking: Boolean): JavaDoubleRDD

     Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.

     Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.

     blocking

     Whether to block until all blocks are deleted.

127.  def unpersist(): JavaDoubleRDD

     Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.

     Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. This method blocks until all blocks are deleted.

128.  def variance(): Double

     Compute the population variance of this RDD's elements.

129.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

130.  final def wait(arg0: Long, arg1: Int): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

131.  final def wait(arg0: Long): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... ) @native()

132.  def wrapRDD(rdd: RDD[Double]): JavaDoubleRDD

     Definition Classes

     JavaDoubleRDD → JavaRDDLike

133.  def zip[U](other: JavaRDDLike[U, _]): JavaPairRDD[Double, 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).

     Definition Classes

     JavaRDDLike

134.  def zipPartitions[U, V](other: JavaRDDLike[U, _], f: FlatMapFunction2[Iterator[Double], 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.

     Definition Classes

     JavaRDDLike

135.  def zipWithIndex(): JavaPairRDD[Double, 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.

     Definition Classes

     JavaRDDLike

136.  def zipWithUniqueId(): JavaPairRDD[Double, 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.

     Definition Classes

     JavaRDDLike