Class/Object

org.apache.spark.api.java

JavaDoubleRDD

Related Docs: object JavaDoubleRDD | package java

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class JavaDoubleRDD extends AbstractJavaRDDLike[Double, JavaDoubleRDD]

Source
JavaDoubleRDD.scala
Linear Supertypes
AbstractJavaRDDLike[Double, JavaDoubleRDD], JavaRDDLike[Double, JavaDoubleRDD], Serializable, Serializable, AnyRef, Any
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Inherited
  1. JavaDoubleRDD
  2. AbstractJavaRDDLike
  3. JavaRDDLike
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Visibility
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Instance Constructors

  1. new JavaDoubleRDD(srdd: RDD[Double])

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Value Members

  1. final def !=(arg0: Any): Boolean

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

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

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

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    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

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    Definition Classes
    Any
  6. def cache(): JavaDoubleRDD

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

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

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    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

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    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]

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    Definition Classes
    JavaDoubleRDDJavaRDDLike
  10. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  11. def coalesce(numPartitions: Int, shuffle: Boolean): JavaDoubleRDD

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    Return a new RDD that is reduced into numPartitions partitions.

  12. def coalesce(numPartitions: Int): JavaDoubleRDD

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    Return a new RDD that is reduced into numPartitions partitions.

  13. def collect(): List[Double]

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    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]]

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    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]]

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    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

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    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

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    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]

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    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]

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    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

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    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]

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    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]

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    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]]

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    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]]

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    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

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

  26. def distinct(): JavaDoubleRDD

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

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

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

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    Definition Classes
    AnyRef → Any
  29. def filter(f: Function[Double, Boolean]): JavaDoubleRDD

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

  30. def finalize(): Unit

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

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    Return the first element in this RDD.

    Return the first element in this RDD.

    Definition Classes
    JavaDoubleRDDJavaRDDLike
  32. def flatMap[U](f: FlatMapFunction[Double, U]): JavaRDD[U]

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    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

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    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]

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    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

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    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

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    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]

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    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

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    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]

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    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]

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    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[_]

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    Definition Classes
    AnyRef → Any
  42. def getNumPartitions: Int

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    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

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    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]]

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    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]]

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    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]]

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    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

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    Definition Classes
    AnyRef → Any
  48. def histogram(buckets: Array[Double], evenBuckets: Boolean): Array[Long]

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  49. def histogram(buckets: Array[Double]): Array[Long]

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    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])

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    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

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    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

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    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

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    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

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    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

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

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    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]

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    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]

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    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]

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    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]

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    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

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    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

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    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]

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    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]

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    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]

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    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

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    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]

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    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

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    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

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    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

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    Compute the mean of this RDD's elements.

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

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    Approximate operation to return the mean within a timeout.

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

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    Return the approximate mean of the elements in this RDD.

  73. def min(): Double

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    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

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    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

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    Definition Classes
    JavaRDDLike
  76. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  77. final def notify(): Unit

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    Definition Classes
    AnyRef
  78. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  79. def partitioner: Optional[Partitioner]

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    The partitioner of this RDD.

    The partitioner of this RDD.

    Definition Classes
    JavaRDDLike
  80. def partitions: List[Partition]

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    Set of partitions in this RDD.

    Set of partitions in this RDD.

    Definition Classes
    JavaRDDLike
  81. def persist(newLevel: StorageLevel): JavaDoubleRDD

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    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]

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    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]

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    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]

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    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]

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    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]

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    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

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    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

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    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]

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    Definition Classes
    JavaDoubleRDDJavaRDDLike
  90. def reduce(f: Function2[Double, Double, Double]): Double

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    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

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    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

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    Return a sampled subset of this RDD.

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

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    Return a sampled subset of this RDD.

  94. def sampleStdev(): Double

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    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

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    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

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    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

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    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

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    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

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    Assign a name to this RDD

  100. val srdd: RDD[Double]

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  101. def stats(): StatCounter

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    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

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

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

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

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

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

  105. def subtract(other: JavaDoubleRDD): JavaDoubleRDD

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    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

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    Add up the elements in this RDD.

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

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    Approximate operation to return the sum within a timeout.

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

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    Approximate operation to return the sum within a timeout.

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

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    Definition Classes
    AnyRef
  110. def take(num: Int): List[Double]

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    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]]

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    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]

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    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]

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    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]

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

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    Definition Classes
    JavaRDDLike
  116. def toDebugString(): String

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    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]

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    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

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    Definition Classes
    AnyRef → Any
  119. def top(num: Int): List[Double]

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    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]

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    Compute the population variance of this RDD's elements.

  129. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  132. def wrapRDD(rdd: RDD[Double]): JavaDoubleRDD

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

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    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]

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    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]

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    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]

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    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

Inherited from AbstractJavaRDDLike[Double, JavaDoubleRDD]

Inherited from JavaRDDLike[Double, JavaDoubleRDD]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped