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Value Members

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

Definition Classes
AnyRef
2. final def !=(arg0: Any): Boolean

Definition Classes
Any
3. final def ##(): Int

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

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

Definition Classes
Any
6. 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
7. final def asInstanceOf[T0]: T0

Definition Classes
Any

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

9. 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
10. 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
11. val classTag: ClassTag[Double]

Definition Classes
12. def clone(): AnyRef

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

Return a new RDD that is reduced into `numPartitions` partitions.

Return a new RDD that is reduced into `numPartitions` partitions.

15. 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
16. 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
17. 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
18. 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
19. def count(): Long

Return the number of elements in the RDD.

Return the number of elements in the RDD.

Definition Classes
JavaRDDLike
20. 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.

Definition Classes
JavaRDDLike
21. 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.

Definition Classes
JavaRDDLike
22. 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
23. 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
24. 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
25. def countByValueApprox(timeout: Long): PartialResult[Map[Double, BoundedDouble]]

(Experimental) Approximate version of countByValue().

(Experimental) Approximate version of countByValue().

Definition Classes
JavaRDDLike
26. def countByValueApprox(timeout: Long, confidence: Double): PartialResult[Map[Double, BoundedDouble]]

(Experimental) Approximate version of countByValue().

(Experimental) Approximate version of countByValue().

Definition Classes
JavaRDDLike

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

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

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

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

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

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

32. def finalize(): Unit

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

Return the first element in this RDD.

Return the first element in this RDD.

Definition Classes
34. 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

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
36. 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
37. 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 and commutative function and a neutral "zero value".

Aggregate the elements of each partition, and then the results for all the partitions, using a given associative and commutative 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. final def getClass(): Class[_]

Definition Classes
AnyRef → Any
44. 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" )
45. def getStorageLevel: StorageLevel

Get the RDD's current storage level, or StorageLevel.

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

Definition Classes
JavaRDDLike
46. 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
47. 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
48. 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
49. def hashCode(): Int

Definition Classes
AnyRef → Any

51. 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.

52. 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.

53. def id: Int

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

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

Definition Classes
JavaRDDLike

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 that this method performs a shuffle internally.

55. def isCheckpointed: Boolean

Return whether this RDD has been checkpointed or not

Return whether this RDD has been checkpointed or not

Definition Classes
JavaRDDLike
56. 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
57. final def isInstanceOf[T0]: Boolean

Definition Classes
Any

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
59. 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
60. 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
61. 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
62. 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
63. 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

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 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
66. 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
67. 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

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
69. 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
70. 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

71. 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
72. def mean(): Double

Compute the mean of this RDD's elements.

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

Approximate operation to return the mean within a timeout.

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

Return the approximate mean of the elements in this RDD.

75. 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

76. 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
77. def name(): String

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

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

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

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

The partitioner of this RDD.

The partitioner of this RDD.

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

Set of partitions in this RDD.

Set of partitions in this RDD.

Definition Classes
JavaRDDLike

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.

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. val rdd: RDD[Double]

Definition Classes
88. 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

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.

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

Return a sampled subset of this RDD.

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

Return a sampled subset of this RDD.

92. 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).

93. 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).

94. 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
95. 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
96. 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

Assign a name to this RDD

99. 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.

100. def stdev(): Double

Compute the standard deviation of this RDD's elements.

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`.

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.

104. def sum(): Double

Add up the elements in this RDD.

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

Approximate operation to return the sum within a timeout.

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

Approximate operation to return the sum within a timeout.

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

Definition Classes
AnyRef
108. 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
109. 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
110. 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
111. 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
112. def takeSample(withReplacement: Boolean, num: Int, seed: Long): List[Double]

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

Definition Classes
JavaRDDLike
114. 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
115. 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
116. def toString(): String

Definition Classes
AnyRef → Any
117. 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
118. 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
119. 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.

Definition Classes
JavaRDDLike
120. 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
121. def treeReduce(f: Function2[Double, Double, Double]): Double

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

Definition Classes
JavaRDDLike
122. 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

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).

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.

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.

126. def variance(): Double

Compute the variance of this RDD's elements.

127. final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Definition Classes
131. 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
132. 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
133. 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
134. 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

Deprecated Value Members

1. def splits: List[Partition]

Definition Classes
JavaRDDLike
Annotations
@deprecated
Deprecated

(Since version 1.1.0) Use partitions() instead.

2. def toArray(): 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
Annotations
@deprecated
Deprecated

(Since version 1.0.0) use collect()