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org.apache.spark.api.java

JavaRDDLike

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

Defines operations common to several Java RDD implementations.

Source
JavaRDDLike.scala
Note

This trait is not intended to be implemented by user code.

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  1. JavaRDDLike
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Abstract Value Members

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

Concrete 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, T, U], combOp: Function2[U, U, U]): U

    Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value".

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

  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def cartesian[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, U]

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

  7. 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.

  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  9. def collect(): List[T]

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

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

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

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

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

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

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

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

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

  12. def context: SparkContext

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

  13. def count(): Long

    Return the number of elements in the RDD.

  14. 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

  15. 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

  16. 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.

  17. def countAsync(): JavaFutureAction[Long]

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

  18. def countByValue(): Map[T, Long]

    Return the count of each unique value in this RDD as a map of (value, count) pairs.

    Return the count of each unique value in this RDD as a map of (value, count) pairs. The final combine step happens locally on the master, equivalent to running a single reduce task.

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

    Approximate version of countByValue().

    Approximate version of countByValue().

    timeout

    maximum time to wait for the job, in milliseconds

    returns

    a potentially incomplete result, with error bounds

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

    Approximate version of countByValue().

    Approximate version of countByValue().

    The confidence is the probability that the error bounds of the result will contain the true value. That is, if countApprox were called repeatedly with confidence 0.9, we would expect 90% of the results to contain the true count. The confidence must be in the range [0,1] or an exception will be thrown.

    timeout

    maximum time to wait for the job, in milliseconds

    confidence

    the desired statistical confidence in the result

    returns

    a potentially incomplete result, with error bounds

  21. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  22. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  23. def first(): T

    Return the first element in this RDD.

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

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

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

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

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

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

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

    Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value".

    Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value". The function op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2.

    This behaves somewhat differently from fold operations implemented for non-distributed collections in functional languages like Scala. This fold operation may be applied to partitions individually, and then fold those results into the final result, rather than apply the fold to each element sequentially in some defined ordering. For functions that are not commutative, the result may differ from that of a fold applied to a non-distributed collection.

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

    Applies a function f to all elements of this RDD.

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

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

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

    Applies a function f to each partition of this RDD.

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

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

  32. def getCheckpointFile(): Optional[String]

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

  33. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  34. def getNumPartitions: Int

    Return the number of partitions in this RDD.

    Return the number of partitions in this RDD.

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

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

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

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

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

    Return an RDD of grouped elements.

    Return an RDD of grouped elements. Each group consists of a key and a sequence of elements mapping to that key.

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

    Return an RDD of grouped elements.

    Return an RDD of grouped elements. Each group consists of a key and a sequence of elements mapping to that key.

  39. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  40. def id: Int

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

  41. def isCheckpointed: Boolean

    Return whether this RDD has been checkpointed or not

  42. 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.

  43. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  44. def iterator(split: Partition, taskContext: TaskContext): Iterator[T]

    Internal method to this RDD; will read from cache if applicable, or otherwise compute it.

    Internal method to this RDD; will read from cache if applicable, or otherwise compute it. This should not be called by users directly, but is available for implementers of custom subclasses of RDD.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    Returns the maximum element from this RDD as defined by the specified Comparator[T].

    Returns the maximum element from this RDD as defined by the specified Comparator[T].

    comp

    the comparator that defines ordering

    returns

    the maximum of the RDD

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

    Returns the minimum element from this RDD as defined by the specified Comparator[T].

    Returns the minimum element from this RDD as defined by the specified Comparator[T].

    comp

    the comparator that defines ordering

    returns

    the minimum of the RDD

  58. def name(): String
  59. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  60. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  61. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  62. def partitioner: Optional[Partitioner]

    The partitioner of this RDD.

  63. def partitions: List[Partition]

    Set of partitions in this RDD.

  64. 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.

  65. 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.

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

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

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

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

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

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

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

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

  70. def saveAsObjectFile(path: String): Unit

    Save this RDD as a SequenceFile of serialized objects.

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

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

  72. def saveAsTextFile(path: String): Unit

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

  73. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  74. def take(num: Int): List[T]

    Take the first num elements of the RDD.

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

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

  75. def takeAsync(num: Int): JavaFutureAction[List[T]]

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

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

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

  76. def takeOrdered(num: Int): List[T]

    Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.

    Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.

    num

    k, the number of top elements to return

    returns

    an array of top elements

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

  77. def takeOrdered(num: Int, comp: Comparator[T]): List[T]

    Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.

    Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.

    num

    k, the number of elements to return

    comp

    the comparator that defines the order

    returns

    an array of top elements

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

  78. def takeSample(withReplacement: Boolean, num: Int, seed: Long): List[T]
  79. def takeSample(withReplacement: Boolean, num: Int): List[T]
  80. def toDebugString(): String

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

  81. def toLocalIterator(): Iterator[T]

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

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

    The iterator will consume as much memory as the largest partition in this RDD.

  82. def toString(): String
    Definition Classes
    AnyRef → Any
  83. def top(num: Int): List[T]

    Returns the top k (largest) elements from this RDD using the natural ordering for T and maintains the order.

    Returns the top k (largest) elements from this RDD using the natural ordering for T and maintains the order.

    num

    k, the number of top elements to return

    returns

    an array of top elements

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

  84. def top(num: Int, comp: Comparator[T]): List[T]

    Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.

    Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.

    num

    k, the number of top elements to return

    comp

    the comparator that defines the order

    returns

    an array of top elements

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

  85. def treeAggregate[U](zeroValue: U, seqOp: Function2[U, T, U], combOp: Function2[U, U, U], depth: Int, finalAggregateOnExecutor: Boolean): U

    org.apache.spark.api.java.JavaRDDLike.treeAggregate with a parameter to do the final aggregation on the executor.

  86. def treeAggregate[U](zeroValue: U, seqOp: Function2[U, T, U], combOp: Function2[U, U, U]): U

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

  87. def treeAggregate[U](zeroValue: U, seqOp: Function2[U, T, U], combOp: Function2[U, U, U], depth: Int): U

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

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

    depth

    suggested depth of the tree

    See also

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

  88. def treeReduce(f: Function2[T, T, T]): T

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

  89. def treeReduce(f: Function2[T, T, T], depth: Int): T

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

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

    depth

    suggested depth of the tree

    See also

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

  90. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  91. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  92. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  93. def zip[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, U]

    Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc.

    Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc. Assumes that the two RDDs have the *same number of partitions* and the *same number of elements in each partition* (e.g. one was made through a map on the other).

  94. def zipPartitions[U, V](other: JavaRDDLike[U, _], f: FlatMapFunction2[Iterator[T], Iterator[U], V]): JavaRDD[V]

    Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions.

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

  95. def zipWithIndex(): JavaPairRDD[T, Long]

    Zips this RDD with its element indices.

    Zips this RDD with its element indices. The ordering is first based on the partition index and then the ordering of items within each partition. So the first item in the first partition gets index 0, and the last item in the last partition receives the largest index. This is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type. This method needs to trigger a spark job when this RDD contains more than one partitions.

  96. def zipWithUniqueId(): JavaPairRDD[T, Long]

    Zips this RDD with generated unique Long ids.

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

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

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