spark.api.java

JavaRDDLike

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

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PairFlatMapWorkaround[T], Serializable, AnyRef, Any
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  1. JavaRDDLike
  2. PairFlatMapWorkaround
  3. Serializable
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Abstract Value Members

  1. implicit abstract val classManifest: ClassManifest[T]

  2. abstract def rdd: RDD[T]

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

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

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

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

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def cartesian[U](other: spark.api.java.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.

  9. def clone(): AnyRef

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

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

  11. def context: SparkContext

    The SparkContext that this RDD was created on.

  12. def count(): Long

    Return the number of elements in the RDD.

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

    (Experimental) Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

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

    (Experimental) Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

  15. 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.

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

    (Experimental) Approximate version of countByValue().

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

    (Experimental) Approximate version of countByValue().

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

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

    Definition Classes
    AnyRef → Any
  20. def finalize(): Unit

    Attributes
    protected[lang]
    Definition Classes
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    @throws()
  21. def first(): T

    Return the first element in this RDD.

  22. def flatMap(f: DoubleFlatMapFunction[T]): JavaDoubleRDD

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

  23. 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.

  24. def flatMap[K, V](f: PairFlatMapFunction[T, K, V]): JavaPairRDD[K, V]

    Definition Classes
    PairFlatMapWorkaround
  25. 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.

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

    Applies a function f to all elements of this RDD.

  27. final def getClass(): java.lang.Class[_]

    Definition Classes
    AnyRef → Any
  28. 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.

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

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

  30. def groupBy[K](f: Function[T, K], numSplits: Int): JavaPairRDD[K, List[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.

  31. def groupBy[K](f: Function[T, K]): JavaPairRDD[K, List[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.

  32. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  33. def id: Int

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

  34. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  35. def iterator(split: Split): Iterator[T]

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

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

  36. def map[K2, V2](f: PairFunction[T, K2, V2]): JavaPairRDD[K2, V2]

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

  37. def map[R](f: DoubleFunction[T]): JavaDoubleRDD

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

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

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

  39. def mapPartitions[K, V](f: PairFlatMapFunction[Iterator[T], K, V]): JavaPairRDD[K, V]

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

  40. def mapPartitions(f: DoubleFlatMapFunction[Iterator[T]]): JavaDoubleRDD

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

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

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

  42. final def ne(arg0: AnyRef): Boolean

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

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

    Definition Classes
    AnyRef
  45. def pipe(command: List[String], env: Map[String, String]): JavaRDD[String]

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

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

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

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

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

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

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

  49. def saveAsObjectFile(path: String): Unit

    Save this RDD as a SequenceFile of serialized objects.

  50. def saveAsTextFile(path: String): Unit

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

  51. def splits: List[Split]

    Set of partitions in this RDD.

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

    Definition Classes
    AnyRef
  53. 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.

  54. def takeSample(withReplacement: Boolean, num: Int, seed: Int): List[T]

  55. def toString(): String

    Definition Classes
    AnyRef → Any
  56. final def wait(): Unit

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

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

    Definition Classes
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    @throws()

Inherited from PairFlatMapWorkaround[T]

Inherited from Serializable

Inherited from AnyRef

Inherited from Any