spark.rdd

NewHadoopRDD

class NewHadoopRDD[K, V] extends RDD[(K, V)]

Linear Supertypes
RDD[(K, V)], Serializable, Serializable, AnyRef, Any
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  1. NewHadoopRDD
  2. RDD
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
Visibility
  1. Public
  2. All

Instance Constructors

  1. new NewHadoopRDD(sc: SparkContext, inputFormatClass: Class[_ <: org.apache.hadoop.mapreduce.InputFormat[K,V]], keyClass: Class[K], valueClass: Class[V], conf: Configuration)

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. def ++(other: RDD[(K, V)]): RDD[(K, V)]

    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).

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

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

    Definition Classes
    Any
  7. def aggregate[U](zeroValue: U)(seqOp: (U, (K, V)) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassManifest[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
    RDD
  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  9. def cache(): RDD[(K, V)]

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

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

    Definition Classes
    RDD
  10. def cartesian[U](other: RDD[U])(implicit arg0: ClassManifest[U]): RDD[((K, V), 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
    RDD
  11. def clone(): AnyRef

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  12. def collect(): Array[(K, V)]

    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
    RDD
  13. def compute(theSplit: Split): Iterator[(K, V)]

    Function for computing a given partition.

    Function for computing a given partition.

    Definition Classes
    NewHadoopRDDRDD
  14. val confBroadcast: Broadcast[SerializableWritable[Configuration]]

  15. def context: SparkContext

    The SparkContext that this RDD was created on.

    The SparkContext that this RDD was created on.

    Definition Classes
    RDD
  16. def count(): Long

    Return the number of elements in the RDD.

    Return the number of elements in the RDD.

    Definition Classes
    RDD
  17. def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble]

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

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

    Definition Classes
    RDD
  18. def countByValue(): Map[(K, V), 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
    RDD
  19. def countByValueApprox(timeout: Long, confidence: Double = 0.95): PartialResult[Map[(K, V), BoundedDouble]]

    (Experimental) Approximate version of countByValue().

    (Experimental) Approximate version of countByValue().

    Definition Classes
    RDD
  20. val dependencies: List[spark.Dependency[_]]

    How this RDD depends on any parent RDDs.

    How this RDD depends on any parent RDDs.

    Definition Classes
    NewHadoopRDDRDD
  21. def distinct(numSplits: Int = splits.size): RDD[(K, V)]

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

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

    Definition Classes
    RDD
  22. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  24. def filter(f: ((K, V)) ⇒ Boolean): RDD[(K, V)]

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

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

    Definition Classes
    RDD
  25. def finalize(): Unit

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  26. def first(): (K, V)

    Return the first element in this RDD.

    Return the first element in this RDD.

    Definition Classes
    RDD
  27. def flatMap[U](f: ((K, V)) ⇒ TraversableOnce[U])(implicit arg0: ClassManifest[U]): RDD[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
    RDD
  28. def fold(zeroValue: (K, V))(op: ((K, V), (K, V)) ⇒ (K, V)): (K, V)

    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.

    Definition Classes
    RDD
  29. def foreach(f: ((K, V)) ⇒ Unit): Unit

    Applies a function f to all elements of this RDD.

    Applies a function f to all elements of this RDD.

    Definition Classes
    RDD
  30. final def getClass(): java.lang.Class[_]

    Definition Classes
    AnyRef → Any
  31. 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
    RDD
  32. def glom(): RDD[Array[(K, V)]]

    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
    RDD
  33. def groupBy[K](f: ((K, V)) ⇒ K)(implicit arg0: ClassManifest[K]): RDD[(K, Seq[(K, V)])]

    Return an RDD of grouped items.

    Return an RDD of grouped items.

    Definition Classes
    RDD
  34. def groupBy[K](f: ((K, V)) ⇒ K, numSplits: Int)(implicit arg0: ClassManifest[K]): RDD[(K, Seq[(K, V)])]

    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
    RDD
  35. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  36. val id: Int

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

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

    Definition Classes
    RDD
  37. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  38. final def iterator(split: Split): Iterator[(K, V)]

    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
    RDD
  39. def map[U](f: ((K, V)) ⇒ U)(implicit arg0: ClassManifest[U]): RDD[U]

    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
    RDD
  40. def mapPartitions[U](f: (Iterator[(K, V)]) ⇒ Iterator[U])(implicit arg0: ClassManifest[U]): RDD[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
    RDD
  41. def mapPartitionsWithSplit[U](f: (Int, Iterator[(K, V)]) ⇒ Iterator[U])(implicit arg0: ClassManifest[U]): RDD[U]

    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
    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. val partitioner: Option[Partitioner]

    Optionally overridden by subclasses to specify how they are partitioned.

    Optionally overridden by subclasses to specify how they are partitioned.

    Definition Classes
    RDD
  46. def persist(): RDD[(K, V)]

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

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

    Definition Classes
    RDD
  47. def persist(newLevel: StorageLevel): RDD[(K, V)]

    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.

    Definition Classes
    RDD
  48. def pipe(command: Seq[String], env: Map[String, String]): RDD[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
    RDD
  49. def pipe(command: Seq[String]): RDD[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
    RDD
  50. def pipe(command: String): RDD[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
    RDD
  51. def preferredLocations(split: Split): WrappedArray[String]

    Optionally overridden by subclasses to specify placement preferences.

    Optionally overridden by subclasses to specify placement preferences.

    Definition Classes
    NewHadoopRDDRDD
  52. def reduce(f: ((K, V), (K, V)) ⇒ (K, V)): (K, V)

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

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

    Definition Classes
    RDD
  53. def sample(withReplacement: Boolean, fraction: Double, seed: Int): RDD[(K, V)]

    Return a sampled subset of this RDD.

    Return a sampled subset of this RDD.

    Definition Classes
    RDD
  54. 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
    RDD
  55. 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
    RDD
  56. def splits: Array[Split]

    Set of partitions in this RDD.

    Set of partitions in this RDD.

    Definition Classes
    NewHadoopRDDRDD
  57. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  58. def take(num: Int): Array[(K, V)]

    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
    RDD
  59. def takeSample(withReplacement: Boolean, num: Int, seed: Int): Array[(K, V)]

    Definition Classes
    RDD
  60. def toArray(): Array[(K, V)]

    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
    RDD
  61. def toString(): String

    Definition Classes
    AnyRef → Any
  62. def union(other: RDD[(K, V)]): RDD[(K, V)]

    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).

    Definition Classes
    RDD
  63. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws()

Inherited from RDD[(K, V)]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any