spark

RDD

abstract class RDD[T] extends Serializable

A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable, partitioned collection of elements that can be operated on in parallel. This class contains the basic operations available on all RDDs, such as map, filter, and persist. In addition, PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; DoubleRDDFunctions contains operations available only on RDDs of Doubles; and SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions when you import spark.SparkContext._.

Internally, each RDD is characterized by five main properties:

All of the scheduling and execution in Spark is done based on these methods, allowing each RDD to implement its own way of computing itself. Indeed, users can implement custom RDDs (e.g. for reading data from a new storage system) by overriding these functions. Please refer to the Spark paper for more details on RDD internals.

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Instance Constructors

  1. new RDD(sc: SparkContext)(implicit arg0: ClassManifest[T])

Abstract Value Members

  1. abstract def compute(split: Split): Iterator[T]

    Function for computing a given partition.

  2. abstract val dependencies: List[spark.Dependency[_]]

    How this RDD depends on any parent RDDs.

  3. abstract def splits: Array[Split]

    Set of partitions in this RDD.

Concrete Value Members

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

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

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  3. final def ##(): Int

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  4. def ++(other: RDD[T]): RDD[T]

    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).

  5. final def ==(arg0: AnyRef): Boolean

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  6. final def ==(arg0: Any): Boolean

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  7. def aggregate[U](zeroValue: U)(seqOp: (U, T) ⇒ 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.

  8. final def asInstanceOf[T0]: T0

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  9. def cache(): RDD[T]

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

  10. def cartesian[U](other: RDD[U])(implicit arg0: ClassManifest[U]): RDD[(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.

  11. def clone(): AnyRef

    Attributes
    protected[lang]
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    @throws()
  12. def collect(): Array[T]

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

  13. def context: SparkContext

    The SparkContext that this RDD was created on.

  14. def count(): Long

    Return the number of elements in the RDD.

  15. 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.

  16. 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.

  17. def countByValueApprox(timeout: Long, confidence: Double = 0.95): PartialResult[Map[T, BoundedDouble]]

    (Experimental) Approximate version of countByValue().

  18. def distinct(numSplits: Int = splits.size): RDD[T]

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

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

    Definition Classes
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  20. def equals(arg0: Any): Boolean

    Definition Classes
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  21. def filter(f: (T) ⇒ Boolean): RDD[T]

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

  22. def finalize(): Unit

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  23. def first(): T

    Return the first element in this RDD.

  24. def flatMap[U](f: (T) ⇒ 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.

  25. def fold(zeroValue: T)(op: (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: (T) ⇒ Unit): Unit

    Applies a function f to all elements of this RDD.

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

    Definition Classes
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  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(): RDD[Array[T]]

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

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

    Return an RDD of grouped items.

  31. def groupBy[K](f: (T) ⇒ K, numSplits: Int)(implicit arg0: ClassManifest[K]): RDD[(K, Seq[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
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  33. val id: Int

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

  34. final def isInstanceOf[T0]: Boolean

    Definition Classes
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  35. final 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[U](f: (T) ⇒ U)(implicit arg0: ClassManifest[U]): RDD[U]

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

  37. def mapPartitions[U](f: (Iterator[T]) ⇒ Iterator[U])(implicit arg0: ClassManifest[U]): RDD[U]

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

  38. def mapPartitionsWithSplit[U](f: (Int, Iterator[T]) ⇒ 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.

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

    Definition Classes
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  40. final def notify(): Unit

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  41. final def notifyAll(): Unit

    Definition Classes
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  42. val partitioner: Option[Partitioner]

    Optionally overridden by subclasses to specify how they are partitioned.

  43. def persist(): RDD[T]

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

  44. def persist(newLevel: StorageLevel): RDD[T]

    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.

  45. def pipe(command: Seq[String], env: Map[String, String]): RDD[String]

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

  46. def pipe(command: Seq[String]): RDD[String]

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

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

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

  48. def preferredLocations(split: Split): Seq[String]

    Optionally overridden by subclasses to specify placement preferences.

  49. def reduce(f: (T, T) ⇒ T): T

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

  50. def sample(withReplacement: Boolean, fraction: Double, seed: Int): RDD[T]

    Return a sampled subset of this RDD.

  51. def saveAsObjectFile(path: String): Unit

    Save this RDD as a SequenceFile of serialized objects.

  52. def saveAsTextFile(path: String): Unit

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

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

    Definition Classes
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  54. def take(num: Int): Array[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.

  55. def takeSample(withReplacement: Boolean, num: Int, seed: Int): Array[T]

  56. def toArray(): Array[T]

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

  57. def toString(): String

    Definition Classes
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  58. def union(other: RDD[T]): RDD[T]

    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).

  59. final def wait(): Unit

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  60. final def wait(arg0: Long, arg1: Int): Unit

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  61. final def wait(arg0: Long): Unit

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