Class/Object

org.apache.spark.rdd

InstrumentedPairRDDFunctions

Related Docs: object InstrumentedPairRDDFunctions | package rdd

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class InstrumentedPairRDDFunctions[K, V] extends Serializable

A version of PairRDDFunctions which enables instrumentation of its operations. For more details and usage instructions see the MetricsContext class.

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Instance Constructors

  1. new InstrumentedPairRDDFunctions(self: RDD[(K, V)])(implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null)

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Value Members

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

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  2. final def ##(): Int

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

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  4. def aggregateByKey[U](zeroValue: U)(seqOp: (U, V) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassTag[U]): RDD[(K, U)]

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  5. def aggregateByKey[U](zeroValue: U, numPartitions: Int)(seqOp: (U, V) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassTag[U]): RDD[(K, U)]

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  6. def aggregateByKey[U](zeroValue: U, partitioner: Partitioner)(seqOp: (U, V) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassTag[U]): RDD[(K, U)]

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  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  9. def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]

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  10. def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]

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  11. def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))]

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  12. def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]

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  13. def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))]

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  14. def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]

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  15. def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]

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  16. def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W]))]

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  17. def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]

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  18. def collectAsMap(): Map[K, V]

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  19. def combineByKey[C](createCombiner: (V) ⇒ C, mergeValue: (C, V) ⇒ C, mergeCombiners: (C, C) ⇒ C): RDD[(K, C)]

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  20. def combineByKey[C](createCombiner: (V) ⇒ C, mergeValue: (C, V) ⇒ C, mergeCombiners: (C, C) ⇒ C, numPartitions: Int): RDD[(K, C)]

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  21. def combineByKey[C](createCombiner: (V) ⇒ C, mergeValue: (C, V) ⇒ C, mergeCombiners: (C, C) ⇒ C, partitioner: Partitioner, mapSideCombine: Boolean, serializer: Serializer): RDD[(K, C)]

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  22. def countApproxDistinctByKey(relativeSD: Double): RDD[(K, Long)]

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  23. def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): RDD[(K, Long)]

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  24. def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)]

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  25. def countApproxDistinctByKey(p: Int, sp: Int, partitioner: Partitioner): RDD[(K, Long)]

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    Annotations
    @Experimental()
  26. def countByKey(): Map[K, Long]

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  27. def countByKeyApprox(timeout: Long, confidence: Double): PartialResult[Map[K, BoundedDouble]]

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    @Experimental()
  28. final def eq(arg0: AnyRef): Boolean

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  29. def equals(arg0: Any): Boolean

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  30. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  31. def flatMapValues[U](f: (V) ⇒ TraversableOnce[U]): RDD[(K, U)]

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  32. def foldByKey(zeroValue: V)(func: (V, V) ⇒ V): RDD[(K, V)]

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  33. def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) ⇒ V): RDD[(K, V)]

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  34. def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) ⇒ V): RDD[(K, V)]

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  35. def fullOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], Option[W]))]

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  36. def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))]

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  37. def fullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Option[V], Option[W]))]

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  38. final def getClass(): Class[_]

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  39. def groupByKey(): RDD[(K, Iterable[V])]

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  40. def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])]

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  41. def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])]

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  42. def groupWith[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]

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  43. def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]

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  44. def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))]

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  45. def hashCode(): Int

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  46. final def isInstanceOf[T0]: Boolean

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  47. def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))]

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  48. def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))]

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  49. def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))]

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  50. def keys: RDD[K]

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  51. def leftOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, Option[W]))]

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  52. def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))]

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  53. def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))]

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  54. def lookup(key: K): Seq[V]

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  55. def mapValues[U](f: (V) ⇒ U): RDD[(K, U)]

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

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  57. final def notify(): Unit

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  58. final def notifyAll(): Unit

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  59. def partitionBy(partitioner: Partitioner): RDD[(K, V)]

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  60. def reduceByKey(func: (V, V) ⇒ V): RDD[(K, V)]

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  61. def reduceByKey(func: (V, V) ⇒ V, numPartitions: Int): RDD[(K, V)]

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  62. def reduceByKey(partitioner: Partitioner, func: (V, V) ⇒ V): RDD[(K, V)]

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  63. def reduceByKeyLocally(func: (V, V) ⇒ V): Map[K, V]

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  64. def rightOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], W))]

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  65. def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))]

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  66. def rightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Option[V], W))]

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  67. def sampleByKey(withReplacement: Boolean, fractions: Map[K, Double], seed: Long): RDD[(K, V)]

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  68. def sampleByKeyExact(withReplacement: Boolean, fractions: Map[K, Double], seed: Long): RDD[(K, V)]

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    Annotations
    @Experimental()
  69. def saveAsHadoopDataset(conf: JobConf): Unit

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  70. def saveAsHadoopFile(path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], conf: JobConf, codec: Option[Class[_ <: CompressionCodec]]): Unit

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  71. def saveAsHadoopFile(path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], codec: Class[_ <: CompressionCodec]): Unit

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  72. def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String, codec: Class[_ <: CompressionCodec])(implicit fm: ClassTag[F]): Unit

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  73. def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]): Unit

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  74. def saveAsNewAPIHadoopDataset(conf: Configuration): Unit

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  75. def saveAsNewAPIHadoopFile(path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], conf: Configuration): Unit

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  76. def saveAsNewAPIHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]): Unit

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  77. def subtractByKey[W](other: RDD[(K, W)], p: Partitioner)(implicit arg0: ClassTag[W]): RDD[(K, V)]

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  78. def subtractByKey[W](other: RDD[(K, W)], numPartitions: Int)(implicit arg0: ClassTag[W]): RDD[(K, V)]

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  79. def subtractByKey[W](other: RDD[(K, W)])(implicit arg0: ClassTag[W]): RDD[(K, V)]

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

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  81. def toString(): String

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  82. def values: RDD[V]

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  83. final def wait(): Unit

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

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

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