spark

SequenceFileRDDFunctions

class SequenceFileRDDFunctions[K, V] extends Logging with Serializable

Extra functions available on RDDs of (key, value) pairs to create a Hadoop SequenceFile, through an implicit conversion. Note that this can't be part of PairRDDFunctions because we need more implicit parameters to convert our keys and values to Writable.

Users should import spark.SparkContext._ at the top of their program to use these functions.

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  1. SequenceFileRDDFunctions
  2. Serializable
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Instance Constructors

  1. new SequenceFileRDDFunctions(self: RDD[(K, V)])(implicit arg0: (K) ⇒ Writable, arg1: ClassManifest[K], arg2: (V) ⇒ Writable, arg3: ClassManifest[V])

Value Members

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

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

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  3. final def ##(): Int

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. def initLogging(): Unit

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  14. final def isInstanceOf[T0]: Boolean

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  15. def log: Logger

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  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  17. def logDebug(msg: ⇒ String): Unit

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  18. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logError(msg: ⇒ String): Unit

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  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logInfo(msg: ⇒ String): Unit

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  22. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  23. def logTrace(msg: ⇒ String): Unit

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  24. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  25. def logWarning(msg: ⇒ String): Unit

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

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  27. final def notify(): Unit

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  28. final def notifyAll(): Unit

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  29. def saveAsSequenceFile(path: String): Unit

    Output the RDD as a Hadoop SequenceFile using the Writable types we infer from the RDD's key and value types.

    Output the RDD as a Hadoop SequenceFile using the Writable types we infer from the RDD's key and value types. If the key or value are Writable, then we use their classes directly; otherwise we map primitive types such as Int and Double to IntWritable, DoubleWritable, etc, byte arrays to BytesWritable, and Strings to Text. The path can be on any Hadoop-supported file system.

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

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  31. def toString(): String

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  32. final def wait(): Unit

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

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

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