SparkContext

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

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

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6. def accumulable[T, R](initialValue: T)(implicit param: AccumulableParam[T, R]): Accumulable[T, R]

   Create an Accumulable shared variable, with a += method

   Create an Accumulable shared variable, with a += method

   T

   accumulator type

   R

   type that can be added to the accumulator

7. def accumulableCollection[R, T](initialValue: R)(implicit arg0: (R) ⇒ Growable[T] with TraversableOnce[T] with Serializable): Accumulable[R, T]

   Create an accumulator from a "mutable collection" type.

   Create an accumulator from a "mutable collection" type.

   Growable and TraversableOnce are the standard APIs that guarantee += and ++=, implemented by standard mutable collections. So you can use this with mutable Map, Set, etc.

8. def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]): Accumulator[T]

   Create an Accumulator variable of a given type, which tasks can "add" values to using the += method.

   Create an Accumulator variable of a given type, which tasks can "add" values to using the += method. Only the master can access the accumulator's value.

9. def addFile(path: String): Unit

   Add a file to be downloaded into the working directory of this Spark job on every node.

   Add a file to be downloaded into the working directory of this Spark job on every node. The path passed can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI.

10. def addJar(path: String): Unit

    Adds a JAR dependency for all tasks to be executed on this SparkContext in the future.

    Adds a JAR dependency for all tasks to be executed on this SparkContext in the future. The path passed can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI.

11. final def asInstanceOf[T0]: T0

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12. def broadcast[T](value: T): Broadcast[T]

    Broadcast a read-only variable to the cluster, returning a spark.Broadcast object for reading it in distributed functions.

    Broadcast a read-only variable to the cluster, returning a spark.Broadcast object for reading it in distributed functions. The variable will be sent to each cluster only once.

13. def clearFiles(): Unit

    Clear the job's list of files added by addFile so that they do not get donwloaded to any new nodes.

14. def clearJars(): Unit

    Clear the job's list of JARs added by addJar so that they do not get downloaded to any new nodes.

15. def clone(): AnyRef

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16. def defaultMinSplits: Int

    Default min number of splits for Hadoop RDDs when not given by user

17. def defaultParallelism: Int

    Default level of parallelism to use when not given by user (e.

    Default level of parallelism to use when not given by user (e.g. for reduce tasks)

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

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19. def equals(arg0: Any): Boolean

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20. def finalize(): Unit

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

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22. def hadoopFile[K, V, F <: InputFormat[K, V]](path: String)(implicit km: ClassManifest[K], vm: ClassManifest[V], fm: ClassManifest[F]): RDD[(K, V)]

    Smarter version of hadoopFile() that uses class manifests to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly.

    Smarter version of hadoopFile() that uses class manifests to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly. Instead, callers can just write, for example,

    val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path)
    

23. def hadoopFile[K, V, F <: InputFormat[K, V]](path: String, minSplits: Int)(implicit km: ClassManifest[K], vm: ClassManifest[V], fm: ClassManifest[F]): RDD[(K, V)]

    Smarter version of hadoopFile() that uses class manifests to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly.

    Smarter version of hadoopFile() that uses class manifests to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly. Instead, callers can just write, for example,

    val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path, minSplits)
    

24. def hadoopFile[K, V](path: String, inputFormatClass: Class[_ <: org.apache.hadoop.mapred.InputFormat[K,V]], keyClass: Class[K], valueClass: Class[V], minSplits: Int): RDD[(K, V)]

    Get an RDD for a Hadoop file with an arbitrary InputFormat

25. def hadoopRDD[K, V](conf: JobConf, inputFormatClass: Class[_ <: org.apache.hadoop.mapred.InputFormat[K,V]], keyClass: Class[K], valueClass: Class[V], minSplits: Int): RDD[(K, V)]

    Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf giving its InputFormat and any other necessary info (e.

    Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf giving its InputFormat and any other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable, etc).

26. def hashCode(): Int

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27. def initLogging(): Unit

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28. final def isInstanceOf[T0]: Boolean

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29. def log: Logger

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

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31. def logDebug(msg: ⇒ String): Unit

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

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33. def logError(msg: ⇒ String): Unit

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

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35. def logInfo(msg: ⇒ String): Unit

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

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37. def logTrace(msg: ⇒ String): Unit

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

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

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40. def makeRDD[T](seq: Seq[T], numSlices: Int)(implicit arg0: ClassManifest[T]): RDD[T]

    Distribute a local Scala collection to form an RDD.

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

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42. def newAPIHadoopFile[K, V, F <: InputFormat[K, V]](path: String, fClass: Class[F], kClass: Class[K], vClass: Class[V], conf: Configuration): RDD[(K, V)]

    Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.

43. def newAPIHadoopFile[K, V, F <: InputFormat[K, V]](path: String)(implicit km: ClassManifest[K], vm: ClassManifest[V], fm: ClassManifest[F]): RDD[(K, V)]

    Get an RDD for a Hadoop file with an arbitrary new API InputFormat.

44. def newAPIHadoopRDD[K, V, F <: InputFormat[K, V]](conf: Configuration, fClass: Class[F], kClass: Class[K], vClass: Class[V]): RDD[(K, V)]

    Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.

45. final def notify(): Unit

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46. final def notifyAll(): Unit

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47. def objectFile[T](path: String, minSplits: Int = defaultMinSplits)(implicit arg0: ClassManifest[T]): RDD[T]

    Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition.

    Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition. This is still an experimental storage format and may not be supported exactly as is in future Spark releases. It will also be pretty slow if you use the default serializer (Java serialization), though the nice thing about it is that there's very little effort required to save arbitrary objects.

48. def parallelize[T](seq: Seq[T], numSlices: Int)(implicit arg0: ClassManifest[T]): RDD[T]

    Distribute a local Scala collection to form an RDD.

49. def runApproximateJob[T, U, R](rdd: RDD[T], func: (TaskContext, Iterator[T]) ⇒ U, evaluator: ApproximateEvaluator[U, R], timeout: Long): PartialResult[R]

    Run a job that can return approximate results.

50. def runJob[T, U](rdd: RDD[T], func: (Iterator[T]) ⇒ U)(implicit arg0: ClassManifest[U]): Array[U]

    Run a job on all partitions in an RDD and return the results in an array.

51. def runJob[T, U](rdd: RDD[T], func: (TaskContext, Iterator[T]) ⇒ U)(implicit arg0: ClassManifest[U]): Array[U]

    Run a job on all partitions in an RDD and return the results in an array.

52. def runJob[T, U](rdd: RDD[T], func: (Iterator[T]) ⇒ U, partitions: Seq[Int], allowLocal: Boolean)(implicit arg0: ClassManifest[U]): Array[U]

    Run a job on a given set of partitions of an RDD, but take a function of type Iterator[T] => U instead of (TaskContext, Iterator[T]) => U.

53. def runJob[T, U](rdd: RDD[T], func: (TaskContext, Iterator[T]) ⇒ U, partitions: Seq[Int], allowLocal: Boolean)(implicit arg0: ClassManifest[U]): Array[U]

    Run a function on a given set of partitions in an RDD and return the results.

    Run a function on a given set of partitions in an RDD and return the results. This is the main entry point to the scheduler, by which all actions get launched. The allowLocal flag specifies whether the scheduler can run the computation on the master rather than shipping it out to the cluster, for short actions like first().

54. def sequenceFile[K, V](path: String, minSplits: Int = defaultMinSplits)(implicit km: ClassManifest[K], vm: ClassManifest[V], kcf: () ⇒ WritableConverter[K], vcf: () ⇒ WritableConverter[V]): RDD[(K, V)]

    Version of sequenceFile() for types implicitly convertible to Writables through a WritableConverter.

    Version of sequenceFile() for types implicitly convertible to Writables through a WritableConverter. For example, to access a SequenceFile where the keys are Text and the values are IntWritable, you could simply write

    sparkContext.sequenceFile[String, Int](path, ...)
    

    WritableConverters are provided in a somewhat strange way (by an implicit function) to support both subclasses of Writable and types for which we define a converter (e.g. Int to IntWritable). The most natural thing would've been to have implicit objects for the converters, but then we couldn't have an object for every subclass of Writable (you can't have a parameterized singleton object). We use functions instead to create a new converter for the appropriate type. In addition, we pass the converter a ClassManifest of its type to allow it to figure out the Writable class to use in the subclass case.

55. def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V]): RDD[(K, V)]

    Get an RDD for a Hadoop SequenceFile with given key and value types.

56. def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V], minSplits: Int): RDD[(K, V)]

    Get an RDD for a Hadoop SequenceFile with given key and value types.

57. val sparkHome: String

    Location where Spark is installed on cluster nodes.

58. def stop(): Unit

    Shut down the SparkContext.

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

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60. def textFile(path: String, minSplits: Int = defaultMinSplits): RDD[String]

    Read a text file from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI, and return it as an RDD of Strings.

61. def toString(): String

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62. def union[T](first: RDD[T], rest: RDD[T]*)(implicit arg0: ClassManifest[T]): RDD[T]

    Build the union of a list of RDDs passed as variable-length arguments.

63. def union[T](rdds: Seq[RDD[T]])(implicit arg0: ClassManifest[T]): RDD[T]

    Build the union of a list of RDDs.

64. final def wait(): Unit

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

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

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