org.apache.spark.bagel

Bagel

object Bagel extends Logging

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

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  3. final def ##(): Int

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

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  6. val DEFAULT_STORAGE_LEVEL: StorageLevel

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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

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  13. def hashCode(): Int

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  14. def initLogging(): Unit

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  15. final def isInstanceOf[T0]: Boolean

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  16. def isTraceEnabled(): Boolean

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  17. def log: Logger

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

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

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

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

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

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

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

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

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

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

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

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  29. final def notify(): Unit

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  30. final def notifyAll(): Unit

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  31. def run[K, V <: Vertex, M <: Message[K]](sc: SparkContext, vertices: RDD[(K, V)], messages: RDD[(K, M)], numPartitions: Int, storageLevel: StorageLevel)(compute: (V, Option[Array[M]], Int) ⇒ (V, Array[M]))(implicit arg0: Manifest[K], arg1: Manifest[V], arg2: Manifest[M]): RDD[(K, V)]

    Runs a Bagel program with no Aggregator, the default org.apache.spark.HashPartitioner and DefaultCombiner

  32. def run[K, V <: Vertex, M <: Message[K]](sc: SparkContext, vertices: RDD[(K, V)], messages: RDD[(K, M)], numPartitions: Int)(compute: (V, Option[Array[M]], Int) ⇒ (V, Array[M]))(implicit arg0: Manifest[K], arg1: Manifest[V], arg2: Manifest[M]): RDD[(K, V)]

    Runs a Bagel program with no Aggregator, default org.apache.spark.HashPartitioner, DefaultCombiner and the default storage level

  33. def run[K, V <: Vertex, M <: Message[K], C](sc: SparkContext, vertices: RDD[(K, V)], messages: RDD[(K, M)], combiner: Combiner[M, C], numPartitions: Int, storageLevel: StorageLevel)(compute: (V, Option[C], Int) ⇒ (V, Array[M]))(implicit arg0: Manifest[K], arg1: Manifest[V], arg2: Manifest[M], arg3: Manifest[C]): RDD[(K, V)]

    Runs a Bagel program with no Aggregator and the default org.apache.spark.HashPartitioner

  34. def run[K, V <: Vertex, M <: Message[K], C](sc: SparkContext, vertices: RDD[(K, V)], messages: RDD[(K, M)], combiner: Combiner[M, C], numPartitions: Int)(compute: (V, Option[C], Int) ⇒ (V, Array[M]))(implicit arg0: Manifest[K], arg1: Manifest[V], arg2: Manifest[M], arg3: Manifest[C]): RDD[(K, V)]

    Runs a Bagel program with no Aggregator, default org.apache.spark.HashPartitioner and default storage level

  35. def run[K, V <: Vertex, M <: Message[K], C](sc: SparkContext, vertices: RDD[(K, V)], messages: RDD[(K, M)], combiner: Combiner[M, C], partitioner: Partitioner, numPartitions: Int, storageLevel: StorageLevel)(compute: (V, Option[C], Int) ⇒ (V, Array[M]))(implicit arg0: Manifest[K], arg1: Manifest[V], arg2: Manifest[M], arg3: Manifest[C]): RDD[(K, V)]

    Runs a Bagel program with no Aggregator

  36. def run[K, V <: Vertex, M <: Message[K], C](sc: SparkContext, vertices: RDD[(K, V)], messages: RDD[(K, M)], combiner: Combiner[M, C], partitioner: Partitioner, numPartitions: Int)(compute: (V, Option[C], Int) ⇒ (V, Array[M]))(implicit arg0: Manifest[K], arg1: Manifest[V], arg2: Manifest[M], arg3: Manifest[C]): RDD[(K, V)]

    Runs a Bagel program with no Aggregator and the default storage level

  37. def run[K, V <: Vertex, M <: Message[K], C, A](sc: SparkContext, vertices: RDD[(K, V)], messages: RDD[(K, M)], combiner: Combiner[M, C], aggregator: Option[Aggregator[V, A]], partitioner: Partitioner, numPartitions: Int, storageLevel: StorageLevel)(compute: (V, Option[C], Option[A], Int) ⇒ (V, Array[M]))(implicit arg0: Manifest[K], arg1: Manifest[V], arg2: Manifest[M], arg3: Manifest[C], arg4: Manifest[A]): RDD[(K, V)]

    Runs a Bagel program.

    Runs a Bagel program.

    K

    key

    V

    vertex type

    M

    message type

    C

    combiner

    A

    aggregator

    sc

    org.apache.spark.SparkContext to use for the program.

    vertices

    vertices of the graph represented as an RDD of (Key, Vertex) pairs. Often the Key will be the vertex id.

    messages

    initial set of messages represented as an RDD of (Key, Message) pairs. Often this will be an empty array, i.e. sc.parallelize(Array[K, Message]()).

    combiner

    Combiner combines multiple individual messages to a given vertex into one message before sending (which often involves network I/O).

    aggregator

    Aggregator performs a reduce across all vertices after each superstep, and provides the result to each vertex in the next superstep.

    partitioner

    org.apache.spark.Partitioner partitions values by key

    numPartitions

    number of partitions across which to split the graph. Default is the default parallelism of the SparkContext

    storageLevel

    org.apache.spark.storage.StorageLevel to use for caching of intermediate RDDs in each superstep. Defaults to caching in memory.

    compute

    function that takes a Vertex, optional set of (possibly combined) messages to the Vertex, optional Aggregator and the current superstep, and returns a set of (Vertex, outgoing Messages) pairs

    returns

    an RDD of (K, V) pairs representing the graph after completion of the program

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

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  39. def toString(): String

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  40. final def wait(): Unit

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

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

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