Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.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.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package graphx

    ALPHA COMPONENT GraphX is a graph processing framework built on top of Spark.

    ALPHA COMPONENT GraphX is a graph processing framework built on top of Spark.

    Definition Classes
    spark
  • package util

    Collections of utilities used by graphx.

    Collections of utilities used by graphx.

    Definition Classes
    graphx
  • GraphGenerators
o

org.apache.spark.graphx.util

GraphGenerators

object GraphGenerators extends Logging

A collection of graph generating functions.

Source
GraphGenerators.scala
Linear Supertypes
Logging, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. GraphGenerators
  2. Logging
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val RMATa: Double
  5. val RMATb: Double
  6. val RMATc: Double
  7. val RMATd: Double
  8. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  9. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  12. def generateRandomEdges(src: Int, numEdges: Int, maxVertexId: Int, seed: Long = -1): Array[Edge[Int]]
  13. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  14. def gridGraph(sc: SparkContext, rows: Int, cols: Int): Graph[(Int, Int), Double]

    Create rows by cols grid graph with each vertex connected to its row+1 and col+1 neighbors.

    Create rows by cols grid graph with each vertex connected to its row+1 and col+1 neighbors. Vertex ids are assigned in row major order.

    sc

    the spark context in which to construct the graph

    rows

    the number of rows

    cols

    the number of columns

    returns

    A graph containing vertices with the row and column ids as their attributes and edge values as 1.0.

  15. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  16. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  17. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  20. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  21. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def logDebug(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def logDebug(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  26. def logError(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logError(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  28. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  30. def logInfo(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  31. def logInfo(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  32. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  33. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  34. def logNormalGraph(sc: SparkContext, numVertices: Int, numEParts: Int = 0, mu: Double = 4.0, sigma: Double = 1.3, seed: Long = -1): Graph[Long, Int]

    Generate a graph whose vertex out degree distribution is log normal.

    Generate a graph whose vertex out degree distribution is log normal.

    The default values for mu and sigma are taken from the Pregel paper:

    Grzegorz Malewicz, Matthew H. Austern, Aart J.C Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: a system for large-scale graph processing. SIGMOD '10.

    If the seed is -1 (default), a random seed is chosen. Otherwise, use the user-specified seed.

    sc

    Spark Context

    numVertices

    number of vertices in generated graph

    numEParts

    (optional) number of partitions

    mu

    (optional, default: 4.0) mean of out-degree distribution

    sigma

    (optional, default: 1.3) standard deviation of out-degree distribution

    seed

    (optional, default: -1) seed for RNGs, -1 causes a random seed to be chosen

    returns

    Graph object

  35. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  36. def logTrace(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  37. def logTrace(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  38. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  39. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  40. def logWarning(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  41. def logWarning(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  42. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  43. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  44. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  45. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  46. def rmatGraph(sc: SparkContext, requestedNumVertices: Int, numEdges: Int): Graph[Int, Int]

    A random graph generator using the R-MAT model, proposed in "R-MAT: A Recursive Model for Graph Mining" by Chakrabarti et al.

    A random graph generator using the R-MAT model, proposed in "R-MAT: A Recursive Model for Graph Mining" by Chakrabarti et al.

    See http://www.cs.cmu.edu/~christos/PUBLICATIONS/siam04.pdf.

  47. def starGraph(sc: SparkContext, nverts: Int): Graph[Int, Int]

    Create a star graph with vertex 0 being the center.

    Create a star graph with vertex 0 being the center.

    sc

    the spark context in which to construct the graph

    nverts

    the number of vertices in the star

    returns

    A star graph containing nverts vertices with vertex 0 being the center vertex.

  48. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  49. def toString(): String
    Definition Classes
    AnyRef → Any
  50. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  51. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  52. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  53. def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
    Attributes
    protected
    Definition Classes
    Logging

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped