Class

net.sansa_stack.rdf.spark.partition.graph.algo

SubjectHashPartition

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class SubjectHashPartition[VD, ED] extends PartitionAlgo[VD, ED] with Serializable

Expanding the partitions by assigning subject triple group of each vertex to partitions

VD

the vertex attribute associated with each vertex in the set.

ED

the edge attribute associated with each edge in the set.

Linear Supertypes
PartitionAlgo[VD, ED], Serializable, Serializable, AnyRef, Any
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  1. SubjectHashPartition
  2. PartitionAlgo
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Visibility
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Instance Constructors

  1. new SubjectHashPartition(graph: Graph[VD, ED], session: SparkSession, numPartitions: Int)(implicit arg0: ClassTag[VD], arg1: ClassTag[ED])

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    Constructs a default instance with default parameters {numIterations: 2}

  2. new SubjectHashPartition(graph: Graph[VD, ED], session: SparkSession)(implicit arg0: ClassTag[VD], arg1: ClassTag[ED])

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    Constructs a default instance with default parameters {numPartitions: graph partitions, numIterations: 2}

  3. new SubjectHashPartition(graph: Graph[VD, ED], session: SparkSession, numPartitions: PartitionID, numIterations: Int)(implicit arg0: ClassTag[VD], arg1: ClassTag[ED])

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    graph

    target graph to be partitioned

    session

    spark session

    numPartitions

    number of partitions

    numIterations

    number of iterations

Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    AnyRef → Any

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

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    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

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    Any

  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    @throws( ... )

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

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  7. def equals(arg0: Any): Boolean

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    AnyRef → Any

  8. def finalize(): Unit

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    Attributes
    protected[java.lang]
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    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

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    Definition Classes
    AnyRef → Any

  10. val graph: Graph[VD, ED]

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    target graph to be partitioned

    target graph to be partitioned

    Definition Classes
    SubjectHashPartition → PartitionAlgo

  11. val groups: Groups[VD, ED]

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  12. def hashCode(): Int

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    AnyRef → Any

  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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    AnyRef

  16. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  17. val numPartitions: PartitionID

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    number of partitions

    number of partitions

    Definition Classes
    SubjectHashPartition → PartitionAlgo

  18. def partitionBy(): Graph[VD, ED]

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    Repartition the edges in the graph and construct a new graph that partitioned by strategy with input number of partitions.

    Repartition the edges in the graph and construct a new graph that partitioned by strategy with input number of partitions.

    Definition Classes
    SubjectHashPartition → PartitionAlgo

  19. val session: SparkSession

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    spark session

    spark session

    Definition Classes
    SubjectHashPartition → PartitionAlgo

  20. def setNumIterations(numIterations: Int): SubjectHashPartition.this.type

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    Sets the number of iterations (default: 2)

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

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  22. def toString(): String

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  23. final def wait(): Unit

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    @throws( ... )

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

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    @throws( ... )

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

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Inherited from PartitionAlgo[VD, ED]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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