Packages

c

org.apache.spark.sql.connector.read.partitioning

KeyGroupedPartitioning

class KeyGroupedPartitioning extends Partitioning

Represents a partitioning where rows are split across partitions based on the partition transform expressions returned by KeyGroupedPartitioning#keys.

Note: Data source implementations should make sure for a single partition, all of its rows must be evaluated to the same partition value after being applied by KeyGroupedPartitioning#keys expressions. Different partitions can share the same partition value: Spark will group these into a single logical partition during planning phase.

Annotations
@Evolving()
Since

3.3.0

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Partitioning, AnyRef, Any
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  1. KeyGroupedPartitioning
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Instance Constructors

  1. new KeyGroupedPartitioning(keys: Array[Expression], numPartitions: Int)

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  8. def finalize(): Unit
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  9. final def getClass(): Class[_]
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  10. def hashCode(): Int
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    @native()
  11. final def isInstanceOf[T0]: Boolean
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  12. def keys(): Array[Expression]

    Returns the partition transform expressions for this partitioning.

  13. final def ne(arg0: AnyRef): Boolean
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  14. final def notify(): Unit
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    @native()
  15. final def notifyAll(): Unit
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    @native()
  16. def numPartitions(): Int

    Returns the number of partitions that the data is split across.

    Returns the number of partitions that the data is split across.

    Definition Classes
    KeyGroupedPartitioning → Partitioning
    Annotations
    @Override()
  17. final def synchronized[T0](arg0: ⇒ T0): T0
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  18. def toString(): String
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  19. final def wait(): Unit
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    @throws( ... )
  20. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  21. final def wait(arg0: Long): Unit
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Inherited from Partitioning

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