case class RoundRobinPartitioning(numPartitions: Int) extends Partitioning with Product with Serializable
Represents a partitioning where rows are distributed evenly across output partitions by starting from a random target partition number and distributing rows in a round-robin fashion. This partitioning is used when implementing the DataFrame.repartition() operator.
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- new RoundRobinPartitioning(numPartitions: Int)
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- val numPartitions: Int
Returns the number of partitions that the data is split across
Returns the number of partitions that the data is split across
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- RoundRobinPartitioning → Partitioning
- def productElementNames: Iterator[String]
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- final def satisfies(required: Distribution): Boolean
Returns true iff the guarantees made by this Partitioning are sufficient to satisfy the partitioning scheme mandated by the
requiredDistribution, i.e.Returns true iff the guarantees made by this Partitioning are sufficient to satisfy the partitioning scheme mandated by the
requiredDistribution, i.e. the current dataset does not need to be re-partitioned for therequiredDistribution (it is possible that tuples within a partition need to be reorganized).A Partitioning can never satisfy a Distribution if its
numPartitionsdoesn't match Distribution.requiredNumPartitions.- Definition Classes
- Partitioning
- def satisfies0(required: Distribution): Boolean
The actual method that defines whether this Partitioning can satisfy the given Distribution, after the
numPartitionscheck.The actual method that defines whether this Partitioning can satisfy the given Distribution, after the
numPartitionscheck.By default a Partitioning can satisfy UnspecifiedDistribution, and AllTuples if the Partitioning only have one partition. Implementations can also overwrite this method with special logic.
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