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trait Partitioning extends AnyRef

An interface to represent the output data partitioning for a data source, which is returned by SupportsReportPartitioning#outputPartitioning(). Note that this should work like a snapshot. Once created, it should be deterministic and always report the same number of partitions and the same "satisfy" result for a certain distribution.

Since

3.0.0

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  1. abstract def numPartitions(): Int

    Returns the number of partitions(i.e., InputPartitions) the data source outputs.

  2. abstract def satisfy(distribution: Distribution): Boolean

    Returns true if this partitioning can satisfy the given distribution, which means Spark does not need to shuffle the output data of this data source for some certain operations.

    Returns true if this partitioning can satisfy the given distribution, which means Spark does not need to shuffle the output data of this data source for some certain operations.

    Note that, Spark may add new concrete implementations of Distribution in new releases. This method should be aware of it and always return false for unrecognized distributions. It's recommended to check every Spark new release and support new distributions if possible, to avoid shuffle at Spark side for more cases.

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