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com.nvidia.spark.rapids

GpuAggregateIterator

object GpuAggregateIterator extends Logging

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  4. def aggregate(helper: AggHelper, preProcessed: SpillableColumnarBatch, metrics: GpuHashAggregateMetrics): SpillableColumnarBatch

    Note

    abstracted away for a unit test..

  5. final def asInstanceOf[T0]: T0
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  6. def clone(): AnyRef
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  7. def computeAggregateAndClose(metrics: GpuHashAggregateMetrics, inputBatch: ColumnarBatch, helper: AggHelper): SpillableColumnarBatch

    Compute the aggregations on the projected input columns, and close input batch.

    Compute the aggregations on the projected input columns, and close input batch.

    metrics

    metrics that will be updated during aggregation

    inputBatch

    input batch to aggregate

    helper

    an internal object that carries state required to execute the aggregate from different parts of the codebase.

    returns

    aggregated batch

    Note

    public for testing

  8. def computeAggregateWithoutPreprocessAndClose(metrics: GpuHashAggregateMetrics, inputBatches: Iterator[ColumnarBatch], helper: AggHelper): Iterator[SpillableColumnarBatch]
  9. def concatenateBatches(metrics: GpuHashAggregateMetrics, toConcat: Seq[SpillableColumnarBatch]): SpillableColumnarBatch

    Concatenates batches after extracting them from SpllableColumnarBatch

    Concatenates batches after extracting them from SpllableColumnarBatch

    metrics

    metrics that will be updated during aggregation

    toConcat

    spillable batches to concatenate

    returns

    concatenated batch result

    Note

    the input batches are not closed as part of this operation

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