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t

org.apache.spark.sql.connector.read

SupportsPushDownAggregates

trait SupportsPushDownAggregates extends ScanBuilder

A mix-in interface for ScanBuilder. Data sources can implement this interface to push down aggregates. Spark assumes that the data source can't fully complete the grouping work, and will group the data source output again. For queries like "SELECT min(value) AS m FROM t GROUP BY key", after pushing down the aggregate to the data source, the data source can still output data with duplicated keys, which is OK as Spark will do GROUP BY key again. The final query plan can be something like this: 

  Aggregate [key#1], [min(min(value)#2) AS m#3]
    +- RelationV2[key#1, min(value)#2]
Similarly, if there is no grouping expression, the data source can still output more than one rows.

When pushing down operators, Spark pushes down filters to the data source first, then push down aggregates or apply column pruning. Depends on data source implementation, aggregates may or may not be able to be pushed down with filters. If pushed filters still need to be evaluated after scanning, aggregates can't be pushed down.

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@Evolving()
Since

3.2.0

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ScanBuilder, AnyRef, Any
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Abstract Value Members

  1. abstract def build(): Scan
    Definition Classes
    ScanBuilder
  2. abstract def pushAggregation(aggregation: Aggregation): Boolean

    Pushes down Aggregation to datasource.

    Pushes down Aggregation to datasource. The order of the datasource scan output columns should be: grouping columns, aggregate columns (in the same order as the aggregate functions in the given Aggregation).

    returns

    true if the aggregation can be pushed down to datasource, false otherwise.

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

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