cloud-fan commented on a change in pull request #26656: [SPARK-27986][SQL]
Support ANSI SQL filter clause for aggregate expression
URL: https://github.com/apache/spark/pull/26656#discussion_r353717031
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File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggUtils.scala
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@@ -135,19 +135,25 @@ object AggUtils {
}
val distinctAttributes = namedDistinctExpressions.map(_.toAttribute)
val groupingAttributes = groupingExpressions.map(_.toAttribute)
+ val filterWithDistinctAttributes =
functionsWithDistinct.flatMap(_.filterAttributes.toSeq)
// 1. Create an Aggregate Operator for partial aggregations.
val partialAggregate: SparkPlan = {
val aggregateExpressions = functionsWithoutDistinct.map(_.copy(mode =
Partial))
val aggregateAttributes = aggregateExpressions.map(_.resultAttribute)
// We will group by the original grouping expression, plus an additional
expression for the
- // DISTINCT column. For example, for AVG(DISTINCT value) GROUP BY key,
the grouping
- // expressions will be [key, value].
+ // DISTINCT column and the referred attributes in the FILTER clause
associated with each
+ // aggregate function. For example:
+ // 1.for the AVG (DISTINCT value) GROUP BY key, the grouping expression
will be [key, value];
+ // 2.for AVG (DISTINCT value) Filter (WHERE value2> 20) GROUP BY key,
the grouping expression
+ // will be [key, value, value2].
Review comment:
I read the source code, seems the workflow is:
1. local aggregate. **input**: records. **group by**: keys and the distinct
column. **output**: agg buffers
2. shuffle aggregate. **input**: agg buffers. **group by**: keys and the
distinct column. **output**: agg buffers
3. local aggregate. **input**: agg buffers. **group by**: keys. **output**:
agg buffers
4. shuffle aggregate. **input**: agg buffers. **group by**: keys.
**output**: agg buffers
So I think we only need to evaluate the filter in the first aggregate.
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