Github user clockfly commented on a diff in the pull request: https://github.com/apache/spark/pull/14753#discussion_r75784293 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala --- @@ -389,3 +389,148 @@ abstract class DeclarativeAggregate def right: AttributeReference = inputAggBufferAttributes(aggBufferAttributes.indexOf(a)) } } + +/** + * Aggregation function which allows **arbitrary** user-defined java object to be used as internal + * aggregation buffer object. + * + * {{{ + * aggregation buffer for normal aggregation function `avg` + * | + * v + * +--------------+---------------+-----------------------------------+ + * | sum1 (Long) | count1 (Long) | generic user-defined java objects | + * +--------------+---------------+-----------------------------------+ + * ^ + * | + * Aggregation buffer object for `TypedImperativeAggregate` aggregation function + * }}} + * + * Work flow (Partial mode aggregate at Mapper side, and Final mode aggregate at Reducer side): + * + * Stage 1: Partial aggregate at Mapper side: + * + * 1. The framework calls `createAggregationBuffer(): T` to create an empty internal aggregation + * buffer object. + * 2. Upon each input row, the framework calls + * `update(buffer: T, input: InternalRow): Unit` to update the aggregation buffer object T. + * 3. After processing all rows of current group (group by key), the framework will serialize + * aggregation buffer object T to SparkSQL internally supported underlying storage format, and + * persist the serializable format to disk if needed. + * 4. The framework moves on to next group, until all groups have been processed. + * + * Shuffling exchange data to Reducer tasks... + * + * Stage 2: Final mode aggregate at Reducer side: + * + * 1. The framework calls `createAggregationBuffer(): T` to create an empty internal aggregation + * buffer object (type T) for merging. + * 2. For each aggregation output of Stage 1, The framework de-serializes the storage + * format and generates one input aggregation object (type T). + * 3. For each input aggregation object, the framework calls `merge(buffer: T, input: T): Unit` + * to merge the input aggregation object into aggregation buffer object. + * 4. After processing all input aggregation objects of current group (group by key), the framework + * calls method `eval(buffer: T)` to generate the final output for this group. + * 5. The framework moves on to next group, until all groups have been processed. + */ +abstract class TypedImperativeAggregate[T >: Null] extends ImperativeAggregate { --- End diff -- I believe so, but I will do a double check
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