Github user clockfly commented on a diff in the pull request: https://github.com/apache/spark/pull/14753#discussion_r75798274 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala --- @@ -389,3 +389,175 @@ 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] extends ImperativeAggregate { + + /** + * Creates an empty aggregation buffer object. This is called before processing each key group + * (group by key). + * + * @return an aggregation buffer object + */ + def createAggregationBuffer(): T + + /** + * In-place updates the aggregation buffer object with an input row. buffer = buffer + input. + * This is typically called when doing Partial or Complete mode aggregation. + * + * @param buffer The aggregation buffer object. + * @param input an input row + */ + def update(buffer: T, input: InternalRow): Unit + + /** + * Merges an input aggregation object into aggregation buffer object. buffer = buffer + input. + * This is typically called when doing PartialMerge or Final mode aggregation. + * + * @param buffer the aggregation buffer object used to store the aggregation result. + * @param input an input aggregation object. Input aggregation object can be produced by + * de-serializing the partial aggregate's output from Mapper side. + */ + def merge(buffer: T, input: T): Unit --- End diff -- @cloud-fan you can find an example at https://github.com/apache/spark/commit/0a777cc8880edd01e209f73d96fe1c644a0c473f
--- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org