Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/14723#discussion_r75586350
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
---
@@ -389,3 +389,89 @@ abstract class DeclarativeAggregate
def right: AttributeReference =
inputAggBufferAttributes(aggBufferAttributes.indexOf(a))
}
}
+
+/**
+ * This traits allow user to define an AggregateFunction which can store
**arbitrary** Java objects
+ * in Aggregation buffer during aggregation of each key group. This trait
must be mixed with
+ * class ImperativeAggregate.
+ *
+ * Here is how it works in a typical aggregation flow (Partial mode
aggregate at Mapper side, and
+ * Final mode aggregate at Reducer side).
+ *
+ * Stage 1: Partial aggregate at Mapper side:
+ *
+ * 1. Upon calling method `initialize(aggBuffer: MutableRow)`, user
stores an arbitrary empty
+ * object, object A for example, in aggBuffer. The object A will be
used to store the
+ * accumulated aggregation result.
+ * 1. Upon calling method `update(mutableAggBuffer: MutableRow, inputRow:
InternalRow)` in
+ * current group (group by key), user extracts object A from
mutableAggBuffer, and then updates
+ * object A with current inputRow. After updating, object A is stored
back to mutableAggBuffer.
+ * 1. After processing all rows of current group, the framework will call
method
+ * `serializeObjectAggregationBufferInPlace(aggregationBuffer:
MutableRow)` to serialize object A
+ * to a serializable format in place.
+ * 1. The framework may spill the aggregationBuffer to disk if there is
not enough memory.
+ * It is safe since we have already convert aggregationBuffer to
serializable format.
+ * 1. Spark 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. Upon calling method `initialize(aggBuffer: MutableRow)`, user
stores a new empty object A1
+ * in aggBuffer. The object A1 will be used to store the accumulated
aggregation result.
+ * 1. Upon calling method `merge(mutableAggBuffer: MutableRow,
inputAggBuffer: InternalRow)`, user
+ * extracts object A1 from mutableAggBuffer, and extracts object A2
from inputAggBuffer. then
+ * user needs to merge A1, and A2, and stores the merged result back to
mutableAggBuffer.
+ * 1. After processing all inputAggBuffer of current group (group by
key), the Spark framework will
+ * call method
`serializeObjectAggregationBufferInPlace(aggregationBuffer: MutableRow)` to
+ * serialize object A1 to a serializable format in place.
+ * 1. The Spark framework may spill the aggregationBuffer to disk if
there is not enough memory.
+ * It is safe since we have already convert aggregationBuffer to
serializable format.
+ * 1. Spark framework moves on to next group, until all groups have been
processed.
+ */
+trait WithObjectAggregateBuffer {
+ this: ImperativeAggregate =>
--- End diff --
oh, seems this trait will be still an java `interface`. But, I think in
general, we do not really need to have this line.
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