Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/14723#discussion_r75612521
--- 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.
--- End diff --
`to a Spark SQL internal format(mostly BinaryType) in place`
---
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
