Github user clockfly commented on a diff in the pull request:
https://github.com/apache/spark/pull/14753#discussion_r75797813
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
---
@@ -389,3 +389,146 @@ 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 storage format (Array[Byte]) and
persist the Array[Byte]
+ * 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 (Array[Byte]) and produces 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
+
+ /**
+ * Generates the final aggregation result value for current key group
with the aggregation buffer
+ * object.
+ *
+ * @param buffer aggregation buffer object.
+ * @return The aggregation result of current key group
+ */
+ def eval(buffer: T): Any
+
+ /** Returns the class of aggregation buffer object */
+ def aggregationBufferClass: Class[T]
+
+ /** Serializes the aggregation buffer object T to Array[Byte] */
+ def serialize(buffer: T): Array[Byte]
--- End diff --
Here we limit the serializable format to `Array[Byte]`
The reason is that SpecialMutableRow will do type check for atomic types
for each `update` call of the aggregation buffer. If we declare the storage
format to be IntegerType, but actually stores an arbitrary object in the
aggregation buffer, then SpecialMutableRow will catch this error and reports
exception.
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