[SPARK-17187][SQL] Supports using arbitrary Java object as internal aggregation buffer object

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala

@@ -389,3 +389,153 @@ 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.
+ *
+ * NOTE: SQL with TypedImperativeAggregate functions is planned in sort based aggregation,
+ * instead of hash based aggregation, as TypedImperativeAggregate use BinaryType as aggregation
+ * buffer's storage format, which is not supported by hash based aggregation. Hash based
+ * aggregation only support aggregation buffer of mutable types (like LongType, IntType that have
+ * fixed length and can be mutated in place in UnsafeRow)
+ */
+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]
+
+  /** De-serializes the serialized format Array[Byte], and produces aggregation buffer object T */
+  def deserialize(storageFormat: Array[Byte]): T
+
+  final override def initialize(buffer: MutableRow): Unit = {
+    val bufferObject = createAggregationBuffer()
+    buffer.update(mutableAggBufferOffset, bufferObject)
+  }
+
+  final override def update(buffer: MutableRow, input: InternalRow): Unit = {
+    val bufferObject = getField(buffer, mutableAggBufferOffset).asInstanceOf[T]
+    update(bufferObject, input)
+  }
+
+  final override def merge(buffer: MutableRow, inputBuffer: InternalRow): Unit = {
+    val bufferObject = getField[T](buffer, mutableAggBufferOffset)
+    val inputObject = deserialize(getField[Array[Byte]](inputBuffer, inputAggBufferOffset))
+    merge(bufferObject, inputObject)
+  }
+
+  final override def eval(buffer: InternalRow): Any = {
+    val bufferObject = getField[AnyRef](buffer, mutableAggBufferOffset)
+    if (bufferObject.getClass == aggregationBufferClass) {
+      // When used in Window frame aggregation, eval(buffer: InternalRow) is called directly
+      // on the object aggregation buffer without intermediate serializing/de-serializing.
+      eval(bufferObject.asInstanceOf[T])
+    } else {
+      eval(deserialize(bufferObject.asInstanceOf[Array[Byte]]))
+    }
+  }
+
+  private[this] val anyObjectType = ObjectType(classOf[AnyRef])
+  private def getField[U](input: InternalRow, fieldIndex: Int): U = {
+    input.get(fieldIndex, anyObjectType).asInstanceOf[U]
+  }
+
+  final override lazy val aggBufferAttributes: Seq[AttributeReference] = {
+    // Underlying storage type for the aggregation buffer object
+    Seq(AttributeReference("buf", BinaryType)())
+  }
+
+  final override lazy val inputAggBufferAttributes: Seq[AttributeReference] =
+    aggBufferAttributes.map(_.newInstance())
+
+  final override def aggBufferSchema: StructType = StructType.fromAttributes(aggBufferAttributes)
+
+  /**
+   * In-place replaces the aggregation buffer object stored at buffer's index
+   * `mutableAggBufferOffset`, with SparkSQL internally supported underlying storage format.
+   *
+   * The framework calls this method every time after updating/merging one group (group by key).
+   */
+  final def serializeAggregateBufferInPlace(buffer: MutableRow): Unit = {
+    val bufferObject = getField(buffer, mutableAggBufferOffset).asInstanceOf[T]
+    buffer(mutableAggBufferOffset) = serialize(bufferObject)
+  }
+}

sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala

@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.aggregate
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateFunction}
+import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateFunction, TypedImperativeAggregate}
 import org.apache.spark.sql.execution.metric.SQLMetric
 
 /**
@@ -54,6 +54,7 @@ class SortBasedAggregationIterator(
     val bufferRowSize: Int = bufferSchema.length
 
     val genericMutableBuffer = new GenericMutableRow(bufferRowSize)
+
     val useUnsafeBuffer = bufferSchema.map(_.dataType).forall(UnsafeRow.isMutable)
 
     val buffer = if (useUnsafeBuffer) {
@@ -90,6 +91,24 @@ class SortBasedAggregationIterator(
   // compared to MutableRow (aggregation buffer) directly.
   private[this] val safeProj: Projection = FromUnsafeProjection(valueAttributes.map(_.dataType))
 
+  // Aggregation function which uses generic aggregation buffer object.
+  // @see [[TypedImperativeAggregate]] for more information
+  private val typedImperativeAggregates: Array[TypedImperativeAggregate[_]] = {
+    aggregateFunctions.collect {
+      case (ag: TypedImperativeAggregate[_]) => ag
+    }
+  }
+
+  // For TypedImperativeAggregate with generic aggregation buffer object, we need to call
+  // serializeAggregateBufferInPlace(...) explicitly to convert the aggregation buffer object
+  // to Spark Sql internally supported serializable storage format.
+  private def serializeTypedAggregateBuffer(aggregationBuffer: MutableRow): Unit = {
+    typedImperativeAggregates.foreach { agg =>
+      // In-place serialization
+      agg.serializeAggregateBufferInPlace(sortBasedAggregationBuffer)
+    }
+  }
+
   protected def initialize(): Unit = {
     if (inputIterator.hasNext) {
       initializeBuffer(sortBasedAggregationBuffer)
@@ -131,6 +150,11 @@ class SortBasedAggregationIterator(
         firstRowInNextGroup = currentRow.copy()
       }
     }
+
+    // Serializes the generic object stored in aggregation buffer for TypedImperativeAggregate
+    // aggregation functions.
+    serializeTypedAggregateBuffer(sortBasedAggregationBuffer)
+
     // We have not seen a new group. It means that there is no new row in the input
     // iter. The current group is the last group of the iter.
     if (!findNextPartition) {
@@ -162,6 +186,9 @@ class SortBasedAggregationIterator(
 
   def outputForEmptyGroupingKeyWithoutInput(): UnsafeRow = {
     initializeBuffer(sortBasedAggregationBuffer)
+    // Serializes the generic object stored in aggregation buffer for TypedImperativeAggregate
+    // aggregation functions.
+    serializeTypedAggregateBuffer(sortBasedAggregationBuffer)
     generateOutput(UnsafeRow.createFromByteArray(0, 0), sortBasedAggregationBuffer)
   }
 }