Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/14753#discussion_r76181435
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/TypedImperativeAggregateSuite.scala
---
@@ -0,0 +1,300 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream,
DataInputStream, DataOutputStream}
+
+import org.apache.spark.sql.TypedImperativeAggregateSuite.TypedMax
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{BoundReference,
Expression, GenericMutableRow, SpecificMutableRow}
+import
org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate
+import org.apache.spark.sql.execution.aggregate.SortAggregateExec
+import org.apache.spark.sql.expressions.Window
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.{AbstractDataType, BinaryType, DataType,
IntegerType, LongType}
+
+class TypedImperativeAggregateSuite extends QueryTest with
SharedSQLContext {
+
+ import testImplicits._
+
+ private val random = new java.util.Random()
+
+ private val data = (0 until 1000).map { _ =>
+ (random.nextInt(10), random.nextInt(100))
+ }
+
+ test("aggregate with object aggregate buffer") {
+ val agg = new TypedMax(BoundReference(0, IntegerType, nullable =
false))
+
+ val group1 = (0 until data.length / 2)
+ val group1Buffer = agg.createAggregationBuffer()
+ group1.foreach { index =>
+ val input = InternalRow(data(index)._1, data(index)._2)
+ agg.update(group1Buffer, input)
+ }
+
+ val group2 = (data.length / 2 until data.length)
+ val group2Buffer = agg.createAggregationBuffer()
+ group2.foreach { index =>
+ val input = InternalRow(data(index)._1, data(index)._2)
+ agg.update(group2Buffer, input)
+ }
+
+ val mergeBuffer = agg.createAggregationBuffer()
+ agg.merge(mergeBuffer, group1Buffer)
+ agg.merge(mergeBuffer, group2Buffer)
+
+ assert(mergeBuffer.value == data.map(_._1).max)
+ assert(agg.eval(mergeBuffer) == data.map(_._1).max)
+
+ // Tests low level eval(row: InternalRow) API.
+ val row = new GenericMutableRow(Array(mergeBuffer): Array[Any])
+
+ // Evaluates directly on row consist of aggregation buffer object.
+ assert(agg.eval(row) == data.map(_._1).max)
+ }
+
+ test("supports SpecificMutableRow as mutable row") {
+ val aggregationBufferSchema = Seq(IntegerType, LongType, BinaryType,
IntegerType)
+ val aggBufferOffset = 2
+ val buffer = new SpecificMutableRow(aggregationBufferSchema)
+ val agg = new TypedMax(BoundReference(ordinal = 1, dataType =
IntegerType, nullable = false))
+ .withNewMutableAggBufferOffset(aggBufferOffset)
+
+ agg.initialize(buffer)
+ data.foreach { kv =>
+ val input = InternalRow(kv._1, kv._2)
+ agg.update(buffer, input)
+ }
+ assert(agg.eval(buffer) == data.map(_._2).max)
+ }
+
+ test("dataframe aggregate with object aggregate buffer, should not use
HashAggregate") {
+ val df = data.toDF("a", "b")
+ val max = new TypedMax($"a".expr)
+
+ // Always uses SortAggregateExec
+ val sparkPlan =
df.select(Column(max.toAggregateExpression())).queryExecution.sparkPlan
+ assert(sparkPlan.isInstanceOf[SortAggregateExec])
+ }
+
+ test("dataframe aggregate with object aggregate buffer, no group by") {
+ val df = data.toDF("key", "value").coalesce(2)
+ val query = df.select(typedMax($"key"), count($"key"),
typedMax($"value"), count($"value"))
+ val maxKey = data.map(_._1).max
+ val countKey = data.size
+ val maxValue = data.map(_._2).max
+ val countValue = data.size
+ val expected = Seq(Row(maxKey, countKey, maxValue, countValue))
+ checkAnswer(query, expected)
+ }
+
+ test("dataframe aggregate with object aggregate buffer, non-nullable
aggregator") {
+ val df = data.toDF("key", "value").coalesce(2)
+
+ // Test non-nullable typedMax
+ val query = df.select(typedMax(lit(null)), count($"key"),
typedMax(lit(null)),
+ count($"value"))
+
+ // typedMax is not nullable
+ val maxNull = Int.MinValue
+ val countKey = data.size
+ val countValue = data.size
+ val expected = Seq(Row(maxNull, countKey, maxNull, countValue))
+ checkAnswer(query, expected)
+ }
+
+ test("dataframe aggregate with object aggregate buffer, nullable
aggregator") {
+ val df = data.toDF("key", "value").coalesce(2)
+
+ // Test nullable nullableTypedMax
+ val query = df.select(nullableTypedMax(lit(null)), count($"key"),
nullableTypedMax(lit(null)),
+ count($"value"))
+
+ // nullableTypedMax is nullable
+ val maxNull = null
+ val countKey = data.size
+ val countValue = data.size
+ val expected = Seq(Row(maxNull, countKey, maxNull, countValue))
+ checkAnswer(query, expected)
+ }
+
+ test("dataframe aggregation with object aggregate buffer, input row
contains null") {
+
+ val nullableData = (0 until 1000).map {id =>
+ val nullableKey: Integer = if (random.nextBoolean()) null else
random.nextInt(100)
+ val nullableValue: Integer = if (random.nextBoolean()) null else
random.nextInt(100)
+ (nullableKey, nullableValue)
+ }
+
+ val df = nullableData.toDF("key", "value").coalesce(2)
+ val query = df.select(typedMax($"key"), count($"key"),
typedMax($"value"),
+ count($"value"))
+ val maxKey = nullableData.map(_._1).filter(_ != null).max
+ val countKey = nullableData.map(_._1).filter(_ != null).size
+ val maxValue = nullableData.map(_._2).filter(_ != null).max
+ val countValue = nullableData.map(_._2).filter(_ != null).size
+ val expected = Seq(Row(maxKey, countKey, maxValue, countValue))
+ checkAnswer(query, expected)
+ }
+
+ test("dataframe aggregate with object aggregate buffer, with group by") {
+ val df = data.toDF("value", "key").coalesce(2)
+ val query = df.groupBy($"key").agg(typedMax($"value"),
count($"value"), typedMax($"value"))
+ val expected = data.groupBy(_._2).toSeq.map { group =>
+ val (key, values) = group
+ val valueMax = values.map(_._1).max
+ val countValue = values.size
+ Row(key, valueMax, countValue, valueMax)
+ }
+ checkAnswer(query, expected)
+ }
+
+ test("dataframe aggregate with object aggregate buffer, empty inputs, no
group by") {
+ val empty = Seq.empty[(Int, Int)].toDF("a", "b")
+ checkAnswer(
+ empty.select(typedMax($"a"), count($"a"), typedMax($"b"),
count($"b")),
+ Seq(Row(Int.MinValue, 0, Int.MinValue, 0)))
+ }
+
+ test("dataframe aggregate with object aggregate buffer, empty inputs,
with group by") {
+ val empty = Seq.empty[(Int, Int)].toDF("a", "b")
+ checkAnswer(
+ empty.groupBy($"b").agg(typedMax($"a"), count($"a"), typedMax($"a")),
+ Seq.empty[Row])
+ }
+
+ test("TypedImperativeAggregate should not break Window function") {
+ val df = data.toDF("key", "value")
+ // OVER (PARTITION BY a ORDER BY b ROW BETWEEN UNBOUNDED PRECEDING AND
CURRENT ROW)
+ val w =
Window.orderBy("value").partitionBy("key").rowsBetween(Long.MinValue, 0)
+
+ val query = df.select(sum($"key").over(w), typedMax($"key").over(w),
sum($"value").over(w),
+ typedMax($"value").over(w))
+
+ val expected = data.groupBy(_._1).toSeq.flatMap { group =>
+ val (key, values) = group
+ val sortedValues = values.map(_._2).sorted
+
+ var outputRows = Seq.empty[Row]
+ var i = 0
+ while (i < sortedValues.size) {
+ val unboundedPrecedingAndCurrent = sortedValues.slice(0, i + 1)
+ val sumKey = key * unboundedPrecedingAndCurrent.size
+ val maxKey = key
+ val sumValue = unboundedPrecedingAndCurrent.sum
+ val maxValue = unboundedPrecedingAndCurrent.max
+
+ outputRows :+= Row(sumKey, maxKey, sumValue, maxValue)
+ i += 1
+ }
+
+ outputRows
+ }
+ checkAnswer(query, expected)
+ }
+
+ private def typedMax(column: Column): Column = {
+ val max = TypedMax(column.expr, nullable = false)
+ Column(max.toAggregateExpression())
+ }
+
+ private def nullableTypedMax(column: Column): Column = {
+ val max = TypedMax(column.expr, nullable = true)
+ Column(max.toAggregateExpression())
+ }
+}
+
+object TypedImperativeAggregateSuite {
+
+ /**
+ * Calculate the max value with object aggregation buffer. This stores
class MaxValue
+ * in aggregation buffer.
+ */
+ private case class TypedMax(
--- End diff --
I thought you want to have the flexibility to still apply some operations
when there is a null input?
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