[SPARK-3891][SQL] Add array support to percentile, percentile_approx and constant inspectors support

Supported passing array to percentile and percentile_approx UDAFs
To support percentile_approx,  constant inspectors are supported for GenericUDAF
Constant folding support added to CreateArray expression
Avoided constant udf expression re-evaluation

Author: Venkata Ramana G <ramana.gollamudihuawei.com>

Author: Venkata Ramana Gollamudi <ramana.gollamudi@huawei.com>

Closes #2802 from gvramana/percentile_array_support and squashes the following commits:

a0182e5 [Venkata Ramana Gollamudi] fixed review comment
a18f917 [Venkata Ramana Gollamudi] avoid constant udf expression re-evaluation - fixes failure due to return iterator and value type mismatch
c46db0f [Venkata Ramana Gollamudi] Removed TestHive reset
4d39105 [Venkata Ramana Gollamudi] Unified inspector creation, style check fixes
f37fd69 [Venkata Ramana Gollamudi] Fixed review comments
47f6365 [Venkata Ramana Gollamudi] fixed test
cb7c61e [Venkata Ramana Gollamudi] Supported ConstantInspector for UDAF Fixed HiveUdaf wrap object issue.
7f94aff [Venkata Ramana Gollamudi] Added foldable support to CreateArray

2 files changed, 37 insertions, 11 deletions

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index ed2e96df8a..93b6ef9fbc 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -159,6 +159,11 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr
     isUDFDeterministic && returnInspector.isInstanceOf[ConstantObjectInspector]
 
   @transient
+  protected def constantReturnValue = unwrap(
+    returnInspector.asInstanceOf[ConstantObjectInspector].getWritableConstantValue(),
+    returnInspector)
+  
+  @transient
   protected lazy val deferedObjects =
     argumentInspectors.map(new DeferredObjectAdapter(_)).toArray[DeferredObject]
 
@@ -166,6 +171,8 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr
 
   override def eval(input: Row): Any = {
     returnInspector // Make sure initialized.
+    if(foldable) return constantReturnValue
+
     var i = 0
     while (i < children.length) {
       val idx = i
@@ -193,12 +200,13 @@ private[hive] case class HiveGenericUdaf(
 
   @transient
   protected lazy val objectInspector  = {
-    resolver.getEvaluator(children.map(_.dataType.toTypeInfo).toArray)
+    val parameterInfo = new SimpleGenericUDAFParameterInfo(inspectors.toArray, false, false)
+    resolver.getEvaluator(parameterInfo)
       .init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors.toArray)
   }
 
   @transient
-  protected lazy val inspectors = children.map(_.dataType).map(toInspector)
+  protected lazy val inspectors = children.map(toInspector)
 
   def dataType: DataType = inspectorToDataType(objectInspector)
 
@@ -223,12 +231,13 @@ private[hive] case class HiveUdaf(
 
   @transient
   protected lazy val objectInspector  = {
-    resolver.getEvaluator(children.map(_.dataType.toTypeInfo).toArray)
+    val parameterInfo = new SimpleGenericUDAFParameterInfo(inspectors.toArray, false, false)
+    resolver.getEvaluator(parameterInfo)
       .init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors.toArray)
   }
 
   @transient
-  protected lazy val inspectors = children.map(_.dataType).map(toInspector)
+  protected lazy val inspectors = children.map(toInspector)
 
   def dataType: DataType = inspectorToDataType(objectInspector)
 
@@ -261,7 +270,7 @@ private[hive] case class HiveGenericUdtf(
   protected lazy val function: GenericUDTF = funcWrapper.createFunction()
 
   @transient
-  protected lazy val inputInspectors = children.map(_.dataType).map(toInspector)
+  protected lazy val inputInspectors = children.map(toInspector)
 
   @transient
   protected lazy val outputInspector = function.initialize(inputInspectors.toArray)
@@ -334,10 +343,13 @@ private[hive] case class HiveUdafFunction(
     } else {
       funcWrapper.createFunction[AbstractGenericUDAFResolver]()
     }
-
-  private val inspectors = exprs.map(_.dataType).map(toInspector).toArray
-
-  private val function = resolver.getEvaluator(exprs.map(_.dataType.toTypeInfo).toArray)
+  
+  private val inspectors = exprs.map(toInspector).toArray
+    
+  private val function = { 
+    val parameterInfo = new SimpleGenericUDAFParameterInfo(inspectors, false, false)
+    resolver.getEvaluator(parameterInfo) 
+  }
 
   private val returnInspector = function.init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors)
 
@@ -350,9 +362,12 @@ private[hive] case class HiveUdafFunction(
   @transient
   val inputProjection = new InterpretedProjection(exprs)
 
+  @transient
+  protected lazy val cached = new Array[AnyRef](exprs.length)
+  
   def update(input: Row): Unit = {
     val inputs = inputProjection(input).asInstanceOf[Seq[AnyRef]].toArray
-    function.iterate(buffer, inputs)
+    function.iterate(buffer, wrap(inputs, inspectors, cached))
   }
 }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
index 5fcaf671a8..5fc8d8dbe3 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
@@ -92,10 +92,21 @@ class HiveUdfSuite extends QueryTest {
   }
 
   test("SPARK-2693 udaf aggregates test") {
-    checkAnswer(sql("SELECT percentile(key,1) FROM src LIMIT 1"),
+    checkAnswer(sql("SELECT percentile(key, 1) FROM src LIMIT 1"),
       sql("SELECT max(key) FROM src").collect().toSeq)
+      
+    checkAnswer(sql("SELECT percentile(key, array(1, 1)) FROM src LIMIT 1"),
+      sql("SELECT array(max(key), max(key)) FROM src").collect().toSeq)
   }
 
+  test("Generic UDAF aggregates") {
+    checkAnswer(sql("SELECT ceiling(percentile_approx(key, 0.99999)) FROM src LIMIT 1"),
+      sql("SELECT max(key) FROM src LIMIT 1").collect().toSeq)
+      
+    checkAnswer(sql("SELECT percentile_approx(100.0, array(0.9, 0.9)) FROM src LIMIT 1"),
+      sql("SELECT array(100, 100) FROM src LIMIT 1").collect().toSeq)
+   }
+  
   test("UDFIntegerToString") {
     val testData = TestHive.sparkContext.parallelize(
       IntegerCaseClass(1) :: IntegerCaseClass(2) :: Nil)