Simplify code

Author: beliefer

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

@@ -21,6 +21,7 @@ import org.apache.spark.sql.catalyst.analysis.{DecimalPrecision, FunctionRegistr
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.trees.UnaryLike
+import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
 
 @ExpressionDescription(
@@ -42,12 +43,8 @@ case class Average(child: Expression) extends DeclarativeAggregate with Implicit
   override def inputTypes: Seq[AbstractDataType] =
     Seq(TypeCollection(NumericType, YearMonthIntervalType, DayTimeIntervalType))
 
-  override def checkInputDataTypes(): TypeCheckResult = child.dataType match {
-    case YearMonthIntervalType | DayTimeIntervalType | NullType => TypeCheckResult.TypeCheckSuccess
-    case dt if dt.isInstanceOf[NumericType] => TypeCheckResult.TypeCheckSuccess
-    case other => TypeCheckResult.TypeCheckFailure(
-      s"function average requires numeric or interval types, not ${other.catalogString}")
-  }
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForAnsiIntervalOrNumericType(child.dataType, "average")
 
   override def nullable: Boolean = true
 

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

@@ -21,6 +21,7 @@ import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.trees.UnaryLike
+import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
 
@@ -48,12 +49,8 @@ case class Sum(child: Expression) extends DeclarativeAggregate with ImplicitCast
   override def inputTypes: Seq[AbstractDataType] =
     Seq(TypeCollection(NumericType, YearMonthIntervalType, DayTimeIntervalType))
 
-  override def checkInputDataTypes(): TypeCheckResult = child.dataType match {
-    case YearMonthIntervalType | DayTimeIntervalType | NullType => TypeCheckResult.TypeCheckSuccess
-    case dt if dt.isInstanceOf[NumericType] => TypeCheckResult.TypeCheckSuccess
-    case other => TypeCheckResult.TypeCheckFailure(
-      s"function sum requires numeric or interval types, not ${other.catalogString}")
-  }
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForAnsiIntervalOrNumericType(child.dataType, "sum")
 
   private lazy val resultType = child.dataType match {
     case DecimalType.Fixed(precision, scale) =>

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala

@@ -61,6 +61,14 @@ object TypeUtils {
     }
   }
 
+  def checkForAnsiIntervalOrNumericType(
+      dt: DataType, funcName: String): TypeCheckResult = dt match {
+    case YearMonthIntervalType | DayTimeIntervalType | NullType => TypeCheckResult.TypeCheckSuccess
+    case dt if dt.isInstanceOf[NumericType] => TypeCheckResult.TypeCheckSuccess
+    case other => TypeCheckResult.TypeCheckFailure(
+      s"function $funcName requires numeric or interval types, not ${other.catalogString}")
+  }
+
   def getNumeric(t: DataType, exactNumericRequired: Boolean = false): Numeric[Any] = {
     if (exactNumericRequired) {
       t.asInstanceOf[NumericType].exactNumeric.asInstanceOf[Numeric[Any]]