diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 586d8167b9272..b838f92255554 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -353,15 +353,6 @@ object CatalystTypeConverters {
     case other => other
   }
 
-  /** 
-   * Converts Catalyst types used internally in rows to standard Scala types
-   * This method is slow, and for batch conversion you should be using converter
-   * produced by createToScalaConverter.
-   */
-  def convertToScala(catalystValue: Any, dataType: DataType): Any = {
-    getConverterForType(dataType).toScala(catalystValue)
-  }
-
   /**
    * Creates a converter function that will convert Catalyst types to Scala type.
    * Typical use case would be converting a collection of rows that have the same schema. You will
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index 634138010fd21..c8f9aacc0baad 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -71,12 +71,16 @@ case class UserDefinedGenerator(
     children: Seq[Expression])
   extends Generator {
 
+  private[this] val inputRow: InterpretedProjection = new InterpretedProjection(children)
+  private[this] val convertToScala: (Row) => Row = {
+    val inputSchema = StructType(children.map(e => StructField(e.simpleString, e.dataType, true)))
+    CatalystTypeConverters.createToScalaConverter(inputSchema)
+  }.asInstanceOf[(Row => Row)]
+
   override def eval(input: Row): TraversableOnce[Row] = {
     // TODO(davies): improve this
     // Convert the objects into Scala Type before calling function, we need schema to support UDT
-    val inputSchema = StructType(children.map(e => StructField(e.simpleString, e.dataType, true)))
-    val inputRow = new InterpretedProjection(children)
-    function(CatalystTypeConverters.convertToScala(inputRow(input), inputSchema).asInstanceOf[Row])
+    function(convertToScala(inputRow(input)))
   }
 
   override def toString: String = s"UserDefinedGenerator(${children.mkString(",")})"