dongjoon-hyun commented on code in PR #48251:
URL: https://github.com/apache/spark/pull/48251#discussion_r1778845957
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala:
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@@ -441,47 +441,53 @@ case class Literal (value: Any, dataType: DataType)
extends LeafExpression {
override def eval(input: InternalRow): Any = value
override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
- val javaType = CodeGenerator.javaType(dataType)
- if (value == null) {
- ExprCode.forNullValue(dataType)
- } else {
- def toExprCode(code: String): ExprCode = {
- ExprCode.forNonNullValue(JavaCode.literal(code, dataType))
- }
- dataType match {
- case BooleanType | IntegerType | DateType | _: YearMonthIntervalType =>
- toExprCode(value.toString)
- case FloatType =>
- value.asInstanceOf[Float] match {
- case v if v.isNaN =>
- toExprCode("Float.NaN")
- case Float.PositiveInfinity =>
- toExprCode("Float.POSITIVE_INFINITY")
- case Float.NegativeInfinity =>
- toExprCode("Float.NEGATIVE_INFINITY")
- case _ =>
- toExprCode(s"${value}F")
- }
- case DoubleType =>
- value.asInstanceOf[Double] match {
- case v if v.isNaN =>
- toExprCode("Double.NaN")
- case Double.PositiveInfinity =>
- toExprCode("Double.POSITIVE_INFINITY")
- case Double.NegativeInfinity =>
- toExprCode("Double.NEGATIVE_INFINITY")
- case _ =>
- toExprCode(s"${value}D")
- }
- case ByteType | ShortType =>
- ExprCode.forNonNullValue(JavaCode.expression(s"($javaType)$value",
dataType))
- case TimestampType | TimestampNTZType | LongType | _:
DayTimeIntervalType =>
- toExprCode(s"${value}L")
- case _ =>
- val constRef = ctx.addReferenceObj("literal", value, javaType)
- ExprCode.forNonNullValue(JavaCode.global(constRef, dataType))
+ def gen(ctx: CodegenContext, ev: ExprCode, dataType: DataType): ExprCode =
{
+ val javaType = CodeGenerator.javaType(dataType)
+ if (value == null) {
+ ExprCode.forNullValue(dataType)
+ } else {
+ def toExprCode(code: String): ExprCode = {
+ ExprCode.forNonNullValue(JavaCode.literal(code, dataType))
+ }
+
+ dataType match {
+ case BooleanType | IntegerType | DateType | _: YearMonthIntervalType
=>
+ toExprCode(value.toString)
+ case FloatType =>
+ value.asInstanceOf[Float] match {
+ case v if v.isNaN =>
+ toExprCode("Float.NaN")
+ case Float.PositiveInfinity =>
+ toExprCode("Float.POSITIVE_INFINITY")
+ case Float.NegativeInfinity =>
+ toExprCode("Float.NEGATIVE_INFINITY")
+ case _ =>
+ toExprCode(s"${value}F")
+ }
+ case DoubleType =>
+ value.asInstanceOf[Double] match {
+ case v if v.isNaN =>
+ toExprCode("Double.NaN")
+ case Double.PositiveInfinity =>
+ toExprCode("Double.POSITIVE_INFINITY")
+ case Double.NegativeInfinity =>
+ toExprCode("Double.NEGATIVE_INFINITY")
+ case _ =>
+ toExprCode(s"${value}D")
+ }
+ case ByteType | ShortType =>
+ ExprCode.forNonNullValue(JavaCode.expression(s"($javaType)$value",
dataType))
+ case TimestampType | TimestampNTZType | LongType | _:
DayTimeIntervalType =>
+ toExprCode(s"${value}L")
+ case udt: UserDefinedType[_] =>
+ gen(ctx, ev, udt.sqlType)
Review Comment:
Do you know how much the generate Java file size grow with this recursion?
For the complex UDF, it could be huge, right?
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