Github user yijieshen commented on a diff in the pull request:
https://github.com/apache/spark/pull/6835#discussion_r32760652
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
---
@@ -248,30 +269,81 @@ case class Hypot(left: Expression, right: Expression)
case class Pow(left: Expression, right: Expression)
extends BinaryMathExpression(math.pow, "POWER") {
override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode):
String = {
- defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.pow($c1, $c2)") +
s"""
- if (Double.valueOf(${ev.primitive}).isNaN()) {
- ${ev.isNull} = true;
- }
- """
+ defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.pow($c1, $c2)")
}
}
case class Logarithm(left: Expression, right: Expression)
extends BinaryMathExpression((c1, c2) => math.log(c2) / math.log(c1),
"LOG") {
- def this(child: Expression) = {
- this(EulerNumber(), child)
- }
- override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode):
String = {
- val logCode = if (left.isInstanceOf[EulerNumber]) {
- defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.log($c2)")
+ override def eval(input: InternalRow): Any = {
+ val evalE1 = left.eval(input)
+ if (evalE1 == null || evalE1.asInstanceOf[Double] <= 0.0) {
+ null
} else {
- defineCodeGen(ctx, ev, (c1, c2) => s"java.lang.Math.log($c2) /
java.lang.Math.log($c1)")
+ val evalE2 = right.eval(input)
+ if (evalE2 == null || evalE2.asInstanceOf[Double] <= 0.0) {
+ null
+ } else {
+ math.log(evalE2.asInstanceOf[Double]) /
math.log(evalE1.asInstanceOf[Double])
+ }
}
- logCode + s"""
- if (Double.valueOf(${ev.primitive}).isNaN()) {
+ }
+
+ override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode):
String = {
+ val eval1 = left.gen(ctx)
+ val eval2 = right.gen(ctx)
+ s"""
+ ${eval1.code}
+ boolean ${ev.isNull} = ${eval1.isNull} || ${eval1.primitive} <= 0.0;
+ ${ctx.javaType(dataType)} ${ev.primitive} =
${ctx.defaultValue(dataType)};
+ if (${ev.isNull}) {
${ev.isNull} = true;
+ } else {
+ ${eval2.code}
+ if (${eval2.isNull} || ${eval2.primitive} <= 0.0) {
+ ${ev.isNull} = true;
+ } else {
+ ${ev.primitive} = java.lang.Math.${funcName}(${eval2.primitive})
/
+ java.lang.Math.${funcName}(${eval1.primitive});
+ }
}
"""
}
+
+ // TODO: Hive's UDFLog doesn't support base in range (0.0, 1.0]
+ // If we want just behaves like Hive, use the code below and turn
`udf_7` on
+
+// override def eval(input: InternalRow): Any = {
+// val evalE1 = left.eval(input)
+// val evalE2 = right.eval(input)
+// if (evalE1 == null || evalE2 == null) {
+// null
+// } else {
+// if (evalE1.asInstanceOf[Double] <= 1.0 ||
evalE2.asInstanceOf[Double] <= 0.0) {
+// null
+// } else {
+// math.log(evalE2.asInstanceOf[Double]) /
math.log(evalE1.asInstanceOf[Double])
+// }
+// }
+// }
--- End diff --
Hive's UDFLog doesn't support base in range (0.0, 1.0], it would just eval
to null in this case, just act as the commented out code below.
I'm not sure it we also want to behave like this.
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