dtenedor commented on code in PR #49210:
URL: https://github.com/apache/spark/pull/49210#discussion_r1904734280
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/randomExpressions.scala:
##########
@@ -330,7 +330,8 @@ object Uniform {
group = "string_funcs")
case class RandStr(
length: Expression, override val seedExpression: Expression, hideSeed:
Boolean)
- extends ExpressionWithRandomSeed with BinaryLike[Expression] with
Nondeterministic {
+ extends ExpressionWithRandomSeed with BinaryLike[Expression] with
Nondeterministic
+ with ExpectsInputTypes {
Review Comment:
Good question! In general I prefer to use `ImplicitCastInputTypes`. But here
I found that the existing `RAND` and `RANDN` functions extend
`ExpectsInputTypes` and return errors when we try to pass numeric values other
than `IntegerType` or `LongType`. So I figured that it would be consistent for
`RANDSTR` to do the same. Let me know if I should think differently.
```
-- !query
SELECT rand(0.1F)
-- !query schema
struct<>
-- !query output
org.apache.spark.sql.catalyst.ExtendedAnalysisException
{
"errorClass" : "DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE",
"sqlState" : "42K09",
"messageParameters" : {
"inputSql" : "\"0.1\"",
"inputType" : "\"FLOAT\"",
"paramIndex" : "first",
"requiredType" : "(\"INT\" or \"BIGINT\")",
"sqlExpr" : "\"rand(0.1)\""
},
"queryContext" : [ {
"objectType" : "",
"objectName" : "",
"startIndex" : 8,
"stopIndex" : 17,
"fragment" : "rand(0.1F)"
} ]
}
```
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