MaxGekk commented on code in PR #36064:
URL: https://github.com/apache/spark/pull/36064#discussion_r842764065
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryCompilationErrors.scala:
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@@ -2264,17 +2258,20 @@ object QueryCompilationErrors {
}
def usingUntypedScalaUDFError(): Throwable = {
- new AnalysisException("You're using untyped Scala UDF, which does not have
the input type " +
- "information. Spark may blindly pass null to the Scala closure with
primitive-type " +
- "argument, and the closure will see the default value of the Java type
for the null " +
- "argument, e.g. `udf((x: Int) => x, IntegerType)`, the result is 0 for
null input. " +
- "To get rid of this error, you could:\n" +
- "1. use typed Scala UDF APIs(without return type parameter), e.g.
`udf((x: Int) => x)`\n" +
- "2. use Java UDF APIs, e.g. `udf(new UDF1[String, Integer] { " +
- "override def call(s: String): Integer = s.length() }, IntegerType)`, " +
- "if input types are all non primitive\n" +
- s"3. set ${SQLConf.LEGACY_ALLOW_UNTYPED_SCALA_UDF.key} to true and " +
- s"use this API with caution")
+ new AnalysisException(
+ errorClass = "INVALID_UDF",
+ messageParameters = Array(
+ "You're using untyped Scala UDF, which does not have the input type " +
Review Comment:
Yep, please make more specific error classes and pass minimum needed info
via parameters.
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