[GitHub] [spark] cloud-fan commented on a change in pull request #28070: [SPARK-31010][SQL] Add Java UDF suggestion in error message of untyped Scala UDF

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cloud-fan commented on a change in pull request #28070: [SPARK-31010][SQL] Add Java UDF suggestion in error message of untyped Scala UDF
URL: https://github.com/apache/spark/pull/28070#discussion_r400082499
 
 

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 File path: sql/core/src/main/scala/org/apache/spark/sql/functions.scala
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 @@ -4864,9 +4864,10 @@ object functions {
         "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. " +
-        "You could use typed Scala UDF APIs (e.g. `udf((x: Int) => x)`) to avoid this problem, " +
-        s"or set ${SQLConf.LEGACY_ALLOW_UNTYPED_SCALA_UDF.key} to true and use this API with " +
-        s"caution."
+        "You could use typed Scala UDF APIs (e.g. `udf((x: Int) => x)`) or Java UDF (e.g. " +
+        "`udf(new UDF1[String, Int] { override def call(s: String): Int = s.length() }, " +
+        "IntegerType)`) if input types are all non primitive to avoid this problem or set " +
+        "${SQLConf.LEGACY_ALLOW_UNTYPED_SCALA_UDF.key} to true and use this API with caution."
 
 Review comment:
   now there are 3 workarounds, can we list them nicely?
   ```
   "To get rid of this error, you could:\n" + 
     "1. use typed Scala UDF, e.g. ...\n" +
     "2. use Java UDF, e.g. ..., if input types... \n" +
     "3. set .... "
   ```

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