Address review: doc note placement, map/struct nested coverage, keep UDF assert

- De-indent the Hive-serde TIME note to align with other datetime types.
- Cover nested TIME in map and struct (not just array) in the directory-write test.
- Keep assert/contains for the wrapped Hive UDF error; the inner UNSUPPORTED_DATATYPE
  has no cause attached on _LEGACY_ERROR_TEMP_3084 (tracked by SPARK-57750).

Co-authored-by: Isaac

Author: MaxGekk

docs/sql-ref-datatypes.md

@@ -48,7 +48,7 @@ Spark SQL and DataFrames support the following data types:
   time-zone.
   - `TimeType(precision)`: Represents values comprising values of fields hour, minute and second with the number of decimal digits `precision` following the decimal point in the seconds field, without a time-zone.
   The range of values is from `00:00:00` to `23:59:59` for min precision `0`, and to `23:59:59.999999999` for max precision `9`. The default precision is `6`.
-    - Note: Apache Hive has no TIME type, so `TimeType` is not supported in Hive SerDe interop. Storing it in a Hive SerDe table (including `INSERT OVERWRITE DIRECTORY ... STORED AS`) or passing it to a Hive UDF/UDAF/UDTF raises an error rather than silently converting the value.
+  - Note: Apache Hive has no TIME type, so `TimeType` is not supported in Hive SerDe interop. Storing it in a Hive SerDe table (including `INSERT OVERWRITE DIRECTORY ... STORED AS`) or passing it to a Hive UDF/UDAF/UDTF raises an error rather than silently converting the value.
   - `TimestampType`: Timestamp with local time zone(TIMESTAMP_LTZ). It represents values comprising values of fields year, month, day,
   hour, minute, and second, with the session local time-zone. The timestamp value represents an
   absolute point in time.

sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertSuite.scala

@@ -710,25 +710,32 @@ class InsertSuite extends QueryTest with TestHiveSingleton with BeforeAndAfter {
 
   test("SPARK-57556: nested TIME type is unsupported when writing to a Hive serde directory") {
     // Exercises HiveFileFormat.supportDataType's recursion into nested types: a TIME nested inside
-    // an array must also be rejected, with the full (array) column type reported.
-    withSQLConf(HiveUtils.CONVERT_METASTORE_INSERT_DIR.key -> "false") {
-      withTempDir { dir =>
-        // InsertIntoHiveDirCommand wraps the failure in a SparkException, so assert on the cause.
-        val e = intercept[SparkException] {
-          sql(
-            s"""
-               |INSERT OVERWRITE LOCAL DIRECTORY '${dir.toURI.getPath}'
-               |STORED AS PARQUET
-               |SELECT array(TIME'12:01:02') AS c
-             """.stripMargin)
+    // an array, map or struct must also be rejected, with the full column type reported.
+    Seq(
+      "array(TIME'12:01:02')" -> ArrayType(TimeType(), containsNull = false),
+      "map('k', TIME'12:01:02')" -> MapType(StringType, TimeType(), valueContainsNull = false),
+      "named_struct('f', TIME'12:01:02')" ->
+        new StructType().add("f", TimeType(), nullable = false)
+    ).foreach { case (selectExpr, columnType) =>
+      withSQLConf(HiveUtils.CONVERT_METASTORE_INSERT_DIR.key -> "false") {
+        withTempDir { dir =>
+          // InsertIntoHiveDirCommand wraps the failure in a SparkException, so assert on the cause.
+          val e = intercept[SparkException] {
+            sql(
+              s"""
+                 |INSERT OVERWRITE LOCAL DIRECTORY '${dir.toURI.getPath}'
+                 |STORED AS PARQUET
+                 |SELECT $selectExpr AS c
+               """.stripMargin)
+          }
+          checkError(
+            exception = e.getCause.asInstanceOf[AnalysisException],
+            condition = "UNSUPPORTED_DATA_TYPE_FOR_DATASOURCE",
+            parameters = Map(
+              "columnName" -> "`c`",
+              "columnType" -> s"\"${columnType.sql}\"",
+              "format" -> "Hive"))
         }
-        checkError(
-          exception = e.getCause.asInstanceOf[AnalysisException],
-          condition = "UNSUPPORTED_DATA_TYPE_FOR_DATASOURCE",
-          parameters = Map(
-            "columnName" -> "`c`",
-            "columnType" -> s"\"${ArrayType(TimeType()).sql}\"",
-            "format" -> "Hive"))
       }
     }
   }

sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala

@@ -411,8 +411,10 @@ class HiveUDFSuite extends QueryTest with TestHiveSingleton {
   test("SPARK-57556: TIME type is unsupported as a Hive UDF argument") {
     withUserDefinedFunction("testGenericUDFHash" -> true) {
       sql(s"CREATE TEMPORARY FUNCTION testGenericUDFHash AS '${classOf[GenericUDFHash].getName}'")
-      // The Hive UDF resolver wraps the underlying failure, but the message must still clearly
-      // identify the unsupported TIME type rather than surfacing a MatchError/internal error.
+      // The Hive UDF resolver wraps the underlying failure in `_LEGACY_ERROR_TEMP_3084` without
+      // attaching it as a cause (see SPARK-57750), so we can only assert on the wrapped message.
+      // It must still clearly identify the unsupported TIME type rather than surfacing a
+      // MatchError/internal error.
       val e = intercept[AnalysisException] {
         sql("SELECT testGenericUDFHash(TIME'12:01:02')").collect()
       }