diff --git a/core/src/main/resources/error/error-classes.json b/core/src/main/resources/error/error-classes.json
index cbdfe5f991374..b6af63c4c65e5 100644
--- a/core/src/main/resources/error/error-classes.json
+++ b/core/src/main/resources/error/error-classes.json
@@ -70,6 +70,9 @@
   "INCOMPATIBLE_DATASOURCE_REGISTER" : {
     "message" : [ "Detected an incompatible DataSourceRegister. Please remove the incompatible library from classpath or upgrade it. Error: %s" ]
   },
+  "INCONSISTENT_BEHAVIOR_CROSS_VERSION" : {
+    "message" : [ "You may get a different result due to the upgrading to Spark >= %s: %s" ]
+  },
   "INDEX_OUT_OF_BOUNDS" : {
     "message" : [ "Index %s must be between 0 and the length of the ArrayData." ],
     "sqlState" : "22023"
@@ -158,6 +161,9 @@
   "UNSUPPORTED_GROUPING_EXPRESSION" : {
     "message" : [ "grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup" ]
   },
+  "UNSUPPORTED_OPERATION" : {
+    "message" : [ "The operation is not supported: %s" ]
+  },
   "WRITING_JOB_ABORTED" : {
     "message" : [ "Writing job aborted" ],
     "sqlState" : "40000"
diff --git a/core/src/main/scala/org/apache/spark/SparkException.scala b/core/src/main/scala/org/apache/spark/SparkException.scala
index aea09e36ade74..8442c8eb8d35d 100644
--- a/core/src/main/scala/org/apache/spark/SparkException.scala
+++ b/core/src/main/scala/org/apache/spark/SparkException.scala
@@ -71,9 +71,22 @@ private[spark] case class ExecutorDeadException(message: String)
 /**
  * Exception thrown when Spark returns different result after upgrading to a new version.
  */
-private[spark] class SparkUpgradeException(version: String, message: String, cause: Throwable)
-  extends RuntimeException("You may get a different result due to the upgrading of Spark" +
-    s" $version: $message", cause)
+private[spark] class SparkUpgradeException(
+    errorClass: String,
+    messageParameters: Array[String],
+    cause: Throwable)
+  extends RuntimeException(SparkThrowableHelper.getMessage(errorClass, messageParameters), cause)
+    with SparkThrowable {
+
+  def this(version: String, message: String, cause: Throwable) =
+    this (
+      errorClass = "INCONSISTENT_BEHAVIOR_CROSS_VERSION",
+      messageParameters = Array(version, message),
+      cause = cause
+    )
+
+  override def getErrorClass: String = errorClass
+}
 
 /**
  * Arithmetic exception thrown from Spark with an error class.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala
index 6424b32efbc39..62b961604a25a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala
@@ -533,30 +533,43 @@ object QueryExecutionErrors {
 
   def sparkUpgradeInReadingDatesError(
       format: String, config: String, option: String): SparkUpgradeException = {
-    new SparkUpgradeException("3.0",
-      s"""
-         |reading dates before 1582-10-15 or timestamps before 1900-01-01T00:00:00Z from $format
-         |files can be ambiguous, as the files may be written by Spark 2.x or legacy versions of
-         |Hive, which uses a legacy hybrid calendar that is different from Spark 3.0+'s Proleptic
-         |Gregorian calendar. See more details in SPARK-31404. You can set the SQL config
-         |'$config' or the datasource option '$option' to 'LEGACY' to rebase the datetime values
-         |w.r.t. the calendar difference during reading. To read the datetime values as it is,
-         |set the SQL config '$config' or the datasource option '$option' to 'CORRECTED'.
-       """.stripMargin, null)
+    new SparkUpgradeException(
+      errorClass = "INCONSISTENT_BEHAVIOR_CROSS_VERSION",
+      messageParameters = Array(
+        "3.0",
+        s"""
+           |reading dates before 1582-10-15 or timestamps before 1900-01-01T00:00:00Z
+           |from $format files can be ambiguous, as the files may be written by
+           |Spark 2.x or legacy versions of Hive, which uses a legacy hybrid calendar
+           |that is different from Spark 3.0+'s Proleptic Gregorian calendar.
+           |See more details in SPARK-31404. You can set the SQL config '$config' or
+           |the datasource option '$option' to 'LEGACY' to rebase the datetime values
+           |w.r.t. the calendar difference during reading. To read the datetime values
+           |as it is, set the SQL config '$config' or the datasource option '$option'
+           |to 'CORRECTED'.
+           |""".stripMargin),
+      cause = null
+    )
   }
 
   def sparkUpgradeInWritingDatesError(format: String, config: String): SparkUpgradeException = {
-    new SparkUpgradeException("3.0",
-      s"""
-         |writing dates before 1582-10-15 or timestamps before 1900-01-01T00:00:00Z into $format
-         |files can be dangerous, as the files may be read by Spark 2.x or legacy versions of Hive
-         |later, which uses a legacy hybrid calendar that is different from Spark 3.0+'s Proleptic
-         |Gregorian calendar. See more details in SPARK-31404. You can set $config to 'LEGACY' to
-         |rebase the datetime values w.r.t. the calendar difference during writing, to get maximum
-         |interoperability. Or set $config to 'CORRECTED' to write the datetime values as it is,
-         |if you are 100% sure that the written files will only be read by Spark 3.0+ or other
-         |systems that use Proleptic Gregorian calendar.
-       """.stripMargin, null)
+    new SparkUpgradeException(
+      errorClass = "INCONSISTENT_BEHAVIOR_CROSS_VERSION",
+      messageParameters = Array(
+        "3.0",
+        s"""
+           |writing dates before 1582-10-15 or timestamps before 1900-01-01T00:00:00Z
+           |into $format files can be dangerous, as the files may be read by Spark 2.x
+           |or legacy versions of Hive later, which uses a legacy hybrid calendar that
+           |is different from Spark 3.0+'s Proleptic Gregorian calendar. See more
+           |details in SPARK-31404. You can set $config to 'LEGACY' to rebase the
+           |datetime values w.r.t. the calendar difference during writing, to get maximum
+           |interoperability. Or set $config to 'CORRECTED' to write the datetime values
+           |as it is, if you are 100% sure that the written files will only be read by
+           |Spark 3.0+ or other systems that use Proleptic Gregorian calendar.
+           |""".stripMargin),
+      cause = null
+    )
   }
 
   def buildReaderUnsupportedForFileFormatError(format: String): Throwable = {
@@ -1617,8 +1630,12 @@ object QueryExecutionErrors {
   }
 
   def timeZoneIdNotSpecifiedForTimestampTypeError(): Throwable = {
-    new UnsupportedOperationException(
-      s"${TimestampType.catalogString} must supply timeZoneId parameter")
+    new SparkUnsupportedOperationException(
+      errorClass = "UNSUPPORTED_OPERATION",
+      messageParameters = Array(
+        s"${TimestampType.catalogString} must supply timeZoneId parameter " +
+          s"while converting to ArrowType")
+    )
   }
 
   def notPublicClassError(name: String): Throwable = {
@@ -1932,7 +1949,9 @@ object QueryExecutionErrors {
   }
 
   def cannotConvertOrcTimestampToTimestampNTZError(): Throwable = {
-    new RuntimeException("Unable to convert timestamp of Orc to data type 'timestamp_ntz'")
+    new SparkUnsupportedOperationException(
+      errorClass = "UNSUPPORTED_OPERATION",
+      messageParameters = Array("Unable to convert timestamp of Orc to data type 'timestamp_ntz'"))
   }
 
   def writePartitionExceedConfigSizeWhenDynamicPartitionError(
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index deb720336a1f0..ed4e9348f1889 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -1737,7 +1737,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
             exprSeq2.foreach(pair =>
               checkExceptionInExpression[SparkUpgradeException](
                 pair._1,
-                  "You may get a different result due to the upgrading of Spark 3.0"))
+                  "You may get a different result due to the upgrading to Spark >= 3.0"))
           } else {
             if (ansiEnabled) {
               exprSeq2.foreach(pair =>
diff --git a/sql/core/src/test/resources/sql-tests/results/ansi/date.sql.out b/sql/core/src/test/resources/sql-tests/results/ansi/date.sql.out
index a21512ffe8b7c..36cf228c6284b 100644
--- a/sql/core/src/test/resources/sql-tests/results/ansi/date.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/ansi/date.sql.out
@@ -641,7 +641,7 @@ select to_date('26/October/2015', 'dd/MMMMM/yyyy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -650,7 +650,7 @@ select from_json('{"d":"26/October/2015"}', 'd Date', map('dateFormat', 'dd/MMMM
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -659,7 +659,7 @@ select from_csv('26/October/2015', 'd Date', map('dateFormat', 'dd/MMMMM/yyyy'))
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/ansi/datetime-parsing-invalid.sql.out b/sql/core/src/test/resources/sql-tests/results/ansi/datetime-parsing-invalid.sql.out
index e6dd07b5658f6..5dc3b85b3a9eb 100644
--- a/sql/core/src/test/resources/sql-tests/results/ansi/datetime-parsing-invalid.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/ansi/datetime-parsing-invalid.sql.out
@@ -17,7 +17,7 @@ select to_timestamp('1', 'yy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '1' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '1' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -35,7 +35,7 @@ select to_timestamp('123', 'yy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '123' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '123' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -44,7 +44,7 @@ select to_timestamp('1', 'yyy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '1' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '1' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -53,7 +53,7 @@ select to_timestamp('1234567', 'yyyyyyy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'yyyyyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'yyyyyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -71,7 +71,7 @@ select to_timestamp('9', 'DD')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '9' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '9' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -89,7 +89,7 @@ select to_timestamp('9', 'DDD')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '9' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '9' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -98,7 +98,7 @@ select to_timestamp('99', 'DDD')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '99' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '99' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -170,7 +170,7 @@ select from_csv('2018-366', 'date Date', map('dateFormat', 'yyyy-DDD'))
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '2018-366' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '2018-366' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/ansi/timestamp.sql.out b/sql/core/src/test/resources/sql-tests/results/ansi/timestamp.sql.out
index 2c47ed3abe2c9..dc25ed9b0d140 100644
--- a/sql/core/src/test/resources/sql-tests/results/ansi/timestamp.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/ansi/timestamp.sql.out
@@ -725,7 +725,7 @@ select to_timestamp('2019-10-06 A', 'yyyy-MM-dd GGGGG')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'yyyy-MM-dd GGGGG' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'yyyy-MM-dd GGGGG' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -734,7 +734,7 @@ select to_timestamp('22 05 2020 Friday', 'dd MM yyyy EEEEEE')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd MM yyyy EEEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd MM yyyy EEEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -743,7 +743,7 @@ select to_timestamp('22 05 2020 Friday', 'dd MM yyyy EEEEE')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -752,7 +752,7 @@ select unix_timestamp('22 05 2020 Friday', 'dd MM yyyy EEEEE')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -761,7 +761,7 @@ select from_json('{"t":"26/October/2015"}', 't Timestamp', map('timestampFormat'
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -770,7 +770,7 @@ select from_csv('26/October/2015', 't Timestamp', map('timestampFormat', 'dd/MMM
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/date.sql.out b/sql/core/src/test/resources/sql-tests/results/date.sql.out
index bd32361eaef06..ad6421a53df21 100644
--- a/sql/core/src/test/resources/sql-tests/results/date.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/date.sql.out
@@ -640,7 +640,7 @@ select to_date('26/October/2015', 'dd/MMMMM/yyyy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -649,7 +649,7 @@ select from_json('{"d":"26/October/2015"}', 'd Date', map('dateFormat', 'dd/MMMM
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -658,7 +658,7 @@ select from_csv('26/October/2015', 'd Date', map('dateFormat', 'dd/MMMMM/yyyy'))
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/datetime-formatting-invalid.sql.out b/sql/core/src/test/resources/sql-tests/results/datetime-formatting-invalid.sql.out
index 9c8553dc0f01f..6649ae3dbaf1c 100644
--- a/sql/core/src/test/resources/sql-tests/results/datetime-formatting-invalid.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/datetime-formatting-invalid.sql.out
@@ -8,7 +8,7 @@ select date_format('2018-11-17 13:33:33.333', 'GGGGG')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'GGGGG' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'GGGGG' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -17,7 +17,7 @@ select date_format('2018-11-17 13:33:33.333', 'yyyyyyy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'yyyyyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'yyyyyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -44,7 +44,7 @@ select date_format('2018-11-17 13:33:33.333', 'MMMMM')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'MMMMM' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'MMMMM' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -53,7 +53,7 @@ select date_format('2018-11-17 13:33:33.333', 'LLLLL')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'LLLLL' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'LLLLL' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -62,7 +62,7 @@ select date_format('2018-11-17 13:33:33.333', 'EEEEE')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -71,7 +71,7 @@ select date_format('2018-11-17 13:33:33.333', 'FF')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'FF' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'FF' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -80,7 +80,7 @@ select date_format('2018-11-17 13:33:33.333', 'ddd')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'ddd' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'ddd' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -89,7 +89,7 @@ select date_format('2018-11-17 13:33:33.333', 'DDDD')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'DDDD' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'DDDD' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -98,7 +98,7 @@ select date_format('2018-11-17 13:33:33.333', 'HHH')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'HHH' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'HHH' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -107,7 +107,7 @@ select date_format('2018-11-17 13:33:33.333', 'hhh')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'hhh' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'hhh' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -116,7 +116,7 @@ select date_format('2018-11-17 13:33:33.333', 'kkk')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'kkk' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'kkk' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -125,7 +125,7 @@ select date_format('2018-11-17 13:33:33.333', 'KKK')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'KKK' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'KKK' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -134,7 +134,7 @@ select date_format('2018-11-17 13:33:33.333', 'mmm')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'mmm' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'mmm' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -143,7 +143,7 @@ select date_format('2018-11-17 13:33:33.333', 'sss')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'sss' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'sss' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -152,7 +152,7 @@ select date_format('2018-11-17 13:33:33.333', 'SSSSSSSSSS')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'SSSSSSSSSS' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'SSSSSSSSSS' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -161,7 +161,7 @@ select date_format('2018-11-17 13:33:33.333', 'aa')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'aa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'aa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -179,7 +179,7 @@ select date_format('2018-11-17 13:33:33.333', 'zzzzz')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'zzzzz' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'zzzzz' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -197,7 +197,7 @@ select date_format('2018-11-17 13:33:33.333', 'ZZZZZZ')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'ZZZZZZ' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'ZZZZZZ' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -260,7 +260,7 @@ select date_format('2018-11-17 13:33:33.333', 'Y')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'Y' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'Y' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -269,7 +269,7 @@ select date_format('2018-11-17 13:33:33.333', 'w')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'w' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'w' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -278,7 +278,7 @@ select date_format('2018-11-17 13:33:33.333', 'W')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'W' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'W' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -287,7 +287,7 @@ select date_format('2018-11-17 13:33:33.333', 'u')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'u' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'u' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/datetime-parsing-invalid.sql.out b/sql/core/src/test/resources/sql-tests/results/datetime-parsing-invalid.sql.out
index c1e1a2c4b2143..33504709c08ec 100644
--- a/sql/core/src/test/resources/sql-tests/results/datetime-parsing-invalid.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/datetime-parsing-invalid.sql.out
@@ -17,7 +17,7 @@ select to_timestamp('1', 'yy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '1' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '1' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -34,7 +34,7 @@ select to_timestamp('123', 'yy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '123' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '123' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -43,7 +43,7 @@ select to_timestamp('1', 'yyy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '1' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '1' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -52,7 +52,7 @@ select to_timestamp('1234567', 'yyyyyyy')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'yyyyyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'yyyyyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -69,7 +69,7 @@ select to_timestamp('9', 'DD')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '9' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '9' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -86,7 +86,7 @@ select to_timestamp('9', 'DDD')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '9' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '9' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -95,7 +95,7 @@ select to_timestamp('99', 'DDD')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '99' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '99' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -160,7 +160,7 @@ select from_csv('2018-366', 'date Date', map('dateFormat', 'yyyy-DDD'))
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '2018-366' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '2018-366' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out b/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out
index e509d4e4cc27b..84610834fa7e7 100644
--- a/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out
@@ -339,7 +339,7 @@ select from_json(
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '02-29' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '02-29' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
@@ -351,7 +351,7 @@ select from_json(
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to parse '02-29' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to parse '02-29' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/timestamp.sql.out b/sql/core/src/test/resources/sql-tests/results/timestamp.sql.out
index 6362a2ac20e0f..282e76351e805 100644
--- a/sql/core/src/test/resources/sql-tests/results/timestamp.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/timestamp.sql.out
@@ -719,7 +719,7 @@ select to_timestamp('2019-10-06 A', 'yyyy-MM-dd GGGGG')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'yyyy-MM-dd GGGGG' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'yyyy-MM-dd GGGGG' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -728,7 +728,7 @@ select to_timestamp('22 05 2020 Friday', 'dd MM yyyy EEEEEE')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd MM yyyy EEEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd MM yyyy EEEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -737,7 +737,7 @@ select to_timestamp('22 05 2020 Friday', 'dd MM yyyy EEEEE')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -746,7 +746,7 @@ select unix_timestamp('22 05 2020 Friday', 'dd MM yyyy EEEEE')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -755,7 +755,7 @@ select from_json('{"t":"26/October/2015"}', 't Timestamp', map('timestampFormat'
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -764,7 +764,7 @@ select from_csv('26/October/2015', 't Timestamp', map('timestampFormat', 'dd/MMM
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd/MMMMM/yyyy' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/timestampNTZ/timestamp-ansi.sql.out b/sql/core/src/test/resources/sql-tests/results/timestampNTZ/timestamp-ansi.sql.out
index 46b51fc9255b9..95120a83931ec 100644
--- a/sql/core/src/test/resources/sql-tests/results/timestampNTZ/timestamp-ansi.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/timestampNTZ/timestamp-ansi.sql.out
@@ -752,7 +752,7 @@ select unix_timestamp('22 05 2020 Friday', 'dd MM yyyy EEEEE')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/timestampNTZ/timestamp.sql.out b/sql/core/src/test/resources/sql-tests/results/timestampNTZ/timestamp.sql.out
index adadae552a7c2..0364f553d2676 100644
--- a/sql/core/src/test/resources/sql-tests/results/timestampNTZ/timestamp.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/timestampNTZ/timestamp.sql.out
@@ -746,7 +746,7 @@ select unix_timestamp('22 05 2020 Friday', 'dd MM yyyy EEEEE')
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'dd MM yyyy EEEEE' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
diff --git a/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/stringCastAndExpressions.sql.out b/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/stringCastAndExpressions.sql.out
index 14e941c074041..fd4f8b2c7a0e3 100644
--- a/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/stringCastAndExpressions.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/typeCoercion/native/stringCastAndExpressions.sql.out
@@ -139,7 +139,7 @@ select to_timestamp('2018-01-01', a) from t
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'aa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'aa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -156,7 +156,7 @@ select to_unix_timestamp('2018-01-01', a) from t
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'aa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'aa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
@@ -173,7 +173,7 @@ select unix_timestamp('2018-01-01', a) from t
 struct<>
 -- !query output
 org.apache.spark.SparkUpgradeException
-You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'aa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
+You may get a different result due to the upgrading to Spark >= 3.0: Fail to recognize 'aa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
 
 
 -- !query
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index 762bc15b4791e..fa246fa79b33c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -516,7 +516,7 @@ class DateFunctionsSuite extends QueryTest with SharedSparkSession {
       Seq(Row(null), Row(null), Row(null)))
     val e = intercept[SparkUpgradeException](df.select(to_date(col("s"), "yyyy-dd-aa")).collect())
     assert(e.getCause.isInstanceOf[IllegalArgumentException])
-    assert(e.getMessage.contains("You may get a different result due to the upgrading of Spark"))
+    assert(e.getMessage.contains("You may get a different result due to the upgrading to Spark"))
 
     // February
     val x1 = "2016-02-29"
@@ -699,7 +699,7 @@ class DateFunctionsSuite extends QueryTest with SharedSparkSession {
           val e = intercept[SparkUpgradeException](invalid.collect())
           assert(e.getCause.isInstanceOf[IllegalArgumentException])
           assert(
-            e.getMessage.contains("You may get a different result due to the upgrading of Spark"))
+            e.getMessage.contains("You may get a different result due to the upgrading to Spark"))
         }
 
         // February
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/errors/QueryExecutionErrorsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/errors/QueryExecutionErrorsSuite.scala
index 429e41e8c997d..eb1b06647aaec 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/errors/QueryExecutionErrorsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/errors/QueryExecutionErrorsSuite.scala
@@ -17,12 +17,22 @@
 
 package org.apache.spark.sql.errors
 
-import org.apache.spark.{SparkException, SparkIllegalArgumentException, SparkRuntimeException, SparkUnsupportedOperationException}
-import org.apache.spark.sql.{DataFrame, QueryTest}
+import java.sql.Timestamp
+
+import org.apache.spark.{SparkException, SparkIllegalArgumentException, SparkRuntimeException, SparkUnsupportedOperationException, SparkUpgradeException}
+import org.apache.spark.sql.{DataFrame, QueryTest, Row}
+import org.apache.spark.sql.execution.datasources.orc.OrcTest
+import org.apache.spark.sql.execution.datasources.parquet.ParquetTest
 import org.apache.spark.sql.functions.{lit, lower, struct, sum}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.internal.SQLConf.LegacyBehaviorPolicy.EXCEPTION
 import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types.{StructField, StructType, TimestampNTZType, TimestampType}
+import org.apache.spark.sql.util.ArrowUtils
+
+class QueryExecutionErrorsSuite extends QueryTest
+  with ParquetTest with OrcTest with SharedSparkSession {
 
-class QueryExecutionErrorsSuite extends QueryTest with SharedSparkSession {
   import testImplicits._
 
   private def getAesInputs(): (DataFrame, DataFrame) = {
@@ -181,4 +191,97 @@ class QueryExecutionErrorsSuite extends QueryTest with SharedSparkSession {
     assert(e2.getSqlState === "0A000")
     assert(e2.getMessage === "The feature is not supported: Pivot not after a groupBy.")
   }
+
+  test("INCONSISTENT_BEHAVIOR_CROSS_VERSION: " +
+    "compatibility with Spark 2.4/3.2 in reading/writing dates") {
+
+    // Fail to read ancient datetime values.
+    withSQLConf(SQLConf.PARQUET_REBASE_MODE_IN_READ.key -> EXCEPTION.toString) {
+      val fileName = "before_1582_date_v2_4_5.snappy.parquet"
+      val filePath = getResourceParquetFilePath("test-data/" + fileName)
+      val e = intercept[SparkException] {
+        spark.read.parquet(filePath).collect()
+      }.getCause.asInstanceOf[SparkUpgradeException]
+
+      val format = "Parquet"
+      val config = SQLConf.PARQUET_REBASE_MODE_IN_READ.key
+      val option = "datetimeRebaseMode"
+      assert(e.getErrorClass === "INCONSISTENT_BEHAVIOR_CROSS_VERSION")
+      assert(e.getMessage ===
+        "You may get a different result due to the upgrading to Spark >= 3.0: " +
+          s"""
+             |reading dates before 1582-10-15 or timestamps before 1900-01-01T00:00:00Z
+             |from $format files can be ambiguous, as the files may be written by
+             |Spark 2.x or legacy versions of Hive, which uses a legacy hybrid calendar
+             |that is different from Spark 3.0+'s Proleptic Gregorian calendar.
+             |See more details in SPARK-31404. You can set the SQL config '$config' or
+             |the datasource option '$option' to 'LEGACY' to rebase the datetime values
+             |w.r.t. the calendar difference during reading. To read the datetime values
+             |as it is, set the SQL config '$config' or the datasource option '$option'
+             |to 'CORRECTED'.
+             |""".stripMargin)
+    }
+
+    // Fail to write ancient datetime values.
+    withSQLConf(SQLConf.PARQUET_REBASE_MODE_IN_WRITE.key -> EXCEPTION.toString) {
+      withTempPath { dir =>
+        val df = Seq(java.sql.Date.valueOf("1001-01-01")).toDF("dt")
+        val e = intercept[SparkException] {
+          df.write.parquet(dir.getCanonicalPath)
+        }.getCause.getCause.getCause.asInstanceOf[SparkUpgradeException]
+
+        val format = "Parquet"
+        val config = SQLConf.PARQUET_REBASE_MODE_IN_WRITE.key
+        assert(e.getErrorClass === "INCONSISTENT_BEHAVIOR_CROSS_VERSION")
+        assert(e.getMessage ===
+          "You may get a different result due to the upgrading to Spark >= 3.0: " +
+            s"""
+               |writing dates before 1582-10-15 or timestamps before 1900-01-01T00:00:00Z
+               |into $format files can be dangerous, as the files may be read by Spark 2.x
+               |or legacy versions of Hive later, which uses a legacy hybrid calendar that
+               |is different from Spark 3.0+'s Proleptic Gregorian calendar. See more
+               |details in SPARK-31404. You can set $config to 'LEGACY' to rebase the
+               |datetime values w.r.t. the calendar difference during writing, to get maximum
+               |interoperability. Or set $config to 'CORRECTED' to write the datetime values
+               |as it is, if you are 100% sure that the written files will only be read by
+               |Spark 3.0+ or other systems that use Proleptic Gregorian calendar.
+               |""".stripMargin)
+      }
+    }
+  }
+
+  test("UNSUPPORTED_OPERATION: timeZoneId not specified while converting TimestampType to Arrow") {
+    val schema = new StructType().add("value", TimestampType)
+    val e = intercept[SparkUnsupportedOperationException] {
+      ArrowUtils.toArrowSchema(schema, null)
+    }
+
+    assert(e.getErrorClass === "UNSUPPORTED_OPERATION")
+    assert(e.getMessage === "The operation is not supported: " +
+      "timestamp must supply timeZoneId parameter while converting to ArrowType")
+  }
+
+  test("UNSUPPORTED_OPERATION - SPARK-36346: can't read Timestamp as TimestampNTZ") {
+    val data = (1 to 10).map { i =>
+      val ts = new Timestamp(i)
+      Row(ts)
+    }
+
+    val actualSchema = StructType(Seq(StructField("time", TimestampType, false)))
+    val providedSchema = StructType(Seq(StructField("time", TimestampNTZType, false)))
+
+    withTempPath { file =>
+      val df = spark.createDataFrame(sparkContext.parallelize(data), actualSchema)
+      df.write.orc(file.getCanonicalPath)
+      withAllNativeOrcReaders {
+        val e = intercept[SparkException] {
+          spark.read.schema(providedSchema).orc(file.getCanonicalPath).collect()
+        }.getCause.asInstanceOf[SparkUnsupportedOperationException]
+
+        assert(e.getErrorClass === "UNSUPPORTED_OPERATION")
+        assert(e.getMessage === "The operation is not supported: " +
+          "Unable to convert timestamp of Orc to data type 'timestamp_ntz'")
+      }
+    }
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcQuerySuite.scala
index 551a3f5a7cc1b..280a88091089b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcQuerySuite.scala
@@ -804,32 +804,6 @@ abstract class OrcQuerySuite extends OrcQueryTest with SharedSparkSession {
     }
   }
 
-  test("SPARK-36346: can't read TimestampLTZ as TimestampNTZ") {
-    val data = (1 to 10).map { i =>
-      val ts = new Timestamp(i)
-      Row(ts)
-    }
-    val answer = (1 to 10).map { i =>
-      // The second parameter is `nanoOfSecond`, while java.sql.Timestamp accepts milliseconds
-      // as input. So here we multiple the `nanoOfSecond` by NANOS_PER_MILLIS
-      val ts = LocalDateTime.ofEpochSecond(0, i * 1000000, ZoneOffset.UTC)
-      Row(ts)
-    }
-    val actualSchema = StructType(Seq(StructField("time", TimestampType, false)))
-    val providedSchema = StructType(Seq(StructField("time", TimestampNTZType, false)))
-
-    withTempPath { file =>
-      val df = spark.createDataFrame(sparkContext.parallelize(data), actualSchema)
-      df.write.orc(file.getCanonicalPath)
-      withAllNativeOrcReaders {
-        val msg = intercept[SparkException] {
-          spark.read.schema(providedSchema).orc(file.getCanonicalPath).collect()
-        }.getMessage
-        assert(msg.contains("Unable to convert timestamp of Orc to data type 'timestamp_ntz'"))
-      }
-    }
-  }
-
   test("SPARK-36346: read TimestampNTZ as TimestampLTZ") {
     val data = (1 to 10).map { i =>
       // The second parameter is `nanoOfSecond`, while java.sql.Timestamp accepts milliseconds
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcTest.scala
index 96932de3275bc..c36bfd9362466 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcTest.scala
@@ -47,7 +47,7 @@ import org.apache.spark.sql.internal.SQLConf.ORC_IMPLEMENTATION
  *       -> HiveOrcPartitionDiscoverySuite
  *   -> OrcFilterSuite
  */
-abstract class OrcTest extends QueryTest with FileBasedDataSourceTest with BeforeAndAfterAll {
+trait OrcTest extends QueryTest with FileBasedDataSourceTest with BeforeAndAfterAll {
 
   val orcImp: String = "native"