gengliangwang commented on a change in pull request #32995: URL: https://github.com/apache/spark/pull/32995#discussion_r655148980
########## File path: sql/core/src/test/resources/sql-tests/results/ansi/datetime.sql.out ########## @@ -936,6 +1198,33 @@ 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 +-- !query +select to_timestamp_ntz('2019-10-06 A', 'yyyy-MM-dd GGGGG') +-- !query schema +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 Review comment: I will have a follow up to improve the error message. This Error message is used in multiple SQL output files and some of them are not related to this PR. ########## File path: sql/core/src/test/resources/sql-tests/results/ansi/datetime.sql.out ########## @@ -936,6 +1198,33 @@ 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 +-- !query +select to_timestamp_ntz('2019-10-06 A', 'yyyy-MM-dd GGGGG') +-- !query schema +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 Review comment: I will have a follow up to improve the error message. This error message is used in multiple SQL output files and some of them are not related to this PR. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
