cloud-fan commented on a change in pull request #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled URL: https://github.com/apache/spark/pull/27889#discussion_r391512138
########## File path: docs/sql-migration-guide.md ########## @@ -70,7 +70,7 @@ license: | - Since Spark 3.0, Proleptic Gregorian calendar is used in parsing, formatting, and converting dates and timestamps as well as in extracting sub-components like years, days and etc. Spark 3.0 uses Java 8 API classes from the java.time packages that based on ISO chronology (https://docs.oracle.com/javase/8/docs/api/java/time/chrono/IsoChronology.html). In Spark version 2.4 and earlier, those operations are performed by using the hybrid calendar (Julian + Gregorian, see https://docs.oracle.com/javase/7/docs/api/java/util/GregorianCalendar.html). The changes impact on the results for dates before October 15, 1582 (Gregorian) and affect on the following Spark 3.0 API: - - Parsing/formatting of timestamp/date strings. This effects on CSV/JSON datasources and on the `unix_timestamp`, `date_format`, `to_unix_timestamp`, `from_unixtime`, `to_date`, `to_timestamp` functions when patterns specified by users is used for parsing and formatting. Since Spark 3.0, the conversions are based on `java.time.format.DateTimeFormatter`, see https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html. New implementation performs strict checking of its input. For example, the `2015-07-22 10:00:00` timestamp cannot be parse if pattern is `yyyy-MM-dd` because the parser does not consume whole input. Another example is the `31/01/2015 00:00` input cannot be parsed by the `dd/MM/yyyy hh:mm` pattern because `hh` supposes hours in the range `1-12`. In Spark version 2.4 and earlier, `java.text.SimpleDateFormat` is used for timestamp/date string conversions, and the supported patterns are described in https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html. The old behavior can be restored by setting `spark.sql.legacy.timeParser.enabled` to `true`. + - Parsing/formatting of timestamp/date strings. This effects on CSV/JSON datasources and on the `unix_timestamp`, `date_format`, `to_unix_timestamp`, `from_unixtime`, `to_date`, `to_timestamp` functions when patterns specified by users is used for parsing and formatting. Since Spark 3.0, the conversions are based on `java.time.format.DateTimeFormatter`, see https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html. New implementation performs strict checking of its input. For example, the `2015-07-22 10:00:00` timestamp cannot be parse if pattern is `yyyy-MM-dd` because the parser does not consume whole input. Another example is the `31/01/2015 00:00` input cannot be parsed by the `dd/MM/yyyy hh:mm` pattern because `hh` supposes hours in the range `1-12`. In Spark version 2.4 and earlier, `java.text.SimpleDateFormat` is used for timestamp/date string conversions, and the supported patterns are described in https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html. The old behavior can be restored by setting `spark.sql.legacy.timeParserPolicy` to `LEGACY`. Review comment: Since we are here, let's make it more clear: ``` Parsing/formatting of timestamp/date strings. This effects on CSV/JSON datasources and on the `unix_timestamp`, `date_format`, `to_unix_timestamp`, `from_unixtime`, `to_date`, `to_timestamp` functions when patterns specified by users is used for parsing and formatting. Since Spark 3.0, we define our own pattern string in `sql-ref-datetime-pattern.md`, which is implemented via `java.time.format.DateTimeFormatter` under the hood. New implementation ... ``` ---------------------------------------------------------------- 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] With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
