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[GitHub] [spark] yaooqinn opened a new pull request #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

yaooqinn opened a new pull request #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889
 
 
   
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   spark.sql.legacy.timeParser.enabled should be removed from SQLConf and the migration guide
   spark.sql.legacy.timeParsePolicy is the right one
   
   ### Why are the changes needed?
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   fix doc
   
   ### Does this PR introduce any user-facing change?
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   no
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   Nah

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[GitHub] [spark] AmplabJenkins commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/spark/pull/27889#issuecomment-598241925
 
 
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[GitHub] [spark] dongjoon-hyun edited a comment on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
dongjoon-hyun edited a comment on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889#issuecomment-598280941
 
 
   Thank you all. Merged to master.

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[GitHub] [spark] cloud-fan commented on a change in pull request #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
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 ...
   ```

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[GitHub] [spark] SparkQA commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889#issuecomment-598220719
 
 
   **[Test build #119712 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119712/testReport)** for PR 27889 at commit [`1bc237a`](https://github.com/apache/spark/commit/1bc237aad1313dc2fd4dedbc3d4d30474020de7c).
    * This patch passes all tests.
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[GitHub] [spark] yaooqinn commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
yaooqinn commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889#issuecomment-598091040
 
 
   cc @cloud-fan @dongjoon-hyun @maropu thanks

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Posted by GitBox <gi...@apache.org>.
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Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889#issuecomment-598111947
 
 
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[GitHub] [spark] yaooqinn commented on a change in pull request #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
yaooqinn 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_r391519667
 
 

 ##########
 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:
   I notice that the pattern we defined in  `sql-ref-datetime-pattern.md` is slightly different from javadoc, e.g.  we have `M` but javadoc has 'M\L' for month-of-year. On any purpurse?

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[GitHub] [spark] dongjoon-hyun edited a comment on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
dongjoon-hyun edited a comment on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889#issuecomment-598280941
 
 
   Thank you all. Merged to master/3.0.
   (I resolved the conflicts due to config version in `branch-3.0`).

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[GitHub] [spark] SparkQA commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889#issuecomment-598091331
 
 
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[GitHub] [spark] xuanyuanking commented on a change in pull request #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
xuanyuanking 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_r395424154
 
 

 ##########
 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:
   I just keep the letters appeared in the old `SimpleDateFormat` for the first version, it's OK to add them back.

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[GitHub] [spark] AmplabJenkins commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

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[GitHub] [spark] AmplabJenkins commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
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[GitHub] [spark] dongjoon-hyun commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
dongjoon-hyun commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889#issuecomment-598280941
 
 
   Thank you all. Merged to master/3.0.

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[GitHub] [spark] dongjoon-hyun closed pull request #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
dongjoon-hyun closed pull request #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889
 
 
   

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[GitHub] [spark] cloud-fan commented on a change in pull request #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
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_r391521163
 
 

 ##########
 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:
   The goal is to be compatible with the old `SimpleDateFormat`, but `L` seems fine. @xuanyuanking do you have any clue?

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[GitHub] [spark] AmplabJenkins commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

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[GitHub] [spark] yaooqinn commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled

Posted by GitBox <gi...@apache.org>.
yaooqinn commented on issue #27889: [SPARK-31131][SQL] Remove the unnecessary config spark.sql.legacy.timeParser.enabled
URL: https://github.com/apache/spark/pull/27889#issuecomment-598096395
 
 
   thanks @dongjoon-hyun 

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