[GitHub] [spark] bart-samwel commented on a change in pull request #27435: [SPARK-26618][SQL][FOLLOWUP] Describe the behavior change of typed `TIMESTAMP`/`DATE` literals

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bart-samwel commented on a change in pull request #27435: [SPARK-26618][SQL][FOLLOWUP] Describe the behavior change of typed `TIMESTAMP`/`DATE` literals
URL: https://github.com/apache/spark/pull/27435#discussion_r374303118
 
 

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 File path: docs/sql-migration-guide.md
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 @@ -77,7 +77,7 @@ license: |
 
     - Formatting of `TIMESTAMP` and `DATE` literals.
 
-    - Creating of typed `TIMESTAMP` and `DATE` literals from strings. Since Spark 3.0, string conversion to typed `TIMESTAMP`/`DATE` literals is performed via casting to `TIMESTAMP`/`DATE` values. For example, `TIMESTAMP '2019-12-23 12:59:30'` is semantically equal to `CAST('2019-12-23 12:59:30' AS TIMESTAMP)`. In Spark version 2.4 and earlier, the `java.sql.Timestamp.valueOf()` and `java.sql.Date.valueOf()` functions are used for the same purpose.
+    - Creating of typed `TIMESTAMP` and `DATE` literals from strings. Since Spark 3.0, string conversion to typed `TIMESTAMP`/`DATE` literals is performed via casting to `TIMESTAMP`/`DATE` values. For example, `TIMESTAMP '2019-12-23 12:59:30'` is semantically equal to `CAST('2019-12-23 12:59:30' AS TIMESTAMP)`. When the input string does not contain information about time zone, the time zone from the SQL config `spark.sql.session.timeZone` is used in that case. In Spark version 2.4 and earlier, the `java.sql.Timestamp.valueOf()` and `java.sql.Date.valueOf()` functions are used for the same purpose, and the conversion is based on JVM system time zone. The different sources of the default time zone may change the behavior of typed `TIMESTAMP` and `DATE` literals.
 
 Review comment:
   I'd suggest striking the fragment "the .... functions are used for the same purpose", since that's an implementation detail. This doc is about the observable behavior, not the implementation.

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