[GitHub] [spark] gatorsmile commented on a change in pull request #27590: [SPARK-30703][SQL][DOCS][FollowUp] Declare the ANSI SQL compliance options as experimental

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gatorsmile commented on a change in pull request #27590: [SPARK-30703][SQL][DOCS][FollowUp] Declare the ANSI SQL compliance options as experimental
URL: https://github.com/apache/spark/pull/27590#discussion_r379847970
 
 

 ##########
 File path: docs/sql-ref-ansi-compliance.md
 ##########
 @@ -19,19 +19,21 @@ license: |
   limitations under the License.
 ---
 
-Spark SQL has two options to comply with the SQL standard: `spark.sql.ansi.enabled` and `spark.sql.storeAssignmentPolicy` (See a table below for details).
+Since Spark 3.0, Spark SQL introduces two experimental options to comply with the SQL standard: `spark.sql.ansi.enabled` and `spark.sql.storeAssignmentPolicy` (See a table below for details).
+
 When `spark.sql.ansi.enabled` is set to `true`, Spark SQL follows the standard in basic behaviours (e.g., arithmetic operations, type conversion, and SQL parsing).
 Moreover, Spark SQL has an independent option to control implicit casting behaviours when inserting rows in a table.
 The casting behaviours are defined as store assignment rules in the standard.
-When `spark.sql.storeAssignmentPolicy` is set to `ANSI`, Spark SQL complies with the ANSI store assignment rules.
+
+When `spark.sql.storeAssignmentPolicy` is set to `ANSI`, Spark SQL complies with the ANSI store assignment rules. This is a separate configuration because its default value is `ANSI`, while the configuration `spark.sql.ansi.enabled` is disabled by default.
 
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
 <tr>
   <td><code>spark.sql.ansi.enabled</code></td>
   <td>false</td>
   <td>
-    When true, Spark tries to conform to the ANSI SQL specification:
+    (Experimental) When true, Spark tries to conform to the ANSI SQL specification:
 
 Review comment:
   In general, this changes make sense to me. If we have more experimental public conf, we should consider doing it in a proper way. 
   
   Regarding the ANSI mode, we need to consider the roadmap of Spark 3.x: which are still missing and what kind of behavior changes we plan to add.

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