[GitHub] [spark] maropu commented on a change in pull request #27489: [SPARK-30703][SQL][DOCS] Add a document for the ANSI mode

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maropu commented on a change in pull request #27489: [SPARK-30703][SQL][DOCS] Add a document for the ANSI mode
URL: https://github.com/apache/spark/pull/27489#discussion_r388643951
 
 

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 File path: docs/sql-ref-ansi-compliance.md
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 @@ -19,6 +19,127 @@ 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).
+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.
+
+<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:
+    1. Spark will throw a runtime exception if an overflow occurs in any operation on integral/decimal field.
+    2. Spark will forbid using the reserved keywords of ANSI SQL as identifiers in the SQL parser.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.sql.storeAssignmentPolicy</code></td>
+  <td>ANSI</td>
+  <td>
+    When inserting a value into a column with different data type, Spark will perform type coercion.
+    Currently, we support 3 policies for the type coercion rules: ANSI, legacy and strict. With ANSI policy,
+    Spark performs the type coercion as per ANSI SQL. In practice, the behavior is mostly the same as PostgreSQL.
+    It disallows certain unreasonable type conversions such as converting string to int or double to boolean.
+    With legacy policy, Spark allows the type coercion as long as it is a valid Cast, which is very loose.
+    e.g. converting string to int or double to boolean is allowed.
+    It is also the only behavior in Spark 2.x and it is compatible with Hive.
+    With strict policy, Spark doesn't allow any possible precision loss or data truncation in type coercion,
+    e.g. converting double to int or decimal to double is not allowed.
+  </td>
+</tr>
+</table>
+
+The following subsections present behaviour changes in arithmetic operations, type conversions, and SQL parsing when the ANSI mode enabled.
+
+### Arithmetic Operations
+
+In Spark SQL, arithmetic operations performed on numeric types (with the exception of decimal) are not checked for overflows by default.
+This means that in case an operation causes overflows, the result is the same that the same operation returns in a Java/Scala program (e.g., if the sum of 2 integers is higher than the maximum value representable, the result is a negative number).
 
 Review comment:
   https://github.com/apache/spark/pull/27819

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