gengliangwang 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_r378449795
##########
File path: docs/sql-ref-ansi-compliance.md
##########
@@ -19,6 +19,87 @@ license: |
limitations under the License.
---
+Spark SQL has two options to comply with the SQL standard:
`spark.sql.ansi.enabled` and `spark.sql.storeAssignmentPolicy`.
+When `spark.sql.ansi.enabled` is set to `true` (`false` by default), 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 and this setting is a default value.
+
+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).
+On the other hand, Spark SQL returns null for decimal overflows.
+When `spark.sql.ansi.enabled` is set to `true` and an overflow occurs in
numeric and interval arithmetic operations, it throws an arithmetic exception
at runtime.
+
+{% highlight sql %}
+-- `spark.sql.ansi.enabled=true`
+SELECT 2147483647 + 1;
+
+ java.lang.ArithmeticException: integer overflow
+
+-- `spark.sql.ansi.enabled=false`
+SELECT 2147483647 + 1;
+
+ +----------------+
+ |(2147483647 + 1)|
+ +----------------+
+ | -2147483648|
+ +----------------+
+
+{% endhighlight %}
+
+### Type Conversion
+
+Spark SQL has three kinds of type conversions: explicit casting, type
coercion, and store assignment casting.
+When `spark.sql.ansi.enabled` is set to `true`, explicit casting by `CAST`
syntax throws a number-format exception at runtime for illegal cast patterns
defined in the standard, e.g. casts from a string to an integer.
Review comment:
number-format exception is not the only runtime exception for ANSI mode
```
spark.sql("select cast(2147483648L as int)").show()
java.lang.ArithmeticException: Casting 2147483648 to int causes overflow
```
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