[GitHub] [spark] gengliangwang edited a comment on issue #25137: [SPARK-28348][SQL] Decimal precision promotion for binary arithmetic with casted decimal type

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gengliangwang edited a comment on issue #25137: [SPARK-28348][SQL] Decimal precision promotion for binary arithmetic with casted decimal type
URL: https://github.com/apache/spark/pull/25137#issuecomment-511822926
 
 
   I am actually slightly -1 with this proposal.
   If this is OK, in following case 
   ```
   scala> Seq(2147483647).toDF("c").createOrReplaceTempView("foobar") // 2147483647 is max int value, so the data type of foobar is Int
   
   scala> spark.sql("select cast(c*c as long) from foobar").show()
   +-----------------------+
   |CAST((c * c) AS BIGINT)|
   +-----------------------+
   |                      1|
   +-----------------------+
   ```
   
   We might need to have a similar new rule to convert the SQL statement as 
   ```
   spark.sql("select cast(c as long)*cast(c as Long) from foobar").show()
   +---------------------------------------+
   |(CAST(c AS BIGINT) * CAST(c AS BIGINT))|
   +---------------------------------------+
   |                    4611686014132420609|
   +---------------------------------------+
   ```
   
   
   I also tried PostgreSQL
   ```
   create table t(i int);
   insert into t values(2147483647);
   select cast(i*i as bigint) from t;
   ```
   And it shows error `integer out of range`. So, for now, I don't think there is such optimization in PostgreSQL.
   
   

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