cloud-fan commented on a change in pull request #29792:
URL: https://github.com/apache/spark/pull/29792#discussion_r496523301
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File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
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@@ -35,18 +35,34 @@ import org.apache.spark.sql.types._
* to be optimized away later and pushed down to data sources.
*
* Currently this only handles cases where:
- * 1). `fromType` (of `fromExp`) and `toType` are of integral types (i.e.,
byte, short, int and
- * long)
+ * 1). `fromType` (of `fromExp`) and `toType` are of numeric types (i.e.,
short, int, float,
+ * decimal, etc)
* 2). `fromType` can be safely coerced to `toType` without precision loss
(e.g., short to int,
* int to long, but not long to int)
*
* If the above conditions are satisfied, the rule checks to see if the
literal `value` is within
* range `(min, max)`, where `min` and `max` are the minimum and maximum value
of `fromType`,
- * respectively. If this is true then it means we can safely cast `value` to
`fromType` and thus
+ * respectively. If this is true then it means we may safely cast `value` to
`fromType` and thus
* able to move the cast to the literal side. That is:
*
* `cast(fromExp, toType) op value` ==> `fromExp op cast(value, fromType)`
*
+ * Note there are some exceptions to the above: if casting from `value` to
`fromType` causes
+ * rounding up or down, the above conversion will no longer be valid. Instead,
the rule does the
+ * following:
+ *
+ * if casting `value` to `fromType` causes rounding up:
+ * - `cast(fromExp, toType) > value` ==> `fromExp >= cast(value, fromType)`
+ * - `cast(fromExp, toType) >= value` ==> `fromExp >= cast(value, fromType)`
+ * - `cast(fromExp, toType) === value` ==> if(isnull(fromExp), null, false)
+ * - `cast(fromExp, toType) <=> value` ==> false (if `fromExp` is
deterministic)
+ * - `cast(fromExp, toType) <=> value` ==> `cast(fromExp, toType) <=> value`
(if `fromExp` is
Review comment:
We can remove this because the rule does nothing for it.
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