maropu commented on a change in pull request #32488:
URL: https://github.com/apache/spark/pull/32488#discussion_r637672796
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
##########
@@ -121,6 +129,49 @@ object UnwrapCastInBinaryComparison extends
Rule[LogicalPlan] {
if canImplicitlyCast(fromExp, toType, literalType) =>
simplifyNumericComparison(be, fromExp, toType, value)
+ // As the analyzer makes sure that the list of In is already of the same
data type, then the
+ // rule can simply check the first literal in `in.list` can implicitly
cast to `toType` or not,
+ // and this rule doesn't convert in when `in.list` is empty.
+ case in @ In(Cast(fromExp, toType: NumericType, _), list @ Seq(firstLit,
_*))
+ if canImplicitlyCast(fromExp, toType, firstLit.dataType) &&
in.inSetConvertible =>
+ val (newValueList, exp) =
+ list.map(lit => unwrapCast(EqualTo(in.value, lit)))
+ .partition {
+ case EqualTo(_, _: Literal) => true
+ case And(IsNull(_), Literal(null, BooleanType)) => false
Review comment:
`case And(IsNull(_), Literal(null, BooleanType)) => false` => `case _ =>
false`?
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
##########
@@ -121,6 +129,49 @@ object UnwrapCastInBinaryComparison extends
Rule[LogicalPlan] {
if canImplicitlyCast(fromExp, toType, literalType) =>
simplifyNumericComparison(be, fromExp, toType, value)
+ // As the analyzer makes sure that the list of In is already of the same
data type, then the
+ // rule can simply check the first literal in `in.list` can implicitly
cast to `toType` or not,
+ // and this rule doesn't convert in when `in.list` is empty.
+ case in @ In(Cast(fromExp, toType: NumericType, _), list @ Seq(firstLit,
_*))
+ if canImplicitlyCast(fromExp, toType, firstLit.dataType) &&
in.inSetConvertible =>
+ val (newValueList, exp) =
+ list.map(lit => unwrapCast(EqualTo(in.value, lit)))
+ .partition {
+ case EqualTo(_, _: Literal) => true
+ case And(IsNull(_), Literal(null, BooleanType)) => false
+ }
+
+ val (nonNullValueList, nullValueList) = newValueList.partition {
+ case EqualTo(_, NonNullLiteral(_, _: NumericType)) => true
+ case EqualTo(_, Literal(null, _)) => false
+ }
+ // make sure the new return list have the same dataType.
+ val newList = {
+ if (nonNullValueList.nonEmpty) {
+ // cast the null value to the dataType of nonNullValueList
+ // when the nonNullValueList is nonEmpty.
+ nullValueList.map {
+ case EqualTo(_, lit) =>
+ Cast(lit,
nonNullValueList.head.asInstanceOf[EqualTo].left.dataType)
+ } ++ nonNullValueList.map {case EqualTo(_, lit) => lit}
+ } else {
+ // the new value list only contains null value,
+ // cast the null value to fromExp.dataType.
+ nullValueList.map {
+ case EqualTo(_, lit) =>
+ Cast(lit, fromExp.dataType)
+ }
+ }
+ }
+
+ val unwrapIn = In(fromExp, newList)
+ // since `exp` are all the same,
+ // convert to a single value `And(IsNull(_), Literal(null,
BooleanType))`.
+ exp.headOption match {
+ case Some(falseIfNotNull) => Or(falseIfNotNull, unwrapIn)
Review comment:
We still need to unwrap casts in this case? IIUC we unwrap casts so that
the later optimizer rules can easily push down predicates into data sources.
But, predicates having `Or` makes it hard to push them down?
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
##########
@@ -121,6 +129,49 @@ object UnwrapCastInBinaryComparison extends
Rule[LogicalPlan] {
if canImplicitlyCast(fromExp, toType, literalType) =>
simplifyNumericComparison(be, fromExp, toType, value)
+ // As the analyzer makes sure that the list of In is already of the same
data type, then the
+ // rule can simply check the first literal in `in.list` can implicitly
cast to `toType` or not,
+ // and this rule doesn't convert in when `in.list` is empty.
+ case in @ In(Cast(fromExp, toType: NumericType, _), list @ Seq(firstLit,
_*))
+ if canImplicitlyCast(fromExp, toType, firstLit.dataType) &&
in.inSetConvertible =>
+ val (newValueList, exp) =
+ list.map(lit => unwrapCast(EqualTo(in.value, lit)))
+ .partition {
+ case EqualTo(_, _: Literal) => true
+ case And(IsNull(_), Literal(null, BooleanType)) => false
+ }
+
+ val (nonNullValueList, nullValueList) = newValueList.partition {
+ case EqualTo(_, NonNullLiteral(_, _: NumericType)) => true
+ case EqualTo(_, Literal(null, _)) => false
Review comment:
ditto: `case EqualTo(_, Literal(null, _)) => false` => `case _ => false`?
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
##########
@@ -21,15 +21,15 @@ import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.Literal.FalseLiteral
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.catalyst.trees.TreePattern.BINARY_COMPARISON
+import org.apache.spark.sql.catalyst.trees.TreePattern.{BINARY_COMPARISON, IN}
import org.apache.spark.sql.types._
/**
- * Unwrap casts in binary comparison operations with patterns like following:
+ * Unwrap casts in binary comparison or `In` operations with patterns like
following:
*
- * `BinaryComparison(Cast(fromExp, toType), Literal(value, toType))`
- * or
- * `BinaryComparison(Literal(value, toType), Cast(fromExp, toType))`
+ * - `BinaryComparison(Cast(fromExp, toType), Literal(value, toType))`
+ * - `BinaryComparison(Literal(value, toType), Cast(fromExp, toType))`
+ * - `In(Cast(fromExp, toType), Seq((v1, toType), (v2, toType), ...)`
Review comment:
`v1` and `v2` should be `Literal`?
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