cloud-fan commented on a change in pull request #31349:
URL: https://github.com/apache/spark/pull/31349#discussion_r565391447
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
##########
@@ -31,126 +31,20 @@ import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types._
+trait TypeCoercionBase {
+ def typeCoercionRules: List[Rule[LogicalPlan]]
-/**
- * A collection of [[Rule]] that can be used to coerce differing types that
participate in
- * operations into compatible ones.
- *
- * Notes about type widening / tightest common types: Broadly, there are two
cases when we need
- * to widen data types (e.g. union, binary comparison). In case 1, we are
looking for a common
- * data type for two or more data types, and in this case no loss of precision
is allowed. Examples
- * include type inference in JSON (e.g. what's the column's data type if one
row is an integer
- * while the other row is a long?). In case 2, we are looking for a widened
data type with
- * some acceptable loss of precision (e.g. there is no common type for double
and decimal because
- * double's range is larger than decimal, and yet decimal is more precise than
double, but in
- * union we would cast the decimal into double).
- */
-object TypeCoercion {
-
- def typeCoercionRules: List[Rule[LogicalPlan]] =
- InConversion ::
- WidenSetOperationTypes ::
- PromoteStrings ::
- DecimalPrecision ::
- BooleanEquality ::
- FunctionArgumentConversion ::
- ConcatCoercion ::
- MapZipWithCoercion ::
- EltCoercion ::
- CaseWhenCoercion ::
- IfCoercion ::
- StackCoercion ::
- Division ::
- IntegralDivision ::
- ImplicitTypeCasts ::
- DateTimeOperations ::
- WindowFrameCoercion ::
- StringLiteralCoercion ::
- Nil
-
- // See https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types.
- // The conversion for integral and floating point types have a linear
widening hierarchy:
- val numericPrecedence =
- IndexedSeq(
- ByteType,
- ShortType,
- IntegerType,
- LongType,
- FloatType,
- DoubleType)
+ def findTightestCommonType(type1: DataType, type2: DataType):
Option[DataType]
Review comment:
we should document it
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