org.apache.spark.sql.catalyst.analysis
Changes numeric values to booleans so that expressions like true = 1 can be evaluated.
Coerces the type of different branches of a CASE WHEN statement to a common type.
Turns Add/Subtract of DateType/TimestampType/StringType and CalendarIntervalType to TimeAdd/TimeSub
Hive only performs integral division with the DIV operator.
This ensure that the types for various functions are as expected.
Coerces the type of different branches of If statement to a common type.
Casts types according to the expected input types for Expressions.
Convert the value and in list expressions to the common operator type by looking at all the argument types and finding the closest one that all the arguments can be cast to.
Promotes strings that appear in arithmetic expressions.
Applies any changes to AttributeReference data types that are made by other rules to instances higher in the query tree.
When encountering a cast from a string representing a valid fractional number to an integral
type the jvm will throw a java.lang.NumberFormatException.
Widens numeric types and converts strings to numbers when appropriate.
Case 1 type widening (see the classdoc comment above for TypeCoercion).
Case 1 type widening (see the classdoc comment above for TypeCoercion).
Find the tightest common type of two types that might be used in a binary expression. This handles all numeric types except fixed-precision decimals interacting with each other or with primitive types, because in that case the precision and scale of the result depends on the operation. Those rules are implemented in DecimalPrecision.
Similar to findTightestCommonType, but can promote all the way to StringType.
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).