Module binary

Coercion rules for matching argument types for binary operators

Functions

binary_numeric_coercion

Coerce lhs_type and rhs_type to a common type where both are numeric

comparison_coercion

Coerce lhs_type and rhs_type to a common type for the purposes of a comparison operation

comparison_coercion_numeric

Similar to comparison_coercion but prefers numeric if compares with numeric and string

decimal_coercion

Decimal coercion rules.

get_input_types

Returns the coerced input types for a binary expression evaluating the op with the left and right hand types

get_result_type

Returns the resulting type of a binary expression evaluating the op with the left and right hand types

get_wider_type

Returns the wider type among arguments lhs and rhs. The wider type is the type that can safely represent values from both types without information loss. Returns an Error if types are incompatible.

like_coercion

Coercion rules for like operations. This is a union of string coercion rules and dictionary coercion rules

regex_coercion

Coercion rules for regular expression comparison operations. This is a union of string coercion rules and dictionary coercion rules

string_coercion

Coercion rules for string view types (Utf8/LargeUtf8/Utf8View): If at least one argument is a string view, we coerce to string view based on the observation that StringArray to StringViewArray is cheap but not vice versa.

try_type_union_resolution

Handle type union resolution including struct type and others.

try_type_union_resolution_with_struct

type_union_resolution

Coerce dissimilar data types to a single data type. UNION, INTERSECT, EXCEPT, CASE, ARRAY, VALUES, and the GREATEST and LEAST functions are examples that has the similar resolution rules. See https://www.postgresql.org/docs/current/typeconv-union-case.html for more information. The rules in the document provide a clue, but adhering strictly to them doesn’t precisely align with the behavior of Postgres. Therefore, we’ve made slight adjustments to the rules to better match the behavior of both Postgres and DuckDB. For example, we expect adjusted decimal precision and scale when coercing decimal types.