datafusion_expr

Module binary

Source
Expand description

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
  • 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.