Data type mapping specification

Data type mapping specification

• A. Chronicles to Clarity Data Type Mapping maps string to VARCHAR; number to NUMERIC/FLOAT; date to DATETIME; time to DATETIME; instant to DATETIME; category to VARCHAR/INTEGER
• B. It maps to SQL types only
• C. It maps to JSON
• D. It maps to XML

Answer: (A)

In a data type mapping specification, the aim is to translate Chronicles types into concrete storage types used by Clarity’s data layer. This means choosing standard SQL-compatible types that reflect how each Chronicle type should be stored and queried. The best mapping spells out: string becomes VARCHAR, number becomes NUMERIC or FLOAT, date becomes DATETIME, time becomes DATETIME, instant becomes DATETIME, and category can be VARCHAR or INTEGER. This covers all the Chronicles primitives with meaningful SQL representations, giving clear guidance on how data will be stored and indexed. The category entry acknowledges that enum-like values can be stored as either text or numeric codes, depending on design. Why the other ideas don’t fit: mapping to SQL types only is too narrow and ignores the variety of Chronicle types that need appropriate storage representations beyond a single SQL category. Mapping to JSON or XML isn’t about how data is stored in a relational sense; those formats describe data interchange, not the concrete column types used in Clarity’s data model.

In a data type mapping specification, the aim is to translate Chronicles types into concrete storage types used by Clarity’s data layer. This means choosing standard SQL-compatible types that reflect how each Chronicle type should be stored and queried.

The best mapping spells out: string becomes VARCHAR, number becomes NUMERIC or FLOAT, date becomes DATETIME, time becomes DATETIME, instant becomes DATETIME, and category can be VARCHAR or INTEGER. This covers all the Chronicles primitives with meaningful SQL representations, giving clear guidance on how data will be stored and indexed. The category entry acknowledges that enum-like values can be stored as either text or numeric codes, depending on design.

Why the other ideas don’t fit: mapping to SQL types only is too narrow and ignores the variety of Chronicle types that need appropriate storage representations beyond a single SQL category. Mapping to JSON or XML isn’t about how data is stored in a relational sense; those formats describe data interchange, not the concrete column types used in Clarity’s data model.