Decisions to take

Core data representation

Issues:

  • Keys
    • atom
  • Values
    • String
      • atom
        • ISO, easy to read write and process
        • Limits (length, number, atom-gc)
        • Needs unnatural true, false and null
      • string (if Prolog has such a type)
        • Unambiguous and compact
        • Non-portable
        • Often requires atom_string/2 to create an atom
      • string(CodesOrChars)
        • ISO
        • Expensive (wrt memory)
        • Often requires atom_codes/2 to create an atom
    • integer
      • Prolog integer
      • What about bounds?
    • float
      • Prolog float
      • What about +-Inf, NaN
      • What about overflows SWI: Controlled by IEEE754 support (Prolog flags)
    • true, false, null
      • If String is not an atom, use and atom.
      • @(true), @(false), @(null) Janus compatibility
      • ‘NULL’(Var) Database NULL semantics. Unclear whether that applies to JSON in general.
    • Objects
      • SWI-Prolog dict
      • Janus {key:value, …}
      • json(ListOfValues)
        • Key: Value
        • Key= Value
        • Key- Value
        • Key(Value)

Options

Base on Janus

  • Decide on null -> @(null). how does this relate to @(none)?
  • Come up with a library for accessing (nested) objects.
    • Find keys
      • Find using path to indicate nesting SWI: Value = Dict.get(a/b/…)
      • Return default if not found SWI: Value = Dict.get(…, Default)
      • Raise error if not found SWI: Value = Dict.Key[.Key]*
    • Add keys
    • Merge objects SWI: Dict.put(New). Adds keys, replaces values.
    • Select sub-objects SWI: _{k1:V1, k2:V2} :< Dict
    • Translate to popular Prolog formats, e.g.
      • pair list
      • assoc
      • …

Type based conversion

  • Define transformation rules in Prolog, e.g., {x:X,y:Y} <-> point(X,Y)
  • Use JSON Schema?
    • E.g., convert enum value to atom, normal string value as above
    • Extend schema with conversion annotations.

Base on one of the above

No obviously best candidate (IMO)


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