Richard Musil writes:
> After some thinking it seems that the float is only case where this
> "loss of a precision" happen.
I don't think so. What's happening here is not restricted to "loss of
precision".
This can happen for any type that has multiple JSON representations of
the same internal value, and therefore you cannot roundtrip from JSON
representation to the same JSON representation for all of them.
This is true of Unicode normalized forms as well as floats. According
to Unicode these are entirely interchangable. If it's convenient for
the Unicode process to change normalization form, according to Unicode
you will get the same *characters* out that you fed in, but you will
not necessarily get byte-for-byte equality. (This bites me all the
time on the Mac, when I write to a file named in NFC and the Mac fs
converts to NFD.)
Python doesn't do any normalization by default, but it's an inherent
feature of Unicode, and there's no (good) way for the codec to know
that it's happening.
As far as I can see, any of the proposals requires the cooperating
systems to coordinate on a nonstandard JSON dialect[1], and for them to
be of practical use, they'll need to have similarly capable internal
representations -- which means the programs have to be designed for
this cooperation. Pick an internal representation for float (probably
Decimal), pick an external (JSON) normalization for Unicode (probably
NFC), write the programs to ensure those representations -- using
conformant JSON, and hope there are no compound types with multiple
JSON representations.
Footnotes:
[1] I write "nonstandard dialect" rather than "non-conformant"
because you can serialize floats as '{"Decimal" : 1.0}', and the
internal processor just needs to know that such a dict should be
automatically converted to Decimal('1.0').
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