Performance
===========

This library is written in Cython for a better performance than a pure-Python implementation could give you.


Decoder Performance
-------------------

The library is a bit slower than the shipped ``json`` module for *pure* JSON data.
If you know that your input does not use JSON5 extension, then this library is probably not what you need.

* Dataset: https://github.com/zemirco/sf-city-lots-json
* Version: Python 3.7.5 (default, Oct 27 2019, 15:43:29)
* CPU: AMD Ryzen 7 2700 @ 3.7GHz
* :func:`pyjson5.decode`: **3.29 s** ± 8.66 ms per loop per loop per loop *(lower is better)*
* :func:`json.loads`: **2.64** s ± 12.7 ms ms per loop
* The decoder works correcty: ``json.loads(content) == pyjson5.loads(content)``


Encoder Performance
-------------------

The encoder generates pure JSON data if there are no infinite or NaN values in the input, which are invalid in JSON.
The serialized data is XML-safe, i.e. there are no cheverons ``<>``, ampersands ``&``, apostrophes ``'`` or control characters in the output.
The output is always ASCII regardless if you call :func:`pyjson5.encode` or :func:`pyjson5.encode_bytes`.

* Dataset: https://github.com/zemirco/sf-city-lots-json
* Version: Python 3.7.5 (default, Oct 27 2019, 15:43:29)
* CPU: AMD Ryzen 7 2700 @ 3.7GHz
* :func:`pyjson5.encode`: **3.42** s ± 11.8 ms per loop *(lower is better)*
* :func:`json.dumps`: **3.77** s ± 14.4 ms per loop
* :func:`json.dumps` + :func:`xml.sax.saxutils.escape`: **4.02** s ± 51.6 ms per loop
* The encoder works correcty: ``obj == json.loads(pyjson5.encode(obj, floatformat='%.16e'))``