Authors:	Ken Robbins <ken@kenrobbins.com>; Lele Gaifax <lele@metapensiero.it>
License:	MIT License
Status:

RapidJSON is an extremely fast C++ JSON parser and serialization library: this module wraps it into a Python 3 extension, exposing its serialization/deserialization (to/from either bytes, str or file-like instances) and JSON Schema validation capabilities.

Latest version documentation is automatically rendered by Read the Docs.

First install python-rapidjson:

$ pip install python-rapidjson

or, if you prefer Conda:

$ conda install -c conda-forge python-rapidjson

Basic usage looks like this:

>>> import rapidjson
>>> data = {'foo': 100, 'bar': 'baz'}
>>> rapidjson.dumps(data)
'{"bar":"baz","foo":100}'
>>> rapidjson.loads('{"bar":"baz","foo":100}')
{'bar': 'baz', 'foo': 100}
>>>
>>> class Stream:
...   def write(self, data):
...      print("Chunk:", data)
...
>>> rapidjson.dump(data, Stream(), chunk_size=5)
Chunk: b'{"foo'
Chunk: b'":100'
Chunk: b',"bar'
Chunk: b'":"ba'
Chunk: b'z"}'

If you want to install the development version (maybe to contribute fixes or enhancements) you may clone the repository:

$ git clone --recursive https://github.com/python-rapidjson/python-rapidjson.git

$ python3 setup.py build --rj-include-dir=/usr/include/rapidjson

A set of makefiles implement most common operations, such as build, check and release; see make help output for a list of available targets.

python-rapidjson tries to be as performant as possible while staying compatible with the json module.

The following tables show a comparison between this module and other libraries with different data sets. Last row (“overall”) is the total time taken by all the benchmarks.

Each number shows the factor between the time taken by each contender and python-rapidjson (in other words, they are normalized against a value of 1.0 for python-rapidjson): the lower the number, the speedier the contender.

In bold the winner.

To run these tests yourself, clone the repo and run:

$ make benchmarks

or

$ make benchmarks-other

The former will focus only on RapidJSON and is particularly handy coupled with the compare past runs functionality of pytest-benchmark:

$ make benchmarks PYTEST_OPTIONS=--benchmark-autosave
# hack, hack, hack!
$ make benchmarks PYTEST_OPTIONS=--benchmark-compare=0001

----------------------- benchmark 'deserialize': 18 tests ------------------------
Name (time in us)                                                            Min…
----------------------------------------------------------------------------------
test_loads[rapidjson-256 Trues array] (NOW)                         5.2320 (1.0)…
test_loads[rapidjson-256 Trues array] (0001)                        5.4180 (1.04)…
…

To reproduce the tables above run make benchmarks-tables

Here are things in the standard json library supports that we have decided not to support:

separators argument

This is mostly used for pretty printing and not supported by RapidJSON so it isn't a high priority. We do support indent kwarg that would get you nice looking JSON anyways.

Coercing keys when dumping

json will stringify a True dictionary key as "true" if you dump it out but when you load it back in it'll still be a string. We want the dump and load to return the exact same objects so we have decided not to do this coercion.

Arbitrary encodings

json.loads() accepts an encoding kwarg determining the encoding of its input, when that is a bytes or bytearray instance. Although RapidJSON is able to cope with several different encodings, we currently supports only the recommended one, UTF-8.