Benchmark of Python JSON libraries

September 29, 2015
dev
python
json

A couple of weeks ago after spending some time with Python profiler, I discovered that Python’s json module is not as fast as I expected. I decided to benchmark alternative JSON libraries.

Libraries #

python-cjson, yajl-py and jsonlib are not included in the benchmark, they are not in active development and don’t support Python 3.

simplejson and ujson may be used as a drop-in replacement for the standard json module, but ujson doesn’t support advanced features like hooks, custom encoders and decoders.

You can change your imports this way to use an alternative library:

import ujson as json

Interpreters #

Python (CPython) 2.7.12
Python (CPython) 3.5.2
PyPy 5.3.0

Methodology #

The tests were performed on MacBook Pro Late 2013 (2.6 GHz Intel Core i5, 8 GB 1600 MHz DDR3, Mac OS 10.11.6). Every test runs 100 times.

Test data
File name	File size	Description
twitter.json	632 KB	Single large JSON (source)
one-json-per-line.jsons.txt	176 KB	Collection of 1000 JSON objects (source)

I published the source code of the benchmark on GitHub. You can clone it and rerun if you want to check it by yourself or if a new version of an alternative JSON library is released.

Results #

Python 2.7
	json	simplejson	ujson
loads (large obj)	1.140	0.441	0.448
dumps (large obj)	0.564	0.630	0.459
loads (small objs)	1.190	0.579	0.195
dumps (small objs)	0.910	1.641	0.304

Python 3.5
	json	simplejson	ujson	rapidjson
loads (large obj)	0.600	0.698	0.605	0.634
dumps (large obj)	0.673	0.629	0.381	0.365
loads (small objs)	0.801	1.091	0.322	0.531
dumps (small objs)	1.213	2.038	0.285	0.234

PyPy 5.3
	json	simplejson
loads (large obj)	0.545	1.876
dumps (large obj)	0.632	3.974
loads (small objs)	0.271	1.651
dumps (small objs)	0.719	2.404

Conclusion #

The numbers speak for themselves. If your application is dealing with a big amount of JSON data and doesn't use any advanced features of built-in json module, you should probably consider switching to ujson.