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Python bindings for the simdjson project.
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Quick-n'dirty Python bindings for simdjson just to see if going down this path might yield some parse time improvements in real-world applications. So far, the results are promising, especially when only part of a document is of interest.

Bindings are currently tested on OS X, Linux, and Windows.

See the latest documentation at


There are binary wheels available for some platforms. On other platforms you'll need a C++17-capable compiler.

pip install pysimdjson

Binary wheels are available for:

Platform py3.4 py3.5 py3.6 py3.7
OS X 10.12 x x x y
Windows x x y y
Linux y y y y

or build from git:

git clone
cd pysimdjson
python install


import simdjson

with open('sample.json', 'rb') as fin:
    doc = simdjson.loads(

However, this doesn't really gain you that much over, say, ujson. You're still loading the entire document and converting the entire thing into a series of Python objects which is very expensive. You can instead use items() to pull only part of a document into Python.

Example document:

    "type": "search_results",
    "count": 2,
    "results": [
        {"username": "bob"},
        {"username": "tod"}
    "error": {
        "message": "All good captain"

And now lets try some queries...

import simdjson

with open('sample.json', 'rb') as fin:
    # Calling ParsedJson with a document is a shortcut for
    # calling pj.allocate_capacity(<size>) and pj.parse(<doc>). If you're
    # parsing many JSON documents of similar sizes, you can allocate
    # a large buffer just once and keep re-using it instead.
    pj = simdjson.ParsedJson(

    pj.items('.type') #> "search_results"
    pj.items('.count') #> 2
    pj.items('.results[].username') #> ["bob", "tod"]
    pj.items('.error.message') #> "All good captain"


simdjson requires AVX2 support to function. Check to see if your OS/processor supports it:

  • OS X: sysctl -a | grep machdep.cpu.leaf7_features
  • Linux: grep avx2 /proc/cpuinfo

Low-level interface

You can use the low-level simdjson Iterator interface directly, just be aware that this interface can change any time. If you depend on it you should pin to a specific version of simdjson. You may need to use this interface if you're dealing with odd JSON, such as a document with repeated non-unique keys.

with open('sample.json', 'rb') as fin:
    pj = simdjson.ParsedJson(
    iter = simdjson.Iterator(pj)
    if iter.is_object():
        if iter.down():

Early Benchmark

Comparing the built-in json module loads on py3.7 to simdjson loads.

File json time pysimdjson time
jsonexamples/apache_builds.json 0.09916733999999999 0.074089268
jsonexamples/canada.json 5.305393378 1.6547515810000002
jsonexamples/citm_catalog.json 1.3718639709999998 1.0438697340000003
jsonexamples/github_events.json 0.04840242700000097 0.034239397999998644
jsonexamples/gsoc-2018.json 1.5382746889999996 0.9597240750000005
jsonexamples/instruments.json 0.24350973299999978 0.13639699600000021
jsonexamples/marine_ik.json 4.505123285000002 2.8965093270000004
jsonexamples/mesh.json 1.0325923849999974 0.38916503499999777
jsonexamples/mesh.pretty.json 1.7129034710000006 0.46509220500000126
jsonexamples/numbers.json 0.16577519699999854 0.04843887400000213
jsonexamples/random.json 0.6930746310000018 0.6175370539999996
jsonexamples/twitter.json 0.6069602610000011 0.41049074900000093
jsonexamples/twitterescaped.json 0.7587005720000022 0.41576198399999953
jsonexamples/update-center.json 0.5577604210000011 0.4961777420000004

Getting subsets of the document is significantly faster. For canada.json getting .type using the naive approach and the items() approach, average over N=100.

Python Time
json.loads(canada_json)['type'] 5.76244878
simdjson.loads(canada_json)['type'] 1.5984486990000004
simdjson.ParsedJson(canada_json).items('.type') 0.3949587819999998

This approach avoids creating Python objects for fields that aren't of interest. When you only care about a small part of the document, it will always be faster.

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