Performance

pysimdjson is fast, typically tying or beating all other Python JSON libraries when simply using simdjson.loads() or simdjson.load().

However, 95% of the time spent loading a JSON document into Python is spent in the creation of Python objects, not the actual parsing of the document. You can avoid all of this overhead by ignoring parts of the document you don’t want.

pysimdjson also has optimizations for loading homogeneous arrays into tools like numpy via simdjson.Array.as_buffer(). This is typically at least 8x faster than other methods.

Don’t load the entire document

pysimdjson supports this in two ways - the use of JSON pointers via at_pointer(), or proxies for objects and lists.

import simdjson
parser = simdjson.Parser()
doc = parser.parse(b'{"res": [{"name": "first"}, {"name": "second"}]}')

For our sample above, we really just want the second entry in res, we don’t care about anything else. We can do this two ways:

assert doc['res'][1]['name'] == 'second' # True
assert doc.at_pointer('/res/1/name') == 'second' # True

Both of these approaches will be much faster than using load/s(), since they avoid loading the parts of the document we didn’t care about.

Both Object and Array have a mini property that returns their entire content as a minified Python str. A message router for example would only parse the document and retrieve a single property, the destination, and forward the payload without ever turning it into a Python object. Here’s a (bad) example:

import simdjson

@app.route('/store', methods=['POST'])
def store():
    parser = simdjson.Parser()
    doc = parser.parse(request.data)
    redis.set(doc['key'], doc.mini)

With this, doc could contain thousands of objects, but the only one loaded into a python object was key, and we even minified the content as we went.

Re-use the parser

One of the easiest performance gains if you’re working on many documents is to re-use the parser.

import simdjson
parser = simdjson.Parser()

for i in range(0, 100):
    doc = parser.parse(b'{"a": "b"}')

This will drastically reduce the number of allocations being made, as it will reuse the existing buffer when possible. If it’s too small, it’ll grow to fit.