simdjson_nodejs

Node.js bindings for simdjson, a gigabytes-per-second JSON parser, possibly the fastest JSON parser at the moment. simdjson as well as simdjson_nodejs work on popular platforms such as OS X, Linux, and Windows.

Installation

The installation can be done in one step with npm:

npm install simdjson

Usage

API Documentation

See index.d.ts for available simdjson binding functions and their input/output types.

Check if a JSON string is valid:

const simdjson = require('simdjson');

const jsonString = "{ \"answer\": 42 }";
const valid = simdjson.isValid(jsonString); // true

Parsing a JSON string (lazily)

Obs.: Please see that the overhead of converting a C++ object to a JS object might make the parsing time in the NodeJS slower for the simdjson. Therefore, parsing it lazily is preferrable. For more information check issue #5.

const simdjson = require('simdjson');

const jsonString = "{   \
  \"foo\": {            \
    \"bar\": [ 0, 42 ]  \
  }                     \
}";
const JSONbuffer = simdjson.lazyParse(jsonString); // external (C++) parsed JSON object
console.log(JSONbuffer.valueForKeyPath("foo.bar[1]")); // 42

Parsing a JSON string

Obs.: Parsing a JSON lazily is preferrable.

const simdjson = require('simdjson');

const jsonString = "{   \
  \"foo\": {            \
    \"bar\": [ 0, 42 ]  \
  }                     \
}";
const parsedJSON = simdjson.parse(jsonString); // parsed JSON object

Benchmarks

Requirements: git, npm, node, a linux-like system, a recent compiler (GCC,clang)

To benchmark directly from the project:

git clone https://github.com/luizperes/simdjson_nodejs.git
cd simdjson_nodejs
npm install
npm run benchmark

filename	filesize (MB)	JSON.parse(ms)	simdjson.lazyParse (ms)	JSON.parse (GB/s)	simdjson.lazyParse (GB/s)	X faster
apache_builds.json	0.13	0.303	0.158	0.42	0.80	1.91
canada.json	2.25	14.332	7.584	0.16	0.30	1.89
citm_catalog.json	1.73	5.387	5.768	0.32	0.30	0.93
github_events.json	0.07	0.192	0.091	0.34	0.71	2.10
gsoc_2018.json	3.33	5.420	3.839	0.61	0.87	1.41
instruments.json	0.22	0.673	0.619	0.33	0.36	1.09
marine_ik.json	2.98	13.169	6.397	0.23	0.47	2.06
mesh_pretty.json	1.58	5.704	3.043	0.28	0.52	1.87
mesh.json	0.72	2.856	1.404	0.25	0.52	2.03
numbers.json	0.15	0.643	0.280	0.23	0.54	2.30
random.json	0.51	1.914	2.447	0.27	0.21	0.78
sf_citylots.json	189.78	1492.166	709.692	0.13	0.27	2.10
twitter.json	0.63	1.621	2.112	0.39	0.30	0.77
twitterescaped.json	0.56	1.924	0.959	0.29	0.59	2.01
update_center.json	0.53	2.803	2.715	0.19	0.20	1.03

Results from a 2018 MacBook Pro with 2.3GHz Intel Core i9.

Ops/sec

apache_builds.json#simdjson x 6,844 ops/sec ±1.61% (90 runs sampled) => 0.146ms
apache_builds.json#JSON x 3,274 ops/sec ±1.83% (90 runs sampled) => 0.305ms
canada.json#simdjson x 183 ops/sec ±7.69% (72 runs sampled) => 5.475ms
canada.json#JSON x 71.87 ops/sec ±0.92% (74 runs sampled) => 13.914ms
citm_catalog.json#simdjson x 155 ops/sec ±2.95% (72 runs sampled) => 6.470ms
citm_catalog.json#JSON x 207 ops/sec ±59.63% (90 runs sampled) => 4.820ms
github_events.json#simdjson x 11,783 ops/sec ±1.80% (90 runs sampled) => 0.085ms
github_events.json#JSON x 120 ops/sec ±192.65% (92 runs sampled) => 8.310ms
gsoc_2018.json#simdjson x 307 ops/sec ±1.81% (79 runs sampled) => 3.255ms
gsoc_2018.json#JSON x 166 ops/sec ±47.75% (81 runs sampled) => 6.029ms
instruments.json#simdjson x 3,691 ops/sec ±2.06% (87 runs sampled) => 0.271ms
instruments.json#JSON x 2,155 ops/sec ±0.83% (93 runs sampled) => 0.464ms
marine_ik.json#simdjson x 140 ops/sec ±3.08% (73 runs sampled) => 7.133ms
marine_ik.json#JSON x 51.20 ops/sec ±60.81% (76 runs sampled) => 19.533ms
mesh_pretty.json#simdjson x 504 ops/sec ±1.93% (87 runs sampled) => 1.985ms
mesh_pretty.json#JSON x 251 ops/sec ±1.02% (85 runs sampled) => 3.987ms
mesh.json#simdjson x 733 ops/sec ±0.99% (92 runs sampled) => 1.363ms
mesh.json#JSON x 349 ops/sec ±0.64% (89 runs sampled) => 2.869ms
numbers.json#simdjson x 4,099 ops/sec ±1.10% (94 runs sampled) => 0.244ms
numbers.json#JSON x 1,372 ops/sec ±38.74% (95 runs sampled) => 0.729ms
random.json#simdjson x 504 ops/sec ±0.97% (91 runs sampled) => 1.983ms
random.json#JSON x 538 ops/sec ±0.93% (91 runs sampled) => 1.859ms
sf_citylots.json#simdjson x 1.76 ops/sec ±4.02% (9 runs sampled) => 569.580ms
sf_citylots.json#JSON x 0.53 ops/sec ±20.38% (6 runs sampled) => 1880.563ms
twitter.json#simdjson x 478 ops/sec ±0.68% (89 runs sampled) => 2.093ms
twitter.json#JSON x 612 ops/sec ±0.90% (91 runs sampled) => 1.633ms
twitterescaped.json#simdjson x 1,203 ops/sec ±1.80% (88 runs sampled) => 0.831ms
twitterescaped.json#JSON x 488 ops/sec ±2.33% (82 runs sampled) => 2.051ms
update_center.json#simdjson x 469 ops/sec ±3.43% (85 runs sampled) => 2.132ms
update_center.json#JSON x 293 ops/sec ±12.87% (57 runs sampled) => 3.410ms

Observation:

Please refer to the the original repository benchmarks for more information about the performance of simdjson https://github.com/lemire/simdjson.

DarkGL/simdjson_nodejs