libpy-simdjson 0.1.0

Python bindings for smidjson, using libpy

libpy Simdjson

Status: Working Alpha

Python bindings for simdjson using libpy.

Requirements

• OS: macOS>10.15, linux.
• Compiler: gcc>=9, clang >= 10 (C++17 code)
• Python: libpy>=0.2.3, numpy.

Usage

from pathlib import Path
import libpy_simdjson as json

doc = json.load(Path("twitter.json"))
# or json.load(b"twitter.json")
# or json.load("twitter.json")
# we also support `loads` for strings.

doc is an Object. Objects act as python dicts with special methods.

isinstance(doc, json.Object)

True

We can grab keys, get the length, grab items, and access specific keys:

len(doc)

2

doc.keys()

[b'statuses', b'search_metadata']

doc[b'search_metadata'].items()

[(b'completed_in', 0.087),
 (b'max_id', 505874924095815700),
 (b'max_id_str', b'505874924095815681'),
 (b'next_results',
  b'?max_id=505874847260352512&q=%E4%B8%80&count=100&include_entities=1'),
 (b'query', b'%E4%B8%80'),
 (b'refresh_url',
  b'?since_id=505874924095815681&q=%E4%B8%80&include_entities=1'),
 (b'count', 100),
 (b'since_id', 0),
 (b'since_id_str', b'0')]

If you every want an actual python dictionary, use as_dict:

doc[b'search_metadata'].as_dict()

{b'completed_in': 0.087,
 b'max_id': 505874924095815700,
 b'max_id_str': b'505874924095815681',
 b'next_results': b'?max_id=505874847260352512&q=%E4%B8%80&count=100&include_entities=1',
 b'query': b'%E4%B8%80',
 b'refresh_url': b'?since_id=505874924095815681&q=%E4%B8%80&include_entities=1',
 b'count': 100,
 b'since_id': 0,
 b'since_id_str': b'0'}

However, we also support JSON Pointer sytnax via at. This will be much faster if you know what you're looking for:

doc.at(b"statuses/50/created_at")

b'Sun Aug 31 00:29:04 +0000 2014'

doc.at(b"statuses/50/text").decode()

'RT @Ang_Angel73: 逢坂「くっ…僕の秘められし右目が…！」\n一同「……………。」'

Let's look at statuses

statuses = doc[b'statuses']

statuses is an Array. Arrays act like python lists with special methods.

Note: statuses and doc share a single parser instance. We cannot parse a new document while these objects are alive (though we can create new parsers via libpy_simdjson.Parser.load.

isinstance(statuses, json.Array)

True

Arrays support length, indexing, iteration:

len(statuses)

100

statuses[0][b'text'].decode()

'@aym0566x \n\n名前:前田あゆみ\n第一印象:なんか怖っ！\n今の印象:とりあえずキモい。噛み合わない\n好きなところ:ぶすでキモいとこ😋✨✨\n思い出:んーーー、ありすぎ😊❤️\nLINE交換できる？:あぁ……ごめん✋\nトプ画をみて:照れますがな😘✨\n一言:お前は一生もんのダチ💖'

for status in statuses:
    # this is a bad example but you get the picture
    if status[b'id'] % 2 == 0:
        print(status[b"text"].decode())
        break
else:
    print("no even ids?")

@aym0566x

名前:前田あゆみ
第一印象:なんか怖っ！
今の印象:とりあえずキモい。噛み合わない
好きなところ:ぶすでキモいとこ😋✨✨
思い出:んーーー、ありすぎ😊❤️
LINE交換できる？:あぁ……ごめん✋
トプ画をみて:照れますがな😘✨
一言:お前は一生もんのダチ💖

If you need to you can convert and Array to a list using as_list:

statuses.as_list()[1][b'metadata']

{b'result_type': b'recent', b'iso_language_code': b'ja'}

However, just like for Objects, we support JSON Pointers via at, which is much faster:

statuses.at(b"33/created_at")

b'Sun Aug 31 00:29:06 +0000 2014'

Benchmarks

---------------------------------------------- benchmark 'Load /home/runner/work/libpy_simdjson/libpy_simdjson/libpy_simdjson/tests/jsonexamples/canada.json': 6 tests ----------------------------------------------
Name (time in ms)                                       Min                Max               Mean            StdDev             Median                IQR            Outliers       OPS            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_benchmark_load[path0-libpy_simdjson-loads]      3.4478 (1.0)      10.1485 (1.0)       4.0615 (1.0)      0.6386 (1.0)       3.9595 (1.0)       0.3985 (1.0)           8;6  246.2156 (1.0)         149           1
test_benchmark_load[path0-orjson-loads]             14.7421 (4.28)     31.9980 (3.15)     21.1131 (5.20)     4.7609 (7.45)     21.8631 (5.52)      8.2455 (20.69)        23;0   47.3639 (0.19)         61           1
test_benchmark_load[path0-pysimdjson-loads]         15.5617 (4.51)     30.0839 (2.96)     22.2207 (5.47)     4.3227 (6.77)     23.6153 (5.96)      8.4906 (21.31)        12;0   45.0031 (0.18)         30           1
test_benchmark_load[path0-ujson-loads]              20.0784 (5.82)     37.2904 (3.67)     27.4904 (6.77)     4.6357 (7.26)     27.7301 (7.00)      8.1542 (20.46)         9;0   36.3763 (0.15)         26           1
test_benchmark_load[path0-rapidjson-loads]          44.7989 (12.99)    69.9204 (6.89)     53.8819 (13.27)    6.2806 (9.83)     54.5078 (13.77)    10.5220 (26.40)         6;0   18.5591 (0.08)         20           1
test_benchmark_load[path0-python_json-loads]        45.6048 (13.23)    58.9150 (5.81)     52.6407 (12.96)    4.2356 (6.63)     53.2421 (13.45)     7.6745 (19.26)         9;0   18.9967 (0.08)         21           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


------------------------------------------------------ benchmark 'Load /home/runner/work/libpy_simdjson/libpy_simdjson/libpy_simdjson/tests/jsonexamples/citm_catalog.json': 6 tests -------------------------------------------------------
Name (time in us)                                           Min                    Max                   Mean                StdDev                 Median                   IQR            Outliers       OPS            Rounds  Iterations
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_benchmark_load[path3-libpy_simdjson-loads]        973.0290 (1.0)       1,696.1500 (1.0)       1,106.7939 (1.0)         70.3023 (1.0)       1,096.5330 (1.0)         55.0015 (1.0)        107;65  903.5106 (1.0)         496           1
test_benchmark_load[path3-orjson-loads]              6,271.9950 (6.45)     18,752.0820 (11.06)     9,199.1053 (8.31)     3,332.8687 (47.41)     7,502.8330 (6.84)     3,940.9760 (71.65)        32;1  108.7062 (0.12)        128           1
test_benchmark_load[path3-pysimdjson-loads]          7,448.6360 (7.66)     21,308.7680 (12.56)    10,668.5839 (9.64)     3,595.1711 (51.14)     8,919.9800 (8.13)     1,307.4410 (23.77)       24;24   93.7332 (0.10)        102           1
test_benchmark_load[path3-ujson-loads]               7,774.9390 (7.99)     17,898.5500 (10.55)    10,364.6843 (9.36)     3,222.6374 (45.84)     8,751.2690 (7.98)     1,562.5480 (28.41)       26;26   96.4815 (0.11)        115           1
test_benchmark_load[path3-python_json-loads]        11,643.7470 (11.97)    23,959.7150 (14.13)    15,714.9961 (14.20)    3,806.9531 (54.15)    13,973.4170 (12.74)    6,292.6375 (114.41)       12;0   63.6335 (0.07)         41           1
test_benchmark_load[path3-rapidjson-loads]          13,983.3210 (14.37)    27,216.4270 (16.05)    17,630.6505 (15.93)    4,016.1918 (57.13)    15,564.2690 (14.19)    2,136.0153 (38.84)       15;15   56.7194 (0.06)         65           1
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


------------------------------------------------- benchmark 'Load /home/runner/work/libpy_simdjson/libpy_simdjson/libpy_simdjson/tests/jsonexamples/github_events.json': 6 tests ------------------------------------------------
Name (time in us)                                        Min                   Max                Mean              StdDev              Median                IQR            Outliers  OPS (Kops/s)            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_benchmark_load[path2-libpy_simdjson-loads]      31.8010 (1.0)      5,766.8830 (6.11)      37.5110 (1.0)       59.9135 (1.24)      37.0010 (1.0)       0.2000 (1.0)        9;3552       26.6588 (1.0)        9200           1
test_benchmark_load[path2-orjson-loads]             229.6080 (7.22)     4,736.2550 (5.02)     266.4467 (7.10)      94.5404 (1.96)     266.1090 (7.19)     40.8512 (204.26)      56;75        3.7531 (0.14)       3243           1
test_benchmark_load[path2-pysimdjson-loads]         291.1090 (9.15)     1,112.7370 (1.18)     340.7878 (9.09)      48.2980 (1.0)      336.6110 (9.10)     33.8510 (169.25)     214;48        2.9344 (0.11)       2187           1
test_benchmark_load[path2-ujson-loads]              300.1100 (9.44)     4,311.1400 (4.57)     342.2005 (9.12)      93.3709 (1.93)     346.5110 (9.36)     50.4020 (252.01)      26;36        2.9223 (0.11)       2258           1
test_benchmark_load[path2-rapidjson-loads]          379.0120 (11.92)    4,312.8390 (4.57)     518.6963 (13.83)    117.7450 (2.44)     507.6160 (13.72)    51.0268 (255.13)      37;40        1.9279 (0.07)       1717           1
test_benchmark_load[path2-python_json-loads]        382.2120 (12.02)      943.6300 (1.0)      439.8152 (11.72)     50.1689 (1.04)     443.7140 (11.99)    82.9020 (414.51)     665;18        2.2737 (0.09)       1894           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


-------------------------------------------------------- benchmark 'Load /home/runner/work/libpy_simdjson/libpy_simdjson/libpy_simdjson/tests/jsonexamples/mesh.json': 6 tests --------------------------------------------------------
Name (time in us)                                          Min                    Max                  Mean                StdDev                Median                 IQR            Outliers       OPS            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_benchmark_load[path4-libpy_simdjson-loads]       993.7280 (1.0)       2,153.3610 (1.0)      1,113.0914 (1.0)        125.6128 (1.0)      1,122.9820 (1.0)      147.0050 (1.0)         64;16  898.3988 (1.0)         898           1
test_benchmark_load[path4-pysimdjson-loads]         3,019.2900 (3.04)     13,713.0090 (6.37)     3,958.4115 (3.56)     1,763.1884 (14.04)    3,619.4070 (3.22)     300.4090 (2.04)        10;14  252.6266 (0.28)        226           1
test_benchmark_load[path4-orjson-loads]             3,075.6900 (3.10)     12,985.8830 (6.03)     4,371.5742 (3.93)     1,528.5850 (12.17)    4,067.1200 (3.62)     444.3125 (3.02)        10;14  228.7506 (0.25)        240           1
test_benchmark_load[path4-ujson-loads]              3,947.6150 (3.97)     13,696.0010 (6.36)     4,954.1335 (4.45)     1,521.1764 (12.11)    4,690.3375 (4.18)     390.0120 (2.65)          8;9  201.8516 (0.22)        218           1
test_benchmark_load[path4-python_json-loads]        7,593.0170 (7.64)     19,002.5420 (8.82)     9,068.5910 (8.15)     1,944.1363 (15.48)    8,763.6505 (7.80)     649.6190 (4.42)          5;5  110.2707 (0.12)        122           1
test_benchmark_load[path4-rapidjson-loads]          8,291.5380 (8.34)     19,017.8470 (8.83)     9,628.5255 (8.65)     1,797.5745 (14.31)    9,276.3670 (8.26)     872.3250 (5.93)          4;4  103.8581 (0.12)        102           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


-------------------------------------------------------- benchmark 'Load /home/runner/work/libpy_simdjson/libpy_simdjson/libpy_simdjson/tests/jsonexamples/twitter.json': 6 tests -------------------------------------------------------
Name (time in us)                                          Min                    Max                  Mean                StdDev                Median                 IQR            Outliers         OPS            Rounds  Iterations
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_benchmark_load[path1-libpy_simdjson-loads]       374.2130 (1.0)      10,169.1400 (1.0)        445.6502 (1.0)        237.7491 (1.0)        443.3150 (1.0)       66.3020 (1.0)         19;29  2,243.9125 (1.0)        1790           1
test_benchmark_load[path1-orjson-loads]             2,788.1970 (7.45)     11,687.4110 (1.15)     3,351.3276 (7.52)     1,117.1151 (4.70)     3,198.9625 (7.22)     351.0120 (5.29)        10;12    298.3892 (0.13)        294           1
test_benchmark_load[path1-ujson-loads]              3,312.1150 (8.85)     12,571.4370 (1.24)     3,973.3347 (8.92)     1,221.4127 (5.14)     3,805.8815 (8.59)     447.3170 (6.75)          7;9    251.6778 (0.11)        258           1
test_benchmark_load[path1-pysimdjson-loads]         3,586.0280 (9.58)     18,704.8590 (1.84)     4,553.9661 (10.22)    1,772.5065 (7.46)     4,182.3480 (9.43)     331.1612 (4.99)         7;17    219.5888 (0.10)        169           1
test_benchmark_load[path1-python_json-loads]        4,573.6530 (12.22)    13,900.1650 (1.37)     5,396.5765 (12.11)    1,236.4753 (5.20)     5,222.7750 (11.78)    554.0430 (8.36)          6;7    185.3027 (0.08)        189           1
test_benchmark_load[path1-rapidjson-loads]          5,447.2870 (14.56)    16,226.5570 (1.60)     6,506.3766 (14.60)    1,495.7694 (6.29)     6,322.1140 (14.26)    544.9407 (8.22)          6;7    153.6954 (0.07)        165           1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Legend:
  Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
  OPS: Operations Per Second, computed as 1 / Mean
================== 71 passed, 1 xfailed, 1 warning in 29.65s ===================