JsonParserByLeo/Test/Ben

Benchmark

This benchmark was built by me as part of testing my library and the MQL5 JSON-parsing ecosystem in general, run on my personal development machine.

Reproduction

MQL5 code

Before anything else, delete the current .log file.

1. For each test/library, do the following:
   • Run UtilHeader with the library name.
   • Run the test several times in Parse mode.
   • Recompile the test in Access mode.
   • Run it several times again.
   • Repeat for the next test.
2. Once all tests are done, run the UtilFinish script. It parses the .log file (with timestamps) and saves the Parse results into the .db file. Access results are saved automatically as they run.

Code in other languages

Requirements:

• VSCode (optional)
• Python 3.11+
• Rust
• C++ (Visual Studio, or GCC — instructions below assume Visual Studio; with GCC you would need to adapt the CMakeLists.txt and the .h/.cpp files under DLL and BenOther/)
• The JsonParserByLeo repo cloned with all its dependencies (assumed already done at this point)

1. C++: this was built with Visual Studio 2026 (used for the simdjson test as an example). Open the BenOther/C++ folder as a project, then Ctrl+S to let the CMake cache build. After that, compiling produces a Test.exe under out/build/....
2. Rust: VSCode was used with the rust-analyzer extension; opening the project pulls dependencies automatically. Then, via CLI:

   cargo build --release
   

   Run the resulting executable from target/. This can all be done purely via CLI too — the above is just the workflow used here; if you know Rust better, feel free to do it differently. Rust knowledge here is basic — just enough to run sonic-rs, serde_json, etc. The benchmark was the only use case.
3. Python: simplest of all — just have Python installed. VSCode's Run button was used here. A standalone .exe can also be generated if preferred.

Performance

Parsing

Benchmark: twitter.json (616.7 KB), 1000 iterations. Values below are total time across all 1000 iterations, sorted fastest to slowest.

Note on the benchmark below: JsonParserByLeo (SIMD) is not validating the input as strictly as simdjson does — notably, it does not perform full UTF-8 validation during the structural scan, and it's a single flat switch-based pipeline with no public error-recovery API surface, unlike simdjson::dom::parser. The timings below reflect the work each parser actually does, not a strict apples-to-apples "same guarantees" comparison — read it as "same ballpark, different tradeoffs," not "beats simdjson at its own game."

Parser	Language	Time (ms, total / 1000 iter)
JsonParserByLeo (with SIMD, via DLL)	MQL5 / C++ DLL	336-337
simdjson::dom::parser (reused parser)	C++	380-384
sonic-rs (typed struct)	Rust	460-461
simdjson.Parser (reused parser)	Python	475
JsonParserByLeo (single array copy)	MQL5	520-521
serde_json (typed struct)	Rust	788-796
YamlParserByLeo, single array copy	MQL5	899-900
simd-json (typed struct)	Rust	915-972
Claude Fable 5 (Effort=Max), generated code (fast JSON lib), single array copy, 3+ iterations with feedback	MQL5	1063.04
FastJson v3.7, single array copy	MQL5	1213.43
GLM 5.2 (Max, deep thinking), generated code (fast JSON lib), single array copy, 14+ iterations with feedback	MQL5	1249.76
orjson	Python	1863
ryml (pure parse time)	C++	2846.55
simdjson.Parser + as_dict	Python	4309
ToyJson3, single string copy, tokenization only	MQL5	4418.49
simdjson.loads	Python	4955
ujson	Python	5322
json (stdlib)	Python	5860
MQL5-JsonLib (ding9736), single string copy, tape parsing only (no DOM built)	MQL5	17547.38
JAson, single array copy	MQL5	21118.68
CJsonNode (MQL5 Articles reference implementation), single array copy	MQL5	86409.04

Access benchmarks

All access benchmarks use test.json, 1000 iterations, and report total time across all iterations in microseconds. Lower is better. Results are sorted fastest to slowest within each table.

Access — Wide object (100 keys)

Repeated key lookups across a flat object with 100 keys (random).

Parser	Language	Time (microseconds, total / 1000 iter)
JsonParserByLeo	MQL5	112
YamlParserByLeo	MQL5	142 (falta revisar)
MQL5-JsonLib	MQL5	235
Claude Fable 5 (Effort=Max), generated code (fast JSON lib), single array copy, 3+ iterations with feedback	MQL5	308
ToyJson3	MQL5	333
FastJson v3.7	MQL5	385
JAson	MQL5	460
CJsonNode (MQL5 Articles reference implementation)	MQL5	523
GLM 5.2 (Max, deep thinking), generated code	MQL5	8826

Access — Large array (10K integer elements)

Sequential/indexed access across a 10,000-element integer array.

Parser	Language	Time (microseconds, total / 1000 iter)
JsonParserByLeo	MQL5	104
YamlParserByLeo	MQL5	115
CJsonNode (MQL5 Articles reference implementation)	MQL5	254
JAson	MQL5	267
ToyJson3	MQL5	373
MQL5-JsonLib	MQL5	384
Claude Fable 5 (Effort=Max), generated code (fast JSON lib), single array copy, 3+ iterations with feedback	MQL5	9758
GLM 5.2 (Max, deep thinking), generated code	MQL5	15550
FastJson v3.7	MQL5	16414

Access — Deep access

Access through deeply nested object/array paths.

Parser	Language	Time (microseconds, total / 1000 iter)
ToyJson3	MQL5	117
CJsonNode (MQL5 Articles reference implementation)	MQL5	227
JAson	MQL5	387
JsonParserByLeo	MQL5	573
YamlParserByLeo	MQL5	755 (falta revisar)
Claude Fable 5 (Effort=Max), generated code (fast JSON lib), single array copy, 3+ iterations with feedback	MQL5	912
MQL5-JsonLib	MQL5	982
FastJson v3.7	MQL5	2236
GLM 5.2 (Max, deep thinking), generated code	MQL5	2522

Access — Mixed trading-data access pattern

A mixed read pattern modeled on typical trading-data access (combination of key lookups, array indexing, and nested paths).

Parser	Language	Time (microseconds, total 500 iterations)
JAson	MQL5	543
CJsonNode (MQL5 Articles reference implementation)	MQL5	658
ToyJson3	MQL5	928
Claude Fable 5 (Effort=Max), generated code (fast JSON lib), single array copy, 3+ iterations with feedback	MQL5	930
MQL5-JsonLib	MQL5	984
JsonParserByLeo	MQL5	1854
YamlParserByLeo	MQL5	2258 (falta revisar)
FastJson v3.7	MQL5	2754
GLM 5.2 (Max, deep thinking), generated code	MQL5	5894

Serialization

Serialization of (test.json) using the files from the previous tests

Parser	Language	Time (ms, total / 1000 iter)
FastJson v3.7	MQL5	555
JsonParserByLeo (DOM mode - with pointers, etc..)	MQL5	750
JAson	MQL5	194068.9 (Caution: must be revised)
MQL5-JsonLib	MQL5	4503261.352 (Caution: must be revised)

Machine

• OS Name: Microsoft Windows 10 Pro
• Version: 10.0.19045 Build 19045
• OS Manufacturer: Microsoft Corporation
• System Manufacturer: LENOVO
• System Model: 81DE
• System Type: x64-based PC
• System SKU: LENOVO_MT_81DE_BU_idea_FM_ideapad 330-15IKB
• Processor: Intel(R) Core(TM) i5-8250U CPU @ 1.60GHz, 1800 MHz, 4 Cores, 8 Logical Processors
• RAM Type (Form Factor): SODIMM
• RAM Speed: 2133 MHz
• Installed Physical Memory (RAM): 8.00 GB
• Total Physical Memory: 7.91 GB
• Available Physical Memory: 2.87 GB
• Total Virtual Memory: 15.2 GB
• Available Virtual Memory: 9.13 GB
• Page File Space: 7.25 GB
• Storage: 13 GB Intel MEMPEI1J016GAL SSD, 224 GB HP SSD S650 240GB SSD
• Graphics Card: AMD Radeon(TM) 530 (2 GB), Intel(R) UHD Graphics 620 (128 MB)

Performance notes

• MQL5 runs: MetaTrader 5 x64, build 5836-6070.
• Python/C++/Rust runs: same machine (Python 3.10.9).
• C++ compiled with optimizations (/O2 in MSVC).
• Rust compiled with maximum optimization (target=native, LTO, opt-level=3, etc.).

Auditing the results

The complete .db file and the raw .log file used to produce it are included in the repository releases, for anyone who wants to independently audit or reproduce these numbers.