Francisco Geiman Thiesen
af2a43610e
* perf(serialization): SIMD-accelerated string escape position finding
Profiling revealed that string serialization was performing redundant
scanning: first calling fast_needs_escaping() (SIMD scan to check IF
escape needed), then find_next_json_quotable_character() (scalar
byte-by-byte scan to find WHERE).
Profile data from Twitter benchmark showed:
- 54.76% time in atom<std::string> (string serialization)
- 24.85% time in find_next_json_quotable_character (scalar position finding)
This optimization unifies both operations into a single SIMD pass that
directly locates the first quotable character position:
- NEON (ARM64): Uses vceqq_u8/vcltq_u8 for character detection, then
extracts position via __builtin_ctzll on 64-bit vector lanes
- SSE2 (x86-64): Uses _mm_cmpeq_epi8/_mm_subs_epu8 for detection, then
_mm_movemask_epi8 + __builtin_ctz for position extraction
The write_string_escaped function now uses the position finder directly,
eliminating the separate fast_needs_escaping check.
Benchmark results (ARM64, Apple Silicon via Docker with p2996 clang):
- Twitter (string-heavy): 4330 -> 5723 MB/s (+32%)
- CITM (numeric-heavy): ~neutral (expected, few strings)
* perf(serialization): batch integer formatting with 4-digit processing
Profiling with perf annotate revealed that the integer-to-string
conversion loop was a significant hotspot in numeric-heavy workloads.
The original implementation processed 2 digits per iteration:
while (pv >= 100) {
memcpy(write_pointer - 1, &decimal_table[(pv % 100) * 2], 2);
write_pointer -= 2;
pv /= 100;
}
Profile data from CITM benchmark showed:
- 31.20% time in atom<unsigned long> (integer formatting)
- 39.07% of integer formatting time in the 2-byte store instruction
(sturh on ARM64)
- CITM integers average 8.8 digits, meaning 4+ store operations per number
This optimization processes 4 digits per iteration, reducing both store
operations and division count by approximately half for large numbers:
while (pv >= 10000) {
q = pv / 10000;
r = pv % 10000;
r_hi = r / 100; // High 2 digits
r_lo = r % 100; // Low 2 digits
memcpy(write_pointer - 1, &decimal_table[r_lo * 2], 2);
memcpy(write_pointer - 3, &decimal_table[r_hi * 2], 2);
write_pointer -= 4;
pv = q;
}
The division by 10000 compiles to an efficient multiply-high instruction
(umulh on ARM64). Applied to both unsigned and signed integer paths.
Benchmark results (ARM64, Apple Silicon via Docker with p2996 clang):
- Twitter: ~neutral (few integers)
- CITM (numeric-heavy): 2912 -> 3086 MB/s (+6%)
* fix(build): add MSVC compatibility for bit manipulation intrinsics
MSVC does not have __builtin_ctz/__builtin_ctzll. Use _BitScanForward
and _BitScanForward64 from <intrin.h> on MSVC instead.
This fixes the build on all Windows configurations (x64, ARM64, Win32).
* refactor: clean up comments to be implementation-focused
Remove references to specific benchmarks and previous implementations
from code comments. Comments now describe what the code does rather
than historical context.
simdjson : Parsing gigabytes of JSON per second
JSON is everywhere on the Internet. Servers spend a lot of time parsing it. We need a fresh approach. The simdjson library uses commonly available SIMD instructions and microparallel algorithms to parse JSON 4x faster than RapidJSON and 25x faster than JSON for Modern C++.
- Fast: Over 4x faster than commonly used production-grade JSON parsers.
- Record Breaking Features: Minify JSON at 6 GB/s, validate UTF-8 at 13 GB/s, NDJSON at 3.5 GB/s.
- Easy: First-class, easy to use and carefully documented APIs.
- Strict: Full JSON and UTF-8 validation, lossless parsing. Performance with no compromises.
- Automatic: Selects a CPU-tailored parser at runtime. No configuration needed.
- Reliable: From memory allocation to error handling, simdjson's design avoids surprises.
- Peer Reviewed: Our research appears in venues like VLDB Journal, Software: Practice and Experience.
This library is part of the Awesome Modern C++ list.
Table of Contents
- Real-world usage
- Quick Start
- Documentation
- Godbolt
- Performance results
- Packages
- Bindings and Ports of simdjson
- About simdjson
- Funding
- Contributing to simdjson
- License
Real-world usage
- Node.js
- ClickHouse
- Meta Velox
- Google Pax
- milvus
- QuestDB
- Clang Build Analyzer
- Shopify HeapProfiler
- StarRocks
- Microsoft FishStore
- Intel PCM
- WatermelonDB
- Apache Doris
- Dgraph
- UJRPC
- fastgltf
- vast
- ada-url
- fastgron
- WasmEdge
- RonDB
- GreptimeDB
- mamba
- Ladybird Browser
If you are planning to use simdjson in a product, please work from one of our releases.
Quick Start
The simdjson library is easily consumable with a single .h and .cpp file.
-
Prerequisites:
g++(version 7 or better) orclang++(version 6 or better), and a 64-bit system with a command-line shell (e.g., Linux, macOS, freeBSD). We also support programming environments like Visual Studio and Xcode, but different steps are needed. Users of clang++ may need to specify the C++ version (e.g.,c++ -std=c++17) since clang++ tends to default on C++98. -
Pull simdjson.h and simdjson.cpp into a directory, along with the sample file twitter.json. You can download them with the
wgetutility:wget https://raw.githubusercontent.com/simdjson/simdjson/master/singleheader/simdjson.h https://raw.githubusercontent.com/simdjson/simdjson/master/singleheader/simdjson.cpp https://raw.githubusercontent.com/simdjson/simdjson/master/jsonexamples/twitter.json -
Create
quickstart.cpp:
#include <iostream>
#include "simdjson.h"
using namespace simdjson;
int main(void) {
ondemand::parser parser;
padded_string json = padded_string::load("twitter.json");
ondemand::document tweets = parser.iterate(json);
std::cout << uint64_t(tweets["search_metadata"]["count"]) << " results." << std::endl;
}
c++ -o quickstart quickstart.cpp simdjson.cpp./quickstart
100 results.
Documentation
Usage documentation is available:
- Basics is an overview of how to use simdjson and its APIs.
- Builder is an overview of how to efficiently write JSON strings using simdjson.
- Performance shows some more advanced scenarios and how to tune for them.
- Implementation Selection describes runtime CPU detection and how you can work with it.
- API contains the automatically generated API documentation.
- Compile-Time Parsing presents our compile-time parsing function (C++26 only).
Godbolt
Some users may want to browse code along with the compiled assembly. You want to check out the following lists of examples:
- C++26 reflection example
- simdjson examples with errors handled through exceptions
- simdjson examples with errors without exceptions
Performance results
The simdjson library uses three-quarters less instructions than state-of-the-art parser RapidJSON. To our knowledge, simdjson is the first fully-validating JSON parser to run at gigabytes per second (GB/s) on commodity processors. It can parse millions of JSON documents per second on a single core.
The following figure represents parsing speed in GB/s for parsing various files on an Intel Skylake processor (3.4 GHz) using the GNU GCC 10 compiler (with the -O3 flag). We compare against the best and fastest C++ libraries on benchmarks that load and process the data. The simdjson library offers full unicode (UTF-8) validation and exact number parsing.
The simdjson library offers high speed whether it processes tiny files (e.g., 300 bytes) or larger files (e.g., 3MB). The following plot presents parsing speed for synthetic files over various sizes generated with a script on a 3.4 GHz Skylake processor (GNU GCC 9, -O3).
All our experiments are reproducible.
For NDJSON files, we can exceed 3 GB/s with our multithreaded parsing functions.
Packages
Bindings and Ports of simdjson
We distinguish between "bindings" (which just wrap the C++ code) and a port to another programming language (which reimplements everything).
- ZippyJSON: Swift bindings for the simdjson project.
- libpy_simdjson: high-speed Python bindings for simdjson using libpy.
- pysimdjson: Python bindings for the simdjson project.
- cysimdjson: high-speed Python bindings for the simdjson project.
- simdjson-rs: Rust port.
- simdjson-rust: Rust wrapper (bindings).
- SimdJsonSharp: C# version for .NET Core (bindings and full port).
- simdjson_nodejs: Node.js bindings for the simdjson project.
- simdjson_php: PHP bindings for the simdjson project.
- simdjson_ruby: Ruby bindings for the simdjson project.
- fast_jsonparser: Ruby bindings for the simdjson project.
- simdjson-go: Go port using Golang assembly.
- rcppsimdjson: R bindings.
- simdjson_erlang: erlang bindings.
- simdjsone: erlang bindings.
- lua-simdjson: lua bindings.
- hermes-json: haskell bindings.
- zimdjson: Zig port.
- simdjzon: Zig port.
- JSON-Simd: Raku bindings.
- JSON::SIMD: Perl bindings; fully-featured JSON module that uses simdjson for decoding.
- gemmaJSON: Nim JSON parser based on simdjson bindings.
- simdjson-java: Java port.
About simdjson
The simdjson library takes advantage of modern microarchitectures, parallelizing with SIMD vector instructions, reducing branch misprediction, and reducing data dependency to take advantage of each CPU's multiple execution cores.
Our default front-end is called On-Demand, and we wrote a paper about it:
- John Keiser, Daniel Lemire, On-Demand JSON: A Better Way to Parse Documents?, Software: Practice and Experience 54 (6), 2024.
Some people enjoy reading the first (2019) simdjson paper: A description of the design and implementation of simdjson is in our research article:
- Geoff Langdale, Daniel Lemire, Parsing Gigabytes of JSON per Second, VLDB Journal 28 (6), 2019.
We have an in-depth paper focused on the UTF-8 validation:
- John Keiser, Daniel Lemire, Validating UTF-8 In Less Than One Instruction Per Byte, Software: Practice & Experience 51 (5), 2021.
We also have an informal blog post providing some background and context.
For the video inclined,
(It was the best voted talk, we're kinda proud of it.)
Citing this work
If you use simdjson in published research, please cite the software library. A suitable BibTeX entry is:
@misc{simdjson,
title={{The simdjson library: Parsing Gigabytes of JSON per Second}},
author={Daniel Lemire and Geoff Langdale and John Keiser and Paul Dreik and Francisco Thiesen and others},
year={2019},
howpublished={Software library},
note={https://github.com/simdjson/simdjson}
}
Funding
The work is supported by the Natural Sciences and Engineering Research Council of Canada under grants RGPIN-2017-03910 and RGPIN-2024-03787.
Contributing to simdjson
Head over to CONTRIBUTING.md for information on contributing to simdjson, and HACKING.md for information on source, building, and architecture/design.
Stars
License
This code is made available under the Apache License 2.0 as well as under the MIT License. As a user, you can pick the license you prefer.
Under Windows, we build some tools using the windows/dirent_portable.h file (which is outside our library code): it is under the liberal (business-friendly) MIT license.
For compilers that do not support C++17, we bundle the string-view library which is published under the Boost license. Like the Apache license, the Boost license is a permissive license allowing commercial redistribution.
For efficient number serialization, we bundle Florian Loitsch's implementation of the Grisu2 algorithm for binary to decimal floating-point numbers. The implementation was slightly modified by JSON for Modern C++ library. Both Florian Loitsch's implementation and JSON for Modern C++ are provided under the MIT license.
For runtime dispatching, we use some code from the PyTorch project licensed under 3-clause BSD.