Daniel Lemire 4dfbf98e4e Using a worker instead of a thread per batch (#920)
In the parse_many function, we have one thread doing the stage 1, while the main thread does stage 2. So if stage 1 and stage 2 take half the time, the parse_many could run at twice the speed. It is unlikely to do so. Still, we see benefits of about 40% due to threading.

To achieve this interleaving, we load the data in batches (blocks) of some size. In the current code (master), we create a new thread for each batch. Thread creation is expensive so our approach only works over sizeable batches. This PR improves things and makes parse_many faster when using small batches.

  This fixes our parse_stream benchmark which is just busted.
  This replaces the one-thread per batch routine by a worker object that reuses the same thread. In benchmarks, this allows us to get the same maximal speed, but with smaller processing blocks. It does not help much with larger blocks because the cost of the thread create gets amortized efficiently.
This PR makes parse_many beneficial over small datasets. It also makes us less dependent on the thread creation time.

Unfortunately, it is going to be difficult to say anything definitive in general. The cost of creating a thread varies widely depending on the OS. On some systems, it might be cheap, in others very expensive. It should be expected that the new code will depend less drastically on the performances of the underlying system, since we create juste one thread.

Co-authored-by: John Keiser <john@johnkeiser.com>
Co-authored-by: Daniel Lemire <lemire@gmai.com>
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Build Status Fuzzing Status Build status CirrusCI Doxygen Documentation

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 2.5x faster than anything else out there.
  • Fast: Over 2.5x faster than other production-grade JSON parsers.
  • Easy: First-class, easy to use API.
  • 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.

This library is part of the Awesome Modern C++ list.

Table of Contents

Quick Start

The simdjson library is easily consumable with a single .h and .cpp file.

  1. Prerequisites: g++ (version 7 or better) or clang++ (version 6 or better), and a 64-bit system with a command-line shell (e.g., Linux, macOS, freeBSD). We also support programming environnements like Visual Studio and Xcode, but different steps are needed.

  2. Pull simdjson.h and simdjson.cpp into a directory, along with the sample file twitter.json.

    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
    
  3. Create quickstart.cpp:

    #include "simdjson.h"
    int main(void) {
      simdjson::dom::parser parser;
      simdjson::dom::element tweets = parser.load("twitter.json");
      std::cout << tweets["search_metadata"]["count"] << " results." << std::endl;
    }
    
  4. c++ -o quickstart quickstart.cpp simdjson.cpp

  5. ./quickstart

    100 results.
    

Documentation

Usage documentation is available:

  • Basics is an overview of how to use simdjson and its APIs.
  • 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.

Performance results

The simdjson library uses three-quarters less instructions than state-of-the-art parser RapidJSON and fifty percent less than sajson. 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 9 compiler (with the -O3 flag). We compare against the best and fastest C++ libraries. The simdjson library offers full unicode (UTF-8) validation and exact number parsing. The RapidJSON library is tested in two modes: fast and exact number parsing. The sajson library offers fast (but not exact) number parsing and partial unicode validation. In this data set, the file sizes range from 65KB (github_events) all the way to 3.3GB (gsoc-2018). Many files are mostly made of numbers: canada, mesh.pretty, mesh, random and numbers: in such instances, we see lower JSON parsing speeds due to the high cost of number parsing. The simdjson library uses exact number parsing which is particular taxing.

On a Skylake processor, the parsing speeds (in GB/s) of various processors on the twitter.json file are as follows, using again GNU GCC 9.1 (with the -O3 flag). The popular JSON for Modern C++ library is particularly slow: it obviously trades parsing speed for other desirable features.

parser GB/s
simdjson 2.5
RapidJSON UTF8-validation 0.29
RapidJSON UTF8-valid., exact numbers 0.28
RapidJSON insitu, UTF8-validation 0.41
RapidJSON insitu, UTF8-valid., exact 0.39
sajson (insitu, dynamic) 0.62
sajson (insitu, static) 0.88
dropbox 0.13
fastjson 0.27
gason 0.59
ultrajson 0.34
jsmn 0.25
cJSON 0.31
JSON for Modern C++ (nlohmann/json) 0.11

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.

Real-world usage

If you are planning to use simdjson in a product, please work from one of our releases.

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).

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.

Some people enjoy reading our 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 also have an informal blog post providing some background and context.

For the video inclined,
simdjson at QCon San Francisco 2019
(it was the best voted talk, we're kinda proud of it).

Funding

The work is supported by the Natural Sciences and Engineering Research Council of Canada under grant number RGPIN-2017-03910.

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.

License

This code is made available under the Apache License 2.0.

Under Windows, we build some tools using the windows/dirent_portable.h file (which is outside our library code): it 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 (http://www.boost.org/LICENSE_1_0.txt). Like the Apache license, the Boost license is a permissive license allowing commercial redistribution.

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