json-stream-rs-tokenizer 0.4.13

A faster tokenizer for the json-stream Python library

NOTE: json-stream-rs-tokenizer is now automatically used by json-stream, so unless you find a bug, you can ignore this package's existence!

json-stream-rs-tokenizer

A faster tokenizer for the json-stream Python library.

It's actually just json-stream's own tokenizer (itself adapted from the NAYA project) ported to Rust almost verbatim and made available as a Python module using PyO3.

On my machine, it speeds up parsing by a factor of 4–10, depending on the nature of the data.

Installation

Implicit

Starting at its 2.0 release, json-stream depends on and uses json-stream-rs-tokenizer by default, so you don't need to install it explicitly anymore.

Explicit

If you use an older json-stream version (which you have no reason to do) or need to install json-stream-rs-tokenizer explicitly for another reason, you can do:

pip install json-stream-rs-tokenizer

The library will be installed as a prebuilt wheel if one is available for your platform. Otherwise, pip will try to build it from the source distribution, which requires a Rust toolchain to be installed and available to succeed.

Note that if the build from source fails, the package installation will be considered successfully completed anyway, but RustTokenizer (see below) won't be available for import. This is so that packages (specifically, json-stream) can depend on the library but fall back to their own implementation if neither a prebuilt wheel is available nor the build succeeds.

You can increase the installation command's verbosity with -v (repeated for even more information, e.g. -vv) to see error messages when the build from source fails.

Note that if the Rust library is compiled in debug mode, it will run slower than the pure-Python tokenizer. The setuptools configuration should make sure this doesn't happen even when installing in development mode, but when in doubt, run installation commands with -v to see the Rust compilation commands and verify that they used --release.

Usage

Implicit

As described above, json-stream-rs-tokenizer is now used by json-stream by default, so you don't have to do anything special to use it. json-stream will fall back to its pure-Python tokenizer when json-stream-rs-tokenizer was not successfully installed, however.

Explicit

For older versions of json-stream, or if you want to ensure the Rust tokenizer is used no matter what, simply pass this package's RustTokenizer as the tokenizer argument to json-stream's load or visit:

from io import StringIO
from json_stream import load
from json_stream_rs_tokenizer import RustTokenizer

json_buf = StringIO('{ "a": [1,2,3,4], "b": [5,6,7] }')

# uses the Rust tokenizer to load JSON:
d = load(json_buf, tokenizer=RustTokenizer)

for k, l in d.items():
  print(f"{k}: {' '.join(str(n) for n in l)}")

Note that the import of RustTokenizer will fail if the Rust extension is not available (i.e., when no prebuilt wheels were available and the installation from the source distribution failed).

Limitations

• For PyPy, the speedup is only 1.0-1.5x (much lower than that for CPython). This has yet to be investigated.
• In builds that don't support PyO3's num-bigint extension (currently only PyPy builds and manual ones against Python's limited C API (Py_LIMITED_API)), conversion of large integers is performed in Python rather than in Rust, at a very small runtime cost.

Benchmarks

The package comes with a script for rudimentary benchmarks on randomly generated JSON data. To run it, you'll need to install the optional benchmark dependencies:

pip install 'json-stream-rs-tokenizer[benchmark]'

You can then run the benchmark as follows:

python -m json_stream_rs_tokenizer.benchmark

Run it with --help to see more information.

Tests

To run the tests, you'll need to install the optional test dependencies:

pip install 'json-stream-rs-tokenizer[test]'

As the test dependencies depend on the benchmark dependencies but the feature enabling such "recursive optional dependencies" was only introduced in Pip 21.3, you'll need a version of Pip at least as recent as that. For older versions, just install the test dependencies manually.

License

MIT license. Refer to the LICENSE file for details.