msgspec
ojg
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msgspec | ojg | |
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31 | 17 | |
1,804 | 779 | |
- | - | |
8.9 | 7.0 | |
19 days ago | 29 days ago | |
Python | Go | |
BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
msgspec
- Htmx, Rust and Shuttle: A New Rapid Prototyping Stack
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Litestar 2.0
Full support for validation and serialisation of attrs classes and msgspec Structs. Where previously only Pydantic models and types where supported, you can now mix and match any of these three libraries. In addition to this, adding support for another modelling library has been greatly simplified with the new plugin architecture
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FastAPI 0.100.0:Release Notes
> Maybe it was very slow before
That is at least partly the case. I maintain msgspec[1], another Python JSON validation library. Pydantic V1 was ~100x slower at encoding/decoding/validating JSON than msgspec, which was more a testament to Pydantic's performance issues than msgspec's speed. Pydantic V2 is definitely faster than V1, but it's still ~10x slower than msgspec, and up to 2x slower than other pure-python implementations like mashumaro.
Recent benchmark here: https://gist.github.com/jcrist/d62f450594164d284fbea957fd48b...
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Pydantic 2.0
While it's definitely much faster than pydantic V1 (which is a huge accomplishment!), it's still not exactly what I'd call "fast".
I maintain msgspec (https://github.com/jcrist/msgspec), a serialization/validation library which provides similar functionality to pydantic. Recent benchmarks of pydantic V2 against msgspec show msgspec is still 15-30x faster at JSON encoding, and 6-15x faster at JSON decoding/validating.
Benchmark (and conversation with Samuel) here: https://gist.github.com/jcrist/d62f450594164d284fbea957fd48b...
This is not to diminish the work of the pydantic team! For many users pydantic will be more than fast enough, and is definitely a more feature-filled tool. It's a good library, and people will be happy using it! But pydantic is not the only tool in this space, and rubbing some rust on it doesn't necessarily make it "fast".
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Need help developing a high performance Redis ORM for Python
https://github.com/jcrist/msgspec so I am using this instead of Pydantic.
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Blog post: Writing Python like it’s Rust
Another thing: why pyserde rather than stuff like msgspec? https://github.com/jcrist/msgspec
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[Guide] A Tour Through the Python Framework Galaxy: Discovering the Stars
Try msgspec | Maat | turbo for fast serialization and validation
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Pydantic V2 rewritten in Rust is 5-50x faster than Pydantic V1
Congratulations to the team, Pydantic is an amazing library.
If you find JSON serialization/deserialization a bottleneck, another interesting library (with much less features) for Python is msgspec: https://github.com/jcrist/msgspec
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Starlite updates March '22 | 2.0 is coming
This feature is yet to be released, but it will allow you to seamlessly use data modelled with for example Pydantic, SQLAlchemy, msgspec or dataclasses in your route handlers, without the need for an intermediary model; The conversion will be handled by the specific DTO "backend" implementation. This new paradigm also makes it trivial to add support for any such modelling library, by simply implementing an appropriate backend.
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
If you're primarily targeting Python as an application layer, you may also want to check out my msgspec library[1]. All the perf benefits of e.g. yyjson, but with schema validation like pydantic. It regularly benchmarks[2] as the fastest JSON library for Python. Much of the overhead of decoding JSON -> Python comes from the python layer, and msgspec employs every trick I know to minimize that overhead.
ojg
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Interactive Examples for Learning Jq
I found Jq to be difficult to use which is why Oj, https://github.com/ohler55/ojg is based on JSONPath. There still are a lot of options but it only takes a couple of help screens to figure out what the options are.
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Building a high performance JSON parser
You might want to take a look at https://github.com/ohler55/ojg. It takes a different approach with a single pass parser. There are some performance benchmarks included on the README.md landing page.
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A Journey building a fast JSON parser and full JSONPath
I like the "Simple Encoding Notation" (SEN) of the underlying library: https://github.com/ohler55/ojg/blob/develop/sen.md
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The fastest tool for querying large JSON files is written in Python (benchmark)
For me OjG (https://github.com/ohler55/ojg) has been great. I regularly use it on files that can not be loaded into memory. The best JSON file format for multiple record is one JSON document per record all in the same file. OjG doesn't care if they are on different lines. It is fast (https://github.com/ohler55/compare-go-json) and uses a fairly complete JSONPath implementation for searches. Similar to jq but using JSONPath instead of a proprietary query language.
I am biased though as I wrote OjG to handle what other tools were not able to do.
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FX: An interactive alternative to jq to process JSON
Another alternative is the oj app (ojg/cmd/oj) which is part of https://github.com/ohler55/ojg. It relies on JSONPath for extraction and manipulation of JSON.
- Go 1.17 Release Notes
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OjG now has a tokenizer that is almost 10 times faster than json.Decode
Check out the SEN https://github.com/ohler55/ojg/blob/develop/sen.md format. You can have the last comma or leave all of them out and it still supports compliant JSON.
I promise to add more examples but in the mean time there are the test files. The one for Unmarshal is https://github.com/ohler55/ojg/blob/develop/oj/unmashall_test.go
What are some alternatives?
pydantic - Data validation using Python type hints
orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
mashumaro - Fast and well tested serialization library
MessagePack - MessagePack serializer implementation for Java / msgpack.org[Java]
marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.
jsonparser - One of the fastest alternative JSON parser for Go that does not require schema
ultrajson - Ultra fast JSON decoder and encoder written in C with Python bindings
jsonic - All you need with JSON
fastjson - Fast JSON parser and validator for Go. No custom structs, no code generation, no reflection
Dask - Parallel computing with task scheduling
ask - A Go package that provides a simple way of accessing nested properties in maps and slices.