json-buffet
ucall
| json-buffet | ucall | |
|---|---|---|
| 2 | 13 | |
| 0 | 990 | |
| - | 1.6% | |
| 3.0 | 6.4 | |
| about 1 year ago | 21 days ago | |
| C++ | C | |
| MIT License | Apache License 2.0 |
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json-buffet
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Analyzing multi-gigabyte JSON files locally
And here's the code: https://github.com/multiversal-ventures/json-buffet
The API isn't the best. I'd have preferred an iterator based solution as opposed to this callback based one. But we worked with what rapidjson gave us for the proof of concept.
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
Ha! Thanks to you, Today I found out how big those uncompressed JSON files really are (the data wasn't accessible to me, so i shared the tool with my colleague and he was the one who ran the queries on his laptop): https://www.dolthub.com/blog/2022-09-02-a-trillion-prices/ .
And yep, it was more or less they way you did with ijson. I found ijson just a day after I finished the prototype. Rapidjson would probably be faster. Especially after enabling SIMD. But the indexing was a one time thing.
We have open sourced the codebase. Here's the link: https://github.com/multiversal-ventures/json-buffet . Since this was a quick and dirty prototype, comments were sparse. I have updated the Readme, and added a sample json-fetcher. Hope this is more useful for you.
Another unwritten TODO was to nudge the data providers towards a more streaming friendly compression formats - and then just create an index to fetch the data directly from their compressed archives. That would have saved everyone a LOT of $$$.
ucall
- Show HN: U)Search Images demo in 200 lines of Python
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Faster JSON-RPC on Linux kernel 5.19+ with io_uring and simdjson
Type checking was included, and union support is trivial to add. We have just added a feature request and will release it in a few days.
- FLiP Stack Weekly for 13 March 2023
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
You are right! For the convenience of Python users, we have to introspect the messages and parse JSON into Python objects. Every member of every dictionary being allocated on heap.
To make it as fast as possible we don't use PyBind, NanoBind, SWIG, or any high-level tooling. Our Python bindings are a pure CPython integration. There is just no way to beat that combo, not that I know.
https://github.com/unum-cloud/ujrpc/blob/main/src/python.c
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Lightweight RPC with `simdjson` and `io_uring` on Linux 5.19 and newer
TLDR: UJRPC reaches 230K TCP/IP round-trips per second on 1 socket. Faster than gRPC and much faster than FastAPI.
- Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19+
What are some alternatives?
japronto - Screaming-fast Python 3.5+ HTTP toolkit integrated with pipelining HTTP server based on uvloop and picohttpparser.
frontman - Frontman is an open-source API gateway written in Go that allows you to manage your microservices and expose them as a single API endpoint. It acts as a reverse proxy and handles requests from clients, routing them to the appropriate backend service.
semi_index - Implementation of the JSON semi-index described in the paper "Semi-Indexing Semi-Structured Data in Tiny Space"
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
is2 - embedded RESTy http(s) server library from Edgio
reddit_mining
simdjson - Parsing gigabytes of JSON per second : used by Facebook/Meta Velox, the Node.js runtime, ClickHouse, WatermelonDB, Apache Doris, Milvus, StarRocks
json_benchmark - Python JSON benchmarking and "correctness".
FrameworkBenchmarks - Source for the TechEmpower Framework Benchmarks project
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
Muonbase - Document Database