json-buffet
data-analysis
json-buffet | data-analysis | |
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2 | 6 | |
0 | 44 | |
- | - | |
3.0 | 7.3 | |
about 1 year ago | 10 months ago | |
C++ | Jupyter Notebook | |
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 $$$.
data-analysis
- Why a public database of hospital prices doesn't exist yet
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Open Database of Hospital Prices
https://github.com/dolthub/data-analysis/tree/main/transpare...
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
Absolutely interested, on my end at least. I wrote this to manage the transparency in coverage files: https://github.com/dolthub/data-analysis/tree/main/transpare... but I'm always looking for better techniques.
Oh wow, I see you used it on those exact files. How about that.
- Healthcare datasets with multiple continuous variables
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Beyond the trillion prices: pricing C-sections in America
Details: data repository, code repository, and notebook. The linked GitHub repo gives you the tools you need to reproduce this analysis or create your own.
- I wrote some tools to find the prices of C-sections in America. Context in README
What are some alternatives?
japronto - Screaming-fast Python 3.5+ HTTP toolkit integrated with pipelining HTTP server based on uvloop and picohttpparser.
json_benchmark - Python JSON benchmarking and "correctness".
semi_index - Implementation of the JSON semi-index described in the paper "Semi-Indexing Semi-Structured Data in Tiny Space"
synthea - Synthetic Patient Population Simulator
is2 - embedded RESTy http(s) server library from Edgio
simdjson-go - Golang port of simdjson: parsing gigabytes of JSON per second
reddit_mining
jsplit - A Go program to split large JSON files into many jsonl files
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML