json-buffet
Apache Arrow
json-buffet | Apache Arrow | |
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2 | 82 | |
0 | 14,508 | |
- | 1.0% | |
3.0 | 10.0 | |
over 1 year ago | 1 day 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 $$$.
Apache Arrow
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Using Polars in Rust for high-performance data analysis
One of the main selling points of Polars over similar solutions such as Pandas is performance. Polars is written in highly optimized Rust and uses the Apache Arrow container format.
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Kotlin DataFrame ❤️ Arrow
Kotlin DataFrame v0.14 comes with improvements for reading Apache Arrow format, especially loading a DataFrame from any ArrowReader. This improvement can be used to easily load results from analytical databases (such as DuckDB, ClickHouse) directly into Kotlin DataFrame.
- Random access string compression with FSST and Rust
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Declarative Multi-Engine Data Stack with Ibis
Apache Arrow
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Shades of Open Source - Understanding The Many Meanings of "Open"
It's this kind of certainty that underscores the vital role of the Apache Software Foundation (ASF). Many first encounter Apache through its pioneering project, the open-source web server framework that remains ubiquitous in web operations today. The ASF was initially created to hold the intellectual property and assets of the Apache project, and it has since evolved into a cornerstone for open-source projects worldwide. The ASF enforces strict standards for diverse contributions, independence, and activity in its projects, ensuring they can withstand the test of time as standards in software development. Many open-source projects strive to become Apache projects to gain the community credibility necessary for adoption as standard software building blocks, such as Apache Tomcat for Java web applications, Apache Arrow for in-memory data representation, and Apache Parquet for data file formatting, among others.
- The Simdjson Library
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Arrow Flight SQL in Apache Doris for 10X faster data transfer
Apache Doris 2.1 has a data transmission channel built on Arrow Flight SQL. (Apache Arrow is a software development platform designed for high data movement efficiency across systems and languages, and the Arrow format aims for high-performance, lossless data exchange.) It allows high-speed, large-scale data reading from Doris via SQL in various mainstream programming languages. For target clients that also support the Arrow format, the whole process will be free of serialization/deserialization, thus no performance loss. Another upside is, Arrow Flight can make full use of multi-node and multi-core architecture and implement parallel data transfer, which is another enabler of high data throughput.
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How moving from Pandas to Polars made me write better code without writing better code
In comes Polars: a brand new dataframe library, or how the author Ritchie Vink describes it... a query engine with a dataframe frontend. Polars is built on top of the Arrow memory format and is written in Rust, which is a modern performant and memory-safe systems programming language similar to C/C++.
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From slow to SIMD: A Go optimization story
I learned yesterday about GoLang's assembler https://go.dev/doc/asm - after browsing how arrow is implemented for different languages (my experience is mainly C/C++) - https://github.com/apache/arrow/tree/main/go/arrow/math - there are bunch of .S ("asm" files) and I'm still not able to comprehend how these work exactly (I guess it'll take more reading) - it seems very peculiar.
The last time I've used inlined assembly was back in Turbo/Borland Pascal, then bit in Visual Studio (32-bit), until they got disabled. Then did very little gcc with their more strict specification (while the former you had to know how the ABI worked, the latter too - but it was specced out).
Anyway - I wasn't expecting to find this in "Go" :) But I guess you can always start with .go code then produce assembly (-S) then optimize it, or find/hire someone to do it.
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Time Series Analysis with Polars
One is related to the heritage of being built around the NumPy library, which is great for processing numerical data, but becomes an issue as soon as the data is anything else. Pandas 2.0 has started to bring in Arrow, but it's not yet the standard (you have to opt-in and according to the developers it's going to stay that way for the foreseeable future). Also, pandas's Arrow-based features are not yet entirely on par with its NumPy-based features. Polars was built around Arrow from the get go. This makes it very powerful when it comes to exchanging data with other languages and reducing the number of in-memory copying operations, thus leading to better performance.
What are some alternatives?
is2 - embedded RESTy http(s) server library from Edgio
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
japronto - Screaming-fast Python 3.5+ HTTP toolkit integrated with pipelining HTTP server based on uvloop and picohttpparser.
h5py - HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.
semi_index - Implementation of the JSON semi-index described in the paper "Semi-Indexing Semi-Structured Data in Tiny Space"
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
json_benchmark - Python JSON benchmarking and "correctness".
FlatBuffers - FlatBuffers: Memory Efficient Serialization Library
reddit_mining
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
jq-zsh-plugin - jq zsh plugin
ClickHouse - ClickHouse® is a real-time analytics DBMS