proposal-resizablearraybuffer
arquero
proposal-resizablearraybuffer | arquero | |
---|---|---|
6 | 8 | |
157 | 1,191 | |
- | 1.8% | |
0.0 | 4.6 | |
6 months ago | about 1 month ago | |
HTML | JavaScript | |
- | BSD 3-clause "New" or "Revised" License |
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proposal-resizablearraybuffer
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Goodbye, Node.js Buffer
Nit: "fixed-length" is no longer true as of very recently [1].
[1] https://github.com/tc39/proposal-resizablearraybuffer
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Updates from the 98th TC39 meeting
Resizable ArrayBuffer: Resizable and growable ArrayBuffers.
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What Is The Importance Of A Buffer Size in .alloc()?
Stage 3 Draft linked above https://tc39.es/proposal-resizablearraybuffer/. Implemented in Chrome and Chromium.
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Deno Joins TC39
This is a great news! Good luck, Luca!
> Better support for explicit resource management
+1
Since everyone is making feature requests, I'd like to point out `ArrayBuffer.transfer`[1] -- ability to effectively move data without copying would do wonders for low-level/high-performance code in JS.
[1] https://github.com/tc39/proposal-resizablearraybuffer
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Updates from 78th meeting of TC39
Resizable ArrayBuffers
arquero
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Show HN: Matrices – explore, visualize, and share large datasets
Hey HN, I'm excited to share a new side project I've been working on.
The product is called Matrices. You can check it out here: https://matrices.com/.
With Matrices, you can *explore*, *visualize*, and *share* large (100k rows) datasets–all without code. Filter data down to just what you want, visualize it with built-in charts, and share your results with one click.
You can use it today (no login or waitlist or anything). Just copy and paste your data from a google sheet or CSV file.
It's hard to describe the feeling of "gliding over data" you get with Matrices, so I'd rather *show* you how it works instead. This 75s video will give you a sense of how it works: https://www.youtube.com/watch?v=Rrh9_I3Ux8E.
Data is stored locally in your browser until you publish it, though small sample does go to the OpenAI APIs for AI-assisted features.
I started building Matrices because I wanted a tool that made it easy to explore new datasets. When I'm first trying to dig into data, I'll have one question... that leads to another... that will invariably lead to five more questions. It's sort of a fractal process, and I couldn't find many good options that were fast, responsive, and visual.
I figured this crowd would be interested in tech stack as well, it's using arquero [1] bindings over apache arrow for in-memory analytics, and visx [2] for visualizations. I'd like to add duckdb-wasm support at some point to open up a wider set of databases. Data is serialized as parquet to save a bit on bandwidth + storage.
Give it a spin, and let me know what you think. This is my first 'serious frontend project' so I appreciate any and all feedback and bug reports. Feel free to comment here (I'll be around most of the day), or shoot me a note: [email protected]
[1]: https://uwdata.github.io/arquero/
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Goodbye, Node.js Buffer
https://github.com/uwdata/arquero
- Arquero is a JavaScript library for query processing and transformation of array-backed data tables
- Arquero – data tables wrangling in JavaScript
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Hal9: Data Science with JavaScript
Transformations: We found out that JavaScript in combination with D3.js has a pretty decent set of data transformation functions; however, it comes nowhere near to Pandas or dplyr. We found out about Tidy.js quite early, loved it, and adopted it. The combination of Tidy.js and D3.js and Plot.js is absolutely amazing for visualizations and data wrangling with small datasets, say 10-100K rows. We were very happy with this for a while; however, once you move away from visualizations into real-world data analysis, we found out 100K rows restrictive, which gets worse when having 100 or 1K columns. So we switched gears and started using Arquero.js, which happens to be columnar and enabled us to process +1M rows in the browser, descent size for real-world data analysis.
- Arquero – Query processing and transformation of array-backed data tables
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Apache Arrow 3.0.0 Release
Take a look at the arquero library from a research group at University of Washington (the same group that D3 came out of). https://github.com/uwdata/arquero
What are some alternatives?
simpatico - Simpatico is an umbrella term for several data-structures and algorithms written in JavaScript
perspective - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
nodejs-polars - nodejs front-end of polars
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
proposal-source-phase-imports - Proposal to enable importing modules at the source phase
hal9ai - Hal9 — Data apps powered by code and LLMs [Moved to: https://github.com/hal9ai/hal9]
proposal-import-assertions - Proposal for syntax to import ES modules with assertions [Moved to: https://github.com/tc39/proposal-import-attributes]
regression-js - Curve Fitting in JavaScript.
proposal-class-static-block - ECMAScript class static initialization blocks
arrow-julia - Official Julia implementation of Apache Arrow
RegExp.escape - Proposal for investigating RegExp escaping for the ECMAScript standard
cylon - Cylon is a fast, scalable, distributed memory, parallel runtime with a Pandas like DataFrame.