explorer
prql
explorer | prql | |
---|---|---|
20 | 106 | |
977 | 9,459 | |
1.2% | 1.0% | |
9.4 | 9.9 | |
6 days ago | 1 day ago | |
Elixir | Rust | |
MIT License | Apache License 2.0 |
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.
explorer
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Polars
The Explorer library [0] in Elixir uses Polars underneath it.
[0] https://github.com/elixir-explorer/explorer
- Unpacking Elixir: Concurrency
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Elixir Livebook is a secret weapon for documentation
To ensure you do not miss this: LiveBook comes with a Vega Lite integration (https://livebook.dev/integrations -> https://livebook.dev/integrations/vega-lite/), which means you get access to a lot of visualisations out of the box, should you need that (https://vega.github.io/vega-lite/).
In the same "standing on giant's shoulders" stance, you can use Explorer (see example LiveBook at https://github.com/elixir-explorer/explorer/blob/main/notebo...), which leverages Polars (https://www.pola.rs), a very fast DataFrame library and now a company (https://www.pola.rs/posts/company-announcement/) with 4M$ seed.
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Does anyone else hate Pandas?
Already exists. Check out https://github.com/elixir-nx/explorer which provides a tidyverse-like API in Elixir using polars as the back end.
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Data wrangling in Elixir with Explorer, the power of Rust, the elegance of R
José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.
My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).
In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:
* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box
* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks
* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)
* Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.
* Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.
Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.
I hope this helps!
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Would you still choose Elixir/Phoenix/LiveView if scaling and performance weren’t an issue to solve for?
There's a package in the Nx ecosystem called Explorer (https://github.com/elixir-nx/explorer). It uses bindings for the rust library, polars, which is much more betterer than Pandas.
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Updated Erlport alternative ?
FWIW around April this year I started using erlport with python polars in a production ETL app because explorer didn't have the features I needed at the time.
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ElixirConf 2022 - That's a wrap!
Machine learning is rapidly expanding within the Elixir ecosystem, with tools such as Nx, Axon, and Explorer being used both by individuals and companies such as Amplified, as mentioned above.
- Dataframes but for Elixir
- Quick candlestick summaries with Elixir's Explorer
prql
- Prolog language for PostgreSQL proof of concept
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SQL is syntactic sugar for relational algebra
> I completely attribute this to SQL being difficult or "backwards" to parse. I mean backwards in the way that in SQL you start with what you want first (the SELECT) rather than what you have and widdling it down.
> The turning point for me was to just accept SQL for what it is.
Or just write PRQL and compile it to SQL
https://github.com/PRQL/prql
- Transpile Any SQL to PostgreSQL Dialect
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Show HN: Open-source, browser-local data exploration using DuckDB-WASM and PRQL
Hey HN! We’ve built Pretzel, an open-source data exploration and visualization tool that runs fully in the browser and can handle large files (200 MB CSV on my 8gb MacBook air is snappy). It’s also reactive - so if, for example, you change a filter, all the data transform blocks after it re-evaluate automatically. You can try it here: https://pretzelai.github.io/ (static hosted webpage) or see a demo video here: https://www.youtube.com/watch?v=73wNEun_L7w
You can play with the demo CSV that’s pre-loaded (GitHub data of text-editor adjacent projects) or upload your own CSV/XLSX file. The tool runs fully in-browser—you can disconnect from the internet once the website loads—so feel free to use sensitive data if you like.
Here’s how it works: You upload a CSV file and then, explore your data as a series of successive data transforms and plots. For example, you might: (1) Remove some columns; (2) Apply some filters (remove nulls, remove outliers, restrict time range etc); (3) Do a pivot (i.e, a group-by but fancier); (4) Plot a chart; (5) Download the chart and the the transformed data. See screenshot: https://imgur.com/a/qO4yURI
In the UI, each transform step appears as a “Block”. You can always see the result of the full transform in a table on the right. The transform blocks are editable - for instance in the example above, you can go to step 2, change some filters and the reactivity will take care of re-computing all the cells that follow, including the charts.
We wanted Pretzel to run locally in the browser and be extremely performant on large files. So, we parse CSVs with the fastest CSV parser (uDSV: https://github.com/leeoniya/uDSV) and use DuckDB-Wasm (https://github.com/duckdb/duckdb-wasm) to do all the heavy lifting of processing the data. We also wanted to allow for chained data transformations where each new block operates on the result of the previous block. For this, we’re using PRQL (https://prql-lang.org/) since it maps 1-1 with chained data transform blocks - each block maps to a chunk of PRQL which when combined, describes the full data transform chain. (PRQL doesn’t support DuckDB’s Pivot statement though so we had to make some CTE based hacks).
There’s also an AI block: This is the only (optional) feature that requires an internet connection but we’re working on adding local model support via Ollama. For now, you can use your own OpenAI API key or use an AI server we provide (GPT4 proxy; it’s loaded with a few credits), specify a transform in plain english and get back the SQL for the transform which you can edit.
Our roadmap includes allowing API calls to create new columns; support for an SQL block with nice autocomplete features, and a Python block (using Pyodide to run Python in the browser) on the results of the data transforms, much like a jupyter notebook.
There’s two of us and we’ve only spent about a week coding this and fixing major bugs so there are still some bugs to iron out. We’d love for you to try this and to get your feedback!
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Pql, a pipelined query language that compiles to SQL (written in Go)
> Looks like PRQL doesn't have a Go library so I guess they just really wanted something in Go?
There's some C bindings and the example in the README shows integration with Go:
https://github.com/PRQL/prql/tree/main/prqlc/bindings/prqlc-...
- FLaNK Stack 26 February 2024
- FLaNK Stack Weekly 19 Feb 2024
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PRQL as a DuckDB Extension
Can someone tell me why PRQL is better? I went here: https://github.com/PRQL/prql
It looks nice, but what's the strengths compared to SQL?
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Shouldn't FROM come before SELECT in SQL?
PRQL [1] is a compile-to-SQL relational querying language that puts FROM first.
[1] https://prql-lang.org
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Vanna.ai: Chat with your SQL database
https://prql-lang.org/ might be an answer for this. As a cross-database pipelined language, it would allow RAG to be intermixed with the query, and the syntax may(?) be more reliable to generate
What are some alternatives?
dplyr - dplyr: A grammar of data manipulation
malloy - Malloy is an experimental language for describing data relationships and transformations.
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Preql - An interpreted relational query language that compiles to SQL.
axon - Nx-powered Neural Networks
bustub - The BusTub Relational Database Management System (Educational)
db-benchmark - reproducible benchmark of database-like ops
tresql - Shorthand SQL/JDBC wrapper language, providing nested results as JSON and more
arrow2 - Transmute-free Rust library to work with the Arrow format
spyql - Query data on the command line with SQL-like SELECTs powered by Python expressions
wasmex - Execute WebAssembly from Elixir
toydb - Distributed SQL database in Rust, written as a learning project