noria VS prql

> Automatically managed, application-transparent, physical denormalisation entirely managed by the database is something I am very, very interested in.

Sounds a bit like Noria: https://github.com/mit-pdos/noria

It feels more than a little bit coincidental to call it Noria when https://github.com/mit-pdos/noria exists (and has been posted about here on HN)... especially with the whole bit about incrementally computing changes.

Is this similar in spirit to Noria?

https://github.com/mit-pdos/noria

I assume you have studied Noria? https://github.com/mit-pdos/noria

If you want to look how databases are implemented in rust try https://github.com/mit-pdos/noria

Only tangentially related, but I wondered what were the difference between ReadySet and Noria, and they address this exact question in their repository I'm really glad to know that the ideas behind Noria didn't die when Noria was abandoned after /u/jonhoo graduated.

:wave: Author of the paper this work is based on here.

I'm so excited to see dynamic, partially-stateful data-flow for incremental materialized view maintenance becoming more wide-spread! I continue to think it's a _great_ idea, and the speed-ups (and complexity reduction) it can yield are pretty immense, so seeing more folks building on the idea makes me very happy.

The PlanetScale blog post references my original "Noria" OSDI paper (https://pdos.csail.mit.edu/papers/noria:osdi18.pdf), but I'd actually recommend my PhD thesis instead (https://jon.thesquareplanet.com/papers/phd-thesis.pdf), as it goes much deeper about some of the technical challenges and solutions involved. It also has a chapter (Appendix A) that covers how it all works by analogy, which the less-technical among the audience may appreciate :) A recording of my thesis defense on this, which may be more digestible than the thesis itself, is also online at https://www.youtube.com/watch?v=GctxvSPIfr8, as well as a shorter talk from a few years earlier at https://www.youtube.com/watch?v=s19G6n0UjsM. And the Noria research prototype (written in Rust) is on GitHub: https://github.com/mit-pdos/noria.

As others have already mentioned in the comments, I co-founded ReadySet (https://readyset.io/) shortly after graduating specifically to build off of Noria, and they're doing amazing work to provide these kinds of speed-ups for general-purpose relational databases. If you're using one of those, it's worth giving ReadySet a look to get these kinds of speedups there! It's also source-available @ https://github.com/readysettech/readyset if you're curious.

It seems similar to MIT's Noria [1]

> Noria is a new streaming data-flow system designed to act as a fast storage backend for read-heavy web applications based on Jon Gjengset's Phd Thesis, as well as this paper from OSDI'18. It acts like a database, but precomputes and caches relational query results so that reads are blazingly fast. Noria automatically keeps cached results up-to-date as the underlying data, stored in persistent base tables, change. Noria uses partially-stateful data-flow to reduce memory overhead, and supports dynamic, runtime data-flow and query change.

[1] https://github.com/mit-pdos/noria

Materialize is really neat, also checkout https://github.com/mit-pdos/noria. It inverts the query problem and processes the data on insert. Exactly like what most applications end up doing using a no-sql solution.

> 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

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!

> 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-...

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?

PRQL [1] is a compile-to-SQL relational querying language that puts FROM first.

[1] https://prql-lang.org

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