octosql-plugin-random_data VS noria

Hey!

> I think the main fundamental difference is that this wants all of the data upfront in a data file.

Absolutely not! Moreover, OctoSQL can push down predicates to databases so that it only has to download a small subset of the table, if the datasource and query allow it.

> Very easy to model HTTP APIs as a table.

"Very easy" is relative, but you can take a look at the random_data[0] datasource which is exactly this. I'm also planning to add a GitHub datasource fairly soon. That said, there is Steampipe[1] for which this is the main use case afaik (hitting API's and exposing them as tables through Postgres FWD's written in Go), so it might be a smoother and more polished experience. There's also tons of plugins already available for it.

> Easy to model basically anything as a table for example files on my filesystem.

Yep, definitely. That's the idea behind OctoSQL. Strive to create a tool for easily exposing anything through SQL (like your machine's processes list, an API, and join that with a file, or database). There's still lot's of documentation work left to do though, in order to make the plugin authoring experience easier.

> A decent query planner so that I can avoid expensive things (like API calls) if I can determine if I need the object based on something cheaper (like a local disk access).

Probably depends on the use-case, and it sometimes needs you to be fairly explicit, but OctoSQL does in fact do that. It will push down predicates to underlying databases, which means joining something small with something very big (while only taking very small amounts of the latter) can be very fast with LOOKUP JOIN's.

> I want something that is easy to extend to sources that are possibly non-listable or at the very least I don't want to have all of the data available.

Doable. An example of this is the `plugins.available_versions` table[2]. It requires you to provide the plugin name as a predicate, as the versions need to be downloaded from the plugin's own repository (and listing all plugin repositories on each query isn't really what you want to be doing). You can also LOOKUP JOIN with the `plugins.available_plugins` table if that is indeed what you want.

[0]: https://github.com/cube2222/octosql-plugin-random_data

[1]: https://steampipe.io

[2]: https://github.com/cube2222/octosql/blob/main/datasources/pl...

> 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.