roapi VS nix

If your use case is read-only I suggest taking a look at roapi[1]. It supports multiple read frontends (GraphQL, SQL, REST) and many backends like SQLite, JSON, google sheets, MySQL, etc.

[1] https://github.com/roapi/roapi

For smaller datasets then anywhere up to a few mb which isn't so bad reasonable with an API but in theory for historic data it could be up to several gb. I've not seen datasette go that high (IIRC it's a 1000 row return limit by default).

That's what got me intrigued with Atlassians offering, as data lakes tend to be something internal to a company, not something I've ever seen offered as an interaction point to users.

I've also tested out roapi [1] which is nice if the data is in some structured format already (Parquet/JSON)

[1] https://github.com/roapi/roapi

https://github.com/roapi/roapi/issues/103?

Very cool and well executed project. Love the sprinkle of Rust in all the other companion projects as well :)

The ROAPI(https://github.com/roapi/roapi) project I built also happened to support a similar feature set, i.e. to expose sqlite through a variety of remote query interfaces including pg wire protocols, rest apis and graphqls.

> why not just accept SQL and cut out all the unnecessary mapping?

You might be interested in what we're building: Seafowl, a database designed for running analytical SQL queries straight from the user's browser, with HTTP CDN-friendly caching [0]. It's a second iteration of the Splitgraph DDN [1] which we built on top of PostgreSQL (Seafowl is much faster for this use case, since it's based on Apache DataFusion + Parquet).

The tradeoff for allowing the client to run any SQL vs a limited API is that PostgREST-style queries have a fairly predictable and low overhead, but aren't as powerful as fully-fledged SQL with aggregations, joins, window functions and CTEs, which have their uses in interactive dashboards to reduce the amount of data that has to be processed on the client.

There's also ROAPI [2] which is a read-only SQL API that you can deploy in front of a database / other data source (though in case of using databases as a data source, it's only for tables that fit in memory).

[0] https://seafowl.io/

[1] https://www.splitgraph.com/connect

[2] https://github.com/roapi/roapi

It could be the NDJSON parser (DF source: [0]) or could be a variety of other factors. Looking at the ROAPI release archive [1], it doesn't ship with the definitive `columnq` binary from your comment, so it could also have something to do with compilation-time flags.

FWIW, we use the Parquet format with DataFusion and get very good speeds similar to DuckDB [2], e.g. 1.5s to run a more complex aggregation query `SELECT date_trunc('month', tpep_pickup_datetime) AS month, COUNT(*) AS total_trips, SUM(total_amount) FROM tripdata GROUP BY 1 ORDER BY 1 ASC)` on a 55M row subset of NY Taxi trip data.

[0]: https://github.com/apache/arrow-datafusion/blob/master/dataf...

[1]: https://github.com/roapi/roapi/releases/tag/roapi-v0.8.0

[2]: https://observablehq.com/@seafowl/benchmarks

> https://github.com/NixOS/nix/pull/9911#issuecomment-19252073...

As we covered in my last post, NixOS is a amazing Linux distribution for creating stable and declared environments. Now while this is amazing for a desktop setup, it is also perfect for a home-server or home-lab.

(Nix itself is slowly chugging along with Windows via MinGW - https://discourse.nixos.org/t/nix-on-windows/1113/108 and https://github.com/NixOS/nix/issues/1320 , for example.)

Nix is a cross-platform package manager. It uses the nix programming language. Nix and NixOs are often used in the same context, but while the first is a package manager, the latter is a linux distribution based on nix.

Today I want to talk to you about Nixos. What is it? Nixos is a declarative and reproducible OS, partly taking the words used on their own page. What does that mean?

Software developers often want to customize:

1. their home environments: for packages (some reach for brew on MacOS) and configurations (dotfiles, and some reach for stow).

2. their development shells: for build dependencies (compilers, SDKs, libraries), tools (LSP, linters, formatters, debuggers), and services (runtime, database). Some reach for devcontainers here.

3. or even their operating systems: for development, for CI, for deployment, or for personal use.

Nix provision all of the above in the same language, with Nixpkgs, NixOS, home-manager, and devShells such as https://devenv.sh/. What's more, Nix is (https://nixos.org/):

- reproducible: what works on your dev machine also works in CI in prod,

- declarative: you version control and review your configurations and infrastructure as code, at a reasonable level of abstraction,

- reliable: all changes are atomic with easy roll back.

Hopping from one distro to another with a different package manager might require some time to adapt. Using a package manager that can be installed on most distro is one way to help you get to work faster. Flatpak is one of them; other alternative are Snap, Nix or Homebrew. Flatpak is a good starter, and if you have a bunch of free time, I suggest trying Nix.

- it reduces bloat, because you can generate an environment or OS image with only the software needed to run a specific program or service

My guess is that a big efficiency gain would come from the second point, because you don't waste CPU on code that you don't use.

Does this make sense? Has anyone explored this?

[0]: https://nixos.org

1) Setting up the development environment - I currently use devcontainers for most things, but may also dig into nix -> isolated, portable, repeatable development environment 2) Exploring Echo - understand routing, requests, response, etc. 3) Incorporate Templ - integration with Echo, template composition, etc. 4) Integrating TailwindCSS - config for use with Echo/Templ, development cycle, deployment, etc. 5) Add in HTMX - endpoints, template structure, concepts, etc. 6) hyperscript for interactivity - client side interactivity