roapi VS docs.rs

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

Working in combination with CodeWhisperer in your IDE, you can send whole code sections to Amazon Q and ask for an explanation of what the selected code does. To show how this works, we open up the file.rs file cloned from this GitHub repository. This is part of an open source project to host documentation of crates for the Rust Programming Language, which is a language we are not familiar with.

Looks like a great initiative – I wish there was a reliable TS/JS equivalent of https://docs.rs (even considering rustdoc's deficiencies[1]).

I went through this exercise recently and so far my experience with trying to produce documentation from a somewhat convoluted TS codebase[2] has been disappointing. I would claim it's a consequence of the library's public (user-facing) API substantially differing from how the actual implementation is structured.

Typedoc produces bad results for that codebase so sphinx-js, which I wanted to use, doesn't have much to work with. I ultimately documented things by hand, for now, the way the API is meant to be used by the user.

Compare:

https://ts-results-es.readthedocs.io/en/latest/reference/api...

vs

https://tsdocs.dev/docs/ts-results-es/4.1.0-alpha.1/index.ht...

Rust Search Extension adds a section on docs.rs menubar which lists the features of a crate in a nice and easy to access format.

📝 Note: At the time of writing this post, I couldn't really locate the init function docs.rs documentation. It didn't seem easily accessible through any of the current HAL implementation documentation. Nevertheless, I reached the signature of the function through the source here.

Rust Crates are a Game-Changer 🎮:The ease of releasing a crate with `cargo publish` and the convenience of rolling out new versions amazed me. The auto-generated docs on Docs.rs. is an amazing tool, especially with docstring formatting. Doc tests serve as a two-fold tool for documenting the code and ensuring it's up-to-date.

I've found I manually type out certain subsets of URLs where possible[0], maybe that's subconsciously associated with my impression that Google Search results have gotten worse and worse over the years.

[0] https://en.wiktionary.org/wiki/ and https://docs.rs/ come to mind.

docs.rs also downloads you crate automatically to generate docs and I would guess lib.rs does something similar

There are also a few key sites you'll want to keep in your back pocket at all times: - The Standard Library Documentation has complete documentation for every std library function in Rust - crates.io is a repository for all third-party packages, and docs.rs has human-readable documentation for the overwhelming majority of them - The Rust Cookbook has some code examples for common tasks you may need to perform - Make sure you are using clippy, which is available through Rustup and can be run with cargo clippy as a replacement to cargo check, it adds additional lints for your Rust code and is very helpful for teaching many of the best practices

OTOH, source-code-generated-docs normalize how code docs are, like the rust docs.rs paradigm, so it sort of forces or encourages package creators/maintainers to write docs.