datasette-stripe VS parquet-wasm

If you want to analyse your payment failures with SQL you could use https://table.dog to download them into a SQLite database.

https://table.dog is a CLI that downloads your Stripe account to a SQLite db.

Would appreciate it if you could test it out if you are interested.

https://table.dog (a CLI to download Stripe to SQLite).

I considered Node.js, C#.

I went with Rust for a few reasons:

- Easy to build small portable binaries (as a primary language feature).

- The type checker ensures type consistency when writing out to SQL tables (SQLite is loosely typed). Code that reads from the SQLite database implicitly benefits from Rusts strong type checks.

- Macros to convert structs to SQL insert/updates.

- Reduce the chance of errors at runtime.

- Leverage as much as SQLite's write throughput as possible.

- When converting Stripes Open API JSON spec into Rust code (using another Node.js program), the Rust type checker ensures I have a well formed HTTP client - the strict compiler makes it a good target for generated programs. Read more about this idea at (https://willcrichton.net/notes/rust-the-new-llvm/).

This is a CLI written in Rust to download your Stripe account to a local SQLite database.

It is intended to enable using SQL queries over your data.

There is also a template for fly.io and Datasette to give you a Sigma like web UI:

https://github.com/tabledog/datasette-stripe

nodejs-polars is node-specific and uses native FFI. polars can be compiled to Wasm but doesn't yet have a js API out of the box.

As for the fastest way to serialize data to Pandas data to the browser, you should use Parquet; it's the fastest to write on the Python side and read on the JS side, while also being compressed. See https://github.com/kylebarron/parquet-wasm (full disclosure, I wrote this)

I'm building WebAssembly bindings to existing Rust libraries [0] and lower-dependency geospatial tools [1]. Rust makes it very easy to bind rust code to both WebAssembly and Python. And by avoiding some large C geospatial dependencies we can get reliable performance in both wasm and Python using the exact same codebase.

[0]: https://github.com/kylebarron/parquet-wasm

[1]: https://github.com/kylebarron/geopolars

Looks to be a great resource. I've been working on a WASM implementation of reading and writing Apache Parquet [0] and it's been difficult being new to WASM to find the best way of distributing the WASM that works on Node and through bundlers like Webpack.

[0]: https://github.com/kylebarron/parquet-wasm