twitter-to-sqlite VS datasette

Keep in mind that all of this is automatically generated from a sqlite table (which itself was automatically generated by running twitter-to-sqlite), greatly reducing the bar for GUI-based, SQL-based EDA.

I've been using the sqlite_utils Python library to build a whole suite of tools for importing data from various different sources into SQLite, including healthkit-to-sqlite, swarm-to-sqlite, twitter-to-sqlite, inaturalist-to-sqlite, google-takeout-to-sqlite, github-to-sqlite, genome-to-sqlite, pocket-to-sqlite, hacker-news-to-sqlite, evernote-to-sqlite.

I wrote an async wrapper around SQLite in Python - I'm using a thread pool: https://github.com/simonw/datasette/blob/main/datasette/data...

I have multiple threads for reads and a single dedicated thread for writes, which I send operations to via a queue. That way I avoid ever having two writes against the same connection at the same time.

I've been working on this for almost six years now. The initial idea was to solve the "get stuff into a SQLite database" problem as effectively as possible, because my https://datasette.io/ project was only useful if you first get your data into SQLite.

It's since grown to handle all manner of manipulations. Possibly the most useful is its support for advanced schema alterations via the "transform" CLI command (and accompanying table.transform() Python method):

https://sqlite-utils.datasette.io/en/stable/cli.html#transfo...

https://sqlite-utils.datasette.io/en/stable/python-api.html#...

This addresses one of the most frequent complaints people have about SQLite - that it doesn't support a range of table alter operations beyond simple things like adding a new column.

sqlite-utils transform is an implementation of the pattern described in the SQLite docs - https://www.sqlite.org/lang_altertable.html#otheralter - where you create a new empty table with the modified schema, then copy the old data across and rename the tables as part of a single transaction.

Simon Willison's github would be a great place to get started imo -

https://github.com/simonw/datasette

I'm a fan on simonw's datasette/dogsheep ecosystem https://datasette.io/

I use Anki the exact same way. After a lifetime of learning I have accepted that I will never read over anything I write for myself voluntarily - so my two options are:

1. Write an article so good I can publish it and look it over myself later on. I did this last year with https://andrew-quinn.me/fzf/, for example.

2. Create Anki cards out of the material. Use the builtin Card Browser or even https://datasette.io/ on the underlying SQLite database in a pinch to search for my notes any time I have to.

Were you aware of, or tempted by https://datasette.io/ for creating your solution?

Browsing HN, GitHub and the like we get to see a huge variety of software products and code bases.

I often see products and think - if this product X, got together with Y, it would be pretty cool - kind of like if they had a kid together.

Not too literally, but more on the conceptual level - my level of programming is low.

E.g. Just some....

- pocketable.io & datasette (+with some more charting) [https://pocketbase.io, https://datasette.io]

You might like the Datasette project: https://datasette.io/

I don't think they are desperate for contributions but it's a welcoming environment and a fun project to hack on. You'll learn a lot just from reading the source and the incredibly informative PRs. The creator is a really talented developer with a great blog which shows up on the HN front page often.