litequeue VS datasette.io

To make sure you that the message you are trying to retrieve hasn't been locked already by another worker.

[0]: https://github.com/litements/litequeue/

[1]: https://github.com/litements/litequeue/blob/3fece7aa9e9a31e4...

SQLite is missing some features like `SELECT FOR UPDATE`, but you can work around some issues with a few extra queries. I wrote litequeue[0] with this specific purpose. I haven't been able to use it a lot, so I don't have real-world numbers of how it scales, but the scaling limits depend on how fast you can insert into the database.

[0]: https://github.com/litements/litequeue

The `RETURNING` is so awesome! I'm implementing a set of data structures on top of SQLite, one of them is a queue[0], and I had to do a transaction to lock a message and then return it, but this makes it easier.

There's one little issue I keep finding with SQLite, and it's that most virtual servers / VM images ship with version 3.22.0, and upgrading often means building from source.

In any case, SQLite is absolutely wonderful. My favorite way of building products is having a folder for all the DBs that I mount to docker-compose. This release makes it even better.

[0] https://github.com/litements/litequeue

Opened an issue exploring alternatives here: https://github.com/simonw/datasette.io/issues/109

I decided to just drop "any size" but keep "any shape".

Here's an example from one of my projects: https://github.com/simonw/datasette.io/blob/main/.github/workflows/deploy.yml

I've come around to almost the opposite approach.

I pull all of the data I can get my hands on (from Twitter, GitHub, Swarm, Apple Health, Pocket, Apple Photos and more) into SQLite database tables that match the schema of the system that they are imported from.

For my own personal Dogsheep (https://simonwillison.net/2020/Nov/14/personal-data-warehous...) that's 119 tables right now.

Then I use SQL queries against those tables to extract and combine data in ways that are useful to me.

If the schema of the systems I am importing from changes, I can update my queries to compensate for the change.

This protects me from having to solve for a standard schema up front - I take whatever those systems give me. But it lets me combine and search across all of the data from disparate systems essentially at runtime.

I even have a search engine for this, which is populated by SQL queries against the different source tables. You can see an example of how that works at https://github.com/simonw/datasette.io/blob/main/templates/d... - which powers the search interface at https://datasette.io/-/beta

I've been exploring this technique more over the past year and I really like it - https://datasette.io (code at https://github.com/simonw/datasette.io ) is a more recent and much more complicated example.

Extracting links from markdown and using them to populate some additional columns or tables at build time would be pretty straight forward.

I run SQLite in serverless environments (Cloud Run, Vercel, Heroku) for dozens of projects... but the trick is that they all treat the database as a read-only asset.

If I want to deploy updated data, I build a brand new image and deploy the application bundled with the data. I tend to run the deploys for these (including the database build) in GitHub Actions workflows.

This works really well, but only for applications that don't need to apply constant updates more than a few times an hour! If you have a constant stream of updates I still think you're better off using a hosted database like Heroku PostgreSQL or Google Cloud SQL.

One example of a site I deploy like that is https://datasette.io/ - it's built and deployed by this GitHub Actions workflow here: https://github.com/simonw/datasette.io/blob/main/.github/wor...