scripts-to-rule-them-all VS sqlite-utils

Personally I like https://github.blog/2015-06-30-scripts-to-rule-them-all/ as a pattern and then let the authors do whatever crazy thing they want from there. In my experience, 99% of repos never move past using simple shell scripts with a few common functions with that pattern, and things are kept fairly simple. A select few repositories tend to mature enough that they are able to invest in swapping towards something more testable than shell scripts, and then you just have a couple people who stick to invoking `make` from the scripts but it's fine and nobody has to think about it except them. We don't stick to that exact set of scripts, but find that as long as you don't use more than like 10ish entrypoints in `script/*`, and have at least `script/bootstrap` it's fine.

I dig the general idea, but question the value add over a directory of `scripts` that follow sane conventions (ie `script/test`, `script/build` etc). Is the main thing that you can do `just -l` to see available commands? I have never really reached for `make` when I've had a choice, as I've done mostly ruby, JS, or java where you have more sane, humane tools (i.e. Rake, Yarn, Maven though that one is never fun).

My general approach is every repo should have something that follows https://github.com/github/scripts-to-rule-them-all, written in sh (maybe bash, its 2023), linted with shellcheck. When you need something fancy Rake is great or grab some nice bash command line helper and source it from all your scripts. Is a command listing really worth another dependency over what you get from `ls script` or `cat script/README` ?

Afaik AzDo cannot run tasks concurrently. From having had to work with azure pipelines I would highly suggest to use the github approach of Scripts to rule them all and avoiding predefined tasks unless absolutely necessary(Things that are complicated to implement and solutions already existing.

GitHub have a pattern for this called "scripts to rule them all" - https://github.com/github/scripts-to-rule-them-all - I've not fully adopted it yet but I probably should, it looks very well thought-out.

People at Github made an attempt to fix this situation: scripts to rule them all. The idea is to have common set of executable scripts for common developer tasks in a script/ directory in the root of every project:

https://github.com/github/scripts-to-rule-them-all

https://github.com/simonw/sqlite-utils

So, his code might not be a good place to find best patterns (for ex, I don't think they are fully typed), but his repos are very pragmatic, and his development process is super insightful (well documented PRs for personal repos!). Best part, he blogs about every non-trivial update, so you get all the context!

Sounds like your problem is with SQLAlchemy, not with SQLite.

My https://sqlite-utils.datasette.io library might be a better fit for you. It's a much thinner abstraction than SQLAlchemy.

There are a few things you can do here.

SQLite is great at JSON - so I often dump JSON structures in a TEXT column and query them using https://www.sqlite.org/json1.html

I also have plugins for running jq() functions directly in SQL queries - https://datasette.io/plugins/datasette-jq and https://github.com/simonw/sqlite-utils-jq

I've been trying to drive the cost of turning semi-structured data into structured SQL queries down as much as possible with https://sqlite-utils.datasette.io - see this tutorial for more: https://datasette.io/tutorials/clean-data

This is also an area that I'm starting to explore with LLMs. I love the idea that you could take a bunch of messy data, tell Datasette Cloud "I want this imported into a table with this schema"... and it does that.

I have a prototype of this working now, I hope to turn it into an open source plugin (and Datasette Cloud feature) pretty soon. It's using this trick: https://til.simonwillison.net/gpt3/openai-python-functions-d...

I've baked a ton of different SQLite tricks - including things like full-text indexing support and advanced alter table methods - into my sqlite-utils CLI tool and Python library: https://sqlite-utils.datasette.io

My Datasette project provides tools for exploring, analyzing and publishing SQLite databases, plus ways to expose them via a JSON API: https://datasette.io

I've also written a ton of stuff about SQLite on my two blogs:

- https://simonwillison.net/tags/sqlite/

- https://til.simonwillison.net/sqlite

This is really fun. I have an experimental branch of my sqlite-utils CLI tool (which has dozens of sub-commands) running with this now and it really did only take 4 lines of code - I'm treating Trogon as an optional dependency because people using my package as a Python library rather than a CLI tool may not want the extra installed components:

https://github.com/simonw/sqlite-utils/commit/ec12b780d5dcd6...

There's an animated GIF demo of the result here: https://github.com/simonw/sqlite-utils/issues/545#issuecomme...

My project https://datasette.io/ is ideal for this kind of thing. You can use https://sqlite-utils.datasette.io/ to load JSON data into a SQLite database, then publish it with Datasette.

I've been using this for about six months now and I absolutely love it.

Make never stuck for me - I couldn't quite get it to fit inside my head.

Just has the exact set of features I want.

Here's one example of one of my Justfiles: https://github.com/simonw/sqlite-utils/blob/fc221f9b62ed8624... - documented here: https://sqlite-utils.datasette.io/en/stable/contributing.htm...

I also wrote about using Just with Django in this TIL: https://til.simonwillison.net/django/just-with-django

SQLite with the open source toolchain I've been building over the past five years:

https://datasette.io as the interface for running queries against (and visualizing) my data.

https://sqlite-utils.datasette.io/ as a set of tools for creating and modifying my databases (inserting JSON or CSV data, enabling full text search text)

https://dogsheep.github.io as a suite of tools for importing my personal data - see also this talk I gave about that project: https://simonwillison.net/2020/Nov/14/personal-data-warehous...

Here's an example: https://github.com/simonw/sqlite-utils/pull/468

> After identifying about 7 commits (with pretty basic/useless messages, and no PR link!), I then had to find the corresponding PRs based on timestamps, and search the PR history for PRs merged around those timestamps.

Not sure if this would save any time, but it is possible to search PRs by commit. For example, say git blame led me to this commit: https://github.com/simonw/sqlite-utils/commit/129141572f249e...

I could have found PR #373 via this search: https://github.com/simonw/sqlite-utils/pulls?q=bb16f52681b6d...

> I thus treat PRs as ephemeral

I think I see what you're saying but as others have pointed out, sometimes you want to add screenshots etc to the context, and you can't capture this kind of info in commit messages. So then you have two choices: issues or PRs.

> Then any review comments are preferably not addressed directly in the PR

I would think that sometimes you really do want to have a back and forth conversation in the PR, rather than just a "make this change" -> "ok done" type of feedback loop.

I view the PR as an decent place for all of this because it's basically a commit of commits, capturing the related changes/conversation/context all in a single place at the point of merge.