alchemy-utils 0.1a0：数据库无关的 sqlite-utils
alchemy-utils 0.1a0
Release: alchemy-utils 0.1a0
发布：alchemy-utils 0.1a0
I've long pondered what a database agnostic version of my sqlite-utils Python library and CLI utility might look like. This morning (literally a shower project) I tasked Codex and GPT-5.6 Sol Ultra with building a prototype:
我一直在思考，我的 sqlite-utils Python 库和 CLI 工具如果做成一个不依赖特定数据库的版本会是什么样子。今天早上（真的是在淋浴时想到的项目），我让 Codex 和 GPT-5.6 Sol Ultra 构建一个原型：
Do a research spike to see what it would take to build a library with the same core API as SQLite-utils - in particular the insert and upsert and insert_all and upsert_all and create and update methods, and the table introspection stuff - but backed by SQLalchemy so it works for multiple database engines
进行一次研究性探索，看看构建一个与 SQLite-utils 核心 API 相同的库需要什么——特别是 insert、upsert、insert_all、upsert_all、create 和 update 方法，以及表内省功能——但底层使用 SQLAlchemy，以便支持多种数据库引擎。
Test against PostgreSQL and SQLite and duckdb
针对 PostgreSQL、SQLite 和 DuckDB 进行测试。
Use ~/dev/sqlite-utils for reference
使用 ~/dev/sqlite-utils 作为参考。
Create a git repo for this and commit and early and often - use uv init to start the project - use red/green TDD and pytest, see ~/dev/django-sql-dashboard for one idea as to how the PostgreSQL tests could work
为此创建一个 git 仓库，并尽早且频繁地提交——使用 uv init 启动项目——使用红/绿 TDD 和 pytest，参见 ~/dev/django-sql-dashboard 了解 PostgreSQL 测试的一种思路。
It took very few follow-up prompts to produce this project in a state good enough to release as an alpha.
只用了很少的后续提示，就把这个项目做到了可以发布为 alpha 版本的状态。
Here's a one-liner I can use to list the rows in a table in my local PostgreSQL copy of my blog's database:
这里有一个一行命令，可以用来列出我本地 PostgreSQL 中博客数据库副本的表中的行：
uvx --with 'alchemy-utils[postgresql]' alchemy-utils rows 'postgresql+psycopg://simon@localhost:5432/simonwillisonblog' redirects_redirect
uvx --with 'alchemy-utils[postgresql]' alchemy-utils rows 'postgresql+psycopg://simon@localhost:5432/simonwillisonblog' redirects_redirect
The output from that starts like this:
该命令的输出开头如下：
[
{
"id": 2328,
"domain": "simonwillison.net",
"path": "2020/May/21/apple-photos-sqlite/",
"target": "/2020/May/21/dogsheep-photos/",
"created": "2020-05-21T13:03:46.591692-07:00"
},
{
"id": 3,
"domain": "feeds.simonwillison.net",
"path": "swn-links",
"target": "https://simonwillison.net/atom/links/",
"created": "2017-10-01T14:12:54.820729-07:00"
}[
{
"id": 2328,
"domain": "simonwillison.net",
"path": "2020/May/21/apple-photos-sqlite/",
"target": "/2020/May/21/dogsheep-photos/",
"created": "2020-05-21T13:03:46.591692-07:00"
},
{
"id": 3,
"domain": "feeds.simonwillison.net",
"path": "swn-links",
"target": "https://simonwillison.net/atom/links/",
"created": "2017-10-01T14:12:54.820729-07:00"
}Or if you'd like a DuckDB database with every tree in San Francisco, schema created automatically to match the file:
或者，如果你想要一个包含旧金山所有树木的 DuckDB 数据库，模式会根据文件自动创建：
curl 'https://raw.githubusercontent.com/simonw/sf-tree-history/refs/heads/main/Street_Tree_List.csv' | uvx --with 'alchemy-utils[duckdb]' alchemy-utils insert 'duckdb:////tmp/trees.db' trees - --csv
curl 'https://raw.githubusercontent.com/simonw/sf-tree-history/refs/heads/main/Street_Tree_List.csv' | uvx --with 'alchemy-utils[duckdb]' alchemy-utils insert 'duckdb:////tmp/trees.db' trees - --csv
(That one took nearly an hour the first time I ran it, so I had Codex optimize it and got it down to around 35 seconds.)
（第一次运行这个命令花了将近一个小时，所以我让 Codex 优化了一下，把它降到了大约 35 秒。）
Tags: databases, postgresql, projects, python, sql, sqlalchemy, sqlite, sqlite-utils, duckdb, coding-agents, codex
标签：databases, postgresql, projects, python, sql, sqlalchemy, sqlite, sqlite-utils, duckdb, coding-agents, codex
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