vim-jumpsuite
aiosql
vim-jumpsuite | aiosql | |
---|---|---|
8 | 10 | |
5 | 1,245 | |
- | - | |
0.0 | 8.7 | |
over 1 year ago | 2 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
vim-jumpsuite
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Parse python traceback in the quickfix list.
Oh, you're in luck because I've actually written this exact plugin, it's called vim-jumpsuite. It is personally one of my special weapons that's becoming completely indispensable for me.
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Which not so well known Python packages do you like to use on a regular basis and why?
vim-jumpsuite: parses python tracebacks and identifies the most "interesting" part of the stack to create a jump list; despite vim being in the name, the python part of the plug-in is usable with any editors that supports parsing grep/quickfix-style output
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Are you a person who loves reinventing a wheel ?
Here is link number 1 - Previous text "my"
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Vim setup as a Python IDE with REPL similar to Spyder/VSCode
vim-jumpsuite for creating a quickfix/loclist jumps out of unittest tracebacks
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IDE Similar to PyCharm for Work
vim-test with lieryan/vim-jumpsuite
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Open Python error in Vim
For more elaborate cases, I wrote a plugin that summarises python traceback into the quickfix list. vim-jumpsuite is designed to be used when writing unittest/pytest; for each failing test, it'll try to pick the three most important locations that you'll want to jump to. You can also configure certain files/functions to never be picked by vim-jumpsuite.
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Python Devs who Use Vim, Share Your Expertise!
Plug: one of the most valuable plugin for me are the vim plugin that I wrote myself: lieryan/vim-jumpsuite. It's a plugin to quickly jump to "interesting" line of code from your test suite by converting unittest reports to a Quickfix jumplist. The plugin uses some customizable heuristics to find the lines from tracebacks that are most likely to be most useful to your code.
aiosql
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Don't use your ORM entities for everything – embrace the SQL
> resort to raw SQL
I'm the opposite, I would rather write SQL than "resorting to" ORM queries, which is why my favourite libraries are aiosql[1] in Python, Hugsql[2] in Clojure and similar: write the queries as SQL in .sql files, which then get exposed as functions to your code.
[1] https://nackjicholson.github.io/aiosql/
[2] https://www.hugsql.org/
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Project template without ORM
I prefer to use aiosql https://nackjicholson.github.io/aiosql/ to organize my SQL and have it in a SQL folder. It looks like this where colons specify variables:
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If you could choose any Python web framework to build APIs for a startup, which one would you choose and why?
I tend to do a lot of data-heavy projects, so I tend to eschew ORM-style code and use a project called aiosql to bind raw SQL to python methods, and offload as much expensive computation to the DB as possible. If I'm prototyping an endpoint (e.g. calculating percentiles for some midsized time-series data), and just need a non-performant working placeholder, it's extremely easy to dump a SQL table to pandas and yeet something together in a few lines - then smoothly replace it with a more performant SQL query down the road. Highly contextual move, but I find it to be an awesome balancing point between flexibility, scalability, performance, productivity, etc.
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Which not so well known Python packages do you like to use on a regular basis and why?
As one of the rare Python developers who actually like SQL, my favourite database library is aiosql
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Database as Code. Not only migrations
Only slightly off-topic, poking around in there led me to aiosql, which takes an idea I'd had and jumps forward a good long way. :-)
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The Data-Oriented Design Process for Game Development
I've been doing something in this vein for a big personal project, using this python library: https://nackjicholson.github.io/aiosql/.
In short, I'm using a run of the mill stack (Caddy/Gunicorn/Flask/Postgres) - but with the twist that all my core logic is defined in plaintext SQL files, which get bound into namespaced Python methods by aiosql. Routing, error handling, templating, etc. are all done in Python - but all data manipulation and processing are outsourced to the DB level. All database object definitions are laid out in a massive, idempotent "init_db" method that gets called at launch, so I can essentially point the app at a fresh instance of Postgres and rebuild from scratch. The design is primarily driven by my personal distaste for ORMs, but I've found it extremely beneficial in terms of rigid typing, integrity checks, and performance.
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Is it bad practice for my flask API to run raw SQL queries against my DB to get/post data?
Definitely check out https://nackjicholson.github.io/aiosql/ if you want to stick with SQL
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Django 4.0 release candidate 1 released
I took that approach on my latest Flask project and it’s gone quite swimmingly. The problem I ran into was that a lot of the ecosystem, and therefore documentation, blog posts, helper libraries, etc., are all written under the assumption that you’re using an ORM. It took a while to figure out how to work around that, but once I did, I was home clear.
I also used a helper library to automatically map namespaced .sql files onto python functions with various return types, which made the development process way more elegant: https://nackjicholson.github.io/aiosql/. Absolute game changer if you plan to go this route - can’t recommend it highly enough.
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FastAPI framework, high perf, easy to learn, fast to code, ready for production
I've been using FastAPI for some time, and now I'm using it as a full web framework (not just for REST APIs). I like writing SQL without ORMs, so the combination of aiosql[0] + FastAPI + Jinja2 works great. Add HTMX[1] and even interactive websites become easy.
That's in fact the stack I am using to build https://drwn.io/ and I couldn't enjoy it more.
Thanks Sebastián for creating it!
[0] https://github.com/nackjicholson/aiosql
What are some alternatives?
pylsp-rope - Extended refactoring capabilities for python-lsp-server using Rope
databases - Async database support for Python. 🗄
Python-mode - Vim python-mode. PyLint, Rope, Pydoc, breakpoints from box.
full-stack-fastapi-template - Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.
Rope - a python refactoring library
django-async-orm - Bringing Async Capabilities to django ORM
vim-test - Run your tests at the speed of thought
fastapi-crudrouter - A dynamic FastAPI router that automatically creates CRUD routes for your models
python-lsp-server - Fork of the python-language-server project, maintained by the Spyder IDE team and the community
Pebble - Java Template Engine
vim-textobj-indent - Vim plugin: Text objects for indented blocks of lines
mangum - AWS Lambda support for ASGI applications