asyncpg
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asyncpg | Flask | |
---|---|---|
15 | 135 | |
6,609 | 66,350 | |
1.5% | 0.8% | |
7.2 | 8.7 | |
14 days ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
asyncpg
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Ask HN: Is Python async/await some kind of joke?
- SqlAlchemy/asyncpg => you can’t use it if you’re using PgBouncer (necessary most of the time with Postgres) in transaction mode? What?? https://github.com/MagicStack/asyncpg/issues/1058
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Differences from Psycopg2
OK I stand corrected, asyncpg has these two C files:
https://github.com/MagicStack/asyncpg/blob/master/asyncpg/pr...
https://github.com/MagicStack/asyncpg/blob/master/asyncpg/pr...
If you are interested here is a post by the psycopg author about psycopg2 and 3 and performance versus asyncpg.
https://www.varrazzo.com/blog/2020/05/19/a-trip-into-optimis...
- Asyncpg – A Fast PostgreSQL Database Client Library for Python/Asyncio
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Ruby Outperforms C: Breaking the Catch-22
This pure Python library claims quite fabulous performance: https://github.com/MagicStack/asyncpg
I believe it because that team have done lots of great stuff but I haven't used it, I just remembered thinking it was interesting the performance was so good. Not sure how related it is to running on the asyncio loop (or which loop they used for benchmarks).
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PgBouncer is useful, important, and fraught with peril
what a great post, we have had a ton of issues with users using pgbouncer and it's not because things are "broken" per se, it's just the situation is very complicated, and pgbouncer's docs are also IMO in need of updating to be more detailed and in a few critical cases less misleading, specifically the prepared statements docs.
This blog post refers to this misleading nature at https://jpcamara.com/2023/04/12/pgbouncer-is-useful.html#pre... .
> PgBouncer says it doesn’t support prepared statements in either PREPARE or protocol-level format. What it actually doesn’t support are named prepared statements in any form.
That's also not really accurate. You can use a named prepared statement just fine in transaction mode. start a transaction (so you aren't in autocommit), use a named statement, works fine. you just can't use it again in another transaction, because it will be "gone" (more accurately, "unmoored" - might be in your session, might be in someone else's session). Making things worse, when the prepared statement is "unmoored", its name can then conflict with another client attempting to use the same name.
so to use named prepared statements, you can less ideally name them with random strings to avoid conflicts, or you can DEALLOCATE the prepared statement(s) you used at the end of your transaction. for our users that use asyncpg, we have them use a uuid for prepared statements to avoid these name conflicts (asyncpg added this feature for us here: https://github.com/MagicStack/asyncpg/issues/837). however, they can just as well use DEALLOCATE ALL, set this as their `server_reset_query`, and then so that happens in transaction mode, also set `server_reset_query_always`, so that it's called at the end of transactions. Where pgbouncer here IMO entirely misleadingly documents this as "This setting is for working around broken setups that run applications that use session features over a transaction-pooled PgBouncer." - which is why nobody uses it, because pgbouncer claims this is "broken". It's not any more broken than it is to switch out the PostgreSQL session underneath a connection that uses multiple transactions. Pgbouncer can do better here and make this clearer and more accommodating of real world database drivers.
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Library to connect Python to Postgresql
asyncpg is another great driver if you're using asyncio and want maximum performance (although they also break with DBAPI, but the tradeoff may be worth it).
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aiopg vs asyncpg vs psycopg3
asyncpg: 5.5k starts, last commit recently, ~150 issues, some incompatibility, few open PRs, extensive README. Includes benchmark showing it's supposedly 3x faster than aiopg and psycopg2, psycopg3 is not mentioned in the benchmark.
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Announcing Quart-DB
Quart-DB uses asyncpg to manage the connections and buildpg to parse the named parameter bindings.
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Should I use TimescaleDB or partitioning is enough?
A major performance boost specifically on inserts with timescaledb was actually starting to use https://github.com/MagicStack/asyncpg.
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Cascade of doom: JIT, and how a Postgres update led to 70% failure on a critical national service
Simple query runs long when DB schema contains thousands of tables #186
Flask
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Ask HN: High quality Python scripts or small libraries to learn from
I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:
https://github.com/pallets/flask
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Rapid Prototyping with Flask, Bootstrap and Secutio
#!/usr/bin/python # # https://flask.palletsprojects.com/en/3.0.x/installation/ # from flask import Flask, jsonify, request contacts = [ { "id": "1", "firstname": "Lorem", "lastname": "Ipsum", "email": "[email protected]", }, { "id": "2", "firstname": "Mauris", "lastname": "Quis", "email": "[email protected]", }, { "id": "3", "firstname": "Donec Purus", "lastname": "Purus", "email": "[email protected]", } ] app = Flask(__name__, static_url_path='', static_folder='public',) @app.route("/contact//save", methods=["PUT"]) def save_contact(id): data = request.json contacts[id - 1] = data return jsonify(contacts[id - 1]) @app.route("/contact/", methods=["GET"]) @app.route("/contact//edit", methods=["GET"]) def get_contact(id): return jsonify(contacts[id - 1]) @app.route('/') def root(): return app.send_static_file('index.html') if __name__ == '__main__': app.run(debug=True)
- Microdot "The impossibly small web framework for Python and MicroPython"
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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10 Github repositories to achieve Python mastery
Explore here.
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Ask HN: What would you use to build a mostly CRUD back end today?
I may use Flask-Admin initially to offload the "CRUD" operations to have an initial prototype fast but then drop it ASAP because I don't want to write a "flask-admin application" to fight against later on. If the application is mainly "CRUD", then Flask-Admin is suitable.
Now...
Would you do a breakdown/list of all the jobs you've done by sector/vertical and by function/role and by application functionality?
- [0]: https://flask.palletsprojects.com
- [1]: https://flask-admin.readthedocs.io/en/latest
- [2]: https://flask.palletsprojects.com/en/2.3.x/patterns/celery
- [3]: https://sentry.io
- [4]: https://posthog.com
- [5]: https://www.docker.com
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Implementing continuous delivery pipelines with GitHub Actions
In the lab to follow, we will be setting up an end-to-end DevOps workflow for a Flask microservice with GitHub Actions, using a self-managed custom runner for maximal control over the pipeline execution environment and automating deployments to a local Kubernetes cluster. Furthermore, we will construct separate pipelines for our "development" and "production" environments to further elaborate on the concepts of continuous deployment and delivery.
- How do you iterate on a library built locally?
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Flask Application Load Balancing using Docker Compose and Nginx
Flask Micro web Framework: You will use Flask to build a Flask web application.
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Open Source Flask-based web applications
In an earlier post I mentioned a bunch of Open Source web applications. Let's now focus on the ones written in Python using Flask the light-weight web framework.
What are some alternatives?
psycopg2 - PostgreSQL database adapter for the Python programming language
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
aiopg - aiopg is a library for accessing a PostgreSQL database from the asyncio
Django - The Web framework for perfectionists with deadlines.
pymssql - Official home for the pymssql source code.
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
awesome-mysql - A curated list of awesome MySQL software, libraries, tools and resources
quart - An async Python micro framework for building web applications.
pgbouncer - lightweight connection pooler for PostgreSQL
starlette - The little ASGI framework that shines. 🌟
mysql-python - MySQLdb is a Python DB API-2.0 compliant library to interact with MySQL 3.23-5.1 (unofficial mirror)
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.