python-mysql-replicati
gevent
python-mysql-replicati | gevent | |
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1 | 5 | |
- | 6,175 | |
- | 0.2% | |
- | 8.1 | |
- | 11 days ago | |
Python | ||
- | GNU General Public License v3.0 or later |
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python-mysql-replicati
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Is anyone using PyPy for real work?
I'm maintaining an internal change-data-capture application that uses a python library to decode mysql binlog and store the change records as json in the data lake (like Debezium). For our most busiest databases a single Cpython process couldn't process the amount of incoming changes in real time (thousands of events per second). It's not something that can be easily parallelized, as the bulk of the work is happening in the binlog decoding library (https://github.com/julien-duponchelle/python-mysql-replicati...).
So we've made it configurable to run some instances with Pypy - which was able to work through the data in realtime, i.e. without generating a lag in the data stream. The downside of using pypy was increased memory usage (4-8x) - which isn't really a problem. An actually problem that I didn't really track down was that the test suite (running pytest) was taking 2-3 times longer with Pypy than with CPython.
A few months ago I upgraded the system to run with CPython 3.11 and the performance improvements of 10-20% that come with that version now actually allowed us to drop Pypy and only run CPython. Which is more convenient and makes the deployment and configuration less complex.
gevent
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Is anyone using PyPy for real work?
A sub-question for the folks here: is anyone using the combination of gevent and PyPy for a production application? Or, more generally, other libraries that do deep monkey-patching across the Python standard library?
Things like https://github.com/gevent/gevent/issues/676 and the fix at https://github.com/gevent/gevent/commit/f466ec51ea74755c5bee... indicate to me that there are subtleties on how PyPy's memory management interacts with low-level tweaks like gevent that have relied on often-implicit historical assumptions about memory management timing.
Not sure if this is limited to gevent, either - other libraries like Sentry, NewRelic, and OpenTelemetry also have low-level monkey-patched hooks, and it's unclear whether they're low-level enough that they might run into similar issues.
For a stack without any monkey-patching I'd be overjoyed to use PyPy - but between gevent and these monitoring tools, practically every project needs at least some monkey-patching, and I think that there's a lack of clarity on how battle-tested PyPy is with tools like these.
- SynchronousOnlyOperation from celery task using gevent execution pool on django orm
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How to Choose the Right Python Concurrency API
I'm not sure how much it replicates the CSP model, but the closest thing I've found to Go-style concurrency in Python is gevent: https://github.com/gevent/gevent
I personally still prefer to use it in all my projects.
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I have a problem with installing Ajenti on a 64bit Ubuntu 21.04 server
Greenlet seems to have some troubles compiling with Python 3.9. https://github.com/gevent/gevent/issues/1627
What are some alternatives?
preshed - 💥 Cython hash tables that assume keys are pre-hashed
eventlet - Concurrent networking library for Python
python-mysql-replication - Pure Python Implementation of MySQL replication protocol build on top of PyMYSQL
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
psycopg2cffi - Port to cffi with some speed improvements
Faust - Python Stream Processing
murmurhash - 💥 Cython bindings for MurmurHash2
Thespian Actor Library - Python Actor concurrency library
sparc-curation - code and files for SPARC curation workflows
kombu - Messaging library for Python.
Tomorrow - Magic decorator syntax for asynchronous code in Python
aiochan - CSP-style concurrency for Python