pottery
memoize
pottery | memoize | |
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5 | 1 | |
1,013 | 64 | |
- | - | |
7.2 | 6.2 | |
about 1 month ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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pottery
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Is Redis om production ready? Or will it be production ready anytime soon?
However, as an alternative, consider my library, Pottery. Pottery offers some similar functionality to Redis OM, and Pottery is production ready.
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Is there any way for hGetAll to return a key-value pair list instead of a simple list?
This isn’t for Node.js… But if you’re using Python, you might want to check out Pottery. Pottery provides the functionality you’re describing and much more.
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What are incredible Python modules you can build entire projects around?
Shameless plug: Pottery: Redis for Humans. Its goal it to make Redis far easier to use by exposing Redis functionality via native Pythonic APIs like dicts, sets, lists, locks, etc.
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Worth wrapping pottery functions for compliance with async?
I have a question about https://github.com/brainix/pottery. It provides a nice Pythonic API by wrapping Redis constructs with Python Redis-backed data structures (Dict, Deque, etc.). I am using it in a Fastapi microservice project, which is obviously async.
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Solving The Three Stooges Problem
Pottery — Pythonic Redis utilities, including a distributed lock
memoize
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Good and Bad Elixir
I totally agree, though I think those articles are a lot harder (eg requiring more skill) to write well because you need to quickly ramp your readers on all of whatever the context is that's necessary to actually appreciate the nuance of the design decisions under discussion. You're basically by definitely going to be out of the realm of "just follow best practice X" or "apply pattern Y or you're doing it wrong."
As a small example, I've been working on a small asyncio-based web service (Python) which is oriented around an expensive process that generates a result, where the result is stashed in sqlite and returned. I knew upfront that I needed a way to track when a particular result was already being prepared so that if I got a second request for it, it would collapse it into the first one and only do the work once. I wrote this as a twenty line memoizing decorator, but it turns out this issue as a name— cache stampeding. Once I realized that, I discovered that there are existing (and much more complicated/tunable) solutions to this problem, such as https://github.com/DreamLab/memoize/, but the article pitching that solution spends quite a bit of time getting to it— enough so that if I'd discovered it before building my own, I'm not sure I would even have appreciated its applicability:
https://tech.ringieraxelspringer.com/blog/open-source/cachin...
What are some alternatives?
fastapi-redis-cache - A simple and robust caching solution for FastAPI that interprets request header values and creates proper response header values (powered by Redis)
httpx-cache - Simple caching transport for httpx
aioredis - asyncio (PEP 3156) Redis support
Tornado-SQLAlchemy - SQLAlchemy support for Tornado
redsync - Distributed mutual exclusion lock using Redis for Go
webssh - :seedling: Web based ssh client
RPA-Python - Python package for doing RPA
turbo - A framework based on tornado for easier development, scaling up and maintenance
Redisson - Redisson - Easy Redis Java client and Real-Time Data Platform. Sync/Async/RxJava/Reactive API. Over 50 Redis based Java objects and services: Set, Multimap, SortedSet, Map, List, Queue, Deque, Semaphore, Lock, AtomicLong, Map Reduce, Bloom filter, Spring Cache, Tomcat, Scheduler, JCache API, Hibernate, RPC, local cache ...
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
reloadium - Hot Reloading and Profiling for Python
cacheme - Asyncio cache framework for Python