pottery VS redsync

However, as an alternative, consider my library, Pottery. Pottery offers some similar functionality to Redis OM, and Pottery is production ready.

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.

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.

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.

Pottery — Pythonic Redis utilities, including a distributed lock

I value and rely on the guarantees of the Redlock algorithm for all my distributed locking.

I thought about using a distributed lock (https://github.com/go-redsync/redsync). Before querying the DB, I would first try to acquire the lock for the resource with id `user_id/resource_id`. Now, the requests for different combinations of `user_id/resource_id` can be done in parallel, but the requests for the same combination of `user_id/resource_id` will be done sequentially.

A client implementation is found here: https://github.com/go-redsync/redsync

Thanks for highlighting this. Yes, I know about this discussion. And I more agree with Salvatore points. There is also Disclaimer in redsync github page I have linked in my readme file. You definitely should understand how it works before using it.

Redis : https://redis.io/topics/distlock

2- We have several implementations of the RedLock algorithm in different languages Redis Official website. and you do not need to implement it again.

https://redis.io/topics/distlock I haven't used Redis specifically for locking, but it appears to be a popular use case. Or you could probably use the locking mechanism of your database to keep track of a shared store of user IDs.

This decorator works by forcing the flow of control through a distributed lock on the request hash, ensuring that no two duplicate requests can proceed concurrently. In this example, we’ve used Pottery’s implementation of Redlock (backed by shared Redis instances) which implements Python’s excellent threading.Lock API as closely as is feasible.