But that is his point.
If you cannot find the session id in redis, you login again.
If your Redis server crash, you start a new one and everyone just login again. No data is lost.
Just because it works for your use case right now doesn’t mean there isn’t room for improvements to support others too.
Oh good, then you don't need to do any of the stuff that you suggested to do
No, redis is a memory cache, with some ACID like features bolt on.
Even real databases have hard time maintaining consistency across nodes. CAP is a real constraint.
You, obviously, don't commit important data only to a session that you can loose, if the application does not allow it.
We use redis as infrastructure. To route events and as a cache.
For us redis could go down and we would merely see a degradation of our service with no data loss.
I recommend using redis like that. And then use a database that supports transactions for real data problems.
But we are different. And that's OK.
But that requires running on multiple instances, which in turn requires to share the data across all replicas.
To put the simple terms... App writes to node-A, node-A (/process on node-A) crashes before change is synced from node-A to node-B, data is lost.
This is true for redis and true for postgresql/ mysql or any similar database. Difference between redis and a "database" is that database protects against this problem by writing change to durable storage before telling app that write is successful. Redis
So if one wants to have a consistent session storage, one should use a proper database or use AOF redis persistense with single node (https://chatgpt.com/s/t_6a24ab818e2881918db959cec8d8cc2d)
Then, I don't understand your point really: Yes, the CAP theorem is a thing. There are compromise solutions available however to enable highly available data storage. Some of them for Redis too, but they are more complicated than those for other database engines. Which is the point of this discussion.
And yes, what you’re saying is technically correct, even a well-tuned single node doesn’t solve the availability problem: if it goes down, you have an outage. To avoid that, you need multiple instances to provide the same data, avoiding downtime if one of the nodes breaks eventually.
> doesn’t pose any constraints on the type of data.
logic, raw disk is also a database. One just need to add block level replication to other nodes to build a replicated/ HA database.
We may not agree, but anything not providing transactions across logically related multiple data read/ update operations is not a database.
> multiple instances to provide the same data
is easy and done by a bunch of software out there, but
> multiple instances to provide the same data on non-shared memory computers, with consistency
is a really hard problem, and no one has been able to solve it yet without introducing other problems to be considered (giving up on fast performance being one of the most visible one) by architects/ developers.