Hacker News Reader

Comment by amtamt | Hacker News Reader

No two processes can guarantee data consistency unless using shared memory with some kind of locking on update. And given two servers don't share memory, two processes running on these servers can not guarantee consistency either.

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)

View on HN

First up, if I wanted to talk to a machine, I would've asked one myself.

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.

Point is... with AOF and RDB enabled, and wait command used in sane manner, one can get reasonable consistency with a significant speed tradeoff and increase in application complexity. So if consistent cache is needed, one can have that with some compromises, but then probably one could use a database straight away.

Again: Redis is a database, not just a cache - it doesn’t care if you store ephemeral cache artifacts or customer records within it. Redis doesn’t pose any constraints on the type of data. Inversely, you can use Postgres as a semi or fully consistent cache.

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.

If redis is a database, then by

> 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.

Reply on news.ycombinator.com