So if you just implement the cache invalidation logic in your monitoring system, you can tell if you got the cache invalidation logic correct in your caching system. That sounds really helpful!

That's not the case though. Polaris acts as a client and only monitors client observable effect, and assumes no knowledge of the server internals.

I am trying to help.

This continues to point out how you just completely don't understand the quote. You very clearly seem to think that the "hard" part of cache invalidation is how to implement invalidating it when you know exactly what needs invalidation. The "hard" part is actually in knowing what needs invalidation. Your grandiose claims make you, your team and org, and your company look bad.

You think figuring out when to invalidate is the harder part of cache invalidation; in that sense Polaris is not checking the “when” logic but rather repeating it. And you are right. This is not the problem we solved in the post. I should have applied a narrower definition to cache invalidation, and I apologize for the confusions.

On the other hand, I don’t think figuring out “when” to invalidate is what makes cache invalidation hard. I shared the same view as Marc https://twitter.com/marcjbrooker/status/1534944340266864640?....

There is a trade off between coordination (in some cases with unbounded write amplification https://twitter.com/uvdn7/status/1534979480363667457?s=21&t=...) and relaxed consistency. I assumed this is a trade off that people just have to make based on their workload (hence you saw me saying in other comments about TTL; it’s about tradeoffs, when the cost of coordination/invalidate surpasses the benefit — higher hit rate, etc.)

> You very clearly seem to think that the "hard" part of cache invalidation is how to implement invalidating it when you know exactly what needs invalidation. The "hard" part is actually in knowing what needs invalidation.

I acknowledge that both are hard. The former can't be avoided. And the latter is easy in certain cases (if you have a simpler data model, etc.). I am not saying the latter is easy. More details in https://news.ycombinator.com/item?id=31676102

And I made the mistake of assuming the definition of “cache invalidation” as it means different things to different people. Marc’s view is a very good one https://twitter.com/marcjbrooker/status/1534944340266864640?... but it’s actually different than some people’s definition on this thread.

I will not defend myself; and you are welcome to speculate. I said it here https://twitter.com/uvdn7/status/1534978702609682432, and other places on this thread, and I will repeat it again.

I should have applied a narrower and more specific definition of cache invalidation in the blog post. The confusion it caused is not intended.

> how to implement invalidating it when you know exactly what needs invalidation

I stand by the claim that this is a hard problem, as explained in the blog post. When you deal with a cache you are inherently dealing with a distributed system (there's cache and the source of truth). And the contribution is making this aspect more manageable, which is common to all invalidation based caches.

https://blog.the-pans.com/when-and-how-to-invalidate-cache/ Does this address the "knowing what needs invalidation" part?