(It’s not really my architecture problem. My architecture problem is that we store pages as grains of sand in a db instead of in a bucket, and that we allow user defined schemas)

Caches have perfectly valid uses, but they are so often used in fundamentally poor ways, especially with databases.

The two questions no one seems to ask are 'do I even need a database?', and 'where do I need my database?'

There are alternate data storage 'patterns' that aren't databases. Though ultimately some sort of (Structure) query language gets invented to query them.

Then there's memoization, often a hack for an algorithm problem.

I once "solved" a huge performance problem with a couple of caches. The stain of it lies on my conscience. It was actually admitting defeat in reorganizing the logic to eliminate the need for the cache. I know that the invalidation logic will have caused bugs for years. I'm sure an engineer will curse my name for as long as that code lives.

For example, let’s say that every web page your CMS produces is created using a computationally expensive compilation. But the final product is more or less static and only gets updated every so often. You can basically have your compilation process pull the data from your source of truth such as your RSBMS but then store the final page (or large fragments of it) in something like MongoDB. In other words the cache replacement happens at generation time and not on demand. This means there is always a cached version available (though possibly slightly stale), and it is always served out of a very fast data store without expensive computation. I prefer this style of caching to on demand caching because it means you avoid cache invalidation issues AND the thundering herd problem.

Of course this doesn’t work for every workflow but I can get you quite far. And yes this example can also be sort of solved with a static site generator but look beyond that at things like document fragments, etc. This works very well for dynamic content where the read to write ratio is high.

I also just think it’s a necessary evil of big systems. Sometimes you need derived data. You can even think about databases as a kind of cache: the “real” data is the stream of every event that ever updated data in the database! (Yes this stretching the meaning of cache lol)

However I agree that caching is often an easy bandaid for a bad architecture.

This talk on Apache Samza completely changed how I think about caching and derived data in general: https://youtu.be/fU9hR3kiOK0?si=t9IhfPtCsSyszscf

And this interview has some interesting insights on the problems that caching faces at super large scale systems (twitter specifically): https://softwareengineeringdaily.com/2023/01/12/caching-at-t...

No it’s ten times worse than that. Adding RAM doesn’t make the task of fixing the memory management problems intrinsically harder. It just makes the problem bigger when you do fix it.

Adding caching to your app makes all of the tools used for detecting and categorizing performance issues much harder to use. We already have too many developers and “engineers” who balk at learning more than the basics of using these tools. Caching is like stirring up sediment in a submarine cave. Now only the most disciplined can still function and often just barely.

When you don’t have caches, data has to flow along the call tree. So if you need a user’s data in three places, that data either flows to those three or you have to look it up three times, which can introduce concurrency issues if the user metadata changes in the middle of a request. But because it’s inefficient there is clear incentive to fix the data propagation issues. Fixing those issues will make testing easier because now the data is passed in instead of having to mock the lookup code.

Then you introduce caching. Now the incentive is mostly gone, since you will only improve cold start performance. And now there is a perverse incentive to never propagate the data again. You start moving backward. Soon there are eight places in the code that use that data, because looking it up was “free” and they are all detached from each other. And now you can’t even turn off the cache, and cache traffic doesn’t tell you what your costs are.

And because the lookup is “free” the user lookup code disappears from your perf data and flame graphs. Only a madman like me will still tackle such a mess, and even I have difficulty finding the motivation.

For these reasons I say with great confidence and no small authority: adding caching to your app is the last major performance improvement most teams will ever see. So if you reach for it prematurely, you’re stuck with what you’ve got. Now a more astute competitor can deliver a faster, cheaper, or both product that eats your lunch and your team will swear there is nothing they can do about it because the app is already as fast as they can make it, and here are the statistics that “prove” it.

Friends don’t let friends put caches on immature apps.

I don’t think this is always true. Sometimes your app simply has data that takes a lot of computation to generate but doesn’t need to be generated often. Any way you solve this is going to be able to be described as a ‘cache’ even if you are just storing calculations in your main database. That doesn’t mean your application has a fundamental design flaw, it could mean your use case has a fundamental cache requirement.

That's what IVM systems like Noria can do. With application + cache, the application stores the final result in the cache. So, with these new IVM systems, you get that precomputed data directly from the database.

Views in Postgres are not materialized right? so every small delta would require refresh of entire view.