Interesting. I hadn’t really thought of global state as being a problem (I mostly think of caches as affecting performance but not semantics but I guess I didn’t really think about cache invalidation/poisoning either). My main worry would be more something like making a cold start very difficult or making things harder to change.
When you design a call tree so that any data used later is passed explicitly down the call tree instead of looked up by ID over and over, then you can be sure that all of the decisions about that data are made on a consistent copy of the data.
When you look up the same value 10 times, you not only pollute the flame graphs and call counts which makes proving that a better algorithm is necessary or has any effect much harder, but more importantly, you could get 3 different states and try to make a bunch of updates based on what should be mutually exclusive states in the system. That's the global shared state problem.
When you look up a value once and remember it throughout a calculation, it may not be the current state, but at least you have a clean snapshot of the data. Which in situations such as cancelling an account immediately after getting one last discount on a purchase, well, we know which scenario the customer probably meant.
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What if other values are looked up deep into the call stack, would that cause actual inconsistency as different values were looked up at different times
It can but it’s very hard to catch. It’s like running your database at the wrong isolation level. By the time the bug happens it’s under heavy load and the system is too noisy to catch the real problem. So you have glitches nobody can explain and they just deal with cleanup.
For this and other reasons I think that in addition to Functional Core, Imperative Shell, you want a “square” call tree in your code. Avoid functions with no fanout, and functions with high fanout. Rearrange code that uses the same data to happen as close together as you can, to improve local reasoning. When functions get unwieldy, or deleted code makes them too small, use the b-tree algorithm as inspiration to rebalance the tree.
Refactor when new features change the coupling of the code.
