>In particular, an error on one line may force you to change a large part of your code.
There's a simple trick to avoid that, use `.clone()` more and use fewer references.
In C++ you would be probably copying around even more data unnecessarily before optimization. In Rust everything is move by default. A few clones here and there can obviate the need to think about lifetimes everywhere and put you roughly on par with normal C++.
You can still optimize later when you solved the problem.
Clone doesn't work when you need to propagate data mutations, which is what you need most of the time.
Another option is to just use cells and treat the execution model similarly to JavaScript where mutation is limited specific scopes.
TikTok's recommender is partly built on European Technology (Apache Flink for real-time feature computation), along with Kafka, and distributed model training infrastructure. The Monolith paper is misleading that the 'online training' is key. It is not. It is that your clicks are made available as features for predicitons in less than 1 second. You need a per-event stream processing architecture for this (like Flink - Feldera would be my modern choice as an incremental streaming engine).
* https://www.youtube.com/watch?v=skZ1HcF7AsM
* Monolith paper - https://arxiv.org/pdf/2209.07663
I don't think any single other platform has as good a search feature as TikTok does.