Traces can be a good place to handle high cardinality observability data. Traces essentially are a particular form of structured logs. Traces support arbitrary tags, and trace analysis systems can be optimized for quick aggregations over high cardinality fields.
I've been trying to push myself to learn Prolog, using this book, because Prolog seems like a very elegant way of modeling the interactions between business rules, compared to the bespoke functions I typically write during web app jobs.
I have no complaints about the book, but I've been struggling to figure out an easy setup for "Prolog as a web app backend." Typical Prolog implementations are not designed to be used as concurrently-accessed databases. Datalog seems to be a nearby technology that is more pointed towards that use case, but even then, there seem to be scant few implementations of Datalog databases with transaction support, and near zero that are open source.
RDF/OWL databases seem to be another conceptually-near possibility, but they also seem to have few implementations that are prepared for high-scale concurrent access.
Overall, it seems that there is a decent amount of interest in "logic-type" query languages as a curiosity, but little attention to implementations that support use in OLTP business applications, which makes adoption difficult.
> When I tell to managers that I’m good at documenting, they always say: great, we need better documentation! But then, one of the following may happen:
I relate so much it hurts. The industry at large seems unable to see the lost opportunity of significant investment in technical documentation.