Essentially that using the graph DB prevented any imposition of order or discipline on the structure of the data, due to the constant need to import new customer data in subtly different structures to keep the business running, which led to a complete inability to deliver anything new at all since no one could make assertions about what's in there. Since they couldn't change it without risking breaking a customer they were migrating one customer at a time to a classic RDBMS. (There were like 200 customers, each of which is a company you've probably heard of).

Many will go "you need a proper ontology" at which point just use a RDBMS. Ontologies are an absolute tarpit, as the semantic web showed. The graph illusion is similar to that academic delusion "It is better to have 100 functions operate on one data structure than 10 functions on 10 data structures." which is one of those quips that makes you appreciate just how theoretical some theoreticians really are.

Having worked with SQL for many years now, I truly disliked using graphql when I used it. Most of the time all I need is just a csv-like structure of the data I'm working with and I had a very difficult time with just getting something like that. Joining tables tended to be difficult where the pivot points between relationships was rather semantically unbounded, so it was difficult for me to create a reasonably rigid mental model of the data I was working with. That was especially noticeable in some of the API documentation I worked with -- where a simple REST-like API just gives a simple, usually flat, json... graphql api responses were deeply nested and often in implementation-dependent ways[1] of thousands of lines. Which has IME made explorability practically impossible.

GraphQL implementations have consistently felt like a hobby project where implementing it in an API becomes a thing to put on a resume rather than making a functionally useful API.

[1] https://graphql-docs-v2.opencollective.com/queries/account (navigate to the variables tab for a real mindfuck)

IME the dreaded out of memory error. This is all they have to say on the matter: https://neo4j.com/developer/kb/recommendations-for-recovery-...