It's funny to see a blog post on HN almost every day where somebody rediscovers the power of columnar query answering engines which are almost the opposite of graph databases.

I've lost count of how many columnar SQL databases have been donated to the apache project and there are so many systems like Actian and Alteryx where data analysts hook together relational operators with boxes and lines.

I had a prototype of a stream processing engine that passed RDF graphs along the lines between the boxes that enable an "object-relational" model, you could eliminate the need for hard-to-maintain joins but I found that firms that had bought multiple columnar processing database companies believed in performance at all cost and couldn't care less for any system that couldn't be implemented with SIMD instructions.

How are they opposite? There are plenty of graph databases out there using columnar storage, even ones directly compatible with GraphQL Federation. Best of both worlds, so to speak.

> So why are we using it for so many naturally non-graph problems? 90%+ of developers' exposure to graphs is through tightly abstract interfaces, I could name maybe 3 graph-related algorithms off the top of my head, but could implement none of them without reading.

It's a reasonable abstraction for structuring related bits of data (like would go in a typical relational database), and that abstraction can align with the developer's mental model easier.

E.g. ORMs basically convert SQL data into an in-memory graph. Likewise, graph database APIs are natively more object-y; you follow the edge from child to parent, instead of making a bit of data the same in both tables and then querying matching rows.

They're not perfect, and shouldn't be used everywhere (nor even many places they currently get used), but I can see the appeal of abusing them.

Because graphs are a good abstraction for relations and with the right tech choices, are much more manageable and malleable than traditional relational databases.