This is pure nonsense. B+Trees are used extensively and by default by 5 out of 5 of the top database systems, according to db-engines.com.
If your database engine is an old design or your data is small by modern standards, then a B+tree will be one of the few indexing algorithms available and if the data is small it will probably work. Modern database kernels targeting modern hardware and storage densities typically aren't using B+trees and the reasons why are well-understood. No one with any sense is using a B+tree to index e.g. a trillion records, which is a pretty ordinary thing to do on a single server in 2022.
You can't just swap out indexing architectures due to their dependency on storage engine and scheduling behavior, so older databases like PostgreSQL will be using B+trees for the indefinite future even if suboptimal.
The transition away from B+tree based architectures in new databases engines started about 10-15 years ago. Back then I used them ubiquitously but I honestly don't remember the last time I've seen one in a new design.
PostgreSQL 14 comes with 6 builtin index types[1]: B-tree, Gist, SP-Gist, Gin, Brin, and Hash. More can be plugged in as extensions.
[1]: Chapters 63-69 of https://www.postgresql.org/docs/14/internals.html
Edited: Fixed the link to version 14.
You said that B-Trees "use in indexing has dwindled with time". This is demonstrably false.
> Back then I used them ubiquitously but I honestly don't remember the last time I've seen one in a new design.
Even if that was true (which it definitely isn't), why would anybody judge the commercial or scientific relevance of B-Trees by looking at what new systems do? There are very few new systems that are intended to be competitive as general purpose systems, which is where most of the market is.
You still haven't actually named a single example of a "modern database kernel" that exemplifies what you're talking about.
Use of B+trees in new database kernels has definitely diminished. I'm not counting the installed base of SQLite etc. Ubiquity doesn't make something the pinnacle of technology -- just as often it means "legacy installed base". I still use PostgreSQL a lot and mod it when I need to but I am not under any illusions about its limitations.
A "modern" database kernel that can efficiently use modern hardware is going to be a thread-per-core architecture with all I/O and execution scheduling done in user space, and the ability to operate on modern storage densities found on database servers, which can exceed a petabyte of direct-attached storage. The implications of storage density and its interaction with indexing drive most of the real changes in the way database kernels are designed. You can find elements of this in open source, but mostly in big data platforms rather than proper database engines.
That said, no one builds new high-end databases for retail anymore, the economics don't make sense. All the money moved to more specialized implementations that cater to smaller audiences where you don't need to advertise. The kernels are fully general, and widely reused, but the interfaces and surrounding bits are purpose-built for particular workloads. Hell, my old storage engines are still used under license by that lot. The days of database billboards on the 101 are an anachronism.
Other than in-memory hash indexing as used by SAP HANA, I’m not aware of any other data structures anywhere near as popular for database engines.
Can you name the data structure(s) that have superseded these?
I never said anything about workloads. All I said was that your statements about B+Trees having dwindling usage are clearly false.
If you make a claim that is self-evidently bogus, then you shouldn't expect anything else that you may have said at the same time to be taken seriously.
https://storage.googleapis.com/pub-tools-public-publication-...
What about relational databases? Most are best suited for OLTP workloads.