Successor to Umbra, I believe.
I know somebody (quite talented) working there. It's likely to kick ass in terms of performance.
But it's hard to get people to pay for a DB these days.
CedarDB is the commercialization of Umbra, the TUM group's in-memory database lead by professor Thomas Neumann. Umbra is a successor to HyPer, so this is the third generation of the system Neumann came up with.
Umbra/CedarDB isn't a completely new way of doing database stuff, but basically a combination of several things that rearchitect the query engine from the ground up for modern systems: A query compiler that generates native code, a buffer pool manager optimized for multi core, push-based DAG execution that divides work into batches ("morsels"), and in-memory Adaptive Radix Tries (never used in a database before, I think).
It also has an advanced query planner that embraces the latest theoretical advances in query optimization, especially some techniques to unnest complex multi-join query plans, especially with queries that have a ton of joins. The TUM group has published some great papers on this.
The part of Umbra I found interesting was the buffer pool, so that's where focused most of my attention when reading though.
I believe Umbra is heavily BTree based, just like its cousin LeanStore.
One of its specific innovations is its buffer pool which uses virtual memory overcommit and multiple possible buffer sizes to squeeze better performance out of page management.
The talk at https://www.youtube.com/watch?v=pS2_AJNIxzU is delightful.
My understanding is the research projects LeanStore & Umbra -- and now I assume the product CedarDB based on the people involved, etc. -- are systems based on the observation that a) existing on-disk systems aren't built well with the characteristics of nVME/SSD drives in mind b) RAM prices up to this year were not dropping at the same rate as they were early in the 2010s, meaning that pure in-memory databases were not so competitive, so it's important to look at how we can squeeze performance out of systems that perform paging. And of course in the last 6 months this has become extremely relevant with the massive spike in RAM prices.
That and the query compilation stuff, I guess, which I know less about.
I am currently writing an on-disk B+tree implementation inspired by LMDB's blazing fast memory-mapped CoW approach, so I'm quite keen to learn what makes TUM's stuff fast.
https://github.com/leanstore/leanstore
Very different approach from lmdb's mmap though. Not limited to single writer. Explicit buffer pool. Big difference from Umbra is fixed sized pages.
I always wondered how good these planners are in practice. The Neumann/Moerkotte papers are top notch (I've implemented several of them myself), but a planner is much more than its theoretical capabilities; you need so much tweaking and tuning to make anything work well, especially in the cost model. Does anyone have any Umbra experience and can say how well it works for things that are not DBT-3?