| To understand the tradeoffs between LSMs, B-Trees, and Fractal Trees, see the references in this previous post on TokuDB and Bε trees... * BetrFS: An in-kernel file system that uses Bε trees to organize on-disk storage https://news.ycombinator.com/item?id=18202935 Memory model considerations and storage architecture design gets even more interesting now that NVMe has become a thing. For example, in addition to LMDB, how much more interesting have things become for Redis on NVMe? * Caching Beyond RAM: The Case for NVMe https://news.ycombinator.com/item?id=17315494 * Intel Optane DC Persistent Memory is officially in Google Cloud https://news.ycombinator.com/item?id=1834816 And there are a few new forward-thinking DB architectures emerging on the scene, some that have been in the works for more than 10 years. Look at the work being done by the Berkeley RISELab team and the architecture behind Fluent DB. * Ground: A Data Context Service (2017) [pdf] (berkeley.edu), https://news.ycombinator.com/item?id=18415456 What might have been conventional wisdom in the realm of DBs years ago will not be the best practices of today. Architectures have changed too much. And this is not just true for storage, it's true for compute too. The availability of CPU/GPU/TPU accelerators in the data centers is driving a rethink in compute toward parallel algorithms in the form of Vector/Matrix/Tensor multiplication. The best way to store and index these arrays is something to consider too. |
At least it doesn't support mmap so removes lmdb.