udisk VS yottaStore

I don't remember exactly why I have any of them saved, but these are some experimental data stores that seems to be fitting what you're looking for somewhat:

- https://github.com/DataManagementLab/ScaleStore - "A Fast and Cost-Efficient Storage Engine using DRAM, NVMe, and RDMA"

- https://github.com/unum-cloud/udisk - "The fastest ACID-transactional persisted Key-Value store designed for NVMe block-devices with GPU-acceleration and SPDK to bypass the Linux kernel."

- https://github.com/capsuleman/ssd-nvme-database - "Columnar database on SSD NVMe"

I'm building a database, and I'm working on the storage format. For each record I have a logical clock which increases by 1 every time there's a write operation on the record, and I would like to be able to compare the clocks of two writes to understand which happened first.

If you're curious to know more, the tree is used for rendezvous based routing, as used by my datastore. I'm doing machine learning on a 200 TB dataset, using around 200 machines.

I copied this approach from several papers, with some improvements, for my datastore.

I have a server with hundreds of routes, representing all the possible operations I can do on a datastore. How can I organize my code better?

Yes I'm building a database where storage and compute are decoupled. I use io_uring to do pseudo-RDMA, and I'm looking to add ePBF to make it even more effective.

I'm currently building an implementation of the dynamo paper, yottastore. Imagine it as a huge, distributed, hash map.

I'm building an implementation of the dynamo paper, yottastore. Given a key, I need to find which NVMe block stores the data. To do that I hash the key to find the shard where I have an in memory array in which at position [hash] I can find a struct with:

I have to be honest, I'm very skeptical about your results and your code. I'm building a database, yottastore, both in javascript, golang and rust so I think I can share my opinion: