[didip]

Very interesting project. Couple of notes:

* What is the back story here? Why create this in the first place?

* Seems like it's less performant than Dragonfly. Why not consolidate effort and help Dragonfly instead?

* To ride the AI wave, y'all need to gain Vector related features, similarity search, etc.

[iamlintaoz]

Great question. Currently, the database landscape is very fragmented. We are faced with a multitude of database choices (different ACID guarantees, data modalities, scalability, and so on). Data pipeline become very complicated, and we believe there must be a better solution. That’s why we developed a common architecture called DataSubstrate and built different APIs on top of it. EloqKV with a Redis API is just one of them; we also provide a MySQL-API RDBMS and a MongoDB-API JSON database (both open-sourced). Our goal is to create the next-generation database foundation to support the growing demand from new generation of applications. We believe future AI agent-driven applications will generate huge volumes of queries and data that will be difficult to handle with existing solutions.

EloqKV is only slightly slower than Dragonfly—about 10–20%—but for good reason. Dragonfly is a pure in-memory database with a highly optimized network layer and a very specialized design. EloqKV, on the other hand, is a full-featured database with all the checkboxes you can think of: fully consistent, durable, distributed transactions, fault-tolerant, tiered storage, and more. Despite this, we incur very little overhead compared with state-of-the-art, purpose-built databases when we have the same workload guarantee. Our thesis is that we may not need dedicated specialized solutions if we can achieve comparable performance (and cost) with general-purpose systems.

We will also add vector support very soon. Please stay tuned.