SSDB VS dragonfly

I've used SSDB[0] in the past for some really stupid large datasets (20TB)_and it worked really well in production

[0] https://github.com/ideawu/ssdb

> Or wanting to shift the architecture entirely to avoid using memory-bound Redis as a queue with an overflow risk.

I wanted to use SSDB[1] instead of Redis for that reason, but it doesn't support the necessary data structures.

[1] https://github.com/ideawu/ssdb

There is also ssdb https://github.com/ideawu/ssdb

It's 2021 and we have extremely fast key-value lookups using LevelDB/RocksDB, but we're still using RAM-based caching tools [1] [2] [3]. It's time to consider RAM-based caching harmful, and start caching with SSDs for larger datasets and lower costs. For ex: SSDB [4]

[1] https://redis.io/

[2] https://memcached.org/

[3] https://news.ycombinator.com/item?id=29532552

[4] https://github.com/ideawu/ssdb#ssdb-vs-redis

Aside from this particular use-case, which is what most people here are talking about -- I had never heard of the SSDB project, but it interests me because I often use Redis for certain things it's able to do, aside from its in-memory storage, and sometimes the fact that the data is not stored on disk is a bit of a drawback for me, something I have to work around.

https://github.com/dragonflydb/dragonfly is another option. Not a fork but API-compatible reimplementation.

Our journey will involve leveraging the capabilities of Dragonfly, a highly efficient drop-in replacement for Redis, known for its ultra-high throughput and multi-threaded share-nothing architecture. Specifically, we'll be utilizing two of Dragonfly's data types: Sorted-Set and Hash. These data structures are perfect for handling real-time data and ranking systems, making them ideal for our leaderboards.

2023 has been a year with remarkable advancements in AI capabilities, and at Dragonfly, we are thrilled to power new use cases with our latest release: Dragonfly Search. This new feature set, debuting in Dragonfly v1.13, is a subset of RediSearch-compatible commands implemented natively in Dragonfly, allowing for both vector search and faceted search use cases in the highly scalable and performant Dragonfly in-memory data store.

If you have not heard about Dragonfly - please check it out. It uses - what I hope - novel and interesting ideas backed up by the research from recent years [1] and [2]. It's meant to fix many problems that exist with Redis today. I have been working on Dragonfly for the last 7 months and it has been one of the more interesting and challenging projects I've ever done!

I recently ran across the the license for Dragonfly [1] which has some restrictions (rights reserved), but 5 years after the license date the license switches to Apache 2.0. Basically a timed-limited rights reservation. I don't hate it. I might even contribute to such a project for free.

I would consider something like this: When I release code, it's rights reserved for 5 years, then open-source (and this baked into an irrevocable license). Anyone may use the software for non-commercial purposes. Anyone may contribute, those who contribute will be granted permission for commercial use if I deem their contributions significant enough. Anyone may distribute the software under these terms.

If such a model became popular, I have a hard time imagining it could make things any worse. It might even accelerate open-source development. You might say, "but it's not open-source", fair enough, but we can view it as open-source contribution with a delay. For example, if this model became wildely popular this year, and we saw great progress with this model, then come 2028 we would be flooded with new open-source software and ultimately might be better off than it would have been without this model.

(And this whole thing makes me rethink copyright and patents and how much they really contribute to society. Perhaps they should be shortened?)

[1]: https://github.com/dragonflydb/dragonfly/blob/main/LICENSE.m...

Maybe check out https://www.dragonflydb.io/ It claims to have a full redis implementation.