[cfors]

I don't disagree that rock solid is a good choice, but there is a ton of innovation necessary for data stores.

Especially in the context of embedding search, which this article is also trying to do. We need database that can efficiently store/query high-dimensional embeddings, and handle the nuance of real-world applications as well such as filtered-ANN. There is a ton of innovation in this space and it's crucial to powering the next generation architectures of just about every company out there. At this point, data-stores are becoming a bottleneck for serving embedding search and I cannot understate that advancements in this are extremely important for enabling these solutions. This is why there is an explosion of vector-databases right now.

This article is a great example of where the actual data-providers are not providing the solutions companies need right now, and there is so much room for improvement in this space.

[whakim]

I do not think data stores are a bottleneck for serving embedding search. I think the raft of new-fangled vector db services (or pgvector or whatever) can be a bottleneck because they are mostly optimized around the long tail of pretty small data. Real internet-scale search systems like ES or Vespa won’t struggle with serving embedding search assuming you have the necessary scale and time/money to invest in them.

[cfors]

Sure they can handle the basic case of ANN. But ANN still doesn’t have good stories for lots of real-world problems.

* filterable ANN, decomposes into prefiltering or postfiltering.

* dynamic updates and versioning is still very difficult

* slow building of graph indexes

* adding other signals into the search, such as query time boosting for recent docs.

I don’t disagree these systems can work but innovation is still necessary. We are not in a “data stores are solved” world.

[mdaniel]

> Real internet-scale search systems like ES

Oh, then you must have the secret sauce that allows scaling ES vector search beyond 10,000 results without requiring infinite RAM. I know their forums would welcome it, because that question comes up a lot

Or I guess that's why you included the qualifier about money to invest