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Comment by whakim | Hacker News Reader

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.

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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.

* Filterable ANN certainly decomposes into pre- and post-filtering, and there is definitely a lot of interesting innovation occurring around filterable ANN. But large-scale search systems currently do a pretty good job with pre-filtering, falling back to brute force search in the case of restrictive filters.

* You'd have to be a bit more exact re: dynamic updates/versioning for me to understand the challenges you're facing.

* Building graph indices can be slow, but in my experience (billions of embeddings) it is possible to build HNSW indices in tens of minutes.

* How is this any different to combining traditional keyword search with, say, recency boosting?

Might be missing my argument here - I stated that there are workable solutions to this like you have pointed out.

But ANN search is still a sledgehammer and building out hybrid solutions that help bridge the gap between this and traditional data stores still have room for innovation.

Fair enough - agreed there's lots of interesting innovations here - but my point is that semantic search and its associated issues don't really differ that much from other types of search problems at scale, and I therefore don't think that the current crop of vector database products add a lot of value from a technical perspective (perhaps they do from an ease-of-use perspective; or they work great at small scale, etc. etc.)

> 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

Would you mind putting aside the snark? I have a couple questions. How large is the corpus? I am also curious about the use-case for top-k ANN, k > 10000?

Not the person you have asked but at work (we are a CRM platform) we allow our clients to arbitrarily query their userbase to find matching users for marketing campaigns (email, sms, whatsapp). These campaigns can some times target a few hundred thousand people. We are on a really ancient version of ES, but it sucks at this job in terms of throughput. Some experimenting with bigquery indicates it is so much better at mass exporting.

Fair; my question was mostly in the context of ANN, since that was the discussion point - I have to assume ES (as a search engine) would not necessarily be the right tool for data warehousing types of workloads.

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