My guess is for most popular uses, vector indexes are indeed more of a feature than a product, so will smooth out over time in big DBs, and curious what that should look like. Seperately, I do think there is a small niche for opinionated vector-first DBs, and not sure what that is either :)
Funny enough I was just reading about this today as a result of the OP article
A commenter here, who apparently works for the pinecone.io vector db platform, states that:
"Pre-filtering in most solutions (eg, Elasticsearch on AWS) requires using an inefficient brute-force search (kNN) through the remaining vectors after they've been filtered, because the original index was built on the unfiltered list and would no longer be useful. This causes sky-high search latencies."
https://www.reddit.com/r/MachineLearning/comments/po3pos/com...
Search latencies are fairly low, even with constantly updating documents.
[1] https://tech.okcupid.com/vespa-vs-elasticsearch-for-matching...
Looking forward to doing some at some point in the near future though.
In all honesty if you are already using ES and you just want nearest-neighbor search for less than 10M documents, just stay with ES.
Things get less obvious when you grow past 10M documents and still want low latency. Or if you need live index updates without downtime, or if you want to apply metadata filters to nearest-neighbor searches.
If you have 100M documents -- not a difficult threshold if you're an enterprise software company or a popular consumer app -- then ES gets ruled out fairly early in the process. We get a lot of those exasperated teams coming to Pinecone after trying their best with ES/OpenSearch.
Why does 100M vectors not work in ES?
- Is this a configuration issue -- common for ES users -- or something fundamental?
- It sounds like latency is the main thing. Any numbers intuition here, and any other dimensions of concern?
AFAICT ES is using the same OSS vector libraries as pinecone, weaviate, etc. ES in general is used for > 100M documents, e.g., logging, so this is surprising.
We are seeing growing interest by our ES/Splunk users in combining our viz tech with vector indexes, so I've been wondering about these, thanks! We currently go out-of-band at the compute tier or dump in our own indexes, but are thinking through managed flows, where fundamental limits gets interesting.
I think that it's important to mention Elasticsearch version in this context :=)
Full disclosure, I work on Vespa.ai, but I have a pretty good understanding of how NN and ANN works in ES.
Elasticsearch 7x only had support for brute-force exact nearest neighbor search (1) which you can surely run with 100M documents, you just need to have a lot of nodes to bring latency down to service latency SLA. Typically, a single threaded/single index segment query with 1M documents with 128 dims using float is 300ms with Elasticsearch 7x (3). Two shards/segments instead would bring it down to 150 ms and so on. But 100M becomes costly, but perfectly doable.
With Elasticsearch 8.0, there is support for doing approximate nearest neighbor search (2) using the HNSW support from Apache Lucene 9. This would bring latency down to low single digit ms (single threaded, single shard) with 1M documents. The biggest disappointment was probably that they don't integrate the approximate nearest neighbor search with regular query filters/terms. This will likely be coming in Elasticsearch 8.1 or so, once it's implemented in Apache Lucene 9.x.
(1) https://www.elastic.co/blog/text-similarity-search-with-vect... (2) https://www.elastic.co/blog/introducing-approximate-nearest-... (3) https://github.com/jobergum/dense-vector-ranking-performance