Scaling HNSWs (via) Salvatore Sanfilippo spent much of this year working on vector sets for Redis , which first shipped in Redis 8 in May.

A big part of that work involved implementing HNSW - Hierarchical NavigableSmall World - an indexing technique first introduced in this 2016 paper by Yu. A. Malkov and D. A. Yashunin.

Salvatore's detailed notes on the Redis implementation here offer an immersive trip through a fascinating modern field of computer science. He describes several new contributions he's made to the HNSW algorithm, mainly around efficient deletion and updating of existing indexes.

Since embedding vectors are notoriously memory-hungry, I particularly appreciated this note about how you can scale a large HNSW vector set across many different nodes and run parallel queries against them for both reads and writes:

[...] if you have different vectors about the same use case split in different instances / keys, you can ask VSIM for the same query vector into all the instances, and add the WITHSCORES option (that returns the cosine distance) and merge the results client-side, and you have magically scaled your hundred of millions of vectors into multiple instances, splitting your dataset N times [One interesting thing about such a use case is that you can query the N instances in parallel using multiplexing, if your client library is smart enough].

Another very notable thing about HNSWs exposed in this raw way, is that you can finally scale writes very easily. Just hash your element modulo N, and target the resulting Redis key/instance. Multiple instances can absorb the (slow, but still fast for HNSW standards) writes at the same time, parallelizing an otherwise very slow process.

It's always exciting to see new implementations of fundamental algorithms and data structures like this make it into Redis because Salvatore's C code is so clearly commented and pleasant to read - here's vector-sets/hnsw.c and vector-sets/vset.c.