VoVAllen
Joined 4 years ago
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Comment · 1 year ago
Comment · 1 year ago
We do have some benchmark number at https://blog.vectorchord.ai/vector-search-over-postgresql-a-.... It varies on different dataset, but most cases it's 2x or more QPS comparing to pgvector's hnsw at same recall.
Comment · 1 year ago
And we do have user hosting 3 Billion vectors with Postgres + VectorChord with sharding. And they're using vectors to save the earth! Check https://blog.vectorchord.ai/3-billion-vectors-in-postgresql-...
Comment · 1 year ago
We at https://github.com/tensorchord/VectorChord solved most of the pgvector issues mentioned in this blog:
- We're IVF + quantization, can support 15x more updates per second comparing to pgvector's HNSW. Insert or delete an element in a posting list is a super light operation comparing to modify a graph (HNSW)
- Our main branch can now index 100M 768-dim vector in 20min with 16vcpu and 32G memory. This enables user to index/reindex in a very efficient way. We'll have a detailed blog about this soon. The core idea is KMeans is just a description of the distribution, so we can do lots of approximation here to accelerate the process.
- For reindex, actually postgres support `CREATE INDEX CONCURRENTLY` or `REINDEX CONCURRENTLY`. User won't experience any data loss or inconsistency during the whole process.
- We support both pre-filtering and post-filtering. Check https://blog.vectorchord.ai/vectorchord-04-faster-postgresql...
- We support hybrid search with BM25 through https://github.com/tensorchord/VectorChord-bm25
The author simplifies the complexity of synchronizing between an existing database and a specialized vector database, as well as how to perform joint queries on them. This is also why we see most users choosing vector solution on PostgreSQL.
Comment · 1 year ago
It's coming in the Postgres 18. https://www.depesz.com/2025/02/28/waiting-for-postgresql-18-...
Comment · 1 year ago
Hi, I'm the tech lead of VectorChord-bm25. It's not based on pg_search (pg_bm25). We just chose the same name during our internal development, and changed it to the formal name VectorChord-bm25 when we released it.
Comment · 1 year ago
I'm not sure I fully understand your point. This can be implemented quite easily by organizing each posting list with specific attributes to locate the values efficiently. For example, you could build each posting list into a B-tree, or similar to a column-oriented format that stores attribute statistics to enable skip scanning. Could you elaborate more?
Comment · 1 year ago
Hi, I'm the author of the article. Please check out our vector search extension in postgres, VectorChord [1]. It's based on RabitQ (a new quantization method) + IVF. It achieves 10ms-level latency for top-10 searches on a 100M dataset and 100ms-level latency when using SSD with limited memory.
[1] https://blog.pgvecto.rs/vectorchord-store-400k-vectors-for-1...
Comment · 1 year ago
Hi, I'm the author of the article. Please check out our vector search extension in postgres, VectorChord [1]. It's based on RabitQ (a new quantization method) + IVF. It achieves 10ms-level latency for top-10 searches on a 100M dataset and 100ms-level latency when using SSD with limited memory.
[1] https://blog.pgvecto.rs/vectorchord-store-400k-vectors-for-1...
The core part is a quantization technique called RaBitQ. We can scan over the bit vector to have an estimation about the real distance between query and data. I'm not sure what do you mean by "miss" here. As the approximate nearest neighbor index, all the index including HNSW will miss some potential candidates.