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Indexing in Postgres is legitimately painful, I don’t think “get moar ram” is a good response to that particular critique.
by hdhshdhshdjd 2y ago
Indexing in Postgres is legitimately painful, I don’t think “get moar ram” is a good response to that particular critique.
- deepsquirrelnet 2y agoNeither is it a good demonstration of things that people who currently maintain postgres are experienced in doing. Companies should be reluctant to manage their own vector indexes until this becomes more a more mainstream skillset. This excellent blog post[1] demonstrates the complexities of scaling HNSW indexes and shows that at a certain point, you need to switch to ivfpq with vastly different performance and accuracy characteristics. https://aws.amazon.com/blogs/big-data/choose-the-k-nn-algorithm-for-your-billion-scale-use-case-with-opensearch/ https://aws.amazon.com/blogs/big-data/choose-the-k-nn-algori...
- darby_nine 2y agoCompared to what?
- ngalstyan4 2y agoAuthor here. > I don’t think “get moar ram” is a good response to that particular critique. I do not think the blog post suggested "get more ram" as a response, but happy to clarify if you could share more details! > Indexing in Postgres is legitimately painful Lantern is here to make the process seamless and remove most of the pain for people building LLM/AI applications. Examples: 1. We build tools to remove the guesswork of HNSW index sizing. E.g. https://lantern.dev/blog/calculator https://lantern.dev/blog/calculator 2. We analyze typical patterns people use when building LLM apps and suggest better practices. E.g. https://lantern.dev/blog/async-embedding-tables https://lantern.dev/blog/async-embedding-tables 3. We build alerts and triggers into our cloud database that automate the discovery of many issues via heuristics.