[HN Gopher] Vector search just got up to 10x faster and vertical...
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       Vector search just got up to 10x faster and vertically scalable
        
       Author : gk1
       Score  : 34 points
       Date   : 2022-08-16 19:45 UTC (3 hours ago)
        
 (HTM) web link (www.pinecone.io)
 (TXT) w3m dump (www.pinecone.io)
        
       | phgn wrote:
       | Side tangent: Pinecone pods seem to cost 15.625% more per hour on
       | AWS compared to GCP.
       | 
       | All the instance types hide away the price differences usually,
       | so this is interesting to see.
       | 
       | Edit: also there is no free tier for Pinecone on AWS :(
        
         | [deleted]
        
       | mammydady wrote:
        
       | opisthenar84 wrote:
       | I don't understand the emphasis here on vertical scaling. Move a
       | database to a bigger machine = more storage and faster querying.
       | Not exactly rocket science. Horizontal scaling is the real
       | challenge here, and the complexity of vector indexes makes it
       | especially challenging. Milvus and Vertex AI both have horizontal
       | scaling ANN search and the ability to do parallel indexing as
       | well. I appreciate the post but this doesn't seem worthy of an
       | announcement.
        
         | ww520 wrote:
         | Bigger machine doesn't automatically mean higher performance.
         | The code needs to scale with the increased number of cores, has
         | share-nothing or share-very-little approach to avoid
         | contention, and uses efficient data structure to utilize the
         | increased memory.
        
       | fdgsdfogijq wrote:
       | Wish I could raise 10M and use it to wrap open source libraries
       | developed by FB and Google
        
         | [deleted]
        
         | MasterIdiot wrote:
         | If you really think that's enough to build a real product, go
         | for it. Even open-source companies (Elastic, Mongo, Scylla)
         | have to build tons of infra around their core codebase in order
         | to make it an actual cloud product.
        
           | fdgsdfogijq wrote:
           | Not that easy, the founder was a director at AWS. This is
           | just devops/obfuscation on top of an open source library:
           | 
           | FAISS
        
             | gk1 wrote:
             | Pinecone doesn't use Faiss, nor ScaNN. We love Faiss and
             | even teach people to use it[1]. There happens to be a
             | sizable population of engineers who need more than what
             | Faiss provides (like live index updates and metadata
             | filtering, for example), and can't be bothered or aren't
             | being paid to customize and manage open-source libraries
             | all day.
             | 
             | [1] https://www.pinecone.io/learn/faiss/
        
               | fdgsdfogijq wrote:
               | So you guys developed and implemented state of the art
               | neural network vector search from scratch? in a year? and
               | something better than libraries with tens of contributors
               | over years of research?
        
             | noogle wrote:
             | I actually built a similar solution supporting similar
             | operations (including filtering by meta-data) using open-
             | source libraries. Took me about 2 weeks net.
             | 
             | I can see a clientele for such database (people who want a
             | turnkey solution), but honestly it looks like an attempt to
             | use a dev-ops solution to address deeper issues with
             | problem formulation: e.g.
             | 
             | 1. Is there really a need to search all items in the
             | database? can subsampling make simple similarity comparison
             | feasible?
             | 
             | 2. Do the embeddings really need to have that many
             | dimensions? Can we reduce their dimensionality and fit them
             | in RAM?
             | 
             | 3. Is embedding accurate enough compared to pairwise
             | comparison? Can we formulate the problem to make the latter
             | feasible?
             | 
             | I also could not find any explanation of the underlying
             | algorithms, especially around meta-data filtering, which is
             | not solved by FAISS as well as their accuracy. (happy to
             | hear otherwise)
        
         | etaioinshrdlu wrote:
         | Which open source libraries is pinecone wrapping?
        
           | gk1 wrote:
           | I'm not sure where the other commenter gets their confidence,
           | but Pinecone is not wrapping any open source vector-search
           | library. We offer three index types (in-memory, in-memory
           | graph-based, hybrid memory + disk), and all are proprietary.
           | 
           | We do have articles about Faiss and HNSW and all sorts of
           | other vector-search and NLP topics, so it's possible that's
           | where the confusion comes from.
        
           | fdgsdfogijq wrote:
           | FAISS
        
             | gk1 wrote:
             | This is incorrect.
        
       | learndeeply wrote:
       | Didn't Milvus (vector db, wrapper around FAISS) come before
       | Pinecone?
        
         | fzliu wrote:
         | Just to clarify, Milvus is much more than a wrapper around
         | FAISS. Our vector search component called Knowhere
         | (https://github.com/milvus-io/knowhere) utilizes FAISS and
         | Annoy and will soon include ScaNN, DiskANN, and in-house vector
         | indexes as well. Milvus uses Knowhere as the compute engine,
         | and implements a variety of database functions such as
         | horizontal scaling, caching, replication, failover, and object
         | storage on top of Knowhere. If you're interested, I recommend
         | checking out our architecture page
         | (https://milvus.io/docs/architecture_overview.md).
         | 
         | [EDIT]: Forgot to mention - Milvus development began in 2018
         | was open sourced in 2019.
        
       | gk1 wrote:
       | For anyone interested in the code walkthrough:
       | https://www.pinecone.io/learn/testing-p2-collections-scaling...
        
       | baobob wrote:
       | Confused by their claim to be the 'first' vector database. These
       | things have been around forever? For example FLANN (not a DB
       | server, but example lib) is from 2009
        
         | opisthenar84 wrote:
         | I think the difference is in the layer of abstraction i.e.
         | FLANN is just the underlying search functionality whereas
         | vector databases are fully managed solutions. Even so, Weaviate
         | came out in 2018, so saying that they are the "first" vector
         | database is just flat out wrong since Pinecone was founded in
         | 2019.
        
           | gk1 wrote:
           | Weaviate calling themselves a vector database is a fairly new
           | thing.
        
             | opisthenar84 wrote:
             | The fact that Weaviate only recently started calling
             | themselves a vector database is completely irrelevant here.
             | They had this type of vector data infrastructure before
             | Pinecone did, and that's all that matters.
             | 
             | Example: I'm going to start a new company called
             | Conifercone and do pretty much exactly what you do, but
             | call it a "vector datastore" instead. Apparently I've now
             | created the first ever vector datastore even though
             | functionally I have done nothing novel.
        
       | 29athrowaway wrote:
       | 10x faster with respect to what?
        
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