This is developer evangelism at its best at the behest of VCs to scale and "productionalize". There are reasons why this problem is a fundamentally difficult and coming out of the blue claiming to have found a 10x solution in a field that has attracted lots of research interest is highly sus. I would love to see a study that actually exposed their methodology, replication from independent parties and more importantly CODE.
Vector search just got up to 10x faster and vertically scalable
41–50 of 54 posts
Re: Vector search just got up to 10x faster and vertically scalable
#4210x faster with respect to what?
It was more than 10x slower than Vespa, Weaviate, or Qdrant before. Maybe they are finally getting close.
Re: Vector search just got up to 10x faster and vertically scalable
#43I 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 does…
Completely true. You have to understand the economics behind this to see why their claim is hyperbole at best and flat out misleading at worst. The fundamentals of scalable vector search is that you are dealing with potentially huge dimensionality and huge datasets, that means that memory consumption will be huge even for a modest (by today's standards) datasets. This problem has garnered lots of research attention,…
On the topic of publishing, these two papers from the Milvus community may be of interest to some folks.
SIGMOD'21 - https://www.cs.purdue.edu/homes/csjgwang/pubs/SIGMOD21_Milvu... This paper talks about the vector database vertical (compute core and user-facing API)
VLDB'22 - https://arxiv.org/pdf/2206.13843.pdf This paper discusses the development of a cloud-native vector database.
Reading through them should help folks understand where the novelty and difficulty in developing an full-fledged vector database comes from.
Disclaimer: I'm a member of the Milvus community.
Re: Vector search just got up to 10x faster and vertically scalable
#44This is developer evangelism at its best at the behest of VCs to scale and "productionalize". There are reasons why this problem is a fundamentally difficult and coming out of the blue claiming to have found a 10x solution in a field that has attracted lots of research interest is highly sus. I would love to see a study that actually exposed their methodology, replication from independent parties and more importantly…
If code and methodology is what you're looking for, there's some great open-source vector databases out there.
Milvus: https://github.com/milvus-io/milvus
Qdrant: https://github.com/qdrant/qdrant
Weaviate: https://github.com/semi-technologies/weaviate
Milvus seems to be the most advanced and best performing vector DB (https://www.farfetchtechblog.com/en/blog/post/powering-ai-wi...). Haven't seen Qdrant benchmarks yet but cool project nonetheless.
FWIW, these open source projects are how I got into the area of vector similarity search to begin with.
Re: Vector search just got up to 10x faster and vertically scalable
#45Its weird to see someone flex vertical scaling...
Re: Vector search just got up to 10x faster and vertically scalable
#46I 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 does…
Completely true. You have to understand the economics behind this to see why their claim is hyperbole at best and flat out misleading at worst. The fundamentals of scalable vector search is that you are dealing with potentially huge dimensionality and huge datasets, that means that memory consumption will be huge even for a modest (by today's standards) datasets. This problem has garnered lots of research attention,…
There are 2 dimensions here :
* fundamental performance
* practical utilization
I led a prod project that uses FAISS at a bank. A huge amount of the work was about making the index practical in a real IT environment. For example we had to build a sharding system to allow it to scale, but also to allow it to be rebuilt with 0 down time. There were many other significant engineering steps required to get it depolyed.
So, I would say that if Pinecone could solve these problems they don't need to have fundamental breakthrough performance vs the open source systems. On top of that, as every dev knows, there are a bunch of hygiene components and features that prod software wants - connectors, admin interfaces, utilities. $50m is probably a bit low to cover all of these and the marketing to be honest - but it will go a long long way and I guess that there's series B funding to get over the line if they don't sell out.
On the otherhand they must avoid over-committing to the indexing approaches of today because if someone does make an algorithmic step forward and Pinecone don't / can't take advantage then the features that they provide that enable deployment are a matter of engineering. Also, at the end of the day I think vectorDB's are going to be an important niche in the enterprise and not at the scale of data warehouses, lakes, or application DB's. I think that fitting them into the enterprise IT puzzle scape is going to be very important in making them commercially successful and good VC investments.
Re: Vector search just got up to 10x faster and vertically scalable
#47I 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 does…
I guess it's about downtime:
> With vertical scaling, pod capacities can be doubled for a live index with zero downtime. Pods are now available in different sizes — x1, x2, x4, and x8 — so you can start with the exact capacity you need and easily scale your index.
Re: Vector search just got up to 10x faster and vertically scalable
#48Earlier quoted context omitted.
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.
Weaviate calling themselves a vector database is a fairly new thing.
I'm affiliated with Weaviate, so maybe nice to get this out here for the record :)
We call Weaviate a "vector search engine" (i.e., we prefer "vector search engine" because it describes the type of database) since around Aug, 2020
Github: https://github.com/semi-technologies/weaviate/tree/a3967aff5...
The reason was simple; our community started to say that the mixed vector and scalar filter search capabilities were what they liked most.
Also, our benchmarks are available for quite some time here: https://weaviate.io/developers/weaviate/current/benchmarks/a...
They are based on ann-benchmarks.com but adjusted for full databases.
Re: Vector search just got up to 10x faster and vertically scalable
#49Earlier quoted context omitted.
FAISS
This is incorrect.
Yeah, what's this landing page about? https://www.pinecone.io/managed-faiss/
Re: Vector search just got up to 10x faster and vertically scalable
#50Earlier quoted context omitted.
Completely true. You have to understand the economics behind this to see why their claim is hyperbole at best and flat out misleading at worst. The fundamentals of scalable vector search is that you are dealing with potentially huge dimensionality and huge datasets, that means that memory consumption will be huge even for a modest (by today's standards) datasets. This problem has garnered lots of research attention,…
There are 2 dimensions here : * fundamental performance * practical utilization I led a prod project that uses FAISS at a bank. A huge amount of the work was about making the index practical in a real IT environment. For example we had to build a sharding system to allow it to scale, but also to allow it to be rebuilt with 0 down time. There were many other significant engineering steps required to get it depolyed. S…
This sounds like a perfect job for jina.ai -> sharding, redudancy, automatic up- and down scaling, security features and most importantly: flexibility to use and switch whatever (vector) database
https://jina.ai/