90x Faster Than Pgvector – Lantern's HNSW Index Creation Time

https://lantern.dev/blog/hnsw-index-creation

by diqi • 3 years ago

109 36 3 years ago

levkk• 3 years ago

Curious about the "outside of the database" index generation part. Is this index WAL-protected eventually?

diqi7• 3 years ago

Yes it is WAL protected: the advantage of external indexing is that the HNSW graph is being constructed externally on multiple cores instead on a single core inside the Postgres process. But eventually the graph is being parsed and processed inside Postgres with all the necessary WAL logs for blocks.

mattashii2• 3 years ago

How does performance scale (vs pgvector) when you have an index and start loading data in parallel? Or how does this scale vs the to-be-released pgvector 0.5.2?

mattashii2• 3 years ago

I'm also concerned about these (tested!) errors:

https://github.com/lanterndata/lantern/blob/040f24253e5a2651...
Operator <-> can only be used inside of an index

Isn't the use of the distance operator in scan+sort critical for generating the expected/correct result that's needed for validating the recall of an ANN-only index?

diqi7• 3 years ago

Ah, thank you for noticing! We actually have typo in the error message. It actually should be the operator <?> instead of <->.

There's some context on the operator <?> here: https://github.com/lanterndata/lantern?tab=readme-ov-file#a-...

diqi7• 3 years ago

We haven't benchmarked against 0.5.2 yet so I can't share exact numbers. We will benchmark it once it is released.

We think our approach will still significantly outperform pgvector because it does less on your production database.

We generate the index remotely, on a compute-optimized machine, and only use your production database for index copy.

Parallel pgvector would have to use your production database resources to run the compute-intensive HNSW index creation workload.

kiwicopple5• 3 years ago

which version of pgvector are you using for these benchmarks?

lettergram2• 3 years ago

As someone who just indexed 6m documents with pgvector, I can say it’s a massive time sync - on the order of days, even with a 32 core 64Gb RDS instance.

cyanydeez• 3 years ago

what was the token sizes for comparison?

lettergram2• 3 years ago

I've done a few 384, 762, 512 all take a few days

Though index creation is not a big deal, I want good queries rapidly for cheap. So IMO RDS with pgvector is the easiest approach.

jn2clark2• 3 years ago

That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.

chatmasta• 3 years ago

That [pgvector index creation time] sounds much longer than it should... I would encourage you to check out Marqo

Your comment makes it sound like Marqo is a way to speed up pgvector indexing, but to be clear, Marqo is just another Vector Database and is unrelated to pgvector.

code_biologist• 3 years ago

The reason I would use pgvector is because I am uninterested in another piece of infrastructure.

netcraft• 3 years ago

So approximately 0% chance I could use this on AWS RDS or Aurora correct?

Still, very impressive

tristan9572• 3 years ago

This extension is licensed under the Business Source License[0], which makes it incompatible with most DBaaS offerings. The BSL is a closed-source license. Good choice for Lantern, but unusable for everyone else.

Some Postgres offerings allow you to bring your own extensions to workaround limitations of these restrictive licenses, for instance Neon[1], where I work. I tried to look at the AWS docs for you, but couldn't find anything about that. I did find Trusted Language Extensions[2], but that seems to be more about writing your own extension. Couldn't find a way to upload arbitrary extensions.

I will add that you could use logical replication[3] to mirror data from your primary database into a Lantern-hosted database (or host your own database with the Lantern extension). This obviously has a couple downsides, but thought I would mention it.

[0] https://github.com/lanterndata/lantern/commit/dda7f064ca80af...

[1] https://neon.tech/docs/extensions/pg-extensions#custom-built...

[2] https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Postg...

[3] https://www.postgresql.org/docs/current/logical-replication....

themanmaran• 3 years ago

Likely as an extension eventually. I know RDS has a variety of postgres extensions you can use. Pg_vector is supported, so likely lantern could get support as well.

[1] https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Postg...

tristan9572• 3 years ago

Unless AWS is going to buy a license, which is probably going to cost them a ton of money, I don't seem them offering Lantern given its licensed under the Business Source License, which is nasty for cloud providers.

AWS is also a big proponent of pgvector, so it is more likely they would put more money into that, in my non-expert opinion.

TuringNYC• 3 years ago

You piqued my interest enough to sign up and try...but now it needs an Access Code to try the DB, any HN special here?

diqi7• 3 years ago

Try YCW24! :)

jbellis• 3 years ago

Nice to see people care about index construction time.

I'm the lead author of JVector, which scales linearly to at least 32 cores and may be the only graph-based vector index designed around nonblocking data structures (as opposed to using locks for thread safety): https://github.com/jbellis/jvector/

JVector looks to be about 2x as fast at indexing as Lantern, ingesting the Sift1M dataset in under 25s on a 32 core aws box (m6i.16xl), compared to 50s for Lantern in the article.

(JVector is based on DiskANN, not HNSW, but the configuration parameters are similar -- both are configured with graph degree and search width.)

VoVAllen• 3 years ago

Sift 1M is too small to make meaningful comparisons. Storing 96 floats * 1M only takes up 800Mb of memory.

justinclift• 3 years ago

nerfborpit3• 3 years ago

This reads like a marketing piece, not an honest technical blogpost.

I agree that Usearch is fast, but it feels pretty dishonest to take credit for someone else's work. Like maybe at least honestly profile what's going on with USearch vs pgvector (..and which settings for pgvector??), and write something interesting about it?

The last time I tried Lantern, it'd segfault when I tried to do anything non-trivial with it, and was incredibly unsafe with how it handled memory. Hopefully that's at least fixed.. but lantern has so many red flags.

diqi7• 3 years ago

Hi, sorry that you didn't have a good experience with Lantern before. We first posted in HN about 3 months ago - Things should be better now, please let us know if you have any issues.

nerfborpit3• 3 years ago

Using ivfflat is much faster for bulk index creation than lantern. There are a lot of trade offs depending on what everyone's specific use case is, but it seems like a pretty massive thing to leave out.

``` postgres=# CREATE INDEX ON sift USING ivfflat (v vector_l2_ops) WITH (lists=1000); CREATE INDEX Time: 65697.411 ms (01:05.697) ```

ngalstyan42• 3 years ago

cofounder here.

You are right that there are many trade-offs between HNSW and IVFFLAT.

E.g. IVFFLAT requires there be significant amount of data in the table, before the index is created, and assumes data distribution does not change with additional inserts (since it chooses centroids during the initial creation and never updates them)

We have also generally had harder time getting high recall with IVFFLAT on vectors from embedding models such as ada-002.

There are trade-offs, some of which we will explore in later blog posts.

This post is about one thing - HNSW index creation time across two systems, at a fixed 99% recall.

ashvardanian• 3 years ago

USearch author here :)

Not sure if it's fair to compare USearch and pgvector. One is an efficient indexing structure, the other is more like a pure database plugin. Not that they can't be used in a similar fashion.

If you are looking for pure indexing benchmarks, you might be interested in USearch vs FAISS HNSW implementation [1]. We run them ourselves (and a couple of other tech companies), so take them with a grain of salt. They might be biased.

As for Lantern vs pgvector, impressed by the result! A lot of people would benefit from having fast vector-search compatible with Postgres! The way to go!

It's wasn't a trivial integration by any means, and the Lantern team was very active - suggesting patches into the upstream version to ease integrations with other databases. Some of those are tricky and have yet to be merged [2]. So stay tuned for the USearch v3. Lots of new features coming :)

[1] https://www.unum.cloud/blog/2023-11-07-scaling-vector-search... [2]: https://github.com/unum-cloud/usearch/pull/171/files

90x Faster Than Pgvector – Lantern's HNSW Index Creation Time

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