I've been working on a graph database in Rust this year actually! I'd love to hear anything you can talk about wrt the query planner and/or how you decided to do cardinality estimation. I decided to go with an EAV graph which makes CE pretty complex, and it's been an interesting challenge to balance quality and speed and expressiveness in the query language
can you host this yourself or do you need to use helix-cloud? the chat thing on the side seems to push me to helix-cloud but it looks like that starts at like $600/mo which is above my experimentation budget.
looking for a db for an agent memory application and i'd probably start with something that's just self-hosted / freeish. postgres is working ok but I want to start ingesting server and chat logs.
If your use case is OLAP based, please check it out PuppyGraph. It’s a graph query engine that sits on top of your Lakehouse (no ETL required). Our benchmark has shown consistently that 10-hop queries across billions of edges in <2 seconds. Our customers including some most data demanding companies like Coinbase, Datadog, Palo Alto Network, Netskope, AMD, etc.
It's not, its actually our prod db with direct user usage - we self host a large dgraph cluster. We have a very large number of people manage their car and car histories with us and host a full replica of the UK MOT Database.
We're fine with clickhouse and redshift for the OLAP work we do. I've been looking at ParaQuery lately if I really want to speed that up.
We’re just two young founders sharing what we’ve been building, so I’ll take the drive-by competitor plug as a compliment :)
Definitely a different focus though. Helix is OLTP, built for operational graph + vector workloads, especially apps/agent memory where low-latency traversals and writes are concerned.
The feature scores are from a paper cited on the site. The ranking features are proprietary to dissuade gaming but are a blend of open public data across a number of different sources.
We’re 100% committed to going back to open-source on an Apache 2.0 license as soon as possible. In the meantime, you can continue to deploy us completely for free, however you like, using the compiled docker container.
For vector search we have warm and cold p99s of approx 20ms and 400ms respectively.
For FTS, warm and cold query p99s of approx 15ms and 250ms respectively.
You can query HelixDB using JSON or directly in your programming language of choice by using our Rust, TypeScript, Go or Python SDKs.
We’ve found AI is very good at working with the SDKs and JSON itself to query, making the development experience much better than before: https://docs.helix-db.com/database/querying
tpuffer is a vector/fts database. Surreal is a bit of an "everything database".
We're a graph database with vector and FTS capabilities. Our vector and FTS benchmarks are comparable with tpuffer, but you would primarily use us for building whole applications, knowledge graphs, or AI memory/retrieval. Anything that is relationship intense.
Let me know if this properly answers your question
can you host this yourself or do you need to use helix-cloud? the chat thing on the side seems to push me to helix-cloud but it looks like that starts at like $600/mo which is above my experimentation budget.
looking for a db for an agent memory application and i'd probably start with something that's just self-hosted / freeish. postgres is working ok but I want to start ingesting server and chat logs.
What's your p99 like for multi hops?
I'm more concerned about if the p99s stay consistent when things get spikey.
dgraph is fine otherwise...
We're fine with clickhouse and redshift for the OLAP work we do. I've been looking at ParaQuery lately if I really want to speed that up.
email us: founders@helix-db.com
We’re just two young founders sharing what we’ve been building, so I’ll take the drive-by competitor plug as a compliment :)
Definitely a different focus though. Helix is OLTP, built for operational graph + vector workloads, especially apps/agent memory where low-latency traversals and writes are concerned.
Looking forward to looking into the generalised AI memory layer when it comes out.
Congrats on the launch!
We’re 100% committed to going back to open-source on an Apache 2.0 license as soon as possible. In the meantime, you can continue to deploy us completely for free, however you like, using the compiled docker container.
For vector search we have warm and cold p99s of approx 20ms and 400ms respectively. For FTS, warm and cold query p99s of approx 15ms and 250ms respectively.
Both of these benchmarks were run on 1m docs.
You can query HelixDB using JSON or directly in your programming language of choice by using our Rust, TypeScript, Go or Python SDKs. We’ve found AI is very good at working with the SDKs and JSON itself to query, making the development experience much better than before: https://docs.helix-db.com/database/querying
We're a graph database with vector and FTS capabilities. Our vector and FTS benchmarks are comparable with tpuffer, but you would primarily use us for building whole applications, knowledge graphs, or AI memory/retrieval. Anything that is relationship intense.
Let me know if this properly answers your question