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.
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
Congrats on the launch George!
Looking forward to looking into the generalised AI memory layer when it comes out.
Currently #5 on gdb-engines.com - definitely worth a look.
How rankings are calculated? HelixDB has zero feature scores, unlike e.g. JanusGraph which is at #27 or Neo4j at #1.
yooo this is awesome. Didn't even realise :)
We've been having some issues with intermittent performance on multi hop queries.
What's your p99 like for multi hops?
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.
This sounds like a perfect usecase. Would love to learn more and see if we can help!
email us: founders@helix-db.com
PuppyGraph is a good fit for OLAP for sure.
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.
And is open source.
In prod we see p99’s of <10ms ms for warm queries and around 50ms per hop for cold queries.
Hmmm... I'll get in touch. Got an email i can reach out to, there doesn't seem to be one listed on your website?
I'm more concerned about if the p99s stay consistent when things get spikey.
dgraph is fine otherwise...
Sure! You can email me personally at george@helix-db.com
Where's the source code for the database itself? Looks like the repo is just a client.
Congrats on the launch!
This was a TEMPORARY decision we made, and I wrote a bit about why we did this here: https://x.com/georgecurtiss/status/2060043184059912470
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.
does it support fts/vector on edges of the graph?
Yes you can put vectors, full text data, secondary and range indexes on both nodes and edges.
when will the graph memory layer be available?
We plan on launching end of month.
How does this compare vs. Turbopuffer?
We see comparable results for vectors and FTS.
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.
what language does this support? cypher/gremlin?
We don't support cypher or gremlin. We can
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
congrats! how does this compare to turbopuffer, surreal or other multi-model ones built on object storage or not
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