Writing it in Rust gets visibility because of the popularity of the language on HN.
Here's why we are not doing it for LadybugDB.
Would love to explore a more gradual/incremental path.
Also focusing on just one query language: strongly typed cypher.
https://vldb.org/cidrdb/2023/kuzu-graph-database-management-...
You can judge for yourself what work has been done in the last 5 months. Many short videos here. New open source contributors who I didn't know before ramping up.
From the commit history it's obvious that this is an AI coded project. It was started a few months ago, 99% of commits are from 1 contributor, and that 1 contributor has some times committed 100,000 lines of code per week. (EDIT: 200,000 lines of code in the first week)
I'm not anti-LLM, but I've done enough AI coding to know that one person submitting 100,000 lines of code a week is not doing deep thought and review on the AI output. I also know from experience that letting AI code the majority of a complex project leads to something very fragile, overly complicated, and not well thought out. I've been burned enough times by investigating projects that turned out to be AI slop with polished landing pages. In some cases the claimed benchmarks were improperly run or just hallucinated by the AI.
So is anyone actually using this? Or is this someone's personal experiment in building a resume portfolio project by letting AI run against a problem for a few months?
https://news.ycombinator.com/item?id=29737326
Kuzu folks took some of these discussions and implemented them. SIP, ASP joins, factorized joins and WCOJ.
Internally it's structured very similar to DuckDB, except for the differences noted above.
DuckDB 1.5 implemented sideways information passing (SIP). And LadybugDB is bringing in support for DuckDB node tables.
So the idea that graph databases have shaky internals stems primarily from pre 2021 incumbents.
4 more years to go to 2030!
As I like to point out, for two decades DARPA has offered to pay many millions of dollars to anyone who can demonstrate a graph database that can handle a sparse trillion-edge graph. That data model easily fits on a single machine. No one has been able to claim the money.
Inexplicably, major advances in this area 15-20 years ago under the auspices of government programs never bled into the academic literature even though it materially improved the situation. (This case is the best example I've seen of obviously valuable advanced research that became lost for mundane reasons, which is pretty wild if you think about it.)
I wonder why no one has claimed it. It's possible to compress large graphs to 1 byte per edge via Graph reordering techniques. So a trillion scale graph becomes 1TB, which can fit into high end machines.
Obviously it won't handle high write rates and mutations well. But with Apache Arrow based compression, it's certainly possible to handle read-only and read-mostly graphs.
Also the single machine constraint feels artificial. For any columnar database written in the last 5 years, implementing object store support is tablestakes.
> There are some additional optimizations that are specific to graphs that a relational DBMS needs to incorporate: [...]
This is essentially what Kuzu implemented and DuckDB tried to implement (DuckPGQ), without touching relational storage.
The jury is out on which one is a better approach.
If you wish to avoid that particular caveat, look for a graph DB which materializes edges within vertices/nodes. The obvious caveat there is that the edges are not normalized, which may or may not be an issue for your particulat application.
Trying to make it optional.
Try
explain match (a)-[b]->(c) return a.rowid, b.rowid, c.rowid;
> We've all seen open source projects drag on for 3 years without shipping anything, that's not necessarily better
There are more options than “never ship anything” and “use AI to slip 200,000 lines of code into a codebase”
Full history here: https://www.linkedin.com/pulse/brief-history-graphs-facebook...
https://archive.fosdem.org/2025/schedule/event/fosdem-2025-5...
Author of ArcadeDB critiques many nominally open source licenses here:
https://www.linkedin.com/posts/garulli_why-arcadedb-will-nev...
What is a graph database is also relevant:
- Does it need index free adjacency?
- Does it need to implement compressed sparse rows?
- Does it need to implement ACID?
- Does translating Cypher to SQL count as a graph database?When you actually need to run graph algorithms against your relational data, you export the subset of that data into something like Grafeo (embedded mode is a big plus here) and run your analysis.
It's possible to run cypher against duckdb (soon postgres as well via duckdb's postgres extension) without having to import anything. That's a game changer when everything is in the same process.
https://github.com/agnesoft/agdb
Ah, yeah, a different query language.
* it is possible to write high quality software using GenAI
* not using GenAI could mean project won't be competitive in current landscape
From examine this codebase it doesn’t appear to be written carefully with AI.
It looks like code that was promoted into existence as fast as possible.
why? this is false in my opinion, iterating fast is not a good indicator of quality nor competitiveness