Story

Hi HN,

I am one of the cofounders of http://turingdb.ai. We built TuringDB while working on large biological knowledge graphs and graph-based digital twins with pharma & hospitals, where existing graph databases were unusable for deep graph traversals with hundreds or thousands of hops on (crappy) machines you can find in a hospital.

https://github.com/turing-db/turingdb

TuringDB is a new in-memory, column-oriented graph database optimised for read-heavy analytical workloads:

- Milliseconds (1) for multi-hop queries on graphs with 10M+ nodes/edges

- Lock-free reads via immutable snapshots

- Git-like versioning for graphs (branch, merge, time travel queries)

- Built-in graph exploration UI for large subgraphs

We wrote TuringDB from scratch in C++ and designed to have predictable memory and concurrency behaviour.

For example, for the Reactome biological knowledge graph, we see ~100× to 300× speedups over Neo4j on multi-hop analytical queries out of the box (details in first comment).

A free Community version is available and runnable locally:

https://docs.turingdb.ai/quickstart

https://github.com/turing-db/turingdb

Happy to answer technical questions.

(1): We actually hit sub-millisecond performance on many queries