TuringDB – The fastest analytical in-memory graph database in C++

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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

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