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/turingdbTuringDB 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 subgraphsWe 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/quickstarthttps://github.com/turing-db/turingdbHappy to answer technical questions.(1): We actually hit sub-millisecond performance on many queries
HN · launched Jan 28, 2026 · 7 upvotes (top 65%) · 1 view
Niche: database management systems and engines · 162 launches, 5 in the last 7 days ▼ -17% · Explore this niche →
Category and tags
- Horizontal
- Data infrastructure
- Developer
- Not AI
- Commercial product
Visit website ↗ Source listing ↗
More like this: all 162 launches in database management systems and engines →
Could you build this?
No Building a high-performance in-memory analytical graph database engine in C++ capable of thousands of hops requires deep systems engineering and advanced graph algorithm expertise.
What it would actually take: A production implementation requires high-performance C++20, custom memory allocators, compressed sparse row (CSR) or cache-conscious index representations, lock-free concurrency, and SIMD-accelerated graph traversal algorithms. The hard part is achieving extreme traversal throughput across massive biological graphs without running into memory bus bottlenecks or latency spikes.
Discussion
2 comments analyzed.
Competitors mentioned: Neo4j, Memgraph
Concerns raised: Not suitable for high-write OLTP scenarios, Limited to analytical read-heavy workloads
Feature requests: Native vector search and embeddings (shipping this week), Support for high-transaction write throughput
Competitors
Other products that read as similar to this one : 542 launches clear the similarity bar, closest 8 shown.
Attention rank: #314 of 543 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 91 days after the earliest competitor.
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