TuringDB
The fastest analytical in-memory graph database in C++
Details
- External ID
- 46796807
- Source
- HN
- Company
- —
- Product
- TuringDB
- Website domain
- github.com
- Launched
- Jan. 28, 2026
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.3544137022397892
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
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
Enrichment
- Theme
- low-level systems and developer tools
- Vertical
- Horizontal
- Function
- Data infrastructure
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Commercial product
- Normalized one-liner
- in-memory graph database
- Manually corrected
- False
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 — 214 launches clear the similarity bar, closest 8 shown.
Attention rank: #118 of 215 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 91 days after the earliest competitor.
- HelixDB · hn · 2026-06-10 · 159 upvotes · similarity 0.54
- TypeGraph · hn · 2026-02-24 · 5 upvotes · similarity 0.49
- LatticeDB · hn · 2026-08-25 · 190 upvotes · similarity 0.48
- BlitzGraph · hn · 2026-06-16 · 15 upvotes · similarity 0.47
- NodeDB · hn · 2026-05-11 · 5 upvotes · similarity 0.46
- Graph-Oriented Generation · hn · 2026-03-06 · 12 upvotes · similarity 0.44
- DAGraph · hn · 2026-05-13 · 6 upvotes · similarity 0.42
- Stratum · hn · 2026-03-12 · 12 upvotes · similarity 0.41
Other launches for this product
- No other launches for this product.
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