🚀 TuringDB Benchmarks
This page showcases the performance of TuringDB vs Neo4j across a series of real-world graph analytics queries using the Reactome biological knowledge graph.
The goal of this first benchmark is to compare the out-of-the-box performance of TuringDB with Neo4J on a few basic queries on the Reactome knowledge graph, without any particular indexing or optimization put in place on either side. Both TuringDB and Neo4J are started in a cold setting out of the box.
🧪 Benchmark Setup
We use the open-source turing-bench suite to evaluate performance. It runs equivalent queries across TuringDB and Neo4j, measuring latency, throughput, and result sizes.
Neo4j may fail to load the Reactome database on first boot.
If this happens: Restart the Neo4j instance manually (details in the Neo4j README)
🔧 How to Run the Benchmarks
# Clone the benchmarking repo
git clone <https://github.com/turing-db/turing-bench>
cd turing-bench
Run a benchmark (Neo4j)
cd src/neo4j
python3 bench.py -q "samples/reactome/neo4j-str-prop-multihop.cypher" -r 3
Run a benchmark (TuringDB)
cd build/build_package
./turing-bench -l reactome -q samples/reactome/str-prop-multihop.cypher -r 100 -p
📊 Summary of Results
📍 Multi-hop Queries From A Set of Seed Nodes
| Query Depth | Neo4j Mean | TuringDB Mean | Speedup |
|---|
| 1-hop | 1390ms | 12ms | 115x |
| 2-hop | 1420ms | 11ms | 129x |
| 4-hop | 1568ms | 14ms | 112x |
| 7-hop | 51264ms | 172ms | 298x |
| 8-hop | 98183ms | 476ms | 206x |
match (n{displayName:"Autophagy"})-->(m) return id(m)
🔖 Labelset Matching & Few-Hop Traversals
| Query | Neo4j Mean | TuringDB Mean | Speedup |
|---|
match (n:Drug) | 525ms | 0.43ms | 1200x |
match (n:Drug:ProteinDrug) | 644ms | 0.16ms | 4025x |
match (n)--(m:Interaction)--(o) | 7519ms | 141ms | 53x |
🧵 Edge-Type Queries
| Query | Neo4j Mean | TuringDB Mean | Speedup |
|---|
match (n)-[:release]->(m) | 3881ms | 822ms | 5x |
match (n)-[:hasEvent]->(m) | 5447ms | 843ms | 6x |
match (n:Pathway)-[:hasEvent]->(m:Event) | 3224ms | 64ms | 50x |
match (r:Reaction)-[:output]->(s:Entity) | 5031ms | 170ms | 30x |
🔁 Deep Multidirectional Traversals
| Query | Neo4j Mean | TuringDB Mean | Speedup |
|---|
match (n:DatabaseObject{isChimeric:false}) return n | 4496ms | 7ms | 642x |
match (b)--(a:Pathway) | 10874ms | 17ms | 640x |
match (c)--(b)--(a:Pathway) return a, c | 78079ms | 8348ms | 9x |
match (c)--(b)--(a:Pathway) return b | 52249ms | 8171ms | 6x |
Observations
- TuringDB is orders of magnitude faster for long-path traversals (e.g., 8-hop queries).
- Cold-start queries in Neo4j are significantly slower due to record-oriented execution and lack of built-in versioning/indexing on properties.
- Columnar memory layout and streaming execution give TuringDB consistent low-latency performance for both analytical and graph-native operations.
TuringDB delivers high performance due to:
- Columnar Execution: Vectorized, SIMD-friendly query engine
- Streaming Pipeline: Efficient execution on batches of node/edge chunks
- Snapshot Isolation: No locks or blocking during deep reads
- DataParts: Enable massive import parallelism & memory efficiency
Learn more:
- Columnar System
- Streaming Execution Engine
- Versioning & Snapshot Isolation
🔍 Benchmark Data
Full benchmarking data and instructions on Github:
📂 TuringDB Benchmark Suite
🧪 Try It Yourself
You can replicate these results on your own instance using the Reactome dataset.
Follow the setup steps in the Benchmark README
❓Need Help?
Join our Discord or open an issue on GitHub.
🛣️ Roadmap
We will be expanding this benchmarks to other graph databases (such as Memgraph or TigerGraph) as well as other criteria to try and build the most reliable benchmark possible and have an honest view of where we may shine and where we may not.
