alibaba

alibaba / neug

Public

High-performance embedded graph database for analytics and real-time transactions

graphscope.ioneug cypher embedded-database graph-analysis graph-database graph-intelligence
12
1
100% credibility
Found Mar 08, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
C++
AI Summary

NeuG is a graph database for both deep analysis and real-time apps, easy to set up with built-in examples and Python support.

How It Works

1
📖 Discover NeuG

You hear about NeuG, a friendly tool for exploring connections in your data like friends or networks.

2
🛠️ Get NeuG ready

Download and set it up on your computer in just a few moments.

3
🗄️ Create your graph space

Open a new area to store your information and relationships.

4
⚡ Add sample data

Load example friends and connections to see it in action instantly.

5
🔍 Ask questions

Run simple queries to find patterns like mutual friends and get clear results.

6
📡 Share with others

Switch to sharing mode so friends or apps can access it anytime.

🎉 Your network thrives

Enjoy fast insights and real-time connections whenever you need them.

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Star Growth

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AI-Generated Review

What is neug?

NeuG is a high-performance embedded graph database built in C++ for hybrid transactional/analytical processing (HTAP), handling both complex analytics like pattern matching and bulk loading, plus real-time transactions via concurrent service mode. Install via `pip install neug` on Python 3.8+, and query with familiar Cypher syntax—load datasets like TinySNB or LDBC, run triangle queries, then switch to server mode on port 8080 for apps. It solves the pain of separate OLTP/OLAP graph stores by unifying them in a lightweight, embeddable package.

Why is it gaining traction?

Alibaba's backing and a world-record LDBC SNB Interactive benchmark (80k+ QPS) make it a standout for C++ high-performance graph workloads on GitHub, borrowing DuckDB's runtime smarts and Kùzu's Cypher compiler for speed without bloat. Developers dig the seamless Python API for quick prototyping, Docker images for x86/arm64, and extensions like JSON scanning—no JVM overhead like Neo4j, just pip-and-go efficiency for high-performance backend graphs.

Who should use this?

Backend engineers building real-time recommendation engines or fraud detection need its service mode for concurrent Cypher queries. Data analysts running graph algorithms on LDBC-scale datasets will love embedded mode's bulk analytics. High-performance computing teams embedding graphs in C++ apps (e.g., simulations or networking) get a credible HTAP alternative to heavier servers.

Verdict

Try it for bleeding-edge graph perf if you're okay with v0.1 maturity—solid docs, CI tests, and coverage, but 12 stars and 1.0% credibility score signal early days; monitor for production stability.

(198 words)

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