NebulaGraph — Distributed Open-Source Graph Database

What it is

NebulaGraph is a horizontally scalable, open-source distributed graph database designed for storing and querying massive graph datasets. It handles billions of vertices and trillions of edges with sub-millisecond latency.

NebulaGraph targets teams building knowledge graphs, social networks, fraud detection systems, recommendation engines, and any application where relationship-heavy data structures outperform relational tables.

The project is actively maintained and suitable for both individual developers and teams looking to integrate it into their existing toolchain. Documentation and community support are available for onboarding.

How it saves time or tokens

NebulaGraph's nGQL query language expresses multi-hop graph traversals in a few lines that would require complex recursive SQL joins in relational databases. Its shared-nothing architecture scales horizontally by adding nodes, so you avoid the vertical scaling ceiling of single-server graph databases.

For teams evaluating multiple tools in the same category, the clear documentation and active community reduce the time spent on research and troubleshooting. Getting started takes minutes rather than hours of configuration.

How to use

1. Deploy NebulaGraph using Docker Compose for development or Kubernetes Helm charts for production.
2. Create a graph space, define tag (vertex) and edge schemas.
3. Insert vertices and edges using nGQL statements.
4. Query with graph traversal patterns (GO, MATCH, LOOKUP) via the NebulaGraph Console or SDK.

Example

-- Create a graph space
CREATE SPACE social_network(partition_num=10, replica_factor=1, vid_type=FIXED_STRING(32));
USE social_network;

-- Define schemas
CREATE TAG person(name string, age int);
CREATE EDGE follows(since datetime);

-- Insert data
INSERT VERTEX person(name, age) VALUES 'alice':('Alice', 30);
INSERT VERTEX person(name, age) VALUES 'bob':('Bob', 25);
INSERT EDGE follows(since) VALUES 'alice'->'bob':(datetime('2026-01-01'));

-- Find who Alice follows
GO FROM 'alice' OVER follows YIELD dst(edge) AS friend;

-- 2-hop friends of friends
GO 2 STEPS FROM 'alice' OVER follows YIELD dst(edge) AS fof;

Related on TokRepo

• AI Tools for Knowledge Graph — Graph databases and knowledge graph tools.
• AI Tools for Database — Compare NebulaGraph with other database solutions.

Common pitfalls

• Using NebulaGraph for simple key-value lookups where a relational or document database would suffice. Graph databases excel at relationship traversals, not flat record retrieval.
• Not planning your partition strategy. NebulaGraph distributes data across partitions. Poor partitioning leads to hot spots and uneven query performance.
• Skipping index creation for property lookups. Without indexes, LOOKUP queries scan entire partitions. Create indexes on frequently queried properties.
• Not reading the changelog before upgrading. Breaking changes between versions can cause unexpected failures in production. Pin your version and review release notes.