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Apache HugeGraph is a distributed graph database supporting billions of vertices and edges with OLTP performance. It offers standalone and distributed deployment modes, supporting Gremlin and Cypher query languages for complex graph traversals.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | apache/hugegraph |
| Owner | apache |
| Primary language | Java |
| License | Apache-2.0 — OSI-approved |
| Stars | 3.1k |
| Forks | 618 |
| Open issues | 356 |
| Latest release | 1.7.0 (2025-11-16) |
| Last updated | 2026-07-08 |
| Source | https://github.com/apache/hugegraph |
What hugegraph is
Java-based graph database with pluggable backend architecture (RocksDB/HBase primary; MySQL/PostgreSQL/Cassandra in legacy ≤v1.5). Provides REST API, TinkerPop 3.5 Gremlin compliance, OpenCypher support, and distributed Raft-based consensus via HugeGraph-PD and HugeGraph-Store modules.
Get the hugegraph source
Clone the repository and explore it locally.
git clone https://github.com/apache/hugegraph.gitcd hugegraph# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Standalone mode (single RocksDB) suitable for <1TB; distributed mode (PD + Store) required for production and scales to <1000TB but adds operational complexity.
- Backend pluggability: RocksDB/HBase are primary; legacy backends (MySQL/PostgreSQL/Cassandra) deprecated in v1.6+. Plan migration path if using older backends.
- Schema management mandatory: VertexLabel, EdgeLabel, PropertyKey, and IndexLabel must be defined upfront; schema-less design not supported.
- Multi-type indexing (exact, range, complex conditions) requires careful planning to avoid performance degradation on large datasets.
- Gremlin and Cypher both supported; team must standardize on query language and ensure driver/SDK compatibility with application stack.
When to avoid it — and what to weigh
- Small transactional datasets (<10GB) with simple relationships — Standalone relational databases or lightweight graph libraries (Neo4j Community) are simpler and faster for small, non-distributed use cases.
- ACID transactions across multiple graphs required — HugeGraph focuses on OLTP performance within a single graph instance; cross-graph ACID semantics and distributed transactions are not emphasized.
- Team without Java/Distributed Systems expertise — Distributed deployment requires operational knowledge of Raft consensus, RocksDB tuning, and multi-node cluster management; steep learning curve for small teams.
- Real-time sub-millisecond query latency mandatory — Designed for scalable throughput over microsecond response times; large graph traversals will not meet ultra-low-latency SLA requirements.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved license: allows commercial use, modification, and distribution with minimal restrictions. Must retain license notice and may not use ASF trademarks.
Apache-2.0 permits commercial deployment without royalties or proprietary restrictions. However, verify integration with any proprietary backend systems (legacy MySQL/PostgreSQL/Cassandra) and assess support model; Apache project provides community support only. For production SLA requirements, consider commercial support alternatives or in-house expertise.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
No explicit security audit data provided. Java-based server; standard JVM security considerations apply (heap, serialization, dependency vulnerabilities). REST API requires authentication/authorization layer (not detailed in README). Distributed consensus (Raft) is cryptographically sound but cluster-to-cluster communication security not specified. No mention of encryption-at-rest or TLS defaults. Evaluate with your security team before production deployment.
Alternatives to consider
Neo4j (Community/Enterprise)
Mature, proprietary graph database with strong OLTP performance and rich ecosystem. Community Edition is free but limited to single-node; Enterprise offers HA. Better for teams preferring managed support and smaller deployments.
Amazon Neptune
Fully managed graph database (AWS); eliminates operational overhead of distributed cluster management. Supports Gremlin and openCypher. Ideal if cloud-native architecture and vendor lock-in are acceptable trade-offs.
TigerGraph
Enterprise graph analytics platform with native distributed architecture and native support for complex graph algorithms. Higher cost but stronger performance on analytic workloads and sub-second query latency claims.
Build on hugegraph with DEV.co software developers
Evaluate HugeGraph's architecture, operational requirements, and integration needs with expert guidance. We help teams architect distributed graph solutions, plan migrations, and optimize deployment for production workloads.
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hugegraph FAQ
Can HugeGraph scale to petabytes?
Is HugeGraph suitable for real-time analytics?
What is the operational overhead of distributed mode?
Can I migrate from legacy backends (MySQL/PostgreSQL) to current versions?
From evaluation to production software
DEV.co builds and maintains open-source databases systems for startups through enterprises. If hugegraph fits your architecture, our custom software development services cover implementation, migration, and the long tail of maintenance.
Ready to Deploy a Large-Scale Graph Database?
Evaluate HugeGraph's architecture, operational requirements, and integration needs with expert guidance. We help teams architect distributed graph solutions, plan migrations, and optimize deployment for production workloads.