Open-Source Databases · apache

hugegraph

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.

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Source: GitHub — github.com/apache/hugegraph

3.1k

GitHub stars

618

Forks

Java

Primary language

Apache-2.0

License (OSI-approved)

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.

Quickstart

Get the hugegraph source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/apache/hugegraph.git2cd hugegraph3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Large-scale knowledge graphs and relationship queries

Efficiently store and query billions of relationships; ideal for recommendation engines, social networks, or semantic knowledge graphs where traversal depth and pattern matching are critical.

Distributed production deployments requiring high availability

Multi-node Raft-based clustering (PD + Store) provides automatic failover and horizontal scaling; suitable for mission-critical graph workloads at petabyte scale.

Big data integration pipelines

Native integration with Flink, Spark, and HDFS enables seamless graph import, computation, and analytics within existing data infrastructure.

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

Security considerations

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.

Software development agency

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hugegraph FAQ

Can HugeGraph scale to petabytes?

Distributed mode claims <1000TB capacity via multi-node Raft clusters (PD + Store). Petabyte-scale is outside stated limits; validate with vendor or community for extreme-scale use cases.

Is HugeGraph suitable for real-time analytics?

HugeGraph is OLTP-optimized; it supports complex queries but is not designed for real-time sub-millisecond analytics. For time-series or real-time streaming analytics, consider purpose-built tools (ClickHouse, Kafka, TimescaleDB).

What is the operational overhead of distributed mode?

Distributed mode requires 3-5 PD nodes (metadata/coordination) and 3+ Store nodes (data + Raft consensus). Each node requires CPU, memory, and network tuning. Estimated ops overhead is 2-3 FTE for medium teams; smaller teams should plan for learning curve.

Can I migrate from legacy backends (MySQL/PostgreSQL) to current versions?

Legacy backends are deprecated in v1.6+. Migration path is not clearly documented; community support exists but plan for custom tooling and validation. Recommend testing migration in non-production environment first.

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