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Dgraph Dgraph is an open-source, distributed native graph database designed for efficient storage and querying of highly connected data. It uses a GraphQL-like query language (DQL) and provides horizontal scalability, making it suitable for complex relationship scenarios such as social networks, recommendation systems, and knowledge graphs.
Core Features:
- Native Graph Database Design: Dgraph is a distributed database built specifically for graph data, using a directed property graph model. It supports flexible modeling of nodes (entities), edges (relationships), and properties (key-value pairs). For example, Person-(FRIEND)->Person{name:"Alice"} can directly represent a social network, naturally suitable for complex relationship queries.
- High-Performance Distributed Architecture: Achieves multi-node data consistency based on the Raft consensus protocol, supporting horizontal scaling. Through data sharding and parallel query execution, it can handle real-time traversal of billions of nodes and edges, with throughput growing linearly with cluster size.
- GraphQL+- Query Language: Provides a declarative query language similar to GraphQL (syntax compatible with GraphQL but extended with graph operation capabilities), supporting deep link queries, filtering, pagination, and aggregation.
- ACID Transaction Support: Ensures strong consistency for read and write across nodes, supporting snapshot isolation levels. Transactions can batch operate multiple nodes/edges, such as updating user information and friend relationships simultaneously, ensuring data integrity.
- Real-Time Incremental Backup and Recovery: Achieves full backups through Export/Import mechanisms, combined with WAL (Write-Ahead Log) for incremental data synchronization, allowing quick rollback to a specified point in time during disaster recovery.
- Multi-Language Client Support: Officially provides client drivers for languages such as Go, Java, Python, and JavaScript, while also supporting gRPC and HTTP APIs, facilitating integration into existing technology stacks.
- Intelligent Indexing and Query Optimization: Automatically creates optimized indexes for frequently queried fields (such as @id, @index), supporting full-text search (@fulltext), fuzzy matching, and geospatial queries. The query planner dynamically optimizes execution paths, reducing graph traversal overhead.
- Cloud-Native and Kubernetes Integration: Provides Helm Charts and Operators to simplify K8s deployment, supporting dynamic scaling, health checks, and monitoring metrics exposure (in Prometheus format), suitable for elastic needs in cloud environments.
- Fine-Grained Permission Control: Implements field-level access control based on JWT or custom authentication logic (such as restricting users to query only their own friend data), meeting compliance requirements like GDPR.
- Real-Time Subscriptions (Live Queries): Clients can subscribe to graph data change events (such as newly added nodes or edges), with updates pushed in real-time to front-end applications, suitable for scenarios like social dynamics and fraud detection.
This project offers pre-configured Dgraph-Distributed graph database，images with Dgraph and its runtime environment pre-installed, along with deployment templates. Follow the guide to enjoy an "out-of-the-box" experience.
Architecture Design:
System Requirements:
- CPU: 4vCPUs or higher
- RAM: 16GB or more
- Disk: At least 50GB
Register a Huawei account and activate Huawei Cloud
| Image Version | Description | Notes |
|---|---|---|
| Dgraph24.1.3-arm-v1.0 | Deployed on Kunpeng servers with Huawei Cloud EulerOS 2.0 64bit |
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