TencentDB for Graph Database
A one-stop database service for storage, computation, and visual analysis of massive amounts of graph data
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TencentDB for Graph Database (Graph Database) is a cloud-based graph database service. Leveraging Tencent's practical experience with massive amounts of graph data, it provides one-stop storage, management, real-time computation and query, and visual analysis capabilities for massive amounts of graph data. In addition, it supports property graph models and the TinkerPop Gremlin query language, helping you quickly complete the modeling, query, and visual analysis of graph data.
Graph Database can store data with tens of billions of vertices and trillions of edges in an ultra-large complex network, linearly scale storage and computing resources, and provide complete error tolerance and recovery capabilities.
Graph Database supports the TinkerPop Gremlin standard query language and modular zero-code query methods.
Graph Database strictly controls access to the graph data and storage and computing resources. Only service instances granted the permission to access data can manipulate data.
Graph Database supports three major graph features: storage, computing, and visualization, for easy management, mining, and analysis of graph data.
Graph Database supports various business scenarios such as financial payment, security risk management, knowledge graph, advertisement and recommendation, and device topology.
Graph Database can linearly scale storage and computing resources and provides various OPS features such as data backup and monitoring.
In social networking scenarios where people form small circles with close interactions, Graph Database can implement the efficient storage and query of such relationships. For example, when you query "who among my friends like singing", traditional relational databases are very inefficient in querying multi-level association data, but Graph Database can efficiently meet the needs in such complex association analysis scenarios.
In financial payment scenarios, associations between entities such as accounts, mobile numbers, and device IDs construct a huge network. Due to the limitations of traditional relational databases with regard to computation and association analysis, it is often difficult for you to mine the associations between entities, local subgraph structures, and specific transaction patterns in graphs, making it inefficient for you to cope with financial fraud and other risks. Based on technologies such as high-performance association query and visual analysis, Graph Database can easily provide graph-related technical support for financial institutions and payment networks to solve many challenges in complex business scenarios.
Knowledge graph is a new-generation knowledge base technology for various industries with a closer link with graph databases and the widest scope of application. By intelligently extracting massive amounts of associated information, it can form large knowledge bases. Graph Database can be used to store such knowledge bases and provide basic storage and query services.
