Graph Databases: Unlocking Complex Data via Relationships
Unlock graph databases: store relationships as first-class data, query in milliseconds, fight fraud & power ML recs.
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In the digital age, the relationships between data are more valuable than the data itself, and the graph database is the magic key to unlocking this value.
Have you ever wondered how Alipay identifies suspicious transactions within seconds? Or how Meituan recommends a restaurant to you with an explanation like, “This place is highly praised by Shandong natives who love Beijing cuisine in Beijing”? Behind these seemingly magical technologies lies a data technology capable of efficiently handling complex relationships: the graph database.
While traditional relational databases are still querying relationships through cumbersome join operations, graph databases have elevated relationships to a first-class citizen. They achieve millisecond-level response times, especially for complex join queries, at a performance level orders of magnitude higher than traditional relational databases.
In computer science, a Graph is a data structure composed of nodes (vertices) and edges. Nodes represent entities, and edges represent the relationships between those entities. Both nodes and edges can possess properties.
The key distinction from traditional database models is that graph databases store relationships directly as first-class entities, allowing for rapid traversal and querying without the need for expensive join operations.
In the graph database model, every node directly contains a list of relationships. This list holds records of the node’s relationships with other nodes. These relationship records are organized by type and direction and can hold additional properties.
