Ant Financial’s Innovations and Practices in Online Graph Computing

Ant Financial’s Online Graph Computing Scenarios

  • First, the construction of a financial-grade capital network. Based on the online capital activity of users, a highly reliable, financial-grade capital network is built in real time.
  • Second, analysis and decision-making based on real-time subgraphs. Based on a highly reliable financial-grade capital network built in real time, real-time analysis and computing can be performed based on subgraphs, and decisions can ultimately be made online.
  • Finally, the construction of a dynamic subgraph network. In the process of analyzing a subgraph, it is necessary to dynamically build and expand subgraphs based on the user’s capital network activity.
  • First, support for millions of concurrent requests is required. Ant Forest currently has more than half a billion users, and needs to support millions of QPS and millisecond-level responses.
  • Second, the storage of trillions of items of relationship data is required. Because Ant Forest has a large data scale, including user relationships, tree planting relationships, and joint planting relationships, there are many, complicated relationships. As a result, it needs to provide a graph storage capability for trillions of records.
  • Finally, consistency is required. For example, in the process of transferring green energy between friends, due to the large amount of concurrency, it is necessary to ensure strong consistency in the real-time update process.
  • First, it needs to provide the ability to store massive amounts of graph data.
  • Second, it provides low-latency I/O access to the stored massive graph data.
  • Finally, after the subgraph relationship is obtained through low-latency I/O, various modes such as stream computing and graph computing are integrated.
  • First, it must support financial-grade disaster recovery and high reliability, such as fault recovery in five data centers across three regions.
  • In addition, it also needs to support low-cost and auto scaling.

Overall Architecture of Ant Financial’s Online Graph Computing

  • First is the computing capability provided by integrating stream computing and graph computing. Stream computing and graph computing must be integrated in one system to implement comprehensive online graph computing.
  • The second is the graph cache with high compression ratio. Through high compression, all graph data can be stored in the memory, enabling fast and efficient subgraph extraction.
  • Last is the financial-grade massive graph database. The financial-grade massive graph database is used to implement storage of massive amounts of relationship data and high reliability in financial scenarios.

Integration of Stream Computing and Graph Computing in Online Graph Computing

Dynamic DAG

High-performance Graph Cache of Online Graph Computing

GeaBase, a Financial Graph Database for Online Graph Computing

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