Flink (VVR) is a commercial version developed based on Apache Flink (referred to as Flink in this topic). VVR is fully compatible with Flink and provides high value-added features such as GeminiStateBackend to improve job performance and stability.

Background information

The core of Flink is a streaming execution engine. The engine provides features such as data distribution, data communication, and fault tolerance for distributed streaming computing. Based on the streaming execution engine, Flink provides APIs of a higher abstraction level to allow you to compile distributed tasks.

Flink (VVR) is fully compatible with Flink. For more information, see the following documents:


Flink is widely used for real-time big data computing. This section describes the use scenarios of Flink from the perspectives of technologies and enterprise applications.

  • Technologies
    From the perspective of technologies, Flink is suitable for the following scenarios:
    • Real-time extract, transform, load (ETL) and data streams
      Data is delivered from point A to point B by using the real-time ETL procedure and data streams. During data delivery, data cleansing and integration may be required, such as real-time indexing in the search system and ETL procedure in real-time data warehousing. Real-time ETL and data streams
    • Real-time data analysis
      Data analysis is a process to extract and integrate required information from raw data to achieve your business objectives. For example, you can view the top 10 products sold per day, the average turn-around time in the warehouse, the average document click rate, and the open rate for push notifications. Real-time data analysis allows you to view real-time reports or dashboards. Real-time data analysis
    • Event-driven applications
      An event-driven application is a system that processes or reacts to subscription events. Event-driven applications depend on internal states and respond to suspicious events detected during fraud detection or in the risk control system or O&M exception detection system. When your behavior triggers a risk control point, the system captures the event and analyzes the current and previous behavior to determine whether to perform risk control over your behavior. Event-driven

    For more information about Apache Flink, visit the Apache Flink official website.

  • Enterprise applications
    From the perspective of enterprise applications, Flink is suitable for the following scenarios:
    • Business department: real-time risk control, real-time recommendation, and real-time indexing of search engines.
    • Data department: real-time data warehousing, real-time reports, and real-time dashboards.
    • O&M department: real-time monitoring, real-time exception detection and alerting, and end-to-end debugging.