Easy to Use
You can edit StreamSQL statements to define the data input, output, and processing. Business logics are implemented quickly, facilitating streaming data analysis.
You can check visualized information on running jobs without the need for cluster O&M.
Pay per Use
The service is priced based on the used SPU resources (1 SPU = 1 core + 4 GB memory) and the service duration (by second).
High Throughput, Low Latency
The Dataflow model of Apache Flink is leveraged to achieve a real-time computing framework.
Real-time Stream Analysis
Real-time stream analysis features ease of use, low latency, and high throughput. It can be achieved based on StreamSQL and user-defined jobs.
Supports online StreamSQL statement editing and provides abundant SQL functions to meet complex service requirements.
Computing clusters are fully hosted by CS, enabling you to focus on stream analysis.
SPU resources specified during job creation are charged by duration (by second).
IoT or edge devices upload data to DIS. CS reads data from DIS, analyzes data (including fault detection and counter warning), and makes the analysis result persistent or reports alarms in real time.
Provides common IoT functions such as area, yaw, and distance detection functions.
Leverages Apache Flink to achieve a complete real-time computing framework.
Isolates tenants from each other to ensure data security.
Aggregate functions, such as window and join, are supported, and SQL is used to express business logics.
CS supports the backpressure mechanism, high throughput, and millisecond-level latency.