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.

Fully Hosted

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.

Application Scenarios

  • Real-time Stream Analysis

  • IoT

Real-time Stream Analysis

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.


  • Easy to Use

    Supports online StreamSQL statement editing and provides abundant SQL functions to meet complex service requirements.

  • Fully Hosted

    Computing clusters are fully hosted by CS, enabling you to focus on stream analysis.

  • Pay per Use

    SPU resources specified during job creation are charged by duration (by second).

Related Services





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.


  • Abundant IoT SQL Functions

    Provides common IoT functions such as area, yaw, and distance detection functions.

  • High Throughput, Low Latency

    Leverages Apache Flink to achieve a complete real-time computing framework.

  • Secure Isolation

    Isolates tenants from each other to ensure data security.

Related Services




StreamSQL Functions

Aggregate functions, such as window and join, are supported, and SQL is used to express business logics.

High Throughput, Low Latency

CS supports the backpressure mechanism, high throughput, and millisecond-level latency.

Rock-solid Security

Security protection mechanisms for tenants ensure operation security.

Distributed Real-time Computing

Large-scale cluster computing and cluster auto scaling significantly reduce the cost.

Video Library

CS: Product Overview

CS: Quick Start

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