Cloud Stream Service

Cloud Stream Service (CS) is a real-time big data stream analysis service running on the Huawei cloud. Computing clusters are fully managed by CS, enabling you to focus on Stream SQL services. CS is compatible with Flink APIs, and CS jobs run in real time, making cloud data easy to calculate and analyze.

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Enterprise users could join the open beta freely.

Ease of Use

StreamSQL can be used to define data inflow, data processing, and data outflow. Service logics can be implemented quickly, facilitating stream data analysis.

Full Management

CS provides visualized information on running jobs.

On-Demand Billing

Pricing is calculated based on units of SPUs. You are charged based on the running duration (with second-level accuracy) of the SPUs specified by you.

High Throughput and Low Latency

The Flink and Dataflow models are used to achieve a complete real-time calculation framework.

Application Scenarios

  • Real-time Stream Analysis

  • IoT

Real-time Stream Analysis

Real-time Stream Analysis

Features ease of use, low latency, and high throughput. Stream analysis can be conducted using Stream SQL and user-defined jobs.


  • Ease of Use

    Online Stream SQL editing is supported and abundant SQL functions are provided to meet complex service requirements.

  • Full Management

    Computing clusters are fully managed without raising user awareness, enabling you to focus on stream analysis.

  • On-Demand Billing

    SPU resources specified during job creation are charged by duration, counted by second.

Combined use



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IoT devices or customer edge (CE) devices upload streaming data to Data Ingestion Server (DIS) or other cloud storage services. Cloud Stream Service reads data directly in DIS, performs data analysis (including fault detection, data cleansing, statistical analysis, and indicator warning), and makes the analysis results persistent or pushes alarm notifications in real time.


  • Abundant IoT SQL Functions

    Common IoT functions such as area, yaw, and distance detection functions are provided.

  • High Throughput and Low Latency

    The Apache Flink execution engine serves as a complete real-time computing framework.

  • Secure Isolation

    Tenants are isolated from each other, ensuring data security.

Combined use



Function Description

Abundant StreamSQL Online Analysis Capabilities

Aggregate functions such as Window and Join are supported. SQL is used to express service logics, enabling quick and easy service implementation.

High Throughput and Low Latency

CS reads data from the DIS, and supports natural backpressure and high-throughput pressure. Millisecond-level latency meets the requirements of real-time calculation scenarios.

Security and Reliability

Triple security protection mechanisms for tenants ensure operation security.

Distributed Real-Time Computing

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