Guest post by KubeEdge Maintainers

Introduction: KubeEdge is an open-source edge computing platform. Based on the native container orchestration and scheduling capabilities of Kubernetes, KubeEdge achieves functionalities such as cloud-edge synergy, edge computing, edge device management, and edge autonomy. KubeEdge is also widely implemented in scenarios such as 5G MEC and AI cloud-edge synergy by using plug-ins.

This article mainly shares practices of edge stream data processing based on KubeEdge and Kuiper.

Kuiper – A stream processing product at the edge

Initiated in early 2019 and released in October 2019, Kuiper has been iteratively developed to be a classic stream processing architecture.

This product is designed to enable the stream processing that runs in the cloud, such as Spark or Flink jobs, to run at the edge.

Architecture Diagram of Kuiper

Architecture diagram of Kuiper

The overall architecture diagram consists of three parts. Sources on the left indicate the data sources, which can be the MQTT macOS broker at a certain edge of KubeEdge, or files, windows, or databases.

Sinks on the right indicate the storage locations after data processing. The target locations can be MQTT, files, databases, or HTTP services.

The middle part is divided into several layers. The top layer is the data service logic processing layer, which provides SQL statements and rule parser. SQL processors process and convert them into SQL plans. The middle layer contains the streaming runtime and SQL runtime, which are used to run the final plan. The bottom is the storage layer, which is used to store outgoing messages.

Application Scenarios of Kuiper

Real-time stream processing at the edge

Rule engine to customize rules for alarm and message forwarding

Data format and protocol conversion for edge and cloud data to integrate IT and OT

KubeEdge and Kuiper Integration

Diagram showing KubeEdge and Kuiper integration

Parts of the architecture

Kuiper is installed at the downstream of the KubeEdge MQTT broker. Both of them run at the edge. There are different underlying mappers, that is, different types of protocols. The edge MQTT broker is used to exchange messages.

Data Processing Types:

The supported data types are int, string, bool, and float.

KubeEdge Model File and Configuration

The following figure shows parts of the configuration file, including the device name, property, name, data type, and description.

KubeEdge model file and configuration

Parts of the configuration file

  1. Save the device model file.
  2. Configure the model file information in ect/mqtt_source.yaml.
  1. Deliver the configurations as a ConfigMap and save them to the related directory.

Using Kuiper

  1. Define the stream, which is similar to defining a table in a database.

DATASOURCE=”$hw/events/device/+/twin/update” is a topic already defined in KubeEdge.

  1. Define and submit the rule.
code example



4 types of time windows + 1 count window

More than 60 SQL functions

  1. Run Kuiper.

Deploying Kuiper in KubeEdge

Method 1: Using Kuiper-Kubernetes-tool

This tool independently runs in containers to execute the command configuration file delivered as a ConfigMap.

Run the file. The tool periodically scans the file and executes the command.

Method 2: Using Kuiper-manager and Cloud-Edge Synergy Management Console

You can also use the management console to manage nodes on which Kuiper runs.

For example, when Kuiper is running in the IoV box that connects many vehicles, you can use Kuiper-manager to connect all instances and update rules in a unified manner.

First, install plug-ins. For example, if you want to connect to an unsupported source, you can write a plug-in with our guides and install it. After that, you can use it on an Android plug-in interface.

Next, create a stream definition.

Specify a location or path for saving data to the file system.

Finally, compile the rule on a visualized editing page.

Use Case: Industrial Internet Big Data Center

Diagram showing Kuiper-manager and Cloud-Edge synergy management console on China Mobile (Shanghai) Industrial Research Institute architecture

This is a typical application scenario. Kubernetes and CloudCore are deployed in the cloud to deliver rules to Kuiper through a management channel. Kuiper is deployed on the MQTT broker to define and clean data. Currently, there are two channels. The first channel is used to send processed messages to the cloud MQTT broker. The second channel is used to store data to the Influxdb database for local data persistence. Some third-party applications at the edge can directly invoke data from Influxdb for visualization. At the bottom layer, mappers are used to connect different data.

Application Scenarios of the Rule Engine in Kuiper

The built-in rule engine LF EdgeX Foundry was officially released in the Geneva version in April 2020.

EdgeX Foundry platform architecture
Built-in rule engine LF EdgeX Foundry architecture

Use Case: Data Format Conversion for Interconnection between Heterogeneous Systems

To realize data exchange with IT systems such as ERP and MES, flexible extensions are provided to easily collect and process data, even the heterogeneous data, using SQL built-in functions or extended functions. After the data is processed, the analysis result can be converted using the data template of the sink to flexibly adapt to various target systems that require different data formats and protocols. For example, for sending an instruction to control the device when the temperature is higher than 30°C to both the device and your WeChat, though different APIs and data are required, the rules are the same. Therefore, you can specify different topics in the same rule in data to send data without complex programming. SAP NetWeaver RFC SDK is used to read data from SAP, process and convert the data, and send the data to other heterogeneous systems.

Performance Data

  1. Kuiper supports concurrent running of thousands of rules.

Source: MQTT

SQL: select temperature from source where temperature>20 (90% data is filtered)

Target: logs

AWS: 2 cores x 4 GB


Memory: 89% ~ 72%; 0.4 MB/rule

GPU: 25%

  1. AWS t2.micro has 10k+/s message throughput.
Screenshot showing AWS and Ubuntu performance data

For more details about KubeEdge, visit