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Welcome to the eKuiper Wiki

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LF Edge eKuiper is

an edge

a lightweight IoT data analytics

/ streaming software implemented by Golang, and it can be run at all kinds of resource constrained

and stream processing engine running on resource-constraint edge devices.


The major goal


for eKuiper is to

migrate the cloud

provide a streaming software

frameworks (such as Apache SparkApache Storm and Apache Flink) to edge side. eKuiper references these cloud streaming frameworks, and also considered special requirement of edge analytics, and introduced rule engine, which is based on SourceSQL (business logic) and Sink, rule engine is used for developing streaming applications at edge side.Image Removed

framework (similar to Apache Flink) in edge side. eKuiper's rule engine allows user to provide either SQL based or graph based (similar to Node-RED) rules to create IoT edge analytics applications within few minutes.

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User scenarios

It can be run at various IoT edge


user scenarios, such as



  • Real-time processing of production line data in the IIoT
  • Gateway of
Connected Vehicle
  • connected vehicle analyze the data from
data-bus in real time;
  • CAN in IoV
  • Real-time analysis of
urban facility
  • wind turbines and smart bulk energy storage data in smart
city scenarios.
  • energy

eKuiper processing at the edge can greatly reduce system response latency, save network bandwidth and storage costs


and improve system security.


  • Lightweight

    • Core server package is only about 4.5M,
    • memory footprint is about 10MB
  • Cross-platform

    • CPU Arch:X86 AMD * 32
, X86 AMD *
    • /64; ARM * 32
, ARM *
    • /64; PPC
The popular
    • Popular Linux distributions, OpenWrt Linux, MacOS and Docker
    • Industrial PC, Raspberry Pi, industrial gateway, home gateway, MEC edge cloud server
  • Data analysis support

    • Support data
extract, transform and filter through SQL
    • ETL
    • Data order, group, aggregation and join with different data sources (the data from databases and files)
    • 60+ functions, includes mathematical, string, aggregate and hash etc
    • 4 time windows & count window
  • Highly

extensibilePlugin system is provided, and it
  • extensible

    It supports to extend at Source, 

SQL functions
  • Functions and Sink with Golang or Python.

    • Source:
embedded support for MQTT, and provide extension points for sources
  • Sink: embedded support for MQTT and HTTP, and provide extension points for sinks
  • UDF functions: embedded support for 60+ functions, and provide extension points for SQL functions
      • allows users to add more data source for analytics.
      • Sink: allows users to send analysis result to different customized systems.
      • UDF functions: allow users to add customized functions for data analysis (for example, AI/ML function invocation)
    • Management


    A nodes, plugins, streams & rules
      • visualized management
      • Plugins, streams and rules management through CLI
      • , REST API and config maps(Kubernetes)
      • Easily be
    integrate and , which bases Kubernetes
    • Integration with

    EMQ X Edge
    • EMQX products

      Seamless integration with

    EMQ X Neuron & EMQ X Edge messaging to analytics.
    • IIoT, IoV

    Quick Start

    Performance Test Result

    MQTT throughput test

    • Using JMeter MQTT plugin to send simulation data to EMQ X Broker, such as: {"temperature": 10, "humidity" : 90}, the value of temperature and humidity are random integer between 0 - 100.
    • eKuiper subscribe from EMQ X Broker, and analyze data with SQL: SELECT * FROM demo WHERE temperature > 50
    • The analysis result are wrote to local file by using file sink plugin.
    DevicesMessage # per secondCPU usageMemory usage
    Raspberry Pi 3B+12ksys+user: 70%20M
    AWS t2.micro( 1 Core * 1 GB)
    10ksys+user: 25%20M

    EdgeX throughput test

    • A Go application is wrote to send data to ZeroMQ message bus, the data is as following.

        "Device": "demo", "Created": 000, …
           {"Name": "Temperature", value: "30", "Created":123 …},
           {"Name": "Humidity", value: "20", "Created":456 …}
    • eKuiper subscribe from EdgeX ZeroMQ message bus, and analyze data with SQL: SELECT * FROM demo WHERE temperature > 50. 90% of data will be filtered by the rule.

    • The analysis result are sent to nop sink, all of the result data will be ignored.

    Message # per secondCPU usageMemory usage
    AWS t2.micro( 1 Core * 1 GB)
    11.4 ksys+user: 75%32M

    Max number of rules support

    • 8000 rules with 800 message/second
    • Configurations
      • 2 core * 4GB memory in AWS
      • Ubuntu
    • Resource usage
      • Memory: 89% ~ 72%
      • CPU: 25%
      • 400KB - 500KB / rule
    • Rule
      • Source: MQTT
      • SQL: SELECT temperature FROM source WHERE temperature > 20 (90% data are filtered)
      • Sink: Log

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