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Welcome to the eKuiper Wiki



Overview

eKuiper is an edge lightweight IoT data analytics / streaming software implemented by Golang, and it can be run at all kinds of resource constrained edge devices. One goal of eKuiper is to migrate the cloud 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.

User scenarios

It can be run at various IoT edge use scenarios, such as real-time processing of production line data in the IIoT; Gateway of Connected Vehicle analyze the data from data-bus in real time; Real-time analysis of urban facility data in smart city scenarios. eKuiper processing at the edge can reduce system response latency, save network bandwidth and storage costs, and improve system security.

Features

  • Lightweight

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

    • CPU Arch:X86 AMD * 32, X86 AMD * 64; ARM * 32, ARM * 64; PPC
    • The 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
    • Data order, group, aggregation and join
    • 60+ functions, includes mathematical, string, aggregate and hash etc
    • 4 time windows & count window
  • Highly extensibile

    Plugin system is provided, and it supports to extend at Source, SQL functions and Sink.

    • 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
  • Management

  • Integration with EMQ X Edge

    Seamless integration with EMQ X Neuron & EMQ X Edge, and provided an end to end solution from messaging to analytics.

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)
Ubuntu18.04
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, …
      "readings": 
      [
         {"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)
Ubuntu18.04
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


Help Us Improve the Wiki

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