EMQ X Kuiper 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 Kuiper is to migrate the cloud streaming software frameworks (such as Apache Spark,Apache Storm and Apache Flink) to edge side. Kuiper references these cloud streaming frameworks, and also considered special requirement of edge analytics, and introduced rule engine, which is based on Source
, SQL (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. Kuiper processing at the edge can reduce system response latency, save network bandwidth and storage costs, and improve system security.
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Lightweight
- Core server package is only about 4.5M, initial memory footprint is about 10MB
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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
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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
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Highly extensibile
Plugin system is provided, and it supports to extend at
Source
,SQL functions
andSink
.- 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
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Management
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Integration with EMQ X Edge
Seamless integration with EMQ X Edge, and provided an end to end solution from messaging to analytics.
Join our Slack, and then join kuiper channel.
- 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. - Kuiper 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.
Devices | Message # per second | CPU usage | Memory usage |
---|---|---|---|
Raspberry Pi 3B+ | 12k | sys+user: 70% | 20M |
AWS t2.micro( 1 Core * 1 GB) Ubuntu18.04 |
10k | sys+user: 25% | 20M |
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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 …} ] }
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Kuiper 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 second | CPU usage | Memory usage | |
---|---|---|---|
AWS t2.micro( 1 Core * 1 GB) Ubuntu18.04 |
11.4 k | sys+user: 75% | 32M |
- 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
- Go version >= 1.13
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Binary:
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Binary:
$ make
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Binary files that support EdgeX:
$ make build_with_edgex
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Packages:
$ make pkg
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Packages:
$ make pkg
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Packages files that support EdgeX:
$ make pkg_with_edgex
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Docker images:
$ make docker
Docker images support EdgeX by default
To using cross-compilation, refer to this doc.