LF Edge eKuiper is a lightweight IoT data analytics and stream processing engine running on resource-constraint edge devices. The major goal for eKuiper is to provide a streaming software 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.
User scenarios
It can be run at various IoT edge user scenarios, such as,
eKuiper processing at the edge can greatly reduce system response latency, save network bandwidth and storage costs and improve system security.
Lightweight
Cross-platform
Data analysis support
Highly extensible
It supports to extend at Source
, Functions
and Sink
with Golang or Python.
Management
Integration with EMQX products
Seamless integration with EMQX, Neuron & NanoMQ, and provided an end-to-end solution from IIoT, IoV
{"temperature": 10, "humidity" : 90}
, the value of temperature and humidity are random integer between 0 - 100.SELECT * FROM demo WHERE temperature > 50
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 |
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 second | CPU usage | Memory usage | |
---|---|---|---|
AWS t2.micro( 1 Core * 1 GB) Ubuntu18.04 | 11.4 k | sys+user: 75% | 32M |
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