Data Collection

We can communicate with deviceShifu via HTTP/gRPC. deviceShifu will convert the request we send into the form of the protocol supported by the device and send it to the device.

When the device receives the command, the data will be transferred to deviceShifu, after that deviceShifu will return the data as the return value of our request, thus realizing the data acquisition.

Automated Data Collection

First, we can write the following program to automate data acquisition. This program is used to perform real-time data acquisition on the previously running edgedevice-thermometer device, parse the temperature data and output it. The program can be written in any language and in any form, and you can store the data in your database or in a file.

package main  

import (  
   "log"   
   "io/ioutil"   
   "net/http"   
   "strconv"   
   "time"
)  

func main() {  
   targetUrl := "http://edgedevice-thermometer/read_value"
   req, _ := http.NewRequest("GET", targetUrl, nil)
   for{
      res, _ := http.DefaultClient.Do(req)
      body, _ := ioutil.ReadAll(res.Body)
      temperature, _ := strconv.Atoi(string(body))     
      if temperature > 20 {
         log.Println("High temperature:", temperature)
      } else if temperature > 15 {
         log.Println("Normal temperature:", temperature)
      } else {
         log.Println("Low temperature:", temperature)
      }
      Body.Close()
      Time.Sleep(2 * time.Second)
   }
}

Use go mod init high-temperature-detector to generate the go.mod file.

For the above program, we can package it as a docker image and load it into the cluster so that it can better communicate with deviceShifu. Create the following Dockerfile file.

# syntax=docker/dockerfile:1  

FROM golang:1.17-alpine  
WORKDIR /app  
COPY go.mod . /  
RUN go mod download  
COPY *.go . /RUN  
RUN go build -o /high-temperature-detector  
EXPOSE 11111  
CMD [ "/high-temperature-detector" ]

To generate a docker image using the Dockerfile file, execute the following command.
```
docker build --tag high-temperature-detector:v0.0.1
```
After that we load the docker image into the cluster with the following command.
```
kind load docker-image high-temperature-detector:v0.0.1
```

To run the data acquisition program we wrote, run the following command.

kubectl run high-temperature-detector --image=high-temperature-detector:v0.0.1

Finally, to view the logs of the program and get the data, we need to execute the following command:

kubectl logs high-temperature-detector -f

The result of the data obtained is as follows.

2021/10/18 10:35:35 High temperature: 24  
2021/10/18 10:35:37 High temperature: 23  
2021/10/18 10:35:39 Low temperature: 15  
2021/10/18 10:35:41 Low temperature: 11  
2021/10/18 10:35:43 Low temperature: 12  
2021/10/18 10:35:45 High temperature: 28  
2021/10/18 10:35:47 Low temperature: 15  
2021/10/18 10:35:49 High temperature: 30  
2021/10/18 10:35:51 High temperature: 30  
2021/10/18 10:35:53 Low temperature: 15

Data Collection

Automated Data Collection​

Automated Data Collection