Python MQTT Interfaces - Developer Guide

Introduction

Python MQTT interface is a developer option provided by the MQTTRoute to extend the MQTT Broker to build IoT Application.  MQTTRoute is the perfect middleware to be used for collecting data from IoT edge devices. It is the highly extendable, customizable, and scalable Python MQTT Broker. This documentation on connecting to the broker provides a complete guide for developers to make use of the python MQTT hooks.

Python MQTT Interfaces

MQTTRoute is not only a simple middleware that just allows you to collect & publish client messages to an MQTT client connected. Instead it is a complete framework which permits you to build any complex IoT application.  MQTTRoute is provided with new powerful extensions that help you to configure MQTT Broker, manage, and build your own IoT / IIoT applications. All custom implementation provided can be done using the additional hooks. These hooks as of now are python based.

Custom Storage Configuration

The Custom Storage Configuration explains how to setup MQTT Broker storage options. MQTTRoute has an option to store data to Elastic by default. It has an extension wherein you can hook the received payload and store the data into any of your analytics / big data engine. If you are planning for storing the data in your own engine, you need to use the data_store.conf to configure your storage.

data_store.conf

[CUSTOM STORAGE]
CUSTOMSTORAGE = DISABLED
# ENABLED || DISABLED

DATASTORE = CUSTOM
# ELASTIC || CUSTOM

[ELASTIC]
HOSTNAME = 127.0.0.1
PORT = 9200
INDEX_NAME = mqtt
BULK_INSERT_TIMING = 2

[CUSTOM] INTERCEPT_FILEPATH = ./../extensions/custom_store.py

 
Enable CUSTOMSTORAGE option in data_store.conf file to send data to the Document data store in addition to the MySQL/SQLite/MS SQL Storage. To store data in the Elastic search, the value of DATASTORE needs to be specified as ELASTIC. For Custom implementation, the value of DATASTORE need to be specified as CUSTOMHOSTNAME, the hostname of the datastore you are using to store data. If you are using Elastic as a custom data store, then the hostname of Elastic search will be mentioned. PORT, the port of the custom data store you are using. INDEX_NAME, the index name in which you want to store data. It similar to DB name in MySQL. You can implement this method in your own file and then specify the path here: INTERCEPT_FILEPATH.

Custom Data Store

The Custom data store explains how to setup MQTT Broker data in any Big Data Engine. MQTT Route provides an option called the custom store to receive data at the back end to be stored as needed. The data received can be modeled and stored in any BIG data engine for further analysis and decision-making. Custom Store implementation is used to hook the received payload from MQTTBroker and store the payload in any of your analytics / big data engine. To store payload in any of your analytics, you need to use a custom_store.py file. To configure, you must enable the CUSTOMSTORAGE in data_store.conf file.

The custom data store hook for Big Data Storage. The Custom data hook can be enabled in the broker.conf inside conf/ folder. The parameter data will be in dict format and the keys are ‘sender’,’topic’, ‘message’, ‘unixtime’, ‘timestamp’.

custom_store.py

global db_cursor

#
# elastic_search cursor
#
global elastic_search
import os, sys

global datasend

SQL Connector will be SQLite / Mssql / Mysql cursor based on your configuration in db.conf. You have to construct your queries accordingly.

custom_store.py

global Client_obj

sys.path.append(os.getcwd()+’/../extensions’)

# Called on the initial call to set the SQL Connector

def setsqlconnector(conf):

global db_cursor
db_cursor=conf[“sql”]
# Called on the initial call to set the Elastic Search Connector

def setelasticconnector(conf):
global elastic_search
elastic_search=conf[“elastic”]
def setwebsocketport(conf):
global web_socket
web_socket=conf[“websocket”]

def setclientobj(obj):
global Client_obj
Client_obj=obj[‘Client_obj’]
#Client_obj

The Client object is used to send/publish the message to any active clients that subscribe to a topic. Simply call the function with parameters like User_name, Client_id, Topic_name, Message, QoS.

custom_store.py

# Importing the custom class into the handler

from customimpl import DataReceiver

datasend = DataReceiver()

def handle_Received_Payload(data):

#
# Write your code here. Use your connection object to
# Send data to your data store

print ” print in the handle_received_payload “,data

result = datasend.receive_data(data)

# if result is none then write failed
def handle_Sent_Payload(data):

#
# Write your code here. Use your connection object to
# Send data to your data store

print ” print in the handle_Sent_payload “,data

result = datasend.sent_data(data)

Now, you need to enable CUSTOMSTORAGE to receive messages from the devices into a python call back handle_Received_Payload(data). The method needs to be implemented in the python file specified in the INTERCEPT_FILEPATH.

Your implementation should receive the data and store it and return the method. We advise you just to store the data or hand over the data to a stream analysis and return the method handle.

Custom Scheduler

ML and AI work best when the data already collected are processed better. The Scheduling module helps in the processing of data on a predefined time interval. The Custom Scheduler will help you create your own schedule in MQTTRoute by adding your own code on the server-side.

def schedule_conf():

schedules={}

schedules={
‘STATUS’:’DISABLE’,
‘SCHEDULES’:[
{‘OnceIn’:1,’methodtocall’:oneminschedule},
{‘OnceIn’:5,’methodtocall’:fiveminschedule}]}

 return schedules

Enable / Disable your schedule by adding value as Enable / Disable in ‘STATUS’. You can add your schedule in MINUTES in ‘OnceIn’. Add your method to call on schedule in ‘methodtocall’.

global elastic_search
# Called on the initial call to set the SQL Connector

global web_socket
# Web_socket
def setsqlconnector(conf):
global db_cursor
db_cursor=conf[“sql”]

def setelasticconnector(conf):
global elastic_search
elastic_search=conf[“elastic”]

def setwebsocketport(conf):
global web_socket
web_socket=conf[“websocket”]

def setclientobj(obj):
global Client_obj
Client_obj=obj[‘Client_obj’]

def oneminschedule():
pass
#Write your code here
#print “extension print”

def fiveminschedule():
pass
#Write your code here
#print “extension print”

Custom UI Server

UI custom server provides an option to customize the user interface. It will help you customize the UI of the MQTTRoute by adding your own code on the server-side. You can alter the code in Custom_ui_server.py  file as you need to customize it. 

custom_ui_server.py

global Client_obj

# Called on the initial call to set the SQL Connector
def setsqlconnector(conf):

global db_cursor
db_cursor=conf[“sql”]

# Called on the initial call to set the Elastic Search Connector

def setelasticconnector(conf):
global elastic_search
elastic_search=conf[“elastic”]

def setclientobj(obj):
global Client_obj
Client_obj=obj[‘Client_obj’]

The Data connectors, SQL Connector will be provided as a cursor global variable for querying the Database and Elastic Search connector for querying Elastic if you have enabled the custom storage option.

custom_ui_server.py

#

# Configure your additional URLs here.
# The default URLs are currently used for the UI.
# Please don’t remove them, if you are building it over the same UI.
#

def custom_urls():

urllist={
“AUTHENTICATION”:’DISABLE’,
“urls”:[{“/extend/url1”:method},
{“/extend/url2”:method1},
{“/extend/url3”:method2}]
}
return urllist

# write your url function codes in the following methods
def method():
return (“BEVYWISE NETWORKS”)

def method1():
return (“BEVYWISE NETWORKS”)

def method2():
return (“BEVYWISE NETWORKS”)

Add your new functionality using the URL and the corresponding method. These URLs can be invoked from your User Interface for manipulating data. We support the GET Http method in this version.

Custom Authentication

MQTT Broker has custom authentication functionality which enables user to integrate MQTT Broker with any central identity access management (IAM) & Single Sign on (SSO).

broker.conf

[CONFIG]
PORT_NO = 1883
WS_PORT_NO = 10443

TLS_ENABLED = FALSE
# TLS_PORT must be 88xx.
TLS_PORT_NO = 8883
WSS_PORT_NO = 11443

########################Device Authentication ######################

[AUTHENTICATION]
AUTHENTICATION_ENABLED = NO
# YES || NO

######################## User Interface Details ######################

[UI]
UI_Http_Port = 8080
LIST_API_CLIENTS = FALSE

[WEBSOCKET]
WEBSOCKET_PORT=8081

############# prefix for Random Clientid Generation #####################

[MQTT]

CLIENTID_PREFIX = Bevywise-

CLEAR_SESSION = DEFAULT
# DEFAULT || DISABLED
################ ######### WEB LOGIN ############################
# Securing the Web login XXXX Need to be removed XXX

[WEB_LOGIN_PAGE]
WEB_LOGIN = ENABLED

WEB_USERNAME = admin
WEB_PASSWORD = admin

# ENABLED || DISABLED

################ #### REMOTE AUTHENTICATION ##################

[REMOTEAUTH]
REMOTEAUTH_ENABLED = NO
# YES || NO
INTERCEPT_FILEPATH = ./../extensions/custom_auth.py

 

How to enable custom authentication option

  • Open Bevywise/MQTTRoute/conf folder
  • In that, open broker.conf file. [ If you are a Windows user, you can either use a note pad or sublime to open the file ]
  • In broker.conf file, enable REMOTE AUTH field. By default it takes the value as NO
  • REMOTEAUTH_ENABLED = YES
  • Save the file and start running the MQTT broker.

Requesting Retries Count

When we attempt to connect to the server, some connection failures may happen eventually. This may be due to entering incorrect login credentials. In that case providing countable retries will be helpful. By entering request retries count, you can add or limit the retries attempt of the user.

 

extensions/custom_auth.py

 

# Request Retries Count
requests.adapters.DEFAULT_RETRIES = 3

# Request URL
url = “https://www.bevywise.com/auth”

# Request Timeout
request_timeout = 0.1

# Request Method
request_auth_method = “POST”
# POST | GET | PUT

 

  • Open Bevywise/MQTTRoute/extensions folder
  • In that, open custom_auth.py file. [ If you are a Windows user, you can either use a note pad or sublime to open the file ]
  • In custom_auth.py file, enter number of request entries as per your need.
  • requests.adapters.DEFAULT_RETRIES = 3 (By default the value will be set as 3)

Setting the request URL

Enter the URL of your authentication landing page. This authenticates the user attempting to connect with their login credentials.

In custom_auth.py file, provide the URL,

url = “https://www.bevywise.com/auth”

 

Request Timeout

Timeout is generally the time duration or interval that an application waits for the response from the client. These values are probably given in seconds or milliseconds.

 To set the request timeout,

 Open custom_auth.py file in extensions folder and enter timeout value in the space given.

request_timeout = 0.1 (By default it carries the value of 0.1)

 

Selecting Request Method

You can select the request method from the set of HTTP’s request methods to indicate the desired method to be performed.

GET – Requesting data from a specified resource.

POST – Submit or send data to the specified resource.

PUT –  Replacing the existing data of the target resource

Open custom_auth.py file in extensions folder and enter auth method in the space given.

request_auth_method = “POST”

Set all your configurations, save the file & start running the broker.

MQTTRoute comes up with the complete internet of things application including user interface customizationdata aggregation & analysisevent data comparison with the processed data. The new IoT application framework will help to build and manage the industrial IoT applications faster and much easier within a single process.

Python MQTT Broker plugin

The ready to use Python MQTT Broker plugins help you connect MQTT broker to the Elastic Search, Mongo DB and Redis. You can test trial these plugins by connecting MQTT Broker with any standard MQTT client or download one from client library.

MongoDB Connector

MongoDB is one of the most widely used Document Storage engines for IoT data analysis. This plugin connects Bevywise MQTT Broker with the MongoDB to store received payload data into MongoDB. It helps you handle complex data in an easy manner and for powerful analysis. The below documentation explains how to configure and setup MQTT Broker in MongoDB.

Configure and Set up MQTTRoute-MongoDB-connector

1. Open plugin.conf and configure the 

  • Update hostname and port no of the MongoDB server in MONGO section
  • If AUTHENTICATION is enabled in MQTTRoute, then update the MongoDB credentials otherwise set AUTHENTICATION_ENABLED = FALSE.
  • Update log file path to your own folder location. [default = Bevywise/MQTTRoute/extensions].

plugin.conf

[MONGO]

HOSTNAME = 127.0.0.1

PORT = 27017

DB_NAME = bevywise

COLLECTION = mqttroute

[AUTHENTICATION]
AUTHENTICATION_ENABLED = FALSE
# TRUE || FALSE
USERNAME = root
PASSWORD = root

[LOG]
LOG_FILE_PATH = ../extensions

2. Copy the folder mongo and paste it into Bevywise/MQTTRoute/extensions.

3. Copy the folder plugin.conf and paste it into Bevywise/MQTTRoute/extensions.

4. Replace custom_store.py with Bevywise/MQTTRoute/extensions/custom_store.py.

5. Open Bevywise/MQTTRoute/conf/data_store.conf.

  • update CUSTOM STORAGE = ENABLED
  • update DATA STORE = CUSTOM

6. Start the MQTTRoute and it will start storing all the payload into the Mongo DB server.

Redis Connector

This Python MQTT plugin connects MQTTRoute with the Redis server to store all the payloads to the Redis server for further processing.

 

Configure and Set up MQTTRoute-Redis-connector

1.Replace”custom_store.py” with Bevywise/MQTTRoute/lib/custom_store.py.

2. In custom_store.py change the server name and port of the Redis if you are running Redis on a different server or port.

custom_store.py

redishost=‘localhost’

redisport=6379

 

3. Then, Open Bevywise / MQTTRoute / conf / data_store.conf

  • update CUSTOM STORAGE = ENABLED
  • update DATA STORE = CUSTOM

4. Start the MQTTRoute and it will start storing all the payload into the Redis server with clientId_unixtime as the key.

Elastic Connector

MQTT Broker store data to Elastic search via custom implementation for better data visualization. The published payload only push to the Elastic search which helps you hook it and send to your data visualization tool.

Configure and Set up MQTTRoute-Elasticsearch-connector

1. Open plugin.conf and configure the 

  • Update hostname and port no of the Elastic search
  • Update log file path to your own folder location. [default = Bevywise/MQTTRoute/extensions].

plugin.conf

[ELASTIC]

HOSTNAME = 127.0.0.1
PORT = 9200
INDEX_NAME = mqttroute

[LOG]
LOG_FILE_PATH = ../extensions

2. Copy the folder plugin.conf and paste it into Bevywise/MQTTRoute/extensions.

3. Copy the folder Elastic and paste it into Bevywise/MQTTRoute/extensions.

4.Replace custom_store.py with Bevywise / MQTTRoute / extensions / custom_store.py.

5.Open Bevywise / MQTTRoute / conf / data_store.conf.

  • update CUSTOM STORAGE = ENABLED
  • update DATA STORE = ELASTIC

6. Start the MQTTRoute and it will start storing all the payload into the Elastic search server.

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