Python MQTT Integration - Developer Guide

Introduction

Internet of things today generates a large amount of data. That data gets exchanged between the broker and clients. We have built Python MQTT interface. It enables application developers to hook in the data from the MQTT Broker. These hooks help build a highly unified application. And at the same time reduce the bandwidth required by half.

This documentation is a comprehensive guide for developers on using Python MQTT hooks. It covers topics such as connecting to the broker, and sending messages. Also it provides a deep tech view on configuring the MQTT Broker. Refer to the sample IoT application that is built using the Python MQTT hooks available in the MQTTRoute.

Python MQTT Interfaces

MQTTRoute is a python based MQTT Broker with a publish subscribe messaging pattern. It provides absolute support for the MQTT protocol . Developers use Python's flexibility to enable an interface for extending the implementation. They use it to build complex IoT applications. Users can extend the MQTT Broker's python modules by connecting to any big data engine. The ready-to-use python interfaces include:

Additional Python interfaces to extend the MQTT Broker & develop complex IoT applications include :

  • Custom UI Server
  • Custom storage
  • Custom scheduler
  • Custom Data Store
  • Custom Authentication

We’ll go over each of these in detail here. The mandate of any IoT Application is

  • Flexibility in Storage of the Data as required for the application
  • Analysis of the data at periodic intervals
  • Integration of authentication like LDAP and other IAM application for device authentication.
  • Customizing the User Interface for visual data presentation.

The MQTT Broker includes the Python MQTT Interface. It helps you achieve complete control over your IoT application development.

Python MQTT Hook 1 : Custom Data Storage

Custom Data Storage allows users to choose a unique database for their application. It collects the data in the database of your choice. With MQTTRoute user has the ability to store data in the following, databases by default.

  • MySQL
  • SQLite
  • PostgreSQL
  • MSSQL
  • ElasticSearch

You can enable these data storage options without a single line of code. You can enable these options in the conf/data_store.conf

We have added the Custom Data Storage Hook. It ensures that MQTT Broker supports all the kind of data storage available. You will be able to enable the Custom storage hook on the same data_store.conf file. The next section will explain how to configure the data storage.

Steps to configure Custom Data Store

  • Open the data_store.conf
  • Make the CUSTOMSTORAGE option as ENABLED. Selecting this option sends received messages from clients to the custom_store.py file. It also includes the default data storage.
  • To store data in the Elastic search, the value of DATASTORE needs to be specified as ELASTIC. The same can be done for all databases. HOSTNAME, the host name or the IP address of the MQTT Broker. MQTT Broker address is 127.0.0.1. PORT, the port of the custom data store you are using. Here it is mentioned as 9200 since the port number of Elastic search is 9200. If you want to utilise a different database, you must specify the appropriate port. The port number for MYSQL is 3306, MSSQL is 1434, and PostgreSQL is 5432. INDEX_NAME specify the name under which data should be stored. This looks like the name of a database. The index name is which you want to store data.
  • If you are planning to have your own data storage, the value of DATASTORE need to be specified as CUSTOM. When you configure custom settings, the MQTT data will be sent to the handle_Received_Payload() method. This method handles the data received from clients. And the data sent to the clients from the broker will be sent to the handle_Sent_Payload() method. This method is present in the python file specified in INTERCEPT_FILEPATH.

data_store.conf


#############################CUSTOM STORAGE###########################

# def handle_Received_Payload(data)

[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

Additional object references

The custom_store.py file offers the following Python object references. These references allow for a closer integration with the MQTT Broker.

  • db_cursor – the DB reference to work with the database. You will be able to read and write data into the database. The reference of the DB set on the conf file will be provided.
  • elastic_search – Elastic Search cursor if the elastic option is enabled
  • datasend – Python object that can be used to send any MQTT messages to the connected mqtt clients.
  • web_socket – UI Websocket object for sending data to the UI for any event notifications. This will be helpful on Custom UI implementation.

Refer to the files below and the same available in the /extensions/ folder for further help

custom_store.py

global db_cursor
#

# elastic_search cursor

#

global elastic_search
import os, sys

global datasend

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

custom_store.py

# Importing the custom client 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)

Implementation Bonus TIP

After configuring the custom data storage, you will receive the python message callback functions in your python file. These callback functions are used for the method handle_Received_Payload(data)

Your implementation should receive the data and store it and return the method. You should do your data analysis in a separate thread.

Python MQTT Hook 2 : Custom Scheduler

When data that has already been acquired is better processed, ML and AI perform best. The Scheduling module assists in the processing of data over a period of time. The Custom Scheduler will assist you in creating your own MQTTRoute schedule. This aids you to aggregate your data by allowing you to add your own code on the server-side.

For example, consider checking the water level in a home. The custom scheduler will help to set a reminder to check the status of the water level in the tank.

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”

Now start adding your query in ‘#Write your code here‘ field.

Python MQTT Hook 3 : Custom UI Server

UI custom server provides an option to customize the user interface. By default, MQTTRoute comes up with the custom dashboard option with rich widgets. Those widgets helps to visualize the data in a way user need. But to customize UI for advanced & high level IoT / IIoT implementation, Custom UI server will be the right option. You can add your own code on the server-side. This allows you to customize the UI of the MQTT server very straightforward. You can alter the code in Custom_ui_server.py file as you need to customize it.

custom_ui_server.py

#
# SQL Connector. It will be sqlite / mssql / mysql cursor based
# on your configuration in db.conf
# Please construct your queries accordingly.

#
import sys
global db_cursor
#username’,’client_send_api’,’topic_name’,’message’,1,0,’10’,0

# elasstic_search cursor.

global elastic_search

from datetime import datetime

import json

import ast
import time
try :
from elasticsearch import Elasticsearch

elastic_search = Elasticsearch(host=”localhost”, port=9200)
except Exception as e :
print(e)
import os, sys

#
#Client object. It used to send/publish client message to any active clients
#Simply call the helper functions with parameters like User_name,Client_id,Topic_name,Message,QOS, to subscribe to a topic / subscribe client

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, like the SQL Connector, will be available as a cursor global variable for querying the Database. The Elastic Search connector will also be available for querying Elastic if you have enabled the custom storage option.

Python MQTT Hook 4 : Custom Authentication

MQTT Broker has custom authentication functionality. It enables user to integrate MQTT Broker with any 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 Client id Generation #####################

[MQTT]

CLIENTID_PREFIX = Bevywise-

# The clean session to be handled by the broker. If the clear_session is marked as # disabled, then every connection will be made fresh.

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. 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

It is 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

There are a set of HTTP's request methods. You can select anyone to indicate the desired method to be performed.

GET – Requesting data from a specified resource.

POST – Submit or publish messages 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 offers a complete Internet of Things application. It includes user interface customization, data aggregation, and analysis. Additionally, it enables event data comparison with the processed data. This IoT application framework will help you build & manage industrial IoT applications faster. It also makes the process much easier within a single process.

Python MQTT Broker plugin

The ready to use plugins help you connect MQTT broker to the Elastic Search, Mongo DB & Redis.You can test trial these plugins by connecting MQTT Broker with any standard MQTT client. For instance, you can use the Paho Python client or Mosquitto client. Otherwise, you can also download one from the 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 MongoDB. It allows the storage of received payload data from connected clients 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 set 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

In the database, the Redis connector uses both clustered and nonclustered connections. Only password-based authentication is supported by Redis Connector. This Python MQTT plugin connects MQTTRoute with the Redis server. It allows 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 & 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. It will start to store 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. This ensures better data visualization. The published payload only push to the Elastic search. This 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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