Use Dash to Build to Web Apps on RabbitMQ Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas module, and the Dash framework, you can build RabbitMQ-connected web applications for RabbitMQ data. This article shows how to connect to RabbitMQ with the CData Connector and use pandas and Dash to build a simple web app for visualizing RabbitMQ data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live RabbitMQ data in Python. When you issue complex SQL queries from RabbitMQ, the driver pushes supported SQL operations, like filters and aggregations, directly to RabbitMQ and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to RabbitMQ Data

Connecting to RabbitMQ data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

About RabbitMQ Management HTTP API

RabbitMQ is an open-source message broker that supports multiple messaging protocols. The RabbitMQ Management HTTP API provides HTTP-based access to management and monitoring data for a RabbitMQ server. The API exposes information about virtual hosts, exchanges, queues, bindings, connections, channels, consumers, users, permissions, policies, and cluster-wide statistics.

The Management plugin must be enabled on the RabbitMQ server for the HTTP API to be available. By default, the management interface listens on port 15672.

Using Basic Authentication

RabbitMQ Management HTTP API uses HTTP Basic authentication. You must supply the username and password of a RabbitMQ management user.

To enable access to the management API:

1. Ensure the RabbitMQ Management plugin is enabled on your server (rabbitmq-plugins enable rabbitmq_management).
2. Use an existing management user or create one with the appropriate management tag (management, policymaker, monitoring, or administrator).
3. Note the full base URL of your RabbitMQ Management HTTP API (e.g., http://localhost:15672).

After configuring your RabbitMQ server, set the following connection properties to connect:

• AuthScheme: Set this to Basic.
• URL: Set this to the base URL of your RabbitMQ Management HTTP API (e.g., http://localhost:15672).
• User: Set this to your RabbitMQ management username (e.g., guest).
• Password: Set this to your RabbitMQ management password.

Example connection string:

Profile=C:\profiles\RabbitMQ.apip;AuthScheme=Basic;URL=http://localhost:15672;User=guest;Password=guest;

Available Tables

The RabbitMQ profile provides access to the following tables:

• Overview - Cluster-wide statistics and information about the RabbitMQ node
• Nodes - Information about individual nodes in the RabbitMQ cluster
• NodeMemory - Detailed memory usage breakdown for a specific cluster node
• Connections - List of all open AMQP connections to the broker
• Channels - List of all open AMQP channels across all connections
• Consumers - List of all consumers registered across all queues
• Exchanges - List of exchanges declared across all virtual hosts
• Queues - List of queues declared across all virtual hosts
• Bindings - List of all bindings between exchanges and queues
• VirtualHosts - List of virtual hosts configured on the broker
• VhostPermissions - User permissions within a specific virtual host
• Users - List of all RabbitMQ users
• Permissions - Permission records for all users across all virtual hosts
• TopicPermissions - Topic-level permission records for all users
• Policies - List of policies applied to queues and exchanges in virtual hosts
• OperatorPolicies - List of operator policies applied to queues in virtual hosts
• Parameters - List of component parameters (e.g., federation, shovel) per virtual host
• GlobalParameters - List of global parameters that apply across all virtual hosts
• VhostLimits - Resource limits configured for specific virtual hosts
• UserLimits - Resource limits configured for specific users
• FeatureFlags - List of feature flags and their enabled/disabled state on the node
• DeprecatedFeatures - List of deprecated features and their usage state
• AuthAttempts - Authentication attempt statistics for the node
• ClusterName - The name of the RabbitMQ cluster
• WhoAmI - Information about the currently authenticated management user
• ExchangeBindingsSource - Bindings for which a specific exchange is the source
• ExchangeBindingsDestination - Bindings for which a specific exchange is the destination
• QueueBindings - Bindings for a specific queue within a virtual host

After installing the CData RabbitMQ Connector, follow the procedure below to install the other required modules and start accessing RabbitMQ through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize RabbitMQ Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData RabbitMQ Connector to create a connection for working with RabbitMQ data.

cnxn = mod.connect("Profile=C:\profiles\\RabbitMQ.apip;AuthScheme=Basic;URL=http://localhost:15672;User=guest;Password=guest;")

Execute SQL to RabbitMQ

Use the read_sql function from pandas to execute any SQL statement and store the result set in a DataFrame.

df = pd.read_sql("SELECT , FROM AuthAttempts WHERE NodeName = 'rabbit@hostname'", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-apiedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our RabbitMQ data and configure the app layout.

trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='RabbitMQ AuthAttempts Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
 app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the RabbitMQ data.

python api-dash.py

👁 RabbitMQ data in a Dash web app (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to RabbitMQ data. Reach out to our Support Team if you have any questions.

---

Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\\RabbitMQ.apip;AuthScheme=Basic;URL=http://localhost:15672;User=guest;Password=guest;")

df = pd.read_sql("SELECT , FROM AuthAttempts WHERE NodeName = 'rabbit@hostname'", cnxn)
app_name = 'dash-apidataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='RabbitMQ AuthAttempts Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
 app.run_server(debug=True)