How to Build an ETL App for RabbitMQ Data in Python with CData

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 and the petl framework, you can build RabbitMQ-connected applications and pipelines for extracting, transforming, and loading RabbitMQ data. This article shows how to connect to RabbitMQ with the CData Python Connector and use petl and pandas to extract, transform, and load 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 petl
pip install pandas

Build an ETL App for RabbitMQ Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL 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 petl as etl
import pandas as pd
import cdata.api as mod

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;")

Create a SQL Statement to Query RabbitMQ

Use SQL to create a statement for querying RabbitMQ. In this article, we read data from the AuthAttempts entity.

sql = "SELECT ,  FROM AuthAttempts WHERE NodeName = 'rabbit@hostname'"

Extract, Transform, and Load the RabbitMQ Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the RabbitMQ data. In this example, we extract RabbitMQ data, sort the data by the column, and load the data into a CSV file.

Loading RabbitMQ Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

etl.tocsv(table2,'authattempts_data.csv')

With the CData API Driver for Python, you can work with RabbitMQ data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

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

Full Source Code

import petl as etl
import pandas as pd
import cdata.api as mod

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

sql = "SELECT ,  FROM AuthAttempts WHERE NodeName = 'rabbit@hostname'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

etl.tocsv(table2,'authattempts_data.csv')