How to connect and process RabbitMQ data from Azure Databricks

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live RabbitMQ data. This article explains how to host the CData JDBC Driver in Azure, as well as connect to and process live RabbitMQ data in Databricks.

With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live RabbitMQ data. When you issue complex SQL queries to 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). Its built-in dynamic metadata querying allows you to work with and analyze RabbitMQ data using native data types.

Install the CData JDBC Driver in Azure

To work with live RabbitMQ data in Databricks, install the driver through Azure Data Lake Storage (ADLS). (Please note that the method of connecting through DBFS, which previous versions of this article described, has been deprecated, but has not published an end-of-life.)

1. Upload the JDBC JAR file to a blob container of your choice (i.e. "jdbcjars" container of the "databrickslibraries" storage account).
2. Fetch the Account Key from the storage account by expanding "Security + networking" and clicking on "Access Keys". Show and copy whichever of the two keys you wish to use.
3. Get the JDBC JAR file's URL by navigating to Containers, opening the specific container storing the JAR, and selecting the entry for the JDBC JAR file. This should open the file's details, where there should be a convenient button to copy the URL button to clipboard. This value will look similar to the below, though the "blob" component may vary depending on storage account type:

   https://databrickslibraries.blob.core.windows.net/jdbcjars/cdata.jdbc.salesforce.jar

   
4. In the Configuration tab of your Databricks cluster, click on the Edit button and expand "Advanced options". From there, add the following Spark option (derived from the JAR URL's domain name) with your copied Account key as its value and click Confirm: spark.hadoop.fs.azure.account.key.databrickslibraries.blob.core.windows.net
5. In the Libraries tab of your Databricks cluster, click on "Install new", and select the ADLS option. Specify the ABFSS URL for the driver JAR (also derived from the JAR URL's domain name), and click Install. The ABFSS URL should resemble the below:

   abfss://[email protected]/cdata.jdbc.salesforce.jar

   

Connect to RabbitMQ from Databricks

With the JAR file installed, we are ready to work with live RabbitMQ data in Databricks. Start by creating a new notebook in your workspace. Name the workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).

Configure the Connection to RabbitMQ

Connect to RabbitMQ by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

driver = "cdata.jdbc.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\\RabbitMQ.apip;AuthScheme=Basic;URL=http://localhost:15672;User=guest;Password=guest;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the RabbitMQ JDBC Driver. Either double-click the JAR file or execute the JAR file from the command-line.

java -jar cdata.jdbc.api.jar

Fill in the connection properties and copy the connection string to the clipboard.

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

Load RabbitMQ Data

Once the connection is configured, you can load RabbitMQ data as a dataframe using the CData JDBC Driver and the connection information.

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "AuthAttempts") \
	.load ()

Display RabbitMQ Data

Check the loaded RabbitMQ data by calling the display function.

display (remote_table.select (""))

Analyze RabbitMQ Data in Azure Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the RabbitMQ data for analysis.

result = spark.sql("SELECT ,  FROM SAMPLE_VIEW WHERE NodeName = 'rabbit@hostname'")

The data from RabbitMQ is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData API Driver for JDBC and start working with your live RabbitMQ data in Azure Databricks. Reach out to our Support Team if you have any questions.