How to connect and process AlloyDB 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 AlloyDB data. This article explains how to host the CData JDBC Driver in Azure, as well as connect to and process live AlloyDB data in Databricks.

With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live AlloyDB data. When you issue complex SQL queries to AlloyDB, the driver pushes supported SQL operations, like filters and aggregations, directly to AlloyDB 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 AlloyDB data using native data types.

Install the CData JDBC Driver in Azure

To work with live AlloyDB 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. 👁 Get Access Key
   
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

   👁 Get JAR URL
   
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 👁 Apply Account Key
   
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

   👁 Install ADLS Library
   

Connect to AlloyDB from Databricks

With the JAR file installed, we are ready to work with live AlloyDB 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).

👁 Attaching to an existing compute resource

Configure the Connection to AlloyDB

Connect to AlloyDB 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 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.alloydb.AlloyDBDriver"
url = "jdbc:alloydb:RTK=5246...;User=alloydb;Password=admin;Database=alloydb;Server=127.0.0.1;Port=5432"

Built-in Connection String Designer

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

java -jar cdata.jdbc.alloydb.jar

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

The following connection properties are usually required in order to connect to AlloyDB.

• Server: The host name or IP of the server hosting the AlloyDB database.
• User: The user which will be used to authenticate with the AlloyDB server.
• Password: The password which will be used to authenticate with the AlloyDB server.

You can also optionally set the following:

• Database: The database to connect to when connecting to the AlloyDB Server. If this is not set, the user's default database will be used.
• Port: The port of the server hosting the AlloyDB database. This property is set to 5432 by default.

Authenticating with Standard Authentication

Standard authentication (using the user/password combination supplied earlier) is the default form of authentication.

No further action is required to leverage Standard Authentication to connect.

Authenticating with pg_hba.conf Auth Schemes

There are additional methods of authentication available which must be enabled in the pg_hba.conf file on the AlloyDB server.

Find instructions about authentication setup on the AlloyDB Server here.

Authenticating with MD5 Authentication

This authentication method must be enabled by setting the auth-method in the pg_hba.conf file to md5.

Authenticating with SASL Authentication

This authentication method must be enabled by setting the auth-method in the pg_hba.conf file to scram-sha-256.

Authenticating with Kerberos

The authentication with Kerberos is initiated by AlloyDB Server when the ∏ is trying to connect to it. You should set up Kerberos on the AlloyDB Server to activate this authentication method. Once you have Kerberos authentication set up on the AlloyDB Server, see the Kerberos section of the help documentation for details on how to authenticate with Kerberos. 👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

Load AlloyDB Data

Once the connection is configured, you can load AlloyDB 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" , "Orders") \
	.load ()

Display AlloyDB Data

Check the loaded AlloyDB data by calling the display function.

display (remote_table.select ("ShipName"))

👁 Displaying AlloyDB Data

Analyze AlloyDB 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 AlloyDB data for analysis.

result = spark.sql("SELECT ShipName, ShipCity FROM SAMPLE_VIEW WHERE ShipCountry = 'USA'")

The data from AlloyDB 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" )

👁 Displaying AlloyDB Data

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