How to connect and process GraphQL 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 GraphQL data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live GraphQL data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live GraphQL data in Databricks, install the driver on your Azure cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "DBFS" as the Library Source and "JAR" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.graphql.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for GraphQL\lib).

Connect to GraphQL from Databricks

With the JAR file installed, we are ready to work with live GraphQL 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 GraphQL

Connect to GraphQL 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.graphql.GraphQLDriver"
url = "jdbc:graphql:RTK=5246...;AuthScheme=Basic;User=username;Password=password;URL=https://mysite.com;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

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

You must specify the URL of the GraphQL service. The driver supports two types of authentication:

• Basic: Set AuthScheme to Basic. You must specify the User and Password of the GraphQL service.
• OAuth 1.0 & 2.0: Take a look at the OAuth section in the Help documentation for detailed instructions.

Load GraphQL Data

Once the connection is configured, you can load GraphQL 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" , "Users") \
	.load ()

Display GraphQL Data

Check the loaded GraphQL data by calling the display function.

display (remote_table.select ("Name"))

Analyze GraphQL 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 GraphQL data for analysis.

result = spark.sql("SELECT Name, Email FROM SAMPLE_VIEW WHERE UserLogin = 'admin'")

The data from GraphQL 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 JDBC Driver for GraphQL and start working with your live GraphQL data in Azure Databricks. Reach out to our Support Team if you have any questions.