How to work with GraphQL Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for GraphQL, Spark can work with live GraphQL data. This article describes how to connect to and query GraphQL data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live GraphQL data due to optimized data processing built into the driver. 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze GraphQL data using native data types.

Install the CData JDBC Driver for GraphQL

Download the CData JDBC Driver for GraphQL installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to GraphQL Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for GraphQL JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for GraphQL/lib/cdata.jdbc.graphql.jar
   

2. With the shell running, you can connect to GraphQL with a JDBC URL and use the SQL Context load() function to read a table.

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

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

   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.

   java -jar cdata.jdbc.graphql.jar
   

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

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to GraphQL, using the connection string generated above.

   scala> val graphql_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:graphql:AuthScheme=Basic;User=username;Password=password;URL=https://mysite.com;InitiateOAuth=GETANDREFRESH;").option("dbtable","Users").option("driver","cdata.jdbc.graphql.GraphQLDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the GraphQL data as a temporary table:

   scala> graphql_df.registerTable("users")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> graphql_df.sqlContext.sql("SELECT Name, Email FROM Users WHERE UserLogin = admin").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for GraphQL in Apache Spark, you are able to perform fast and complex analytics on GraphQL data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.