How to work with MarkLogic 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 MarkLogic, Spark can work with live MarkLogic data. This article describes how to connect to and query MarkLogic data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live MarkLogic data due to optimized data processing built into the driver. When you issue complex SQL queries to MarkLogic, the driver pushes supported SQL operations, like filters and aggregations, directly to MarkLogic 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 MarkLogic data using native data types.

Install the CData JDBC Driver for MarkLogic

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

Start a Spark Shell and Connect to MarkLogic Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for MarkLogic/lib/cdata.jdbc.marklogic.jar
   

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

   Set User, Password, and Server to the credentials for the MarkLogic account and the address of the server you want to connect to. You should also specify the REST API Port if you want to use a specific instance of a REST Server.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.marklogic.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 MarkLogic, using the connection string generated above.

   scala> val marklogic_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:marklogic:User='myusername';Password='mypassword';Server='http://marklogic';").option("dbtable","Customer").option("driver","cdata.jdbc.marklogic.MarkLogicDriver").load()
   

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

4. Register the MarkLogic data as a temporary table:

   scala> marklogic_df.registerTable("customer")

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

   scala> marklogic_df.sqlContext.sql("SELECT Name, TotalDue FROM Customer WHERE Id = 1").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 MarkLogic in Apache Spark, you are able to perform fast and complex analytics on MarkLogic 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.