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

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

Getting Started

---

Install the CData JDBC Driver for MongoDB

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

Start a Spark Shell and Connect to MongoDB Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for MongoDB/lib/cdata.jdbc.mongodb.jar
   

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

   Set the Server, Database, User, and Password connection properties to connect to MongoDB. To access MongoDB collections as tables you can use automatic schema discovery or write your own schema definitions. Schemas are defined in .rsd files, which have a simple format. You can also execute free-form queries that are not tied to the schema.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.mongodb.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 MongoDB, using the connection string generated above.

   scala> val mongodb_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:mongodb:Server=MyServer;Port=27017;Database=test;User=test;Password=Password;").option("dbtable","restaurants").option("driver","cdata.jdbc.mongodb.MongoDBDriver").load()
   

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

4. Register the MongoDB data as a temporary table:

   scala> mongodb_df.registerTable("restaurants")

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

   scala> mongodb_df.sqlContext.sql("SELECT borough, cuisine FROM restaurants WHERE Name = Morris Park Bake Shop").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 MongoDB in Apache Spark, you are able to perform fast and complex analytics on MongoDB 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.