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

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

Install the CData JDBC Driver for Cosmos DB

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

Start a Spark Shell and Connect to Cosmos DB Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Cosmos DB JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Cosmos DB/lib/cdata.jdbc.cosmosdb.jar
2. With the shell running, you can connect to Cosmos DB with a JDBC URL and use the SQL Context load() function to read a table.

   To obtain the connection string needed to connect to a Cosmos DB account using the SQL API, log in to the Azure Portal, select Azure Cosmos DB, and select your account. In the Settings section, click Connection String and set the following values:

   • AccountEndpoint: The Cosmos DB account URL from the Keys blade of the Cosmos DB account
   • AccountKey: In the Azure portal, navigate to the Cosmos DB service and select your Azure Cosmos DB account. From the resource menu, go to the Keys page. Find the PRIMARY KEY value and set AccountKey to this value.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.cosmosdb.jar

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

   Configure the connection to Cosmos DB, using the connection string generated above.

   scala> val cosmosdb_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:cosmosdb:AccountEndpoint=myAccountEndpoint;AccountKey=myAccountKey;").option("dbtable","Customers").option("driver","cdata.jdbc.cosmosdb.CosmosDBDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Cosmos DB data as a temporary table:

   scala> cosmosdb_df.registerTable("customers")

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

   scala> cosmosdb_df.sqlContext.sql("SELECT City, CompanyName FROM Customers WHERE Name = Morris Park Bake Shop").collect.foreach(println)

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

Using the CData JDBC Driver for Cosmos DB in Apache Spark, you are able to perform fast and complex analytics on Cosmos DB data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.