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

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

Install the CData JDBC Driver for Sugar CRM

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

Start a Spark Shell and Connect to Sugar CRM Data

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

   The User and Password properties, under the Authentication section, must be set to valid SugarCRM user credentials. This will use the default OAuth token created to allow client logins. OAuthClientId and OAuthClientSecret are required if you do not wish to use the default OAuth token.

   You can generate a new OAuth consumer key and consumer secret in Admin -> OAuth Keys. Set the OAuthClientId to the OAuth consumer key. Set the OAuthClientSecret to the consumer secret.

   Additionally, specify the URL to the SugarCRM account.

   Note that retrieving SugarCRM metadata can be expensive. It is advised that you store the metadata locally as described in the "Caching Metadata" chapter of the help documentation.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.sugarcrm.jar

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

   Configure the connection to Sugar CRM, using the connection string generated above.

   scala> val sugarcrm_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:sugarcrm:User=MyUser;Password=MyPassword;URL=MySugarCRMAccountURL;CacheMetadata=True;").option("dbtable","Accounts").option("driver","cdata.jdbc.sugarcrm.SugarCRMDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Sugar CRM data as a temporary table:

   scala> sugarcrm_df.registerTable("accounts")

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

   scala> sugarcrm_df.sqlContext.sql("SELECT Name, AnnualRevenue FROM Accounts WHERE Name = Bob").collect.foreach(println)

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

Using the CData JDBC Driver for Sugar CRM in Apache Spark, you are able to perform fast and complex analytics on Sugar CRM 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.