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

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

Getting Started

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Install the CData JDBC Driver for Salesforce

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

Start a Spark Shell and Connect to Salesforce Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Salesforce/lib/cdata.jdbc.salesforce.jar
   

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

   There are several authentication methods available for connecting to Salesforce: OAuth, Login (or basic), and SSO. The Login method requires you to have the username, password, and security token of the user.

   OAuth Authentication (default)

   The default authentication mechanism (and the one preferred by Salesforce) is OAuth. To use OAuth with CData's embedded OAuth application, leave the connection properties blank. If you have configured your own custom OAuth application with Salesforce (see the Help documentation for more information), set OAuthClientId, OAuthClientSecret, and CallbackURL to the properties for you application. Set InitiateOAuth to the desired OAuth flow ("GETANDREFRESH" will have the connector manage the entire OAuth flow).

   Login (or Basic) Authentication

   If you do not wish do not wish to use OAuth authentication, you can use Login (or basic) authentication. Set AuthScheme to Basic, and set the User, Password, and SecurityToken properties. You can configure your security token in Salesforce.

   SSO (single sign-on) Authentication

   SSO (single sign-on) can be used by setting the SSOProperties, SSOLoginUrl, and SSOExchangeURL connection properties, which allow you to authenticate to an identity provider. See the "Getting Started" chapter in the Help documentation for more information.

   Multi-Factor Authentication (MFA)

   If your Salesforce org has MFA enforcement enabled, set MFACode to the time-based one-time passcode (TOTP) generated by your authenticator app (such as Salesforce Authenticator or Google Authenticator). MFACode applies to both OAuth and Login authentication flows.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.salesforce.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 Salesforce, using the connection string generated above.

   scala> val salesforce_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:salesforce:InitiateOAuth=GETANDREFRESH;MFACode=YourMFACode").option("dbtable","Account").option("driver","cdata.jdbc.salesforce.SalesforceDriver").load()
   

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

4. Register the Salesforce data as a temporary table:

   scala> salesforce_df.registerTable("account")

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

   scala> salesforce_df.sqlContext.sql("SELECT Industry, AnnualRevenue FROM Account WHERE Name = GenePoint").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 Salesforce in Apache Spark, you are able to perform fast and complex analytics on Salesforce 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.