How to work with LDAP Objects 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 LDAP, Spark can work with live LDAP objects. This article describes how to connect to and query LDAP objects from a Spark shell.

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

Install the CData JDBC Driver for LDAP

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

Start a Spark Shell and Connect to LDAP Objects

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

   $ spark-shell --jars /CData/CData JDBC Driver for LDAP/lib/cdata.jdbc.ldap.jar
   

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

   To establish a connection, the following properties under the Authentication section must be provided:

   • Valid User and Password credentials (e.g., Domain\BobF or cn=Bob F,ou=Employees,dc=Domain).
   • Server information, including the IP or host name of the Server, as well as the Port.

   • BaseDN: This will limit the scope of LDAP searches to the height of the distinguished name provided.

     Note: Specifying a narrow BaseDN may greatly increase performance; for example, cn=users,dc=domain will only return results contained within cn=users and its children.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.ldap.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 LDAP, using the connection string generated above.

   scala> val ldap_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:ldap:User=Domain\BobF;Password=bob123456;Server=10.0.1.1;Port=389;").option("dbtable","User").option("driver","cdata.jdbc.ldap.LDAPDriver").load()
   

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

4. Register the LDAP objects as a temporary table:

   scala> ldap_df.registerTable("user")

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

   scala> ldap_df.sqlContext.sql("SELECT Id, LogonCount FROM User WHERE CN = Administrator").collect.foreach(println)

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

   👁 Objects in Apache Spark (Salesforce is shown)
   

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