Work with EnterpriseDB Data in Apache Spark Using SQL

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EnterpriseDB JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with EnterpriseDB.



Access and process EnterpriseDB Data in Apache Spark using the CData JDBC Driver.

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for EnterpriseDB, Spark can work with live EnterpriseDB data. This article describes how to connect to and query EnterpriseDB data from a Spark shell.

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

Install the CData JDBC Driver for EnterpriseDB

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

Start a Spark Shell and Connect to EnterpriseDB Data

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

    The following connection properties are required in order to connect to data.

    • Server: The host name or IP of the server hosting the EnterpriseDB database.
    • Port: The port of the server hosting the EnterpriseDB database.

    You can also optionally set the following:

    • Database: The default database to connect to when connecting to the EnterpriseDB Server. If this is not set, the user's default database will be used.

    Connect Using Standard Authentication

    To authenticate using standard authentication, set the following:

    • User: The user which will be used to authenticate with the EnterpriseDB server.
    • Password: The password which will be used to authenticate with the EnterpriseDB server.

    Connect Using SSL Authentication

    You can leverage SSL authentication to connect to EnterpriseDB data via a secure session. Configure the following connection properties to connect to data:

    • SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
    • SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
    • SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
    • SSLClientCertType: The certificate type of the client store.
    • SSLServerCert: The certificate to be accepted from the server.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.enterprisedb.jar

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

    Configure the connection to EnterpriseDB, using the connection string generated above.

    scala> val enterprisedb_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:enterprisedb:User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5444").option("dbtable","Orders").option("driver","cdata.jdbc.enterprisedb.EnterpriseDBDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the EnterpriseDB data as a temporary table:

    scala> enterprisedb_df.registerTable("orders")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> enterprisedb_df.sqlContext.sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = USA").collect.foreach(println)

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

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