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Work with FTP Data in Apache Spark Using SQL

Access and process FTP 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 FTP, Spark can work with live FTP data. This article describes how to connect to and query FTP data from a Spark shell.

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

Install the CData JDBC Driver for FTP

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

Start a Spark Shell and Connect to FTP Data

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

    To connect to FTP or SFTP servers, specify at least RemoteHost and FileProtocol. Specify the port with RemotePort.

    Set User and Password to perform Basic authentication. Set SSHAuthMode to use SSH authentication. See the Getting Started section of the data provider help documentation for more information on authenticating via SSH.

    Set SSLMode and SSLServerCert to secure connections with SSL.

    The data provider lists the tables based on the available folders in your FTP server. Set the following connection properties to control the relational view of the file system:

    • RemotePath: Set this to the current working directory.
    • TableDepth: Set this to control the depth of folders to list as views.
    • FileRetrievalDepth: Set this to retrieve and list files recursively from the root table.

    Stored Procedures are available to download files, upload files, and send protocol commands. See the Data Model chapter of the FTP data provider documentation for more information.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.ftp.jar

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

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

    scala> val ftp_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:ftp:RemoteHost=MyFTPServer;").option("dbtable","MyDirectory").option("driver","cdata.jdbc.ftp.FTPDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the FTP data as a temporary table:

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

    scala> ftp_df.sqlContext.sql("SELECT Filesize, Filename FROM MyDirectory WHERE FilePath = /documents/doc.txt").collect.foreach(println)

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

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