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

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

Install the CData JDBC Driver for SFTP

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

Start a Spark Shell and Connect to SFTP Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for SFTP/lib/cdata.jdbc.sftp.jar
   

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

   SFTP can be used to transfer files to and from SFTP servers using the SFTP Protocol. To connect, specify the RemoteHost;. service uses the User and Password and public key authentication (SSHClientCert). Choose an SSHAuthMode and specify connection values based on your selection.

   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 subfolders to report as views.
   • FileRetrievalDepth: Set this to retrieve files recursively and list them in the Root table.
   Stored Procedures are available to download files, upload files, and send protocol commands. See gdatamodel for more on using SQL to interact with the server.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.sftp.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 SFTP, using the connection string generated above.

   scala> val sftp_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:sftp:RemoteHost=MyFTPServer;").option("dbtable","MyDirectory").option("driver","cdata.jdbc.sftp.SFTPDriver").load()
   

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

4. Register the SFTP data as a temporary table:

   scala> sftp_df.registerTable("mydirectory")

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

   scala> sftp_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:

   👁 Data in Apache Spark (Salesforce is shown)
   

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