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Work with CSV Data in Apache Spark Using SQL

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

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

Install the CData JDBC Driver for CSV

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

Start a Spark Shell and Connect to CSV Data

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

    The DataSource property must be set to a valid local folder name.

    Also, specify the IncludeFiles property to work with text files having extensions that differ from .csv, .tab, or .txt. Specify multiple file extensions in a comma-separated list. You can also set Extended Properties compatible with the Microsoft Jet OLE DB 4.0 driver. Alternatively, you can provide the format of text files in a Schema.ini file.

    Set UseRowNumbers to true if you are deleting or updating in CSV. This will create a new column with the name RowNumber which will be used as key for that table.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.csv.jar

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

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

    scala> val csv_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:csv:DataSource=MyCSVFilesFolder;").option("dbtable","Customer").option("driver","cdata.jdbc.csv.CSVDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the CSV data as a temporary table:

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

    scala> csv_df.sqlContext.sql("SELECT City, TotalDue FROM Customer WHERE FirstName = Bob").collect.foreach(println)

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

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