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Work with Wasabi Data in Apache Spark Using SQL

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

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

Install the CData JDBC Driver for Wasabi

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

Start a Spark Shell and Connect to Wasabi Data

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

    To authorize Wasabi requests, provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id. Set SecretKey to the secret access key.

    Note: You can connect as the AWS account administrator, but it is recommended to use IAM user credentials to access AWS services.

    For information on obtaining the credentials and other authentication methods, refer to the Getting Started section of the Help documentation.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.wasabi.jar

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

    scala> val wasabi_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:wasabi:AccessKey=a123;SecretKey=s123;").option("dbtable","Buckets").option("driver","cdata.jdbc.wasabi.WasabiDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the Wasabi data as a temporary table:

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

    scala> wasabi_df.sqlContext.sql("SELECT Name, OwnerId FROM Buckets WHERE Name = TestBucket").collect.foreach(println)

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

Using the CData JDBC Driver for Wasabi in Apache Spark, you are able to perform fast and complex analytics on Wasabi data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 160+ CData JDBC Drivers and get started today.