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Work with Amazon S3 Data in Apache Spark Using SQL

Access and process Amazon S3 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 Amazon S3, Spark can work with live Amazon S3 data. This article describes how to connect to and query Amazon S3 data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Amazon S3 data due to optimized data processing built into the driver. When you issue complex SQL queries to Amazon S3, the driver pushes supported SQL operations, like filters and aggregations, directly to Amazon S3 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 Amazon S3 data using native data types.

Install the CData JDBC Driver for Amazon S3

Download the CData JDBC Driver for Amazon S3 installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Amazon S3 Data

  1. Open a terminal and start the Spark shell with the CData JDBC Driver for Amazon S3 JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Amazon S3/lib/cdata.jdbc.amazons3.jar
  2. With the shell running, you can connect to Amazon S3 with a JDBC URL and use the SQL Context load() function to read a table.




    Built-in Connection String Designer

    For assistance in constructing the JDBC URL, use the connection string designer built into the Amazon S3 JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

    java -jar cdata.jdbc.amazons3.jar


    scala> val amazons3_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:amazons3:AccessKey=a123;SecretKey=s123;").option("dbtable","ObjectsACL").option("driver","cdata.jdbc.amazons3.AmazonS3Driver").load()
  4. Register the Amazon S3 data as a temporary table:

    scala> amazons3_df.registerTable("objectsacl")

    scala> amazons3_df.sqlContext.sql("SELECT Name, OwnerId FROM ObjectsACL WHERE Name = TestBucket").collect.foreach(println)


Using the CData JDBC Driver for Amazon S3 in Apache Spark, you are able to perform fast and complex analytics on Amazon S3 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.