How to work with Amazon S3 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 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.

   To authorize Amazon S3 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 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
   

   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 Amazon S3, using the connection string generated above.

   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()
   

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

4. Register the Amazon S3 data as a temporary table:

   scala> amazons3_df.registerTable("objectsacl")

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

   scala> amazons3_df.sqlContext.sql("SELECT Name, OwnerId FROM ObjectsACL WHERE Name = TestBucket").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 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 hundreds of CData JDBC Drivers and get started today.