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

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

Install the CData JDBC Driver for Aha

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

Start a Spark Shell and Connect to Aha Data

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

   Start by setting the Profile connection property to the location of the Aha! Profile on disk (e.g. C:\profiles\aha.apip). Next, set the ProfileSettings connection property to the connection string for Aha! (see below).

   Aha! API Profile Settings

   The Aha! API uses OAuth-based authentication.

   You will first need to register an OAuth app with Aha!. This can be done from your Aha! account under 'Settings' > 'Personal' > 'Developer' > 'OAuth Applications'. Additionally, you will need to set the Domain, found in the domain name of your Aha account. For example if your Aha account is acmeinc.aha.io, then the Domain should be 'acmeinc'.

   After setting the following in the connection string, you are ready to connect:

   • AuthScheme: Set this to OAuth.
   • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
   • OAuthClientId: Set this to the client_id that is specified in you app settings.
   • OAuthClientSecret: Set this to the client_secret that is specified in you app settings.
   • CallbackURL: Set this to the Redirect URI you specified in your app settings.
   • Domain: Set this in the ProfileSettings to your Aha domain.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.api.jar

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

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\aha.apip;ProfileSettings='Domain=acmeinc';Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;").option("dbtable","Ideas").option("driver","cdata.jdbc.api.APIDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Aha data as a temporary table:

   scala> api_df.registerTable("ideas")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Ideas WHERE AssignedToUserId = my_user_id").collect.foreach(println)

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

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