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

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

Install the CData JDBC Driver for Strava

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

Start a Spark Shell and Connect to Strava Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Strava JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Strava/lib/cdata.jdbc.api.jar
   

2. With the shell running, you can connect to Strava with a JDBC URL and use the SQL Context load() function to read a table.

   To authenticate to Strava, and connect to your own data or to allow other users to connect to their data, you can use the OAuth standard.

   Using OAuth Authentication

   You must create a custom OAuth application to connect to Strava. To create a custom OAuth application:

   1. Log into the Strava API Settings page
   2. Create a new application or select an existing application
   3. Set the "Authorization Callback Domain" to your callback URL domain (e.g. localhost)
   4. Note down the Client ID and Client Secret

   After setting the following connection properties, 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 from your Strava API application.
   • OAuthClientSecret: Set this to the Client Secret from your Strava API application.
   • CallbackURL: Set this to the redirect URI matching your application's callback domain.

   Example connection string:

   Profile=C:\profiles\Strava.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackURL=http://localhost:33333;
   

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Strava 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 Strava, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Strava.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackURL=http://localhost:33333;").option("dbtable","Athlete").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 Strava data as a temporary table:

   scala> api_df.registerTable("athlete")

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

   scala> api_df.sqlContext.sql("SELECT ,  FROM Athlete WHERE  = ").collect.foreach(println)

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

   

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