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

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

Install the CData JDBC Driver for Foursquare

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

Start a Spark Shell and Connect to Foursquare Data

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

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

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

   Using API Key Authentication

   Foursquare Places API uses Service Key (Bearer token) authentication. To obtain a Service Key:

   1. Go to the Foursquare Developer Console at https://foursquare.com/developers/
   2. Create a new project or select an existing one
   3. Navigate to the API Keys section
   4. Generate a new Service Key for the Places API

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

   • AuthScheme: Set this to APIKey.
   • ServiceKey: Set this to your Foursquare Service Key obtained from the Developer Console.
   • XPlacesApiVersion: (Optional) Set this to the API version date. Defaults to 2025-06-17.

   Example APIKey connection string

   Profile=C:\profiles\Foursquare.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_personal_access_token';
   

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Foursquare.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_personal_access_token';").option("dbtable","Autocomplete").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 Foursquare data as a temporary table:

   scala> api_df.registerTable("autocomplete")

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

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

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

   

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