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

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

Install the CData JDBC Driver for Pushover

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

Start a Spark Shell and Connect to Pushover Data

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

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

2. With the shell running, you can connect to Pushover 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 Pushover Profile on disk (e.g. C:\profiles\Pushover.apip). Next, set the ProfileSettings connection property to the connection string for Pushover (see below).

   Pushover API Profile Settings

   Pushover uses API Key authentication. Your Pushover Application API Token is used to authenticate all requests. You can create and manage API tokens in the Pushover dashboard at pushover.net by registering an application under Settings > Your Applications.

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

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your 30-character Pushover Application API Token.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("groupmembers")

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

   scala> api_df.sqlContext.sql("SELECT User, Memo FROM GroupMembers WHERE GroupKey = your_group_key").collect.foreach(println)

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

   

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