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

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

Install the CData JDBC Driver for CleverPush

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

Start a Spark Shell and Connect to CleverPush Data

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

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

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

   Cleverpush API Profile Settings

   CleverPush uses private API keys to authenticate requests. Your API key is passed as the Authorization request header value on every API call.

   You can find your private API key in the CleverPush dashboard under Settings > API. Use the private key (not the public key) for server-side access.

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

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your CleverPush private API key.

   Optional Connection Properties

   • ChannelId: Set this to your default CleverPush channel identifier. Most tables require a channel filter. Setting this property allows queries without specifying ChannelId in every WHERE clause.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("segments")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Segments WHERE ChannelId = your_channel_id").collect.foreach(println)

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

   

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