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

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

Install the CData JDBC Driver for Placid

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

Start a Spark Shell and Connect to Placid Data

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

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

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

   Placid uses API Key authentication to control access to the API. API tokens are project-specific and can be obtained from your project settings on placid.app.

   Using API Key Authentication

   To obtain your API key, log in to placid.app, navigate to your project, open the project settings, and generate an API token from the API section. Note that each API token is scoped to a specific project.

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

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Placid project API token.

   Example connection string:

   Profile=C:\profiles\Placid.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_project_api_token';
   

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Placid 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.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

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

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

   scala> api_df.registerTable("collections")

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

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

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

   👁 Data in Apache Spark (Salesforce is shown)
   

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