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

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

Install the CData JDBC Driver for Flowlu

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

Start a Spark Shell and Connect to Flowlu Data

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

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

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

   Flowlu API Profile Settings

   In your Flowlu account, navigate to System Settings > API Settings > Create to generate an API key. Your Company name is the subdomain from your Flowlu account URL.

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Flowlu.apip;ProfileSettings='APIKey=your_api_key;Company=your_company_subdomain';").option("dbtable","AgileCategories").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 Flowlu data as a temporary table:

   scala> api_df.registerTable("agilecategories")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM AgileCategories WHERE ProjectId = 1").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 Flowlu in Apache Spark, you are able to perform fast and complex analytics on Flowlu 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.