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

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

Install the CData JDBC Driver for Wrike

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

Start a Spark Shell and Connect to Wrike Data

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

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

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

   Wrike API Profile Settings

   Obtain a permanent API token from the Wrike App Console by clicking Obtain Token.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("account")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Account WHERE SubscriptionType = Pro").collect.foreach(println)

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

   

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