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

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

Install the CData JDBC Driver for Slack

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

Start a Spark Shell and Connect to Slack Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Slack/lib/cdata.jdbc.slack.jar
   

2. With the shell running, you can connect to Slack with a JDBC URL and use the SQL Context load() function to read a table. Slack uses the OAuth authentication standard. To authenticate using OAuth, create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the Getting Started section of the help documentation for an authentication guide.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Slack JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.slack.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 Slack, using the connection string generated above.

   scala> val slack_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:slack:OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;").option("dbtable","Channels").option("driver","cdata.jdbc.slack.SlackDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Slack data as a temporary table:

   scala> slack_df.registerTable("channels")

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

   scala> slack_df.sqlContext.sql("SELECT Id, Name FROM Channels WHERE IsPublic = True").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 Slack in Apache Spark, you are able to perform fast and complex analytics on Slack 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.