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

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

Install the CData JDBC Driver for Sentry

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

Start a Spark Shell and Connect to Sentry Data

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

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

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

   Using API Key Authentication

   Sentry uses token-based authentication. To obtain an Auth Token:

   1. Log in to your Sentry account at https://sentry.io
   2. Navigate to Settings > Auth Tokens
   3. Click "Create New Token"
   4. Select the required scopes and click "Create Token"
   5. Copy the generated token (it will only be shown once)

   After obtaining your Auth Token, set the following connection properties:

   • AuthScheme: Set this to APIKey.
   Set the following in the ProfileSettings connection property:
   • APIKey: Set this to your Sentry Auth Token.
   • OrganizationId: Set this to your Sentry organization slug or ID.

   Example Connection String

   Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";
   

   Connecting to Sentry

   Once the authentication is configured, you can connect to Sentry and query data from any of the available tables such as Organizations, Projects, Issues, and Events.

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";").option("dbtable","UserOrganizations").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 Sentry data as a temporary table:

   scala> api_df.registerTable("userorganizations")

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

   scala> api_df.sqlContext.sql("SELECT , FROM UserOrganizations 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 Sentry in Apache Spark, you are able to perform fast and complex analytics on Sentry 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.