Analyze Sentry Data in R via JDBC

Access Sentry data with pure R script and standard SQL on any machine where R and Java can be installed. You can use the CData JDBC Driver for Sentry and the RJDBC package to work with remote Sentry data in R. By using the CData Driver, you are leveraging a driver written for industry-proven standards to access your data in the popular, open-source R language. This article shows how to use the driver to execute SQL queries to Sentry and visualize Sentry data by calling standard R functions.

Install R

You can match the driver's performance gains from multi-threading and managed code by running the multithreaded Microsoft R Open or by running open R linked with the BLAS/LAPACK libraries. This article uses Microsoft R Open 3.2.3, which is preconfigured to install packages from the Jan. 1, 2016 snapshot of the CRAN repository. This snapshot ensures reproducibility.

Load the RJDBC Package

To use the driver, download the RJDBC package. After installing the RJDBC package, the following line loads the package:

library(RJDBC)

Connect to Sentry as a JDBC Data Source

You will need the following information to connect to Sentry as a JDBC data source:

• Driver Class: Set this to cdata.jdbc.api.APIDriver
• Classpath: Set this to the location of the driver JAR. By default this is the lib subfolder of the installation folder.

The DBI functions, such as dbConnect and dbSendQuery, provide a unified interface for writing data access code in R. Use the following line to initialize a DBI driver that can make JDBC requests to the CData JDBC Driver for Sentry:

driver <- JDBC(driverClass = "cdata.jdbc.api.APIDriver", classPath = "MyInstallationDir\lib\cdata.jdbc.api.jar", identifier.quote = "'") 

You can now use DBI functions to connect to Sentry and execute SQL queries. Initialize the JDBC connection with the dbConnect function.

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.)

Below is a sample dbConnect call, including a typical JDBC connection string:

conn <- dbConnect(driver,"jdbc:api:Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";")

Schema Discovery

The driver models Sentry APIs as relational tables, views, and stored procedures. Use the following line to retrieve the list of tables:

dbListTables(conn)

Execute SQL Queries

You can use the dbGetQuery function to execute any SQL query supported by the Sentry API:

userorganizations <- dbGetQuery(conn,"SELECT , FROM UserOrganizations WHERE = ''")

You can view the results in a data viewer window with the following command:

View(userorganizations)

Plot Sentry Data

You can now analyze Sentry data with any of the data visualization packages available in the CRAN repository. You can create simple bar plots with the built-in bar plot function:

par(las=2,ps=10,mar=c(5,15,4,2))
barplot(userorganizations$, main="Sentry UserOrganizations", names.arg = userorganizations$, horiz=TRUE)

👁 A basic bar plot. (Salesforce is shown.)