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The Datadog plugin for JetBrains IDEs is available for IntelliJ IDEA, GoLand, PhpStorm, and PyCharm. It helps you improve software performance by providing meaningful code-level insights directly in the IDE based on real-time observability data.
The Code Insights view keeps you informed about:
The Continuous Profiler helps you to reduce latency and lower cloud costs by highlighting code lines that:
The Logs support shows observed logs in source code, as detected from Log patterns, and provides links to the Datadog Log Explorer to view logs generated by a specific line of code.
The CI Test Runs feature opens the Continuous Integration Visibility Explorer to show recent runs for a selected test.
The Static Analysis integration analyzes your code (locally) against predefined rules to detect and fix problems before you commit changes.
Datadog.Alternatively, you can install the plugin from the JetBrains Marketplace.
After installing the Datadog plugin and restarting the IDE, log in to Datadog:
Note: For most users, one login is all that is required. If you’re using a multi-org setup, check to ensure that the correct account is active. To find out which login your IDE is using, click Settings -> Tools -> Datadog, and check which account is active.
To provide relevant data from the Datadog platform, add related services to your project:
To remove a service, select it in the Services table and click the minus icon (-).
The Code Insights tab displays insights generated by the Datadog platform that are relevant to your current project. The insights are grouped into three categories: performance, reliability, and security.
Code Insights include a detailed description for each issue, and links to:
You can dismiss individual insights and set filters to view the categories of insights that you are interested in.
The Continuous Profiler tab shows profiling information for the service in a selected environment, aggregated over a specific time frame. Available views are:
You can specify the following parameters for the profiling data:
The available profiling types usually include options like CPU Time and Allocated Memory, but are determined by the platform and vary by language.
The Top List sub-tab shows the methods that consume the most resources based on the aggregated profile data loaded from the Datadog servers. These are the methods that are most likely candidates for optimization.
The call tree to the right of the Top List shows the paths that lead to (and from) the selected method.
The default Caller Hierarchy view shows the callers (or predecessors) of the target method and the frequency with which they appear in the call stack. To view the callees (or successors), click the Callee Hierarchy button on the toolbar.
Right-click on a method in the call tree to see options to navigate to the source editor or flame graph.
A flame graph is a visualization of profiling samples that shows stack traces and their relative frequency during the sample period. The Datadog plugin collects multiple individual profiles from the requested time frame, and aggregates them. Each individual profile covers a 60 second interval within the requested time frame.
Each time you change the profile type, the time frame, or the environment, the Datadog plugin generates a new flame graph.
You can navigate the flame graph in several ways:
Hovering over a method displays a tooltip with the following information:
Profiling samples include stack trace and line number information. Use the Separate Flame Graph by button to switch between separating frames by method or line number.
When the Continuous Profiler tab is active, the plugin adds code highlights to the source code editor margin. For Top Methods, an icon appears in the editor margin, and line-level highlights appear in the code based on the active Profiling data.
The active Profiling tab also affects the project tree view, which is annotated with the selected profile’s metrics:
Log patterns from Datadog are matched directly to lines of code in your editor for your Java, Go, and Python source files:
A popup shows runtime values from the log entries:
Click the log icon to open the Log Explorer on the Datadog platform with a pre-filled query that matches the logger name, log level, and log message as closely as possible:
You can view recent test runs in the Continuous Integration Visibility Explorer by navigating directly from your source files. Look for the View Test Runs links following test method declarations in your source code:
Clicking the link opens the Test Runs tab showing the recent history for one test case.
The View in IDE feature provides a link from the Datadog platform directly to the source files in your IDE. Look for the button next to frames in stack traces displayed on the platform (for example, in Error Tracking):
The Datadog plugin runs Static Analysis rules on your source files as you edit them. The goal is to detect and fix problems such as maintainability issues, bugs, or security vulnerabilities in your code before you commit your changes.
Static Analysis supports scanning for many programming languages. For a complete list, see Static Analysis Rules. For file types belonging to supported languages, issues are shown in the source code editor with the JetBrains inspection system, and suggested fixes can be applied directly:
Additionally, all issues detected by this feature are listed in the standard Problems view.
When you start editing a source file supported by Static Analysis, the plugin checks for static-analysis.datadog.yml at your source repository’s root. It prompts you to create the file if necessary:
Once the configuration file is created, the static analyzer runs automatically in the background.
You can give feedback in the discussion forum, or send an e-mail to team-ide-integration@datadoghq.com.
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