Digma Developer Guide
  • Welcome to the Digma Docs!
  • What is a Continuous Feedback platform?
  • Digma Quickstart
  • Installation
    • Local Install
      • Local Install Architecture
      • Installation Troubleshooting
    • Central (on-prem) Install
      • Resource Requirements
  • INSTRUMENTATION
    • Instrumenting your code for tracing
    • Java
      • Automatic Instrumentation in the IDE (IntelliJ)
      • Spring, Spring Boot, Dropwizard
        • Instrumenting your code in CI/Staging or the terminal
        • Instrumenting your application in Docker Compose
        • Instrumenting your application on Kubernetes
        • Covering more of your code with Observability
        • Using GitHub Actions (beta)
        • Using Micrometer Tracing (Spring Boot 3.x only)
        • Instrumenting code running in CLI
      • Quarkus, Micronaut, OpenLiberty
    • .NET
    • Correlating observability and source code commits
    • Sending Data to Digma using the OTEL Collector
    • Sending Data to Digma Using the Datadog agent
  • Use Cases
    • Design and write code more efficiently by understanding the system flows
    • Get early feedback on bottlenecks and code issues
    • Prioritize Technical Debt
  • Digma Core Concepts
    • Environments
    • Assets
    • Analytics vs. Issues
  • Digma Features
    • Issues
      • Suspected N+1
      • Excessive API calls (chatty API)
      • Bottleneck
      • Scaling Issue
      • Session In View Query Detected
      • Query Optimization Suggested
      • High number of queries
      • Slow Endpoint
    • Analytics
      • Top Usage
      • Request Breakdown
      • Duration
      • Code Nexus
      • Duration Breakdown
      • Endpoint Low/High Usage
    • Performance Impact
    • Test observability
    • Issue Criticality
  • Sample Projects
    • Spring Boot
  • Troubleshooting
    • Reporting Plugin Issues
    • Digma Overload Warning
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On this page
  • See runtime usages
  • Traces tied to the code
  • Browse all assets used by a specific endpoint
  • Find dead code
  1. Use Cases

Design and write code more efficiently by understanding the system flows

Code usage analytics can be critical to understand how the code works and roll out changes faster and without issues.

PreviousSending Data to Digma Using the Datadog agentNextGet early feedback on bottlenecks and code issues

Last updated 1 year ago

See runtime usages

Digma overlays observability over code, so developers are able to see the application flows triggering any code location, query, http call, or any other asset. This provides an automatic view of any runtime dependencies.

Traces tied to the code

Each flow include a sample trace for that specific flow, which in turn is fully connected back to the code, so developers can transition from code to trace and vice versa while staying inside the IDE.

Browse all assets used by a specific endpoint

Find dead code

Any code that is not triggered will have the Never reached code lens attached to it. This is a good way to detect dead code that might be a candidate for removal.

Note: Areas of the code that are especially risky to change will have the insight.

You can navigate the tree or take an overview of an entire endpoint request to list all of the different unique assets that take part in its execution flow. Assets could be sorted by recency, performance impact, and other criteria. For each of the assets, you can navigate to the related code, see other flows it affects

Code Nexus
assets