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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  • Issue criticality
  • Asset performance impact
  1. Use Cases

Prioritize Technical Debt

PreviousGet early feedback on bottlenecks and code issuesNextEnvironments

Last updated 1 year ago

Beyond simply identifying issues. Digma also analyzes their effect on the application to determine their criticality.

Issue criticality

Each issue is assessed for criticality based on its overall effect on the application. In local environments, this is merely measured by the severity of the issue and the scope of different application flows affected by it. In shared environments such as CI, staging, or production, however, actual usage is also measured to determine the true impact of the issue.

The criticality of each issue is reflected by the color coding of the issue icon. Hovering over the icon will also reveal the criticality score. When reviewing the overall issue list, you can choose to sort by latest or by the most critical issues to help prioritize the backlog and avoid micro-optimizations:

Asset performance impact

Digma assesses each asset (query, code location, endpoint etc.) to determine its performance impact on the application. This helps identify the best candidates for optimization that would carry the most 'punch for the bucks' if their performance is improved. Assessment of performance impact is available only in shared environments such as CI/staging or production and relies on measuring actual usage.

For example, a slow query that is rarely used or has a marginal effect on the overall request would be ranked lower than a slow query that is heavily used and critically affects multiple flows.

Using this feature requires installing and collecting data from a shared environment such as CI, Staging, Testing, or Production.

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