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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What is a Continuous Feedback platform?

PreviousWelcome to the Digma Docs!NextDigma Quickstart

Last updated 1 year ago

Digma's focus is analyzing observability. The basic premise is that there is a lot of useful information in logs traces and metrics - they just require a lot more processing to glean any insights you can actually use as a developer. You can think of Digma as a data pipeline. Its main concern is not storing a lot of raw data but aggregating and processing it continuously, looking for anomalies, changes, and useful analytics.

Digma ingests observability based on the (which it can also automatically collect using the IDE), processes the data, and then exposes insights to the IDE plugin which can map and connect it with the code.

OTEL standard