Typical Trace Information of an Observability platform - unix1998/technical_notes GitHub Wiki

To effectively visualize trace information for your applications and external access using platforms like Grafana or Datadog, you typically need to capture and include several key pieces of information in your traces. Here’s a list of typical information you should collect and visualize:

Typical Trace Information

  1. Trace ID: A unique identifier for the entire trace, which ties together all the spans (individual units of work) involved in a single transaction or request.

  2. Span ID: A unique identifier for each span within a trace. Each span represents a single operation or step within the overall trace.

  3. Parent Span ID: The ID of the parent span, which helps build the hierarchical structure of the trace.

  4. Service Name: The name of the service or application component generating the trace. This helps differentiate between various services in a microservices architecture.

  5. Operation Name: A description of the specific operation or request being traced (e.g., "HTTP GET /api/user").

  6. Start Time: The timestamp when the span started.

  7. Duration: The length of time the span took to complete.

  8. Status: The outcome of the span, indicating success, error, or failure.

  9. Error Details: Information about any errors that occurred, including error messages and stack traces.

  10. Tags/Attributes: Key-value pairs providing additional context about the span. Common tags include:

    • http.method: The HTTP method (e.g., GET, POST).
    • http.url: The URL of the request.
    • http.status_code: The HTTP status code of the response.
    • db.statement: The database query being executed.
    • db.type: The type of database (e.g., SQL, NoSQL).
    • peer.address: The IP address and port of the peer (client or server).
    • user.id: The identifier of the user making the request.
    • environment: The environment in which the service is running (e.g., production, staging).

  11. Logs: Log entries associated with the span, providing detailed information about events that occurred during the span’s execution.

  12. Baggage Items: Key-value pairs that are propagated across the entire trace, providing context that is shared across different spans and services.

Visualizing Trace Information

When visualizing trace information using platforms like Grafana or Datadog, you can use various types of visualizations to gain insights:

  1. Trace Map/Service Map: A graphical representation showing the relationships and interactions between different services. It helps identify bottlenecks and performance issues.

  2. Timeline View: A detailed view of a single trace, showing the timing and hierarchy of spans. It allows you to see how long each operation took and how they relate to each other.

  3. Metrics and Dashboards: Aggregated metrics derived from trace data, such as request rates, error rates, and latency distributions. These metrics can be displayed on dashboards to provide an overview of application performance.

  4. Heatmaps: Visualizations showing the distribution of latency or error rates across different services or endpoints, helping to identify hotspots.

  5. Logs: Correlating logs with traces to provide detailed context about specific spans or errors.

Example Using Datadog

In Datadog, you can visualize trace information as follows:

  1. Service Map: Datadog automatically generates a service map showing how services interact with each other.
  2. Trace View: You can drill down into individual traces to see the details of each span, including tags, logs, and error information.
  3. Dashboards: Create custom dashboards to monitor key metrics such as request rates, error rates, and latency.
  4. Analytics: Use trace analytics to filter and analyze trace data based on various attributes, such as service name, operation name, or user ID.

Example Using Grafana

In Grafana, you can use plugins like Jaeger or Tempo to visualize traces:

  1. Trace Search and View: Search for traces based on trace ID, service name, or operation name and view the details of each trace.
  2. Dashboards: Create dashboards to display trace metrics and visualize trends over time.
  3. Heatmaps: Use heatmaps to visualize latency distributions and identify performance issues.

By capturing and visualizing these key pieces of information, you can gain deep insights into the performance and behavior of your applications, identify bottlenecks, and troubleshoot issues effectively.