In this lesson, you will learn how to connect your trace data to a visualization backend like Jaeger or Tempo to transform raw span data into actionable insights for bottleneck analysis.
The Role of the Trace Backend
While the OpenTelemetry Collector acts as a courier, moving data from your applications to storage, it does not provide an interface for human interaction. To identify why a specific microservice request is slow, you need a visual representation. Jaeger and Grafana Tempo act as both the storage engine and the frontend. They reconstruct individual "spans"—the tiny snippets of execution time you instrumented earlier—into a hierarchical "trace" tree.
Connecting the Collector to the Backend
To visualize your data, you must point your OpenTelemetry Collector to the ingestion endpoint of your chosen backend. For example, if you are running Tempo in your Kubernetes cluster, you configure the exporters section of your otel-collector-config.yaml:
exporters:
otlp/tempo:
endpoint: "tempo-distributor.monitoring.svc.cluster.local:4317"
tls:
insecure: true
service:
pipelines:
traces:
receivers: [otlp]
processors: [batch]
exporters: [otlp/tempo]
Analyzing Latency and Bottlenecks
Once the data is flowing, you can open the Jaeger UI or the Grafana Explore tab. By looking at a trace, you see a horizontal "waterfall" chart.
- Long spans: Identify the specific service or database call causing the delay.
- Sequential vs. Parallel blocks: Spot opportunities for performance optimization (e.g., turning sequential API calls into concurrent ones).
- Error propagation: Trace exactly which service in the chain triggered a 500 error code.
By inspecting these diagrams, you move from guessing where a bottleneck exists to seeing the exact line of code or remote service call responsible for the latency. In a CI/CD environment, this is critical; it allows you to correlate performance regressions directly with new deployment versions, ensuring your cluster health remains optimal as you ship new code.
Key Takeaway: Visualizing traces turns raw span data into a waterfall chart, allowing you to visually pinpoint exactly which service or network call is creating latency in your distributed system.