Trace Correlation

Definition

Trace correlation connects spans within a distributed operation and links those spans to other telemetry.

Signals

• Logs
• Metrics
• Traces
• Profiles

Why use it

Find which dependency contributed to a slow or failed request.

Common use cases

• Investigate cross-service latency
• connect spans to logs
• inspect downstream failures.

Benefits

• Shows execution context and dependencies for recorded requests.

Limitations

• Sampling, missing instrumentation, and broken propagation can produce partial traces. A span error does not by itself identify the root cause.

Practical pattern

Propagate context across HTTP and messaging boundaries. Preserve service attributes and configure links to related log data.

Supported by

Documented examples, not an exhaustive compatibility list. Features require suitable instrumentation and configuration; availability can depend on the runtime, backend, and subscription.

• OpenTelemetry — Propagated context connects spans across services.
• Grafana with Tempo and Loki — Span-to-log links add error details to a trace investigation.

Related concepts

• Context Propagation
• Log Correlation
• Metric Correlation

Investigation patterns

• Metrics to Traces
• Services to Dependencies
• Trace to Profiles
• Service to Database
• User Session to Backend Trace
• Queue Producer to Consumer

Related guides

• What is Observability?
• What is MTTR?
• OpenTelemetry: Logs to Traces in Python
• Grafana: Metrics to Traces with Exemplars
• OpenTelemetry: Queue Context, Retries and Batches
• Troubleshoot: Log Trace ID Not Found
• Troubleshoot: Exemplars Missing in Grafana

FAQ

Does a trace capture every request?

Only when collection and retention policies allow it. Sampling and export failures can remove requests or spans.