Corréler les traces, les journaux et les métriques

Observability rests on three pillars: metrics (what is wrong), traces (where it is wrong), and logs (why it is wrong). Used in isolation they are useful; correlated together they are powerful.

• Metrics: aggregate trends, e.g. p99 latency rose
• Traces: the path of one request across services
• Logs: detailed events at a single point

Debugging means moving fluidly between them.

The key to correlation is a shared trace ID propagated through every service and stamped onto every log line and span. It is the thread that ties the three pillars together.

Trace context travels in request headers. Each service reads it, continues the trace, and passes it downstream so the whole journey shares one ID.

Inject the active trace ID into structured logs so a log line can be tied back to the exact request that produced it.

Exemplars attach sample trace IDs to metric data points. Click a spike on a latency chart and jump straight to a trace that experienced it.

The workflow: a metric alert fires for high error rate, an exemplar takes you to a slow trace, you spot the failing span, then its trace ID pulls the precise log line with the stack trace.

OpenTelemetry generates traces, metrics, and logs with consistent context, making correlation work out of the box instead of being hand-wired per service.

Correlation also relies on shared dimensions: the same service.name, environment, and version labels across all three signals. Inconsistent tags break the joins.

Without correlation, engineers manually hunt across three disconnected tools under time pressure. With it, one click chains the full story, turning hours of distributed debugging into minutes.

Tracing every request is expensive. Use tail-based sampling to keep traces that are slow or errored while dropping routine ones, preserving the interesting correlations without overwhelming cost.

Test your understanding of observability correlation.

You learned to correlate the three pillars: metrics show what, traces show where, logs show why. A propagated trace ID plus exemplars and consistent tags let you pivot from a metric spike to a trace to the exact log line. OpenTelemetry makes this work cohesively, turning distributed debugging from hours into minutes.