Metrics summarize behavior over time

Metrics are numeric aggregations collected over fixed time windows. They track rates, averages, and percentiles such as request count, error rate, CPU utilization, and latency distributions. Because they are aggregated, metrics remain compact even at high volume and are ideal for dashboards and alerting. A sudden rise in p99 latency or a drop in successful requests immediately signals that system health has shifted.

The limitation of metrics is that aggregation hides individual events. A metric may show that 5 percent of requests failed without identifying which specific calls or downstream services were responsible. Teams therefore treat metrics as the first detection layer rather than the final diagnostic tool.

  • Use metrics for capacity planning and SLO tracking because they scale efficiently.
  • Avoid relying on metrics alone for root-cause analysis when high-cardinality attributes are involved.
  • Default OpenTelemetry aggregations balance memory cost against useful statistical summaries.

Traces follow individual requests across services

A trace records the complete path of a single request as it moves through multiple services. Each span captures the start time, duration, and attributes of one operation, while parent-child relationships connect the spans into a directed acyclic graph. This structure makes nondeterministic problems visible that cannot be reproduced locally in a single process.

Traces excel at dependency analysis and latency investigation. When a metric alert fires, the corresponding trace quickly narrows the search to the slowest span or the failing downstream call. Without traces, engineers must guess which service introduced the delay.

  • Root spans represent the full request lifecycle from the client perspective.
  • Child spans provide context about each step, including network calls and database queries.
  • Sampling decisions affect storage cost; full-fidelity traces require careful retention policies.

Logs capture discrete events with context

Logs are timestamped records emitted by application code or infrastructure components. They contain free-form messages, structured attributes, severity levels, and stack traces. Unlike metrics, logs preserve the exact wording of an error or the state of a variable at a moment in time. Unlike traces, logs are not required to be associated with any particular request.

Logs become far more powerful when they carry trace and span identifiers. Correlation allows an engineer to jump from a slow span directly to the log lines that were written during that operation. Without this linkage, searching logs for a high-cardinality field quickly becomes expensive and slow.

  • Include trace context in every log statement to enable correlation.
  • Prefer structured logs over plain text for machine parsing and filtering.
  • High log volume requires sampling or aggregation strategies to control cost.

Combining the signals produces complete workflows

Effective troubleshooting follows a consistent sequence. A metric threshold triggers an alert. The alert links to a trace that isolates the problematic service and operation. The trace identifier filters the relevant logs, revealing the exact error message or unexpected state. This pattern works because the three signals share resource attributes and trace context propagated by OpenTelemetry.

Organizations that instrument only one or two signals leave gaps. Metrics without traces cannot locate the source of latency. Traces without logs cannot explain why a particular span failed. Logs without metrics or traces become an undifferentiated flood of data during an incident.

  • Define a shared trace ID format across all services and logging libraries.
  • Store resource attributes such as service name and deployment environment on every signal.
  • Review cardinality of metric attributes regularly to avoid unexpected memory growth.

Practical limits and next steps

Instrumentation must be added before an incident occurs. Adding spans or log statements during an outage rarely succeeds under time pressure. Teams should therefore adopt the OpenTelemetry specification early and treat observability as a first-class requirement alongside functional correctness.

Storage and query costs grow with data volume. Metrics remain cheapest, traces require selective sampling, and logs benefit from structured indexing. Governance policies that balance retention with investigation needs keep observability sustainable as systems scale.

  • Start with automatic instrumentation where available, then add manual spans for business-critical operations.
  • Test correlation end-to-end in staging before relying on it in production.
  • Document which signal answers which question so on-call engineers follow a repeatable path.