Logs, Metrics, Traces, and Audit Evidence
Goal
Use complementary evidence to understand workload health, request behavior, and control-plane change.
The Short Version
Observability is the ability to investigate a system through the evidence it produces. Metrics summarize behavior over time, logs record events with context, traces follow work across boundaries, and audit records show who changed what in the platform or application. None is a universal substitute for the others.
Collect evidence to answer operational questions, not because a tool can ingest it. Start with critical flows and likely investigations, then decide which signals, dimensions, retention, and access controls are required.
Complementary Signals
| Evidence | Best at answering | Typical weakness |
|---|---|---|
| Metrics | Is behavior changing, and how widely? | Limited detail about one event |
| Logs | What happened at a component or decision point? | Volume, inconsistency, and search cost |
| Traces | Where did a request spend time or fail? | Sampling and context-propagation gaps |
| Audit records | Who or what changed a controlled resource? | Does not explain all runtime behavior |
Use shared identifiers to connect these views: workload, environment, version, region, tenant where appropriate, request or trace ID, and deployment ID. Keep cardinality under control; an unbounded user ID or request value can make metric storage expensive and queries ineffective.
Architecture Meaning
Instrument user journeys and dependency boundaries. Record duration, outcome, and failure category without placing secrets or unnecessary personal data in telemetry. Protect evidence with access control, encryption, retention, integrity, and deletion rules. Security and compliance teams may need longer audit retention than operators need for high-volume debug logs.
AWS CloudWatch and X-Ray, and Azure Monitor with Application Insights, are provider implementations of these responsibilities. AWS CloudTrail and Azure Activity Log focus on control-plane and audit activity. Product names matter during implementation, but the durable design is the signal model and investigation path.
Example: Slow Order Submission
An alert shows rising order latency. Metrics establish that the problem affects one region and began after a deployment. Traces show most time in an inventory dependency. Structured logs reveal timeout categories and the affected version. Audit records confirm that no network policy changed. Together, the evidence narrows the response faster than any single signal.
Common Mistakes
Logging everything without defining useful fields, retention, or queries.
Putting secrets, tokens, or sensitive payloads into diagnostic records.
Using host CPU as the primary measure of customer experience.
Breaking trace context at queues, background jobs, or third-party calls.
If Someone Asks You
Metrics show patterns, logs explain events, traces connect distributed work, and audit records establish change history. Design them around operational questions and correlate them with consistent context while controlling sensitive data, volume, and retention.
What You Should Remember
Useful observability is connected evidence for specific decisions, not maximum telemetry volume.
Questions to Check Your Understanding
Which signal best exposes where a distributed request spent time?
Why should audit and debug data have different retention policies?
What makes a telemetry dimension operationally dangerous?
Further Study
OpenTelemetry signals - Review the vendor-neutral definitions of traces, metrics, logs, and related context.