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Telemetry26 October 20265 min read

Clean telemetry: sampling, filtering and high-signal data

More data is not more insight. Clean telemetry means each signal is there for a reason, is consistent across services and can be trusted when something goes wrong.

In this post
  1. Principles
  2. Techniques
  3. Keep it clean
  4. Sources and further reading

Principles

  • Purpose: every signal answers a question someone asks.
  • Consistency: the same names, units and labels across services.
  • Context: traces linked to logs and metrics through shared identifiers.
  • Restraint: drop what is never used.

Techniques

  • Head sampling decides at the start of a trace; tail sampling decides once the trace is complete and can keep errors and slow requests.
  • Filtering and redaction in the Collector remove noise and sensitive data.
  • Structured logs with stable fields are easier to search and cheaper to store.

Keep it clean

  1. Write down the telemetry standard and share it.
  2. Review new instrumentation like new code.
  3. Check periodically which data is used.
  4. Remove what is not.

Sources and further reading

  1. OpenTelemetry. Sampling. opentelemetry.io/docs/concepts/sampling/.

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