Observability / Basic: signals and measurement meaning

Observability β€” Basic: signals and measurement meaning

Understand questions, metric semantics, structured events, traces, distributions, and collection loss.

6 lessons Β· No account required

What you will learn

  • Choose signals for explicit diagnostic questions.
  • Preserve units, time boundaries, and evidence limitations.

  1. Observability starts with questions

    Choose telemetry from an explicit diagnostic question and explain the difference between observing a signal, forming a hypothesis, and establishing a cause.

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  2. Metric types, units, and temporality

    Choose instruments according to what a quantity means, then preserve units, aggregation, and cumulative or delta semantics through collection and queries.

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  3. Structured logs and event semantics

    Design bounded event records with stable fields, timestamps, severity, and safe correlation while keeping sensitive payloads outside routine telemetry.

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  4. Traces, spans, and propagated context

    Read a trace as related timed operations, distinguish parentage from wall-clock nesting, and preserve context safely across process boundaries.

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  5. Histograms and honest latency summaries

    Choose distribution boundaries for a question, distinguish bucket representations, and avoid invalid percentile aggregation or unsupported precision.

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  6. Collection pipelines and visible data loss

    Map telemetry from instrumentation to query and distinguish source silence, intentional filtering, buffering, export failure, and backend visibility.

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