Prometheus / Basic: collection and time-series meaning

Prometheus β€” Basic: collection and time-series meaning

Follow collection boundaries, series identity, metric types, targets, selectors, and counter rates.

6 lessons Β· No account required

What you will learn

  • Define collection and user-outcome boundaries.
  • Interpret labels, selectors, types, and rate evidence.

  1. The pull model and measurement boundaries

    Follow a metric from an instrumented target through scraping, storage, and query evaluation while distinguishing monitoring evidence from the user operation itself.

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  2. Time series, labels, and identity

    Define a series by its complete label set and explain why changing dimensions creates new history rather than editing an existing series in place.

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  3. Counters, gauges, and exposition contracts

    Choose a metric type from quantity semantics, read a text exposition fragment, and separate declared type metadata from the query you must apply.

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  4. Jobs, targets, and scrape health

    Interpret collection configuration and up without confusing discovered targets, attempted scrapes, successful exposition, and user-visible availability.

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  5. Selectors, vectors, and evaluation time

    Read instant and range selectors as population-and-time contracts, including label matching, absent labels, and the distinction between query range and data range.

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  6. Counter rates, increases, and resets

    Interpret counter-derived rates with reset and window semantics, and avoid treating a simple subtraction or Python model as the PromQL engine algorithm.

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