Metrics and Time Series

Metrics are the foundation of observability. They are numeric measurements collected at regular intervals and stored as time series — sequences of data points indexed by time. Metrics are lightweight, efficient to store, and ideal for dashboards and alerting.

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What is a Time Series?

A time series is a sequence of values recorded at successive points in time:

timestamp              value
2025-03-15T10:00:00Z   0.42
2025-03-15T10:00:15Z   0.51
2025-03-15T10:00:30Z   0.38
2025-03-15T10:00:45Z   0.67

Each time series is uniquely identified by a metric name and a set of labels (also called tags or dimensions):

http_request_duration_seconds{service="api", method="GET", endpoint="/users"}

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Metric Types

Most metrics systems define four core metric types:

Counter

A monotonically increasing value that only goes up (or resets to zero on restart).

http_requests_total{method="GET", status="200"} 14523

Use counters for: total requests, errors, bytes transferred.

Gauge

A value that can go up or down — a snapshot of a current measurement.

temperature_celsius{location="server-room"} 22.5

Use gauges for: CPU usage, memory usage, queue depth, temperature.

Histogram

Samples observations (e.g., request latency) and counts them in configurable buckets.