Glossary
Observation density
Observations that exist, against observations the schedule implies.
Definition
Observation density is the ratio of observations actually recorded in a window to the number the probe schedule implies should exist there: total_observations divided by (window_seconds / probe_interval). It is a measure of the measurement, not of the endpoint.
The problem
Availability is blind to the absence of measurement. A scheduler that stops issuing probes produces no failed observations and therefore no availability deficit - the record stays green while the evidence stops accumulating, and an availability figure computed over a near-empty window is indistinguishable from one computed over a full one.
Why it matters
Density is the signal that separates an endpoint failing from a measurement pipeline failing. They have different causes, different owners and different fixes, and they produce opposite readings on the same availability chart.
Practical example
On 11 September 2026 the RELIASTRA public record for one dependency returned 277 observations in a 24-hour window against 288 expected (96.2% density) and 595 in a 90-day window against 25,920 expected (2.3%). Availability read 100.0% in both. The falling density, not the availability, was the finding.
How RELIASTRA approaches it
RELIASTRA publishes the observation count beside every availability figure and audits its own records for density. The three-check audit - window monotonicity, history depth, expected-versus-observed density - is published with its script and its captured data so anyone can run it against their own monitoring supplier.
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