prometheus

Prometheus asks your service for numbers on a schedule rather than waiting to be told. That one design choice explains service discovery, exporters, staleness and why short-lived jobs are the awkward case.

The observability failure that scales with your success. Cardinality is the cost driver in every metrics system, and the fix has to be structural or it comes back next quarter.

A question with a right answer and a subtle reason. Histogram versus summary is the part that catches people, and it turns on whether you can aggregate across instances.

The pull model is the famous part and explains none of the operational pain. The storage engine is where Prometheus hurts in production: crash recovery, cold queries, disk death.

Anyone can divide bad requests by total. This answer shows the aggregation mistakes that quietly corrupt SLO dashboards, the rate-versus-increase traps that make budgets lie, and when subqueries cost too much.

Forty healthy islands equal one blind estate. Compare the consolidation patterns honestly, keep alerting local, curate what crosses the WAN, and make the global view read-only and cheap.