You’re storing metrics at 1-second granularity. After a year, that’s 31 million data points per metric. Nobody looks at second-level data from 6 months ago. But you still need the trends.

Your monitoring system ingests 100,000 metrics per second. Each is a timestamp, a name, and a value. A regular database buckles. Time-series databases are designed for exactly this shape of data.

Read-your-writes and monotonic reads aren’t just buzzwords. They’re the difference between a database that feels broken and one that makes sense to users.

Users see stale data after writes. It’s not a bug, it’s replication lag. Learn to handle read-after-write problems and causality violations in production.

Eventual consistency doesn’t mean milliseconds. Understand linearizability, causal consistency, and quorum reads to pick the right consistency model.