Time as a First-Class Dimension Time-series systems treat time as a primary design axis. Ingestion, storage layout, indexing, compression, and query execution are all built around the assumption that data arrives as a stream of observations ordered in time. A time-series record carries the state of a process at a specific instant: CPU load on a node, vibration amplitude on a turbine, voltage on a feeder line, latency of a REST endpoint. New observations arrive as append-only writes, often at fixed sampling intervals or triggered by events. Historical data remains largely immutable, while queries scan contiguous time ranges: “last 5 minutes”, “previous 30 days”, “compare this week with last week”. The resulting workload differs sharply from OLTP (random point reads and writes) and from classic OLAP (batch analytics over slowly changing fact tables).…