A building-management system emits temperature, humidity, and CO2 readings from a gallery's HVAC controller, and all three values for a given moment always carry the exact same timestamp because they are sampled together. An engineer wants to group and combine those three simultaneous readings into a single row per timestamp, without introducing any time-span logic. Which windowing function matches this need?

A snapshot window groups events that carry an identical timestamp value into a single result, with no notion of a duration or a sliding span at all — it is purely a same-instant grouping. Because the HVAC controller emits temperature, humidity, and CO2 readings that are stamped with the exact same moment by design, a snapshot window is the direct match: it combines those three readings into one row per timestamp without introducing any artificial time-bucket logic.

A tumbling window with a one-minute duration would work only by coincidence if all three readings always land in the same one-minute bucket, but it also risks bundling in unrelated readings from other moments within that same minute, which is not what the requirement asks for. A hopping window adds overlap on top of that same imprecision, making it an even worse fit for a same-instant grouping. A session window keys off gaps of inactivity between events, which is irrelevant here since the three readings are simultaneous by construction rather than separated by any gap to measure.

A caveat: a snapshot window only behaves correctly when the source truly guarantees identical timestamps for the values that should be grouped; if the controller's three sensors were ever stamped even a millisecond apart, they would land in separate snapshot groups. Operationally, spot-check a handful of grouped rows to confirm each one contains exactly the temperature, humidity, and CO2 reading for a single shared timestamp, with none split across rows.