`rolling_sum`

Generic Description

Computes a trailing sum over a fixed-size window. It is documented separately because time construction, sequence ordering, window parameters, and as-of behavior materially change analytical meaning.

Simple example:

WITH [10.0, 13.0, 9.0, 16.0] AS readings
RETURN rolling_sum(readings, 3) AS trailing_sum

Consumer-Level Explanation

Use it for recent activity volume, spend, or count intensity over a moving frame. Prefer this function when the temporal assumption should be visible to the planner, especially for event streams, freshness checks, and sequence features.

More Detailed Explanation

rolling_sum works on ordered temporal or sequence-style values inside Cypher. The practical contract is to assemble the sequence deliberately, choose the time or window parameter explicitly, and return named fields so downstream planner tooling does not invent unsupported window syntax.

Advanced Example

This example names the event stream and operational context before applying rolling_sum, which helps planner tooling preserve the intended sequence semantics.

WITH 'sensor-1' AS subject_id,
     [{ts: 1735689600000, value: 10.0, state: 'ok'}, {ts: 1735689660000, value: 13.0, state: 'ok'}, {ts: 1735689840000, value: 9.0, state: 'warn'}, {ts: 1735690200000, value: 16.0, state: 'ok'}] AS readings
RETURN subject_id,
       rolling_sum(readings, 3) AS trailing_sum

Real Use Cases

Real Limitations And Tradeoffs