`sliding_window`

Generic Description

Returns overlapping list windows of a chosen size and step. It is documented separately because time construction, sequence ordering, window parameters, and as-of behavior materially change analytical meaning.

Simple example:

WITH [10, 13, 9, 16] AS readings
RETURN sliding_window(readings, 3, 1) AS windows

Consumer-Level Explanation

Use it when later logic needs the actual window contents, not only a scalar summary. 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

sliding_window 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 sliding_window, 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,
       sliding_window(readings, 3, 1) AS windows

Real Use Cases

Real Limitations And Tradeoffs