`derivative`

Generic Description

Computes rate of value change between consecutive temporal points. It is documented separately because time construction, sequence ordering, window parameters, and as-of behavior materially change analytical meaning.

Simple example:

WITH [{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 derivative(readings, 'value') AS per_second_change

Consumer-Level Explanation

Use it for slope over time when the raw value changes are time-spaced irregularly. 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

derivative 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 derivative, 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,
       derivative(readings, 'value') AS per_second_change

Real Use Cases

Real Limitations And Tradeoffs