`frequency`
Generic Description
Computes event rate over the full sequence span. 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 frequency(readings) AS events_per_second
Consumer-Level Explanation
Use it for overall cadence, not burst-local intensity. 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
frequency 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 frequency, 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,
frequency(readings) AS events_per_second
Real Use Cases
- sessionizing user or device events before feature extraction
- smoothing, rate calculation, or anomaly screening on irregular telemetry
- joining observations to the correct historical context without future leakage
Real Limitations And Tradeoffs
- sequence functions assume the input list is already the intended ordered series
- window size, inactivity threshold, and tolerance values are modeling choices that change results materially
- these helpers are not a replacement for registering temporal properties when the workload needs cataloged timeseries history