WINDOW

Generic Description

Bucket rows into time windows for later aggregation.

Simple example:

MATCH (r:Reading) WINDOW TUMBLING 300 ON r.ts RETURN r

Consumer-Level Explanation

Use WINDOW when the query must bucket rows into time windows for later aggregation and the planner needs to see that operation as part of the Cypher row pipeline. Keep the clause explicit because it controls row grain, variable scope, and what later clauses are allowed to reference.

More Detailed Explanation

WINDOW is the bridge from raw event history to rollups. In the current AST and docs, tumbling windows are the explicit implemented strategy, which already covers a large class of operational and analytics queries.

What this clause is really for:

it defines one concrete stage in the Cypher row pipeline, so variables available before and after WINDOW must be clear
it should make graph structure, temporal filters, document payload shaping, or procedure output explicit instead of relying on client-side interpretation
planner tooling depends on this clause boundary to know row grain, variable scope, and whether later expressions are reads, writes, schema operations, or projections

Advanced Example

This example keeps WINDOW inside a complete query pipeline so the clause boundary, visible variables, and returned row shape are clear to planner tooling.

MATCH (s:Sensor)-[r:EMITS]->(m:Measurement)
TIME r.ts BETWEEN datetime('2025-01-01T00:00:00Z') AND datetime('2025-01-07T00:00:00Z')
WITH s, r, m, date_trunc('hour', r.ts) AS bucket, properties(m) AS payload
RETURN s.sensor_id, bucket, keys(payload) AS payload_keys, unpivot(payload) AS metrics
ORDER BY bucket DESC

Real Use Cases

per-minute or per-hour rollups
alerting and threshold dashboards
smoothing raw event history into business periods

Real Limitations And Tradeoffs

window width choice strongly changes output meaning
windowing alone does not aggregate; it usually needs WITH/RETURN aggregation after it
query readers need the bucket semantics explained clearly