---
title: `context_alignment_join`
url: https://nexyron.tech/docs/cypher/functions/temporal/context-alignment-join
description: Joins each left event to the nearest right side context within a tolerance. It is documented separately because time construction, sequence ordering, window parameters, and as of b
---

# `context_alignment_join`

## Generic Description

Joins each left event to the nearest right-side context within a tolerance. It is documented separately because time construction, sequence ordering, window parameters, and as-of behavior materially change analytical meaning.

Simple example:

```cypher
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, [{ts: 1735689300000, context: 'baseline'}, {ts: 1735689720000, context: 'release'}] AS context
RETURN context_alignment_join(readings, context, 900000) AS joined_rows
```

## Consumer-Level Explanation

Use it when closest-in-time context is appropriate rather than strictly prior context. 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

`context_alignment_join` 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 `context_alignment_join`, which helps planner tooling preserve the intended sequence semantics.

```cypher
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,
     [{ts: 1735689300000, context: 'baseline'}, {ts: 1735689720000, context: 'release'}] AS context
RETURN subject_id,
       context_alignment_join(readings, context, 900000) AS joined_rows
```

## 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