spotlight

This page explains how to use the spotlight function in APL to compare a selected set of events against a baseline and surface the most significant differences.

Spotlight lets you set up an analysis inside a query. You define a comparison set of events and compare it to the implicit baseline (the rest of the events in scope). Spotlight evaluates every field you pass in, scores differences, and returns the most informative contrasts. You use it when you want fast root-cause analysis, anomaly investigation, or pattern discovery without hand-rolling many ad-hoc aggregations.

Spotlight is useful when you:

Investigate spikes or dips in a time series and want to know what changed
Explain why a subset of traces is slow or error-prone
Find which attributes distinguish suspicious requests from normal traffic

This page explains the Spotlight APL function. For more information about how Spotlight works in the Axiom Console, see Spotlight.

Usage

Syntax

summarize spotlight(SelectionPredicate, Field1, Field2, ..., FieldN)

You use spotlight inside summarize. The first argument defines the comparison set. The remaining arguments list the fields to analyze.

Parameters

Name	Type	Description
`SelectionPredicate`	Boolean expression	Defines the comparison set (selected cohort). Spotlight compares events where the predicate evaluates to `true` against the baseline (events where it evaluates to `false`) within the current query scope.
`Field1 ... FieldN`	field references	One or more fields to analyze. Include string or categorical fields (for proportions) and numeric or timespan fields (for distributional differences). Use `*` as a wildcard to analyze all fields.

Returns

Bar charts for categorical fields (strings, Booleans)
Boxplots for numeric fields (integers, floats, timespans) with many distinct values

Use case examples

Find what distinguishes error responses from normal traffic in the last 15 minutes.

Query

['sample-http-logs']
| where _time >= now(-15m)
| summarize spotlight(status startswith "5", ['geo.country'], ['geo.city'], method, uri, req_duration_ms)

Run in Playground

This query keeps the last 15 minutes of traffic in scope and compares error responses to everything else. Spotlight ranks the strongest differences, pointing to endpoints, regions, and latency ranges associated with the errors.

Explain why some spans are slow or erroring in the last 30 minutes.

Query

['otel-demo-traces']
| where _time >= now(-30m)
| summarize spotlight(duration > 500ms, ['service.name'], kind, status_code, duration)

Run in Playground

The query compares spans that ran longer than 500 ms to all other spans in the time window. Spotlight highlights the service, kind, and duration range that most distinguish the selected spans.

Best practices

Keep the where scope broad enough that the baseline remains meaningful. Over-filtering reduces contrast.
Pass only fields that carry signal. Very high-cardinality identifiers can drown out more actionable attributes.
Include numeric fields like req_duration_ms or duration to let Spotlight detect distribution shifts, not just categorical skews.

where: Filters events before Spotlight runs. Use it to scope the time window or dataset; use spotlight to compare selected vs baseline inside that scope.
summarize: Runs aggregations over events. spotlight is an aggregation you call within summarize.
top: Returns the most frequent values. Use top for simple frequency counts; use spotlight to contrast a cohort against its baseline with lift and significance.
lookup: Enriches events with reference attributes. Use lookup to add context before running spotlight across enriched fields.