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Reconstruct AI search incident timelines without making up the story

A practical workflow to correlate logs, metrics, traces, and OpenSearch when a vector-search regression degrades an API.

Turn scattered telemetry into a verifiable timeline: incident window, falsifiable hypotheses, trace anchors, correlated logs, saturation metrics, and mitigation validation.

Created: July 1, 2026

Published: July 1, 2026

Estimated time45 min

LevelIntermediate

Before you startRead access to Prometheus or a PromQL-compatible backend

PlatformsLinux / Docker

WhatsApp X LinkedIn

Linux

CLI workflow to freeze the incident window, extract evidence, and build a reproducible timeline table.

curljqdateread-only access to Prometheus/Loki/Tempo/OpenSearch

Prepare the incident window

export START="2026-07-01T07:42:00Z"
export END="2026-07-01T08:18:00Z"
export SERVICE="search-api"

Query p99 and errors

curl -G "$PROM/api/v1/query_range" --data-urlencode "query=histogram_quantile(0.99, sum by (le)(rate(http_server_duration_seconds_bucket{service=\"$SERVICE\"}[5m])))" --data-urlencode "start=$START" --data-urlencode "end=$END" | jq .

Also available inReconstruir timelines de incidentes de búsqueda AI sin inventarte la película

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