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feat: system coverage, deploy verdicts and incidents

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
go
Área
backend-api-design, observability

Línea de trabajo

Start with the existing System overview dashboard, deploy markers and split, alert rules, webhook delivery, and email sending described in the issue. Then trace how Fanout accepts and processes OpenTelemetry data, and review the Collector receiver and CI/CD semantic convention details referenced here. Done means the database, queue, and TLS panels, deploy verdicts, panel alerts, incidents, and email delivery work end to end as listed; the issue says each part ships separately.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

enhancement

Goal

Fanout becomes the one place to see a whole system and act on what goes wrong in it. It keeps doing only what it does today — accept OpenTelemetry data and do everything after that — and builds on what already exists: the System overview default dashboard, deploy markers and the deploy split, alert rules with webhook delivery, and Fanout's email sending.

Principle

The boundary is the input, not the feature set. Telemetry arrives only as OpenTelemetry, a standard, so anything that sends OTLP works without a Fanout integration. On top of that data Fanout should be rich and full-featured: dashboards, analysis, alerts, deploy verdicts and incidents all belong here.

1. More of the system on System overview

Recognise standard Collector receiver output and present it, with no per-source integration in Fanout:

  • Databases and queues from the postgresql, mysql, redis and kafkametrics receivers: connections, slow or failed queries, replication lag, consumer lag. Panels appear when their data exists and their descriptions name the receiver otherwise.
  • TLS certificate expiry from the tlscheck receiver: days until expiry per endpoint, bad when close.
  • The Send telemetry guide gains the receiver configuration for each.

2. Know what changed, and whether it broke something

  • Real change events. Accept deploys and pipeline runs that arrive as OpenTelemetry data (the CI/CD semantic conventions, or the Collector's github receiver) and draw them as deploy markers, alongside today's markers inferred from a new service.version.
  • A verdict for each deploy — pass, fail or inconclusive — from the existing before-and-after deploy split, shown on the marker and in the System overview versions table.

3. Act on problems: incidents on top of alerts

  • Alerts on dashboard panels, so a System overview panel turning bad (a filesystem above 90%, a host not reporting, a check with no recent result) can fire. Today alert rules see only per-service span and log rollups.
  • Incidents: a firing alert opens one, with acknowledgement, an owner, and a timeline that includes nearby deploys; it is recovered only once the alert has stayed resolved.
  • Email notifications next to webhooks, using the email sending Fanout already has.

Out of scope

Performance work (a scale benchmark, per-minute summaries, faster metric queries), AI and API cost tracking, and checks run by Fanout itself — Fanout does not gather or probe.

Done when

  • A Collector with the receivers above produces database, queue and TLS-expiry panels on System overview, verified end to end through OTLP.
  • A deploy event arriving as OpenTelemetry data appears as a marker with a pass, fail or inconclusive verdict.
  • A panel-based alert opens an incident, it can be acknowledged and assigned, and it closes only after recovery holds; email and webhook delivery both work.

Each part ships on its own, in this order.

Lenguaje dominante
HTML
Estrellas
6
Forks
0
Merge medio
2 h 25 min
PR fusionados (30 d)
25

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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