feat(docs): Add comprehensive documentation for Vexer, Vulnerability Explorer, and Zastava modules
- Introduced AGENTS.md, README.md, TASKS.md, and implementation_plan.md for Vexer, detailing mission, responsibilities, key components, and operational notes. - Established similar documentation structure for Vulnerability Explorer and Zastava modules, including their respective workflows, integrations, and observability notes. - Created risk scoring profiles documentation outlining the core workflow, factor model, governance, and deliverables. - Ensured all modules adhere to the Aggregation-Only Contract and maintain determinism and provenance in outputs.
This commit is contained in:
261
docs/modules/scheduler/operations/worker-grafana-dashboard.json
Normal file
261
docs/modules/scheduler/operations/worker-grafana-dashboard.json
Normal file
@@ -0,0 +1,261 @@
|
||||
{
|
||||
"title": "Scheduler Worker – Planning & Rescan",
|
||||
"uid": "scheduler-worker-observability",
|
||||
"schemaVersion": 38,
|
||||
"version": 1,
|
||||
"editable": true,
|
||||
"timezone": "",
|
||||
"graphTooltip": 0,
|
||||
"time": {
|
||||
"from": "now-24h",
|
||||
"to": "now"
|
||||
},
|
||||
"templating": {
|
||||
"list": [
|
||||
{
|
||||
"name": "datasource",
|
||||
"type": "datasource",
|
||||
"query": "prometheus",
|
||||
"hide": 0,
|
||||
"refresh": 1,
|
||||
"current": {}
|
||||
},
|
||||
{
|
||||
"name": "mode",
|
||||
"label": "Mode",
|
||||
"type": "query",
|
||||
"datasource": {
|
||||
"type": "prometheus",
|
||||
"uid": "${datasource}"
|
||||
},
|
||||
"query": "label_values(scheduler_planner_runs_total, mode)",
|
||||
"refresh": 1,
|
||||
"multi": true,
|
||||
"includeAll": true,
|
||||
"allValue": ".*",
|
||||
"current": {
|
||||
"selected": false,
|
||||
"text": "All",
|
||||
"value": ".*"
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"annotations": {
|
||||
"list": []
|
||||
},
|
||||
"panels": [
|
||||
{
|
||||
"id": 1,
|
||||
"title": "Planner Runs per Status",
|
||||
"type": "timeseries",
|
||||
"datasource": {
|
||||
"type": "prometheus",
|
||||
"uid": "${datasource}"
|
||||
},
|
||||
"fieldConfig": {
|
||||
"defaults": {
|
||||
"unit": "ops",
|
||||
"displayName": "{{status}}"
|
||||
},
|
||||
"overrides": []
|
||||
},
|
||||
"options": {
|
||||
"legend": {
|
||||
"displayMode": "table",
|
||||
"placement": "bottom"
|
||||
}
|
||||
},
|
||||
"targets": [
|
||||
{
|
||||
"expr": "sum by (status) (rate(scheduler_planner_runs_total{mode=~\"$mode\"}[5m]))",
|
||||
"legendFormat": "{{status}}",
|
||||
"refId": "A"
|
||||
}
|
||||
],
|
||||
"gridPos": {
|
||||
"h": 8,
|
||||
"w": 12,
|
||||
"x": 0,
|
||||
"y": 0
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"title": "Planner Latency P95 (s)",
|
||||
"type": "timeseries",
|
||||
"datasource": {
|
||||
"type": "prometheus",
|
||||
"uid": "${datasource}"
|
||||
},
|
||||
"fieldConfig": {
|
||||
"defaults": {
|
||||
"unit": "s"
|
||||
},
|
||||
"overrides": []
|
||||
},
|
||||
"options": {
|
||||
"legend": {
|
||||
"displayMode": "table",
|
||||
"placement": "bottom"
|
||||
}
|
||||
},
|
||||
"targets": [
|
||||
{
|
||||
"expr": "histogram_quantile(0.95, sum by (le) (rate(scheduler_planner_latency_seconds_bucket{mode=~\"$mode\"}[5m])))",
|
||||
"legendFormat": "p95",
|
||||
"refId": "A"
|
||||
}
|
||||
],
|
||||
"gridPos": {
|
||||
"h": 8,
|
||||
"w": 12,
|
||||
"x": 12,
|
||||
"y": 0
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 3,
|
||||
"title": "Runner Segments per Status",
|
||||
"type": "timeseries",
|
||||
"datasource": {
|
||||
"type": "prometheus",
|
||||
"uid": "${datasource}"
|
||||
},
|
||||
"fieldConfig": {
|
||||
"defaults": {
|
||||
"unit": "ops",
|
||||
"displayName": "{{status}}"
|
||||
},
|
||||
"overrides": []
|
||||
},
|
||||
"options": {
|
||||
"legend": {
|
||||
"displayMode": "table",
|
||||
"placement": "bottom"
|
||||
}
|
||||
},
|
||||
"targets": [
|
||||
{
|
||||
"expr": "sum by (status) (rate(scheduler_runner_segments_total{mode=~\"$mode\"}[5m]))",
|
||||
"legendFormat": "{{status}}",
|
||||
"refId": "A"
|
||||
}
|
||||
],
|
||||
"gridPos": {
|
||||
"h": 8,
|
||||
"w": 12,
|
||||
"x": 0,
|
||||
"y": 8
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 4,
|
||||
"title": "New Findings per Severity",
|
||||
"type": "timeseries",
|
||||
"datasource": {
|
||||
"type": "prometheus",
|
||||
"uid": "${datasource}"
|
||||
},
|
||||
"fieldConfig": {
|
||||
"defaults": {
|
||||
"unit": "ops",
|
||||
"displayName": "{{severity}}"
|
||||
},
|
||||
"overrides": []
|
||||
},
|
||||
"options": {
|
||||
"legend": {
|
||||
"displayMode": "table",
|
||||
"placement": "bottom"
|
||||
}
|
||||
},
|
||||
"targets": [
|
||||
{
|
||||
"expr": "sum(rate(scheduler_runner_delta_critical_total{mode=~\"$mode\"}[5m]))",
|
||||
"legendFormat": "critical",
|
||||
"refId": "A"
|
||||
},
|
||||
{
|
||||
"expr": "sum(rate(scheduler_runner_delta_high_total{mode=~\"$mode\"}[5m]))",
|
||||
"legendFormat": "high",
|
||||
"refId": "B"
|
||||
},
|
||||
{
|
||||
"expr": "sum(rate(scheduler_runner_delta_total{mode=~\"$mode\"}[5m]))",
|
||||
"legendFormat": "total",
|
||||
"refId": "C"
|
||||
}
|
||||
],
|
||||
"gridPos": {
|
||||
"h": 8,
|
||||
"w": 12,
|
||||
"x": 12,
|
||||
"y": 8
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 5,
|
||||
"title": "Runner Backlog by Schedule",
|
||||
"type": "table",
|
||||
"datasource": {
|
||||
"type": "prometheus",
|
||||
"uid": "${datasource}"
|
||||
},
|
||||
"fieldConfig": {
|
||||
"defaults": {
|
||||
"displayName": "{{scheduleId}}",
|
||||
"unit": "none"
|
||||
},
|
||||
"overrides": []
|
||||
},
|
||||
"options": {
|
||||
"showHeader": true
|
||||
},
|
||||
"targets": [
|
||||
{
|
||||
"expr": "max by (scheduleId) (scheduler_runner_backlog{mode=~\"$mode\"})",
|
||||
"format": "table",
|
||||
"refId": "A"
|
||||
}
|
||||
],
|
||||
"gridPos": {
|
||||
"h": 8,
|
||||
"w": 12,
|
||||
"x": 0,
|
||||
"y": 16
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 6,
|
||||
"title": "Active Runs",
|
||||
"type": "stat",
|
||||
"datasource": {
|
||||
"type": "prometheus",
|
||||
"uid": "${datasource}"
|
||||
},
|
||||
"fieldConfig": {
|
||||
"defaults": {
|
||||
"unit": "none"
|
||||
},
|
||||
"overrides": []
|
||||
},
|
||||
"options": {
|
||||
"orientation": "horizontal",
|
||||
"textMode": "value"
|
||||
},
|
||||
"targets": [
|
||||
{
|
||||
"expr": "sum(scheduler_runs_active{mode=~\"$mode\"})",
|
||||
"refId": "A"
|
||||
}
|
||||
],
|
||||
"gridPos": {
|
||||
"h": 8,
|
||||
"w": 12,
|
||||
"x": 12,
|
||||
"y": 16
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,42 @@
|
||||
groups:
|
||||
- name: scheduler-worker
|
||||
interval: 30s
|
||||
rules:
|
||||
- alert: SchedulerPlannerFailuresHigh
|
||||
expr: sum(rate(scheduler_planner_runs_total{status="failed"}[5m]))
|
||||
/
|
||||
sum(rate(scheduler_planner_runs_total[5m])) > 0.05
|
||||
for: 10m
|
||||
labels:
|
||||
severity: critical
|
||||
service: scheduler-worker
|
||||
annotations:
|
||||
summary: "Planner failure ratio above 5%"
|
||||
description: "More than 5% of planning runs are failing. Inspect scheduler logs and ImpactIndex connectivity before queues back up."
|
||||
- alert: SchedulerPlannerLatencyHigh
|
||||
expr: histogram_quantile(0.95, sum by (le) (rate(scheduler_planner_latency_seconds_bucket[5m]))) > 45
|
||||
for: 10m
|
||||
labels:
|
||||
severity: warning
|
||||
service: scheduler-worker
|
||||
annotations:
|
||||
summary: "Planner latency p95 above 45s"
|
||||
description: "Planning latency p95 stayed above 45 seconds for 10 minutes. Check ImpactIndex, Mongo, or external selectors to prevent missed SLAs."
|
||||
- alert: SchedulerRunnerBacklogGrowing
|
||||
expr: max_over_time(scheduler_runner_backlog[15m]) > 500
|
||||
for: 15m
|
||||
labels:
|
||||
severity: warning
|
||||
service: scheduler-worker
|
||||
annotations:
|
||||
summary: "Runner backlog above 500 images"
|
||||
description: "Runner backlog exceeded 500 images over the last 15 minutes. Verify runner workers, scanner availability, and rate limits."
|
||||
- alert: SchedulerRunStuck
|
||||
expr: sum(scheduler_runs_active) > 0 and max_over_time(scheduler_runs_active[30m]) == min_over_time(scheduler_runs_active[30m])
|
||||
for: 30m
|
||||
labels:
|
||||
severity: warning
|
||||
service: scheduler-worker
|
||||
annotations:
|
||||
summary: "Scheduler runs stuck without progress"
|
||||
description: "Active runs count has remained flat for 30 minutes. Investigate stuck segments or scanner timeouts."
|
||||
82
docs/modules/scheduler/operations/worker.md
Normal file
82
docs/modules/scheduler/operations/worker.md
Normal file
@@ -0,0 +1,82 @@
|
||||
# Scheduler Worker – Observability & Runbook
|
||||
|
||||
## Purpose
|
||||
Monitor planner and runner health for the Scheduler Worker (Sprint 16 telemetry). The new .NET meters surface queue throughput, latency, backlog, and delta severities so operators can detect stalled runs before rescan SLAs slip.
|
||||
|
||||
> **Grafana note:** Import `docs/modules/scheduler/operations/worker-grafana-dashboard.json` into the Prometheus-backed Grafana stack that scrapes the OpenTelemetry Collector.
|
||||
|
||||
---
|
||||
|
||||
## Key metrics
|
||||
|
||||
| Metric | Use case | Suggested query |
|
||||
| --- | --- | --- |
|
||||
| `scheduler_planner_runs_total{status}` | Planner throughput & failure ratio | `sum by (status) (rate(scheduler_planner_runs_total[5m]))` |
|
||||
| `scheduler_planner_latency_seconds_bucket` | Planning latency (p95 / p99) | `histogram_quantile(0.95, sum by (le) (rate(scheduler_planner_latency_seconds_bucket[5m])))` |
|
||||
| `scheduler_runner_segments_total{status}` | Runner success vs retries | `sum by (status) (rate(scheduler_runner_segments_total[5m]))` |
|
||||
| `scheduler_runner_delta_{critical,high,total}` | Newly-detected findings | `sum(rate(scheduler_runner_delta_critical_total[5m]))` |
|
||||
| `scheduler_runner_backlog{scheduleId}` | Remaining digests awaiting runner | `max by (scheduleId) (scheduler_runner_backlog)` |
|
||||
| `scheduler_runs_active{mode}` | Active runs in-flight | `sum(scheduler_runs_active)` |
|
||||
|
||||
Reference queries power the bundled Grafana dashboard panels. Use the `mode` template variable to focus on `analysisOnly` versus `contentRefresh` schedules.
|
||||
|
||||
---
|
||||
|
||||
## Grafana dashboard
|
||||
|
||||
1. Import `docs/modules/scheduler/operations/worker-grafana-dashboard.json` (UID `scheduler-worker-observability`).
|
||||
2. Point the `datasource` variable to the Prometheus instance scraping the collector. Optional: pin the `mode` variable to a specific schedule mode.
|
||||
3. Panels included:
|
||||
- **Planner Runs per Status** – visualises success vs failure ratio.
|
||||
- **Planner Latency P95** – highlights degradations in ImpactIndex or Mongo lookups.
|
||||
- **Runner Segments per Status** – shows retry pressure and queue health.
|
||||
- **New Findings per Severity** – rolls up delta counters (critical/high/total).
|
||||
- **Runner Backlog by Schedule** – tabulates outstanding digests per schedule.
|
||||
- **Active Runs** – stat panel showing the current number of in-flight runs.
|
||||
|
||||
Capture screenshots once Grafana provisioning completes and store them under `docs/assets/dashboards/` (pending automation ticket OBS-157).
|
||||
|
||||
---
|
||||
|
||||
## Prometheus alerts
|
||||
|
||||
Import `docs/modules/scheduler/operations/worker-prometheus-rules.yaml` into your Prometheus rule configuration. The bundle defines:
|
||||
|
||||
- **SchedulerPlannerFailuresHigh** – 5%+ of planner runs failed for 10 minutes. Page SRE.
|
||||
- **SchedulerPlannerLatencyHigh** – planner p95 latency remains above 45 s for 10 minutes. Investigate ImpactIndex, Mongo, and Feedser/Vexer event queues.
|
||||
- **SchedulerRunnerBacklogGrowing** – backlog exceeded 500 images for 15 minutes. Inspect runner workers, Scanner availability, and rate limiting.
|
||||
- **SchedulerRunStuck** – active run count stayed flat for 30 minutes while remaining non-zero. Check stuck segments, expired leases, and scanner retries.
|
||||
|
||||
Hook these alerts into the existing Observability notification pathway (`observability-pager` routing key) and ensure `service=scheduler-worker` is mapped to the on-call rotation.
|
||||
|
||||
---
|
||||
|
||||
## Runbook snapshot
|
||||
|
||||
1. **Planner failure/latency:**
|
||||
- Check Planner logs for ImpactIndex or Mongo exceptions.
|
||||
- Verify Feedser/Vexer webhook health; requeue events if necessary.
|
||||
- If planner is overwhelmed, temporarily reduce schedule parallelism via `stella scheduler schedule update`.
|
||||
2. **Runner backlog spike:**
|
||||
- Confirm Scanner WebService health (`/healthz`).
|
||||
- Inspect runner queue for stuck segments; consider increasing runner workers or scaling scanner capacity.
|
||||
- Review rate limits (schedule limits, ImpactIndex throughput) before changing global throttles.
|
||||
3. **Stuck runs:**
|
||||
- Use `stella scheduler runs list --state running` to identify affected runs.
|
||||
- Drill into Grafana panel “Runner Backlog by Schedule” to see offending schedule IDs.
|
||||
- If a segment will not progress, use `stella scheduler segments release --segment <id>` to force retry after resolving root cause.
|
||||
4. **Unexpected critical deltas:**
|
||||
- Correlate `scheduler_runner_delta_critical_total` spikes with Notify events (`scheduler.rescan.delta`).
|
||||
- Pivot to Scanner report links for impacted digests and confirm they match upstream advisories/policies.
|
||||
|
||||
Document incidents and mitigation in `ops/runbooks/INCIDENT_LOG.md` (per SRE policy) and attach Grafana screenshots for post-mortems.
|
||||
|
||||
---
|
||||
|
||||
## Checklist
|
||||
|
||||
- [ ] Grafana dashboard imported and wired to Prometheus datasource.
|
||||
- [ ] Prometheus alert rules deployed (see above).
|
||||
- [ ] Runbook linked from on-call rotation portal.
|
||||
- [ ] Observability Guild sign-off captured for Sprint 16 telemetry (OWNER: @obs-guild).
|
||||
|
||||
Reference in New Issue
Block a user