# Metrics & SLOs (DOCS-OBS-51-001)

Last updated: 2025-12-15

## Core metrics (platform-wide)
- **Requests**: `http_requests_total{tenant,workload,route,status}` (counter); latency histogram `http_request_duration_seconds`.
- **Jobs**: `worker_jobs_total{tenant,queue,status}`; `worker_job_duration_seconds`.
- **DB**: `db_query_duration_seconds{db,operation}`; `db_pool_in_use`, `db_pool_available`.
- **Cache**: `cache_requests_total{result=hit|miss}`; `cache_latency_seconds`.
- **Queue depth**: `queue_depth{tenant,queue}` (gauge).
- **Errors**: `errors_total{tenant,workload,code}`.
- **Custom module metrics**: keep namespaced (e.g., `riskengine_score_duration_seconds`, `notify_delivery_attempts_total`).

## SLOs (suggested)
- API availability: 99.9% monthly per public service.
- P95 latency: <300 ms for read endpoints; <1 s for write endpoints.
- Worker job success: >99% over 30d; P95 job duration set per queue (document locally).
- Queue backlog: alert when `queue_depth` > 1000 for 5 minutes per tenant/queue.
- Error budget policy: 28-day rolling window; burn-rate alerts at 2× and 14× budget.

## Alert examples
- High error rate: `rate(errors_total[5m]) / rate(http_requests_total[5m]) > 0.02`.
- Latency regression: `histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le,route)) > 0.3`.
- Queue backlog: `queue_depth > 1000` for 5m.
- Job failures: `rate(worker_jobs_total{status="failed"}[10m]) > 0.01`.

## UX KPIs (triage TTFS)
- Targets:
  - TTFS first evidence p95: <= 1.5s
  - TTFS skeleton p95: <= 0.2s
  - Clicks-to-closure median: <= 6
  - Evidence completeness avg: >= 90% (>= 3.6/4)

```promql
# TTFS first evidence p50/p95
histogram_quantile(0.50, sum(rate(stellaops_ttfs_first_evidence_seconds_bucket[5m])) by (le))
histogram_quantile(0.95, sum(rate(stellaops_ttfs_first_evidence_seconds_bucket[5m])) by (le))

# Clicks-to-closure median
histogram_quantile(0.50, sum(rate(stellaops_clicks_to_closure_bucket[5m])) by (le))

# Evidence completeness average percent (0-4 mapped to 0-100)
100 * (sum(rate(stellaops_evidence_completeness_score_sum[5m])) / clamp_min(sum(rate(stellaops_evidence_completeness_score_count[5m])), 1)) / 4

# Budget violations by phase
sum(rate(stellaops_performance_budget_violations_total[5m])) by (phase)
```

- Dashboard: `ops/devops/observability/grafana/triage-ttfs.json`
- Alerts: `ops/devops/observability/triage-alerts.yaml`

## TTFS Metrics (time-to-first-signal)
- Core metrics:
  - `ttfs_latency_seconds{surface,cache_hit,signal_source,kind,phase,tenant_id}` (histogram)
  - `ttfs_signal_total{surface,cache_hit,signal_source,kind,phase,tenant_id}` (counter)
  - `ttfs_cache_hit_total{surface,cache_hit,signal_source,kind,phase,tenant_id}` (counter)
  - `ttfs_cache_miss_total{surface,cache_hit,signal_source,kind,phase,tenant_id}` (counter)
  - `ttfs_slo_breach_total{surface,cache_hit,signal_source,kind,phase,tenant_id}` (counter)
  - `ttfs_error_total{surface,cache_hit,signal_source,kind,phase,tenant_id,error_type,error_code}` (counter)

- SLO targets:
  - P50 < 2s, P95 < 5s (all surfaces)
  - Warm path P50 < 700ms, P95 < 2.5s
  - Cold path P95 < 4s

```promql
# TTFS latency p50/p95
histogram_quantile(0.50, sum(rate(ttfs_latency_seconds_bucket[5m])) by (le))
histogram_quantile(0.95, sum(rate(ttfs_latency_seconds_bucket[5m])) by (le))

# SLO breach rate (per minute)
60 * sum(rate(ttfs_slo_breach_total[5m]))
```

## Offline Kit (air-gap) metrics
- `offlinekit_import_total{status,tenant_id}` (counter)
- `offlinekit_attestation_verify_latency_seconds{attestation_type,success}` (histogram)
- `attestor_rekor_success_total{mode}` (counter)
- `attestor_rekor_retry_total{reason}` (counter)
- `rekor_inclusion_latency{success}` (histogram)

```promql
# Import rate by status
sum(rate(offlinekit_import_total[5m])) by (status)

# Import success rate
sum(rate(offlinekit_import_total{status="success"}[5m])) / clamp_min(sum(rate(offlinekit_import_total[5m])), 1)

# Attestation verify p95 by type (success only)
histogram_quantile(0.95, sum(rate(offlinekit_attestation_verify_latency_seconds_bucket{success="true"}[5m])) by (le, attestation_type))

# Rekor inclusion latency p95 (by success)
histogram_quantile(0.95, sum(rate(rekor_inclusion_latency_bucket[5m])) by (le, success))
```

Dashboard: `docs/observability/dashboards/offline-kit-operations.json`

## Observability hygiene
- Tag everything with `tenant`, `workload`, `env`, `region`, `version`.
- Keep metric names stable; prefer adding labels over renaming.
- No high-cardinality labels (avoid `user_id`, `path`, raw errors); bucket or hash if needed.
- Offline: scrape locally (Prometheus/OTLP); ship exports via bundle if required.

## Dashboards
- Golden signals per service: traffic, errors, saturation, latency (P50/P95/P99).
- Queue dashboards: depth, age, throughput, success/fail rates.
- Tracing overlays: link span `status` to error metrics; use exemplars where supported.

## Validation checklist
- [ ] Metrics emitted with required tags.
- [ ] Cardinality review completed (no unbounded labels).
- [ ] Alerts wired to error budget policy.
- [ ] Dashboards cover golden signals and queue health.