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- Introduced `all-edge-reasons.json` to test edge resolution reasons in .NET. - Added `all-visibility-levels.json` to validate method visibility levels in .NET. - Created `dotnet-aspnetcore-minimal.json` for a minimal ASP.NET Core application. - Included `go-gin-api.json` for a Go Gin API application structure. - Added `java-spring-boot.json` for the Spring PetClinic application in Java. - Introduced `legacy-no-schema.json` for legacy application structure without schema. - Created `node-express-api.json` for an Express.js API application structure.
114 lines
5.1 KiB
Markdown
114 lines
5.1 KiB
Markdown
# Metrics & SLOs (DOCS-OBS-51-001)
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Last updated: 2025-12-15
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## Core metrics (platform-wide)
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- **Requests**: `http_requests_total{tenant,workload,route,status}` (counter); latency histogram `http_request_duration_seconds`.
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- **Jobs**: `worker_jobs_total{tenant,queue,status}`; `worker_job_duration_seconds`.
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- **DB**: `db_query_duration_seconds{db,operation}`; `db_pool_in_use`, `db_pool_available`.
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- **Cache**: `cache_requests_total{result=hit|miss}`; `cache_latency_seconds`.
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- **Queue depth**: `queue_depth{tenant,queue}` (gauge).
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- **Errors**: `errors_total{tenant,workload,code}`.
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- **Custom module metrics**: keep namespaced (e.g., `riskengine_score_duration_seconds`, `notify_delivery_attempts_total`).
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## SLOs (suggested)
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- API availability: 99.9% monthly per public service.
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- P95 latency: <300 ms for read endpoints; <1 s for write endpoints.
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- Worker job success: >99% over 30d; P95 job duration set per queue (document locally).
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- Queue backlog: alert when `queue_depth` > 1000 for 5 minutes per tenant/queue.
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- Error budget policy: 28-day rolling window; burn-rate alerts at 2× and 14× budget.
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## Alert examples
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- High error rate: `rate(errors_total[5m]) / rate(http_requests_total[5m]) > 0.02`.
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- Latency regression: `histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le,route)) > 0.3`.
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- Queue backlog: `queue_depth > 1000` for 5m.
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- Job failures: `rate(worker_jobs_total{status="failed"}[10m]) > 0.01`.
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## UX KPIs (triage TTFS)
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- Targets:
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- TTFS first evidence p95: <= 1.5s
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- TTFS skeleton p95: <= 0.2s
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- Clicks-to-closure median: <= 6
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- Evidence completeness avg: >= 90% (>= 3.6/4)
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```promql
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# TTFS first evidence p50/p95
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histogram_quantile(0.50, sum(rate(stellaops_ttfs_first_evidence_seconds_bucket[5m])) by (le))
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histogram_quantile(0.95, sum(rate(stellaops_ttfs_first_evidence_seconds_bucket[5m])) by (le))
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# Clicks-to-closure median
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histogram_quantile(0.50, sum(rate(stellaops_clicks_to_closure_bucket[5m])) by (le))
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# Evidence completeness average percent (0-4 mapped to 0-100)
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100 * (sum(rate(stellaops_evidence_completeness_score_sum[5m])) / clamp_min(sum(rate(stellaops_evidence_completeness_score_count[5m])), 1)) / 4
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# Budget violations by phase
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sum(rate(stellaops_performance_budget_violations_total[5m])) by (phase)
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```
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- Dashboard: `ops/devops/observability/grafana/triage-ttfs.json`
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- Alerts: `ops/devops/observability/triage-alerts.yaml`
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## TTFS Metrics (time-to-first-signal)
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- Core metrics:
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- `ttfs_latency_seconds{surface,cache_hit,signal_source,kind,phase,tenant_id}` (histogram)
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- `ttfs_signal_total{surface,cache_hit,signal_source,kind,phase,tenant_id}` (counter)
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- `ttfs_cache_hit_total{surface,cache_hit,signal_source,kind,phase,tenant_id}` (counter)
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- `ttfs_cache_miss_total{surface,cache_hit,signal_source,kind,phase,tenant_id}` (counter)
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- `ttfs_slo_breach_total{surface,cache_hit,signal_source,kind,phase,tenant_id}` (counter)
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- `ttfs_error_total{surface,cache_hit,signal_source,kind,phase,tenant_id,error_type,error_code}` (counter)
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- SLO targets:
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- P50 < 2s, P95 < 5s (all surfaces)
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- Warm path P50 < 700ms, P95 < 2.5s
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- Cold path P95 < 4s
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```promql
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# TTFS latency p50/p95
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histogram_quantile(0.50, sum(rate(ttfs_latency_seconds_bucket[5m])) by (le))
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histogram_quantile(0.95, sum(rate(ttfs_latency_seconds_bucket[5m])) by (le))
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# SLO breach rate (per minute)
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60 * sum(rate(ttfs_slo_breach_total[5m]))
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```
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## Offline Kit (air-gap) metrics
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- `offlinekit_import_total{status,tenant_id}` (counter)
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- `offlinekit_attestation_verify_latency_seconds{attestation_type,success}` (histogram)
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- `attestor_rekor_success_total{mode}` (counter)
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- `attestor_rekor_retry_total{reason}` (counter)
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- `rekor_inclusion_latency{success}` (histogram)
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```promql
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# Import rate by status
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sum(rate(offlinekit_import_total[5m])) by (status)
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# Import success rate
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sum(rate(offlinekit_import_total{status="success"}[5m])) / clamp_min(sum(rate(offlinekit_import_total[5m])), 1)
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# Attestation verify p95 by type (success only)
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histogram_quantile(0.95, sum(rate(offlinekit_attestation_verify_latency_seconds_bucket{success="true"}[5m])) by (le, attestation_type))
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# Rekor inclusion latency p95 (by success)
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histogram_quantile(0.95, sum(rate(rekor_inclusion_latency_bucket[5m])) by (le, success))
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```
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Dashboard: `docs/observability/dashboards/offline-kit-operations.json`
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## Observability hygiene
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- Tag everything with `tenant`, `workload`, `env`, `region`, `version`.
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- Keep metric names stable; prefer adding labels over renaming.
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- No high-cardinality labels (avoid `user_id`, `path`, raw errors); bucket or hash if needed.
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- Offline: scrape locally (Prometheus/OTLP); ship exports via bundle if required.
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## Dashboards
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- Golden signals per service: traffic, errors, saturation, latency (P50/P95/P99).
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- Queue dashboards: depth, age, throughput, success/fail rates.
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- Tracing overlays: link span `status` to error metrics; use exemplars where supported.
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## Validation checklist
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- [ ] Metrics emitted with required tags.
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- [ ] Cardinality review completed (no unbounded labels).
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- [ ] Alerts wired to error budget policy.
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- [ ] Dashboards cover golden signals and queue health.
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