|
| 1 | +--- |
| 2 | +title: Monitoring |
| 3 | +description: "Plan monitoring for Layer5 Cloud self-hosted deployments: metrics, logs, tracing, dashboards, and alerts." |
| 4 | +categories: [Self-Hosted] |
| 5 | +tags: [monitoring] |
| 6 | +weight: 4 |
| 7 | +--- |
| 8 | + |
| 9 | +Monitoring is essential to operate a reliable Layer5 Cloud deployment. Plan for metrics, logs, traces, dashboards, alerting, and retention so that you can detect and resolve issues quickly, understand capacity, and meet compliance needs. |
| 10 | + |
| 11 | +## Objectives |
| 12 | + |
| 13 | +- Establish observability for core services (API, UI, real-time collaboration, identity, database, cache, ingress) and infrastructure (Kubernetes, nodes, storage, networking) |
| 14 | +- Provide dashboards for SLOs and golden signals (latency, traffic, errors, saturation) |
| 15 | +- Configure actionable alerts with clear ownership and runbooks |
| 16 | +- Size and retain telemetry data according to compliance and cost constraints |
| 17 | + |
| 18 | +## Metrics |
| 19 | + |
| 20 | +Collect system and application metrics. Common choices include Prometheus or any OpenMetrics-compatible backend. |
| 21 | + |
| 22 | +- Kubernetes: kube-state-metrics, cAdvisor/node-exporter, API server, etcd, ingress controller |
| 23 | +- Layer5 Cloud services: HTTP latency and error rates, request throughput, worker queue depth, WebSocket/WebRTC health |
| 24 | +- Datastores: database query latency, connections, cache hit ratio |
| 25 | + |
| 26 | +Recommended metrics and SLOs: |
| 27 | + |
| 28 | +- Request success rate (5xx, 4xx) per route and service; target ≥ 99.9% over 30 days |
| 29 | +- p50/p90/p99 latency per route and service; budget aligned to user experience goals |
| 30 | +- Resource saturation: CPU, memory, pod restarts, HPA activity; queue length where applicable |
| 31 | +- Collaboration health: signaling availability, peer connection success, message delivery error rate |
| 32 | + |
| 33 | +## Logs |
| 34 | + |
| 35 | +Use a centralized, searchable logging stack (e.g., Loki, Elasticsearch, or a managed service). Ensure structured logs (JSON) for Layer5 Cloud services and infrastructure components. |
| 36 | + |
| 37 | +- Retention tiers: short-term hot (7–14 days), longer-term warm/cold per compliance |
| 38 | +- Privacy: scrub/omit secrets and PII; apply data minimization and access control |
| 39 | +- Context: include request IDs, user/session IDs (where appropriate), and correlation IDs |
| 40 | + |
| 41 | +## Tracing |
| 42 | + |
| 43 | +Enable distributed tracing with OpenTelemetry to diagnose cross-service latency and failures. |
| 44 | + |
| 45 | +- Propagate W3C Trace Context across ingress → services → dependencies |
| 46 | +- Sample rates: start with 1–5% head sampling; use tail-based sampling for errors/latency outliers |
| 47 | +- Storage/backends: Tempo/Jaeger/managed APM |
| 48 | + |
| 49 | +## Dashboards |
| 50 | + |
| 51 | +Provide Grafana (or equivalent) dashboards for: |
| 52 | + |
| 53 | +- Service health overview: error rate, latency, throughput, saturation |
| 54 | +- Ingress and API gateway performance by route |
| 55 | +- Real-time collaboration: signaling uptime, peer connection success, message RTT |
| 56 | +- Identity/OIDC: login success, token issuance errors, external IdP health |
| 57 | +- Database/cache: latency, throughput, errors, saturation |
| 58 | +- Kubernetes: cluster/node/pod health, HPA activity, pending pods, eviction events |
| 59 | + |
| 60 | +## Alerts |
| 61 | + |
| 62 | +Create multi-level alerts (warning/critical) with clear runbooks and ownership. |
| 63 | + |
| 64 | +- Availability: elevated 5xx rate or failure rate by route/service |
| 65 | +- Latency: p99 above budget for sustained periods |
| 66 | +- Saturation: CPU/memory pressure, pod crashloops, queue backlogs |
| 67 | +- Dependencies: database unreachable, cache error spikes, external IdP failures |
| 68 | +- Collaboration: signaling down, degraded connection success, message delivery failures |
| 69 | + |
| 70 | +Alert destinations may include Slack, email, PagerDuty, or your incident tool. Include links to dashboards and logs in notifications. |
| 71 | + |
| 72 | +## Sizing and Retention |
| 73 | + |
| 74 | +Estimate telemetry volume early to avoid unexpected costs. |
| 75 | + |
| 76 | +- Metrics: number of time series × scrape interval; downsample older data |
| 77 | +- Logs: average line size × events/sec; apply sampling/filters and retention tiers |
| 78 | +- Traces: sample strategically; store only spans needed for SLOs and investigations |
| 79 | + |
| 80 | +## Security and Compliance |
| 81 | + |
| 82 | +- Restrict telemetry access by role; audit access to sensitive logs |
| 83 | +- Encrypt in transit and at rest; segregate prod/staging data |
| 84 | +- Redact secrets and PII at the source where possible |
| 85 | + |
| 86 | +## Reference Architecture (example) |
| 87 | + |
| 88 | +- Metrics: Prometheus + Alertmanager; long-term storage via remote-write (e.g., Thanos, Mimir) |
| 89 | +- Logs: Loki (or Elasticsearch) with LogQL saved views and retention tiers |
| 90 | +- Traces: Tempo/Jaeger with OpenTelemetry SDKs/collectors |
| 91 | +- Dashboards: Grafana with folders for platform, services, and business metrics |
| 92 | + |
| 93 | +This setup is vendor-neutral and can be substituted with managed offerings from your cloud provider or APM vendor. |
| 94 | + |
| 95 | + |
0 commit comments