Monitoring and Observability Setup with Grafana, Prometheus, and Alerts
Complete monitoring stack setup with metrics, logs, and alerts for production applications.
Implement full observability that detects issues before users notice them, with actionable dashboards and intelligent alerting.
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Prompt objective
Implement full observability that detects issues before users notice them, with actionable dashboards and intelligent alerting.
Real use case
A SaaS platform serving [NUMBER] active users experiences database storage issues at 3am with no alerting in place. The on-call team only discovers the problem when customers start reporting issues at 8am, resulting in significant user churn and revenue loss. They need proactive monitoring to catch infrastructure problems before business impact.
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Prompt
Configure a complete observability stack for [PROJECT NAME], a [APPLICATION TYPE] application with [NUMBER] active users running on [DOCKER/KUBERNETES/VPS].\\\\\\\\n\\\\\\\\n**Services to monitor:**\\\\\\\\n- [LIST: e.g. Node.js API, PostgreSQL, Redis, Nginx, Workers]\\\\\\\\n- Infrastructure: CPU, memory, disk, network\\\\\\\\n\\\\\\\\n**1) Metrics (Prometheus + Grafana):**\\\\\\\\n\\\\\\\\n**Prometheus:**\\\\\\\\n- prometheus.yml configuration with targets\\\\\\\\n- Scrape interval per service\\\\\\\\n- Required exporters: node_exporter, postgres_exporter, redis_exporter\\\\\\\\n- Application custom metrics (prom-client for Node.js):\\\\\\\\n - \\\\\\\\\\\\\\\\\\\\\\\`http_requests_total\\\\\\\\\\\\\\\\\\\\\\\` (counter by route, method, status)\\\\\\\\n - \\\\\\\\\\\\\\\\\\\\\\\`http_request_duration_seconds\\\\\\\\\\\\\\\\\\\\\\\` (histogram)\\\\\\\\n - \\\\\\\\\\\\\\\\\\\\\\\`active_connections\\\\\\\\\\\\\\\\\\\\\\\` (gauge)\\\\\\\\n - \\\\\\\\\\\\\\\\\\\\\\\`business_events_total\\\\\\\\\\\\\\\\\\\\\\\` (counter: signups, orders, payments)\\\\\\\\n - \\\\\\\\\\\\\\\\\\\\\\\`queue_size\\\\\\\\\\\\\\\\\\\\\\\` (gauge per queue)\\\\\\\\n- Retention and storage sizing\\\\\\\\n\\\\\\\\n**Grafana Dashboards:**\\\\\\\\n- Dashboard 1: Overview (uptime, request rate, error rate, latency p50/p95/p99)\\\\\\\\n- Dashboard 2: Infrastructure (CPU, RAM, disk, network per container)\\\\\\\\n- Dashboard 3: Database (connections, query duration, cache hit ratio, dead tuples)\\\\\\\\n- Dashboard 4: Business (revenue/hour, conversions, churn indicators)\\\\\\\\n- For each dashboard: exportable JSON with template variables\\\\\\\\n\\\\\\\\n**2) Logs (Grafana Loki or ELK):**\\\\\\\\n- Structured log format (JSON)\\\\\\\\n- Correct log levels: ERROR (failures), WARN (degradation), INFO (events), DEBUG (dev)\\\\\\\\n- Correlation ID per request (trace ID)\\\\\\\\n- Log rotation and retention policy\\\\\\\\n- Query examples for common troubleshooting scenarios\\\\\\\\n\\\\\\\\n**3) Alerts (Alertmanager):**\\\\\\\\n- Alert rules by severity:\\\\\\\\n - **CRITICAL** (PagerDuty/SMS): downtime, error rate > 5%, disk > 95%\\\\\\\\n - **WARNING** (Slack/Email): latency p95 > 2s, CPU > 80%, memory > 85%\\\\\\\\n - **INFO** (Slack): deploy completed, backup finished, cron executed\\\\\\\\n- Routing: who receives which alert (on-call rotation)\\\\\\\\n- Silencing and inhibition rules\\\\\\\\n- Runbooks linked to each alert (what to do when triggered)\\\\\\\\n\\\\\\\\n**4) Uptime Monitoring:**\\\\\\\\n- Standardized health check endpoints (/health, /ready)\\\\\\\\n- External ping (UptimeRobot/Better Uptime)\\\\\\\\n- Public status page for customers\\\\\\\\n\\\\\\\\n**5) docker-compose for the monitoring stack:**\\\\\\\\n- Prometheus + Grafana + Loki + Alertmanager\\\\\\\\n- Persistent volumes for data\\\\\\\\n- Network configuration\\\\\\\\n\\\\\\\\nProvide all configuration files and dashboard JSON exports.
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- 1Replace the key placeholders first: PROJECT NAME, APPLICATION TYPE, NUMBER, DOCKER/KUBERNETES/VPS.
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