Deploying Monitoring Stacks

Overview

Deploy production monitoring stacks (Prometheus + Grafana, Datadog, or Victoria Metrics) with metric collection, custom dashboards, and alerting rules. Configure exporters, scrape targets, recording rules, and notification channels for comprehensive infrastructure and application observability.

Prerequisites

• Target infrastructure identified: Kubernetes cluster, Docker hosts, or bare-metal servers

• Metric endpoints accessible from the monitoring platform (application /metrics , node exporters)

• Storage backend capacity planned for time-series data (Prometheus TSDB, Thanos, or Cortex for long-term)

• Alert notification channels defined: Slack webhook, PagerDuty integration key, or email SMTP

• Helm 3+ for Kubernetes deployments using kube-prometheus-stack or similar charts

Instructions

• Select the monitoring platform: Prometheus + Grafana for open-source self-hosted, Datadog for managed SaaS, Victoria Metrics for high-cardinality workloads

• Deploy the monitoring stack: helm install kube-prometheus-stack prometheus-community/kube-prometheus-stack or Docker Compose for non-Kubernetes

• Install exporters on monitored systems: node-exporter for host metrics, kube-state-metrics for Kubernetes object states, application-specific exporters

• Configure scrape targets in prometheus.yml : define job names, scrape intervals, and relabeling rules for service discovery

• Create recording rules for frequently queried aggregations to reduce dashboard query load

• Define alerting rules with meaningful thresholds: high CPU (>80% for 5m), high memory (>90%), error rate (>1%), latency P99 (>500ms)

• Configure Alertmanager with routing, grouping, and notification channels (Slack, PagerDuty, email)

• Build Grafana dashboards: RED metrics (Rate, Errors, Duration) for services, USE metrics (Utilization, Saturation, Errors) for resources

• Set up data retention: configure TSDB retention period (15-30 days local), set up Thanos/Cortex for long-term storage if needed

• Test the full pipeline: trigger a test alert and verify notification delivery

Output

• Helm values file or Docker Compose for the monitoring stack

• Prometheus configuration with scrape targets, recording rules, and alerting rules

• Alertmanager configuration with routing tree and notification receivers

• Grafana dashboard JSON files for infrastructure and application metrics

• Exporter deployment manifests (node-exporter DaemonSet, application ServiceMonitor)

Error Handling

Error Cause Solution

No data points in dashboard

Scrape target not reachable or metric name wrong Check Targets page in Prometheus UI; verify service discovery and metric name

Too many time series (high cardinality)

Labels with unbounded values (user IDs, request IDs) Remove high-cardinality labels with metric_relabel_configs ; use recording rules for aggregation

Alert condition met but no notification

Alertmanager routing or receiver misconfigured Verify Alertmanager config with amtool check-config ; test receiver with amtool silence

Prometheus OOMKilled

Insufficient memory for series count Increase memory limits; reduce scrape targets or retention; add WAL compression

Grafana datasource connection failed

Wrong Prometheus URL or network policy blocking access Verify datasource URL in Grafana; check Kubernetes service name and port; review network policies

Examples

• "Deploy kube-prometheus-stack on Kubernetes with alerts for node CPU > 80%, pod restart count > 5, and API error rate > 1%, sending to Slack."

• "Set up Prometheus + Grafana on Docker Compose for monitoring 10 application servers with node-exporter and custom application metrics."

• "Create Grafana dashboards for the four golden signals (latency, traffic, errors, saturation) for a microservices application."

Resources

• Prometheus documentation: https://prometheus.io/docs/

• Grafana documentation: https://grafana.com/docs/grafana/latest/

• kube-prometheus-stack: https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack

• Alerting best practices: https://prometheus.io/docs/practices/alerting/

• Datadog documentation: https://docs.datadoghq.com/