Infrastructure Monitoring Best Practices Cheat Sheet

Core practices and tooling patterns for monitoring servers, containers, and cloud infrastructure with metrics, dashboards, and alerts.

2 PagesIntermediateFeb 5, 2026

The Four Golden Signals

Google SRE's foundational metrics for any monitored system.

Latency- Time to service a request; track successful vs failed request latency separately
Traffic- Demand on the system, e.g. requests/sec, throughput
Errors- Rate of failed requests, explicit (5xx) or implicit (wrong content)
Saturation- How "full" a resource is, e.g. CPU, memory, disk I/O, queue depth

Prometheus Scrape Config

Basic scrape configuration for a target.

yaml

global:  scrape_interval: 15s  evaluation_interval: 15sscrape_configs:  - job_name: 'node-exporter'    static_configs:      - targets: ['localhost:9100']    relabel_configs:      - source_labels: [__address__]        target_label: instance

PromQL Query Basics

Common query patterns for alerting and dashboards.

promql

# CPU usage rate over 5 minutesrate(node_cpu_seconds_total{mode="idle"}[5m])# 95th percentile request latencyhistogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))# Alert if error rate > 5% over 5msum(rate(http_requests_total{status=~"5.."}[5m]))  / sum(rate(http_requests_total[5m])) > 0.05

Metric Types

Prometheus/StatsD-style metric type semantics.

Counter- Monotonically increasing value, e.g. total requests
Gauge- Value that can go up or down, e.g. memory in use
Histogram- Samples observations into configurable buckets, supports quantile estimation server-side
Summary- Similar to histogram but calculates quantiles client-side, not aggregatable across instances

Pro Tip

Alert on symptoms (SLO burn rate, user-facing latency/errors) rather than causes (high CPU) — causes generate noisy pages that don't always correlate with actual user impact.

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