Prometheus Cheat Sheet

Reference for PromQL query syntax, scrape configuration, alerting rules, and common metric types used in Prometheus monitoring.

2 PagesIntermediateFeb 18, 2026

Scrape Configuration

prometheus.yml defining targets to scrape.

yaml

global:  scrape_interval: 15sscrape_configs:  - job_name: 'node'    static_configs:      - targets: ['localhost:9100']  - job_name: 'api'    metrics_path: /metrics    static_configs:      - targets: ['api1:8080', 'api2:8080']

PromQL Basics

Common PromQL query patterns.

sql

# Current value of a metricup# Rate of increase over 5 minutes (for counters)rate(http_requests_total[5m])# Sum across all instances, grouped by jobsum by (job) (rate(http_requests_total[5m]))# 95th percentile latency from a histogramhistogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))# Alert-style comparisonnode_filesystem_free_bytes / node_filesystem_size_bytes < 0.1

Alerting Rule

A rule that fires when error rate exceeds a threshold.

yaml

groups:  - name: api-alerts    rules:      - alert: HighErrorRate        expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.05        for: 10m        labels:          severity: critical        annotations:          summary: "High 5xx error rate on {{ $labels.instance }}"

Metric Types

The four core Prometheus metric types.

Counter- Monotonically increasing value (e.g. total requests); use rate() to analyze
Gauge- A value that can go up or down (e.g. memory usage, queue length)
Histogram- Samples observations into configurable buckets; supports histogram_quantile()
Summary- Similar to histogram but calculates quantiles client-side over a sliding window

Pro Tip

Never take rate() of a gauge or irate()/rate() of raw counter values without a range vector — always apply rate() before aggregating with sum(), otherwise counter resets from restarts will produce misleading spikes.

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