Prometheus Concepts

TL;DR

Explanation

Metric types: Counter (monotonically increasing, e.g. requests total), Gauge (up/down, e.g. memory), Histogram (bucketed distribution, e.g. latency), Summary (client-side quantiles). Labels: dimensions for filtering (method='GET', status='200'). Cardinality: high-cardinality labels (user_id) cause performance issues — use sparingly. PromQL: sum(rate(http_requests_total[5m])) by (route). Scrape: Prometheus pulls /metrics every 15-60s. Alertmanager: evaluates rules, routes alerts. PHP: promphp/prometheus_client_php, default Laravel integration. Push gateway: for short-lived jobs that don't live long enough to be scraped.

Common Misconception

Why It Matters

Common Mistakes

• High-cardinality labels — user_id, request_id in labels = millions of time series.
• Not using histograms for latency — use Histogram, not Gauge, for percentile queries.
• Scraping too frequently — default 15s is fine; 1s creates unnecessary load.

Code Examples

// High cardinality — one series per user:
$counter->labels(['user_id' => $userId])->inc(); // Millions of series

// Low cardinality — aggregate labels:
$counter->labels(['route' => $route, 'method' => $method, 'status' => $statusCode])->inc();

// Histogram for latency:
$histogram = new Histogram($registry, 'http_duration_seconds', 'Request duration',
    ['route'], [.005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5]);
$histogram->labels([$route])->observe($duration);

References

• ↗ https://prometheus.io/docs/concepts/metric_types/

Tags

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