Prometheus Metric Types Explained

Four metric types, infinite confusion. Let me break it down.

Counter - Only Goes Up

from prometheus_client import Counter

requests_total = Counter(
    'http_requests_total',
    'Total HTTP requests',
    ['method', 'endpoint', 'status']
)

requests_total.labels(method='GET', endpoint='/api', status='200').inc()

Use for: Request counts, error counts, bytes processed

Gauge - Goes Up and Down

from prometheus_client import Gauge

active_connections = Gauge(
    'active_connections',
    'Currently active connections'
)

active_connections.inc()
active_connections.dec()
active_connections.set(42)

Use for: Temperature, memory usage, queue size

Histogram - Distribution Buckets

from prometheus_client import Histogram

request_latency = Histogram(
    'http_request_duration_seconds',
    'Request latency in seconds',
    buckets=[0.01, 0.05, 0.1, 0.5, 1.0, 5.0]
)

with request_latency.time():
    process_request()

Use for: Latency percentiles, size distributions

Summary - Client-Side Quantiles

Generally avoid. Histograms are more flexible and aggregatable.

Quick Reference

Type	Aggregatable	Use Case
Counter	Yes	Events
Gauge	Limited	Current state
Histogram	Yes	Latency/sizes
Summary	No	Avoid