# Histograms and Summaries — Prometheus + Grafana

Source: https://www.skillbyai.com/en/prometheus-grafana/i-histogram

> Distributions such as latency.

## Buckets for percentiles

Averages hide slow requests, so latency is recorded as a distribution. A **histogram** counts observations into cumulative buckets (`le` labels such as 0.1, 0.25, 0.5 seconds) and exposes `_bucket`, `_sum` and `_count` series; percentiles are estimated at query time with `histogram_quantile`, and buckets can be aggregated across instances. A **summary** calculates quantiles inside the client, which cannot be meaningfully aggregated across instances. Prefer histograms, and choose buckets around your latency targets. Newer Prometheus versions also support native histograms with automatic buckets.

## Histogram series for one route

Illustrative exposition output.

```text
http_request_duration_seconds_bucket{route="/api/orders",le="0.1"}  9200
http_request_duration_seconds_bucket{route="/api/orders",le="0.25"} 10100
http_request_duration_seconds_bucket{route="/api/orders",le="0.5"}  10380
http_request_duration_seconds_bucket{route="/api/orders",le="1"}    10440
http_request_duration_seconds_bucket{route="/api/orders",le="+Inf"} 10449
http_request_duration_seconds_sum{route="/api/orders"}   1187.4
http_request_duration_seconds_count{route="/api/orders"} 10449
```

## Put a bucket at your SLO threshold

If the target is 300 ms, a 0.3 bucket lets you count good requests exactly.

**Quiz:** Why prefer histograms over summaries for multi-instance services?

- [ ] Summaries cannot record latency
- [x] Histogram buckets can be aggregated across instances before computing percentiles
- [ ] Histograms need no buckets
- [ ] Summaries are not supported by Prometheus

*Answer:* Histogram buckets can be aggregated across instances before computing percentiles. You cannot average percentiles.