prometheus-summary

Prometheus-summary library adds support of quantiles calculation for the Summary metric. It is fully compatible with the official Python Prometheus client library.

Installation

pip install prometheus-summary==0.1.6

This package can be found on PyPI.

Collecting

Basic usage

from prometheus_summary import Summary

s = Summary("request_latency_seconds", "Description of summary")
s.observe(4.7)

Usage with labels

from prometheus_summary import Summary

s = Summary("request_latency_seconds", "Description of summary", ["method", "endpoint"])
s.labels(method="GET", endpoint="/profile").observe(1.2)
s.labels(method="POST", endpoint="/login").observe(3.4)

Usage with custom quantiles and precisions

By default, metrics are observed for the following (quantile, precision (inaccuracy)) pairs:

((0.50, 0.05), (0.90, 0.01), (0.99, 0.001))

You can also provide your own values when creating the metric.

Usage with custom time window settings

Typically, you don’t want to have a Summary representing the entire runtime of the application, but you want to look at a reasonable time interval. This Summary metric implement a configurable sliding time window.

The default is a time window of 10 minutes and 5 age buckets, i.e. the time window is 10 minutes wide, and we slide it forward every 10 / 5 = 2 minutes, but you can configure this values for your own purposes.

Querying

Suppose we have a metric:

from prometheus_summary import Summary

s = Summary("request_latency_seconds", "Description of summary", ["method", "endpoint"])

To show request latency by method, endpoint and quantile use the following PromQL query:

max by (method, endpoint, quantile) (request_latency_seconds)

To show only 99-th quantile:

max by (method, endpoint) (request_latency_seconds{quantile="0.99"})