Aioprometheus summary with quantiles over configurable sliding time window
pip install aioprometheus-summary==0.1.0
This package can be found on PyPI.
from aioprometheus_summary import Summary
s = Summary("request_latency_seconds", "Description of summary")
s.observe({}, 4.7)from aioprometheus_summary import Summary
s = Summary("request_latency_seconds", "Description of summary")
s.observe({"method": "GET", "endpoint": "/profile"}, 1.2)
s.observe({"method": "POST", "endpoint": "/login"}, 3.4)By default, metrics are observed for next quantile-precision pairs
((0.50, 0.05), (0.90, 0.01), (0.99, 0.001))
but you can provide your own value when creating the metric.
from aioprometheus_summary import Summary
s = Summary(
"request_latency_seconds", "Description of summary",
invariants=((0.50, 0.05), (0.75, 0.02), (0.90, 0.01), (0.95, 0.005), (0.99, 0.001)),
)
s.observe({}, 4.7)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. Summary metrics 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 2 minutes, but you can configure this values for your own purposes.
from aioprometheus_summary import Summary
s = Summary(
"request_latency_seconds", "Description of summary",
# time window 5 minutes wide with 10 age buckets (sliding every 30 seconds)
max_age_seconds=5 * 60,
age_buckets=10,
)
s.observe({}, 4.7)Suppose we have a metric:
from aioprometheus_summary import Summary
s = Summary("request_latency_seconds", "Description of summary")To show request latency by method, endpoint and quntile use next 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")