A Prometheus Python client library for asyncio-based applications

Overview
https://github.com/claws/aioprometheus/workflows/Python%20Package%20Workflow/badge.svg?branch=master https://readthedocs.org/projects/aioprometheus/badge/?version=latest

aioprometheus

aioprometheus is a Prometheus Python client library for asyncio-based applications. It provides metrics collection and serving capabilities, supports multiple data formats and pushing metrics to a gateway.

The project documentation can be found on ReadTheDocs.

Install

$ pip install aioprometheus

A Prometheus Push Gateway client and ASGI service are also included, but their dependencies are not installed by default. You can install them alongside aioprometheus by running:

$ pip install aioprometheus[aiohttp]

Prometheus 2.0 removed support for the binary protocol, so in version 20.0.0 the dependency on prometheus-metrics-proto, which provides binary support, is now optional. If you want binary response support, for use with an older Prometheus, you will need to specify the 'binary' optional extra:

$ pip install aioprometheus[binary]

Multiple optional dependencies can be listed at once, such as:

$ pip install aioprometheus[aiohttp,binary]

Example

The example below shows a single Counter metric collector being created and exposed via the optional aiohttp service endpoint.

#!/usr/bin/env python
"""
This example demonstrates how a single Counter metric collector can be created
and exposed via a HTTP endpoint.
"""
import asyncio
import socket
from aioprometheus import Counter, Service


if __name__ == "__main__":

    async def main(svr: Service) -> None:

        events_counter = Counter(
            "events", "Number of events.", const_labels={"host": socket.gethostname()}
        )
        svr.register(events_counter)
        await svr.start(addr="127.0.0.1", port=5000)
        print(f"Serving prometheus metrics on: {svr.metrics_url}")

        # Now start another coroutine to periodically update a metric to
        # simulate the application making some progress.
        async def updater(c: Counter):
            while True:
                c.inc({"kind": "timer_expiry"})
                await asyncio.sleep(1.0)

        await updater(events_counter)

    loop = asyncio.get_event_loop()
    svr = Service()
    try:
        loop.run_until_complete(main(svr))
    except KeyboardInterrupt:
        pass
    finally:
        loop.run_until_complete(svr.stop())
    loop.close()

In this simple example the counter metric is tracking the number of while loop iterations executed by the updater coroutine. In a realistic application a metric might track the number of requests, etc.

Following typical asyncio usage, an event loop is instantiated first then a metrics service is instantiated. The metrics service is responsible for managing metric collectors and responding to metrics requests.

The service accepts various arguments such as the interface and port to bind to. A collector registry is used within the service to hold metrics collectors that will be exposed by the service. The service will create a new collector registry if one is not passed in.

A counter metric is created and registered with the service. The service is started and then a coroutine is started to periodically update the metric to simulate progress.

This example and demonstration requires some optional extra to be installed.

$ pip install aioprometheus[aiohttp,binary]

The example script can then be run using:

(venv) $ cd examples
(venv) $ python simple-example.py
Serving prometheus metrics on: http://127.0.0.1:5000/metrics

In another terminal fetch the metrics using the curl command line tool to verify they can be retrieved by Prometheus server.

By default metrics will be returned in plan text format.

$ curl http://127.0.0.1:5000/metrics
# HELP events Number of events.
# TYPE events counter
events{host="alpha",kind="timer_expiry"} 33

Similarly, you can request metrics in binary format, though the output will be hard to read on the command line.

$ curl http://127.0.0.1:5000/metrics -H "ACCEPT: application/vnd.google.protobuf; proto=io.prometheus.client.MetricFamily; encoding=delimited"

The metrics service also responds to requests sent to its / route. The response is simple HTML. This route can be useful as a Kubernetes /healthz style health indicator as it does not incur any overhead within the service to serialize a full metrics response.

$ curl http://127.0.0.1:5000/
<html><body><a href='/metrics'>metrics</a></body></html>

The aioprometheus package provides a number of convenience decorator functions that can assist with updating metrics.

The examples directory contains many examples showing how to use the aioprometheus package. The app-example.py file will likely be of interest as it provides a more representative application example than the simple example shown above.

Examples in the examples/frameworks directory show how aioprometheus can be used within various web application frameworks without needing to create a separate aioprometheus.Service endpoint to handle metrics. The FastAPI example is shown below.

#!/usr/bin/env python
"""
Sometimes you may not want to expose Prometheus metrics from a dedicated
Prometheus metrics server but instead want to use an existing web framework.

This example uses the registry from the aioprometheus package to add
Prometheus instrumentation to a FastAPI application. In this example a registry
and a counter metric is instantiated and gets updated whenever the "/" route
is accessed. A '/metrics' route is added to the application using the standard
web framework method. The metrics route renders Prometheus metrics into the
appropriate format.

Run:

  $ pip install fastapi uvicorn
  $ uvicorn fastapi_example:app

"""

from aioprometheus import render, Counter, Registry
from fastapi import FastAPI, Header, Response
from typing import List


app = FastAPI()
app.registry = Registry()
app.events_counter = Counter("events", "Number of events.")
app.registry.register(app.events_counter)


@app.get("/")
async def hello():
    app.events_counter.inc({"path": "/"})
    return "hello"


@app.get("/metrics")
async def handle_metrics(response: Response, accept: List[str] = Header(None)):
    content, http_headers = render(app.registry, accept)
    return Response(content=content, media_type=http_headers["Content-Type"])

License

aioprometheus is released under the MIT license.

aioprometheus originates from the (now deprecated) prometheus python package which was released under the MIT license. aioprometheus continues to use the MIT license and contains a copy of the original MIT license from the prometheus-python project as instructed by the original license.

A Python pickling decompiler and static analyzer

Fickling Fickling is a decompiler, static analyzer, and bytecode rewriter for Python pickle object serializations. Pickled Python objects are in fact

Trail of Bits 162 Dec 13, 2022
Publish Xarray Datasets via a REST API.

Xpublish Publish Xarray Datasets via a REST API. Serverside: Publish a Xarray Dataset through a rest API ds.rest.serve(host="0.0.0.0", port=9000) Clie

xarray-contrib 106 Jan 06, 2023
Auth for use with FastAPI

FastAPI Auth Pluggable auth for use with FastAPI Supports OAuth2 Password Flow Uses JWT access and refresh tokens 100% mypy and test coverage Supports

David Montague 95 Jan 02, 2023
FastAPI on Google Cloud Run

cloudrun-fastapi Boilerplate for running FastAPI on Google Cloud Run with Google Cloud Build for deployment. For all documentation visit the docs fold

Anthony Corletti 139 Dec 27, 2022
TODO aplication made with Python's FastAPI framework and Hexagonal Architecture

FastAPI Todolist Description Todolist aplication made with Python's FastAPI framework and Hexagonal Architecture. This is a test repository for the pu

Giovanni Armane 91 Dec 31, 2022
Example app using FastAPI and JWT

FastAPI-Auth Example app using FastAPI and JWT virtualenv -p python3 venv source venv/bin/activate pip3 install -r requirements.txt mv config.yaml.exa

Sander 28 Oct 25, 2022
volunteer-database

This is the official CSM (Crowd source medical) database The What Now? We created this in light of the COVID-19 pandemic to allow volunteers to work t

32 Jun 21, 2022
[rewrite 중] 코로나바이러스감염증-19(COVID-19)의 국내/국외 발생 동향 조회 API | Coronavirus Infectious Disease-19 (COVID-19) outbreak trend inquiry API

COVID-19API 코로나 바이러스 감염증-19(COVID-19, SARS-CoV-2)의 국내/외 발생 동향 조회 API Corona Virus Infectious Disease-19 (COVID-19, SARS-CoV-2) outbreak trend inquiry

Euiseo Cha 28 Oct 29, 2022
Online Repo Browser

MSYS2 Web Interface A simple web interface for browsing the MSYS2 repos. Rebuild CSS/JS (optional): cd frontend npm install npm run build Run for Dev

MSYS2 64 Dec 30, 2022
I'm curious if pydantic + fast api can be sensibly used with DDD + hex arch methodology

pydantic-ddd-exploration I'm curious if pydantic + fast api can be sensibly used with DDD + hex arch methodology Prerequisites nix direnv (nix-env -i

Olgierd Kasprowicz 2 Nov 17, 2021
A complete end-to-end machine learning portal that covers processes starting from model training to the model predicting results using FastAPI.

Machine Learning Portal Goal Application Workflow Process Design Live Project Goal A complete end-to-end machine learning portal that covers processes

Shreyas K 39 Nov 24, 2022
Example of integrating Poetry with Docker leveraging multi-stage builds.

Poetry managed Python FastAPI application with Docker multi-stage builds This repo serves as a minimal reference on setting up docker multi-stage buil

Michael Oliver 266 Dec 27, 2022
API & Webapp to answer questions about COVID-19. Using NLP (Question Answering) and trusted data sources.

This open source project serves two purposes. Collection and evaluation of a Question Answering dataset to improve existing QA/search methods - COVID-

deepset 329 Nov 10, 2022
Code for my JWT auth for FastAPI tutorial

FastAPI tutorial Code for my video tutorial FastAPI tutorial What is FastAPI? FastAPI is a high-performant REST API framework for Python. It's built o

José Haro Peralta 8 Dec 16, 2022
FastAPI + Django experiment

django-fastapi-example This is an experiment to demonstrate one potential way of running FastAPI with Django. It won't be actively maintained. If you'

Jordan Eremieff 78 Jan 03, 2023
Money Transaction is a system based on the recent famous FastAPI.

moneyTransfer Overview Money Transaction is a system based on the recent famous FastAPI. techniques selection System's technique selection is as follo

2 Apr 28, 2021
A Jupyter server based on FastAPI (Experimental)

jupyverse is experimental and should not be used in place of jupyter-server, which is the official Jupyter server.

Jupyter Server 122 Dec 27, 2022
api versioning for fastapi web applications

fastapi-versioning api versioning for fastapi web applications Installation pip install fastapi-versioning Examples from fastapi import FastAPI from f

Dean Way 472 Jan 02, 2023
Lung Segmentation with fastapi

Lung Segmentation with fastapi This app uses FastAPI as backend. Usage for app.py First install required libraries by running: pip install -r requirem

Pejman Samadi 0 Sep 20, 2022
Python supercharged for the fastai library

Welcome to fastcore Python goodies to make your coding faster, easier, and more maintainable Python is a powerful, dynamic language. Rather than bake

fast.ai 810 Jan 06, 2023