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.

EML analyzer is an application to analyze the EML file

EML analyzer EML analyzer is an application to analyze the EML file which can: Analyze headers. Analyze bodies. Extract IOCs (URLs, domains, IP addres

Manabu Niseki 162 Dec 28, 2022
FastAPI Skeleton App to serve machine learning models production-ready.

FastAPI Model Server Skeleton Serving machine learning models production-ready, fast, easy and secure powered by the great FastAPI by Sebastián Ramíre

268 Jan 01, 2023
JSON-RPC server based on fastapi

Description JSON-RPC server based on fastapi: https://fastapi.tiangolo.com Motivation Autogenerated OpenAPI and Swagger (thanks to fastapi) for JSON-R

199 Dec 30, 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
Admin Panel for GinoORM - ready to up & run (just add your models)

Gino-Admin Docs (state: in process): Gino-Admin docs Play with Demo (current master 0.2.3) Gino-Admin demo (login: admin, pass: 1234) Admin

Iuliia Volkova 46 Nov 02, 2022
signal-cli-rest-api is a wrapper around signal-cli and allows you to interact with it through http requests

signal-cli-rest-api signal-cli-rest-api is a wrapper around signal-cli and allows you to interact with it through http requests. Features register/ver

Sebastian Noel Lübke 31 Dec 09, 2022
First API using FastApi

First API using FastApi Made this Simple Api to store and Retrive Student Data of My College Ncc-Bim To View All the endpoits Visit /docs To Run Local

Sameer Joshi 2 Jun 21, 2022
The template for building scalable web APIs based on FastAPI, Tortoise ORM and other.

FastAPI and Tortoise ORM. Powerful but simple template for web APIs w/ FastAPI (as web framework) and Tortoise-ORM (for working via database without h

prostomarkeloff 95 Jan 08, 2023
A rate limiter for Starlette and FastAPI

SlowApi A rate limiting library for Starlette and FastAPI adapted from flask-limiter. Note: this is alpha quality code still, the API may change, and

Laurent Savaete 565 Jan 02, 2023
This is a FastAPI application that provides a RESTful API for the Podcasts from different podcast's RSS feeds

The Podcaster API This is a FastAPI application that provides a RESTful API for the Podcasts from different podcast's RSS feeds. The API response is i

Sagar Giri 2 Nov 07, 2021
A simple docker-compose app for orchestrating a fastapi application, a celery queue with rabbitmq(broker) and redis(backend)

fastapi - celery - rabbitmq - redis - Docker A simple docker-compose app for orchestrating a fastapi application, a celery queue with rabbitmq(broker

Kartheekasasanka Kaipa 83 Dec 19, 2022
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
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
Social Distancing Detector using deep learning and capable to run on edge AI devices such as NVIDIA Jetson, Google Coral, and more.

Smart Social Distancing Smart Social Distancing Introduction Getting Started Prerequisites Usage Processor Optional Parameters Configuring AWS credent

Neuralet 129 Dec 12, 2022
sample web application built with FastAPI + uvicorn

SPARKY Sample web application built with FastAPI & Python 3.8 shows simple Flask-like structure with a Bootstrap template index.html also has a backgr

mrx 21 Jan 03, 2022
ReST based network device broker

The Open API Platform for Network Devices netpalm makes it easy to push and pull state from your apps to your network by providing multiple southbound

368 Dec 31, 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
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
[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
Recommend recipes based on what ingredients you have at home

🌱 MyChef 📦 Overview MyChef is an application that helps you decide what meal to make based on what you have at home. Simply enter in ingredients you

Logan Connolly 44 Nov 08, 2022