prometheus-python 1.0.2

Prometheus client wrapper for django or django rest framework based applications.

prometheus

Python prometheus library for django and django rest framework. This helps in monitoring the application on a granular level. You can customize which part of the application you want to monitor. Through this you can monitor a REST API, a python function , a code segment.

Usage

Requirements

• Django >= 1.8
• djangorestframework >= 3.0
• prometheus_client >= 0.7.1

Installation

Install with:

pip install prometheus-python

Or, if you're using a development version cloned from this repository:

git clone https://github.com/harshittrivedi78/prometheus.git
python prometheus/setup.py install

This will install Django >= 1.8 and djangorestframework >= 3.0 and prometheus_client as a dependency if not installed already.

Quickstart

In your settings.py:

INSTALLED_APPS = [
   ...
   'prometheus',
   ...
]

In your urls.py:

urlpatterns = [
    ...
    url('', include('prometheus.urls')),
]

In your views.py:

from rest_framework import generics, status
from rest_framework.response import Response
from prometheus import monitor

class TestAPIView(generics.RetrieveAPIView):

    @monitor(app_name="test") # app_name should be unique through out the application.
    def retrieve(self, request, *args, **kwargs):
        data = {}
        return Response(data, status=status.HTTP_200_OK)

So as you can see in the above example I have decorated the retrieve function by our monitor decorator which will provide monitoring metrics for this function only. And you can identify how much time this function is taking to execute, how many requests are in progress currently, how many request totally served till now.

Default list of monitored metrics

* request_count
* request_latency
* request_in_progress
* response_by_status_total

Configuration

Prometheus uses Histogram based grouping for monitoring latencies. The default buckets are here: https://github.com/prometheus/client_python/blob/master/prometheus_client/core.py

You can define custom buckets for latency, adding more buckets decreases performance but increases accuracy: https://prometheus.io/docs/practices/histograms/

In your settings.py

PROMETHEUS_LATENCY_BUCKETS = (.1, .2, .5, .6, .8, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.5, 9.0, 12.0, 15.0, 20.0, 30.0, float("inf"))

Monitor in multiprocess mode (uWSGI, Gunicorn)

In your settings.py

PROMETHEUS_MULTIPROC_MODE = True # default is False
PROMETHEUS_MULTIPROC_DIR = /path/to/prometheus_multiproc_dir # default it will save db files in prometheus/multiproc_dir/