Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/willkg/markus

Markus is a Python library for generating metrics
https://github.com/willkg/markus

library metrics python statsd

Last synced: 10 days ago
JSON representation

Markus is a Python library for generating metrics

Awesome Lists containing this project

README 

        
======

Markus

======



Markus is a Python library for generating metrics.



:Code:          https://github.com/willkg/markus

:Issues:        https://github.com/willkg/markus/issues

:License:       MPL v2

:Documentation: http://markus.readthedocs.io/en/latest/



Goals

=====



Markus makes it easier to generate metrics in your program by:



* providing multiple backends (Datadog statsd, statsd, logging, logging rollup,

  and so on) for sending data to different places



* sending metrics to multiple backends at the same time



* providing a testing framework for easy testing



* providing a decoupled architecture making it easier to write code to generate

  metrics without having to worry about making sure creating and configuring a

  metrics client has been done--similar to the Python logging Python logging

  module in this way



I use it at Mozilla in the collector of our crash ingestion pipeline. Peter used

it to build our symbols lookup server, too.



Install

=======



To install Markus, run::



    $ pip install markus



(Optional) To install the requirements for the

``markus.backends.statsd.StatsdMetrics`` backend::



    $ pip install 'markus[statsd]'



(Optional) To install the requirements for the

``markus.backends.datadog.DatadogMetrics`` backend::



    $ pip install 'markus[datadog]'



Quick start

===========



Similar to using the logging library, every Python module can create a

``markus.main.MetricsInterface`` (loosely equivalent to a Python

logging logger) at any time including at module import time and use that to

generate metrics.



For example::



    import markus



    metrics = markus.get_metrics(__name__)



Creating a ``markus.main.MetricsInterface`` using ``__name__``

will cause it to generate all stats keys with a prefix determined from

``__name__`` which is a dotted Python path to that module.



Then you can use the ``markus.main.MetricsInterface`` anywhere in that

module::



    @metrics.timer_decorator("chopping_vegetables")

    def some_long_function(vegetable):

        for veg in vegetable:

            chop_vegetable()

            metrics.incr("vegetable", value=1)



At application startup, configure Markus with the backends you want and any

options they require to publish metrics.



For example, let us configure Markus to publish metrics to the Python logging

infrastructure and Datadog::



    import markus



    markus.configure(

        backends=[

            {

                # Publish metrics to the Python logging infrastructure

                "class": "markus.backends.logging.LoggingMetrics",

            },

            {

                # Publish metrics to Datadog

                "class": "markus.backends.datadog.DatadogMetrics",

                "options": {

                    "statsd_host": "example.com",

                    "statsd_port": 8125,

                    "statsd_namespace": ""

                }

            }

        ]

    )



Once you've added code that publishes metrics, you'll want to test it and make

sure it's working correctly. Markus comes with a ``markus.testing.MetricsMock``

to make testing and asserting specific outcomes easier::



    from markus.testing import MetricsMock



    def test_something():

        with MetricsMock() as mm:

            # ... Do things that might publish metrics



            # Make assertions on metrics published

            mm.assert_incr_once("some.key", value=1)