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Metrics system for generating statistics about your app

Project description

Markus is a Python library for generating metrics.

License:MPL v2


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.


To install Markus, run:

$ pip install markus

(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 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 MetricsImplementation 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 MetricsImplementation anywhere in that module:

def some_long_function(vegetable):
    for veg in vegetable:
        metrics.incr('vegetable', 1)

At application startup, configure Markus with the backends you want to use to publish metrics and any options they require.

For example, lets configure metrics to publish to logs and Datadog:

import markus

            # Log metrics to the logs
            'class': 'markus.backends.logging.LoggingMetrics',
            # Log metrics to Datadog
            'class': 'markus.backends.datadog.DatadogMetrics',
            'options': {
                'statsd_host': '',
                'statsd_port': 8125,
                'statsd_namespace': ''

When you’re writing your tests, use the MetricsMock to make testing easier:

import markus
from markus.testing import MetricsMock

def test_something():
    with MetricsMock() as mm:
        # ... Do things that might publish metrics

        # This helps you debug and write your test

        # Make assertions on metrics published
        assert mm.has_metric(markus.INCR, 'some.key', value=1)


1.2.0 (April 27th, 2018)


  • Add .clear() to MetricsMock making it easier to build a pytest fixture with the MetricsMock context and manipulate records for easy testing. #29

Bug fix

  • Update Cloudwatch backend fixing .timing() and .histogram() to send histogram metrics type which Datadog now supports. #31

1.1.2 (April 5th, 2018)

Typo fix

  • Fix the date from the previous release. Ugh.

1.1.1 (April 5th, 2018)


  • Official switch to semver.

Bug fixes

  • Fix MetricsMock so it continues to work even if configure is called. #27

1.1 (November 13th, 2017)


  • Added markus.utils.generate_tag utility function

1.0 (October 30th, 2017)


  • Added support for Python 2.7.
  • Added a markus.backends.statsd.StatsdMetrics backend that uses pystatsd client for statsd pings. Thank you, Javier!

Bug fixes

  • Added LoggingRollupMetrics to docs.
  • Mozilla has been running Markus in production for 6 months so we can mark it production-ready now.

0.2 (April 19th, 2017)


  • Added a markus.backends.logging.LoggingRollupMetrics backend that rolls up metrics and does some light math on them. Possibly helpful for light profiling for development.

Bug fixes

  • Lots of documentation fixes. Thank you, Peter!

0.1 (April 10th, 2017)

Initial writing.

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