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)

Contents

Contents:

Indices and tables