Markus¶
Markus is a Python library for generating metrics.
- Code
- Issues
- License
MPL v2
- Documentation
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.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:
@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 to use to publish metrics and any options they require.
For example, lets configure metrics to publish to logs and Datadog:
import markus
markus.configure(
backends=[
{
# Log metrics to the logs
'class': 'markus.backends.logging.LoggingMetrics',
},
{
# Log metrics to Datadog
'class': 'markus.backends.datadog.DatadogMetrics',
'options': {
'statsd_host': 'example.com',
'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
mm.print_records()
# Make assertions on metrics published
assert mm.has_record(markus.INCR, 'some.key', value=1)