pymetrics 1.0.3

PyMetrics is versatile metrics collection library for Python that encapsulates the collection of counters, gauges, histograms, and timers into a generic interface with pluggable publishers so that you can helpfully instrument your applications without suffering vendor lock.

Publishing metrics is a straightforward process involving two steps. First, configure your metrics and publisher(s):

METRICS_CONFIG = {
    'version': 2,
    'error_logger_name': 'pymetrics',
    'publishers': [
        {
            'path': 'pymetrics.publishers.datadog.DogStatsdPublisher',
            'kwargs': {
                'host': 'localhost',
                'port': 8135,
            },
        },
    ],
}

Then, use a pymetrics.recorders.base.MetricsRecorder in your application to collect and publish:

from pymetrics.recorders.default import DefaultMetricsRecorder

metrics = DefaultMetricsRecorder(config=settings.METRICS_CONFIG)

metrics.counter('counter.name').increment()

metrics.gauge('gauge.name', tag_name1='tag_value1', tag_name2='tag_value2').set(12)

metrics.histogram('histogram.name').set(1730)

with metrics.timer('timer.name'):
    do_something()

cumulative_timer = metrics.timer('cumulative_timer.name')
for item in items:
    do_something_without_timing()
    with cumulative_timer:
        do_something_with_timing()

metrics.publish_all()

Provided publisher plugins include Statsd, Datadog, Python Logging, SQLite, and a null publisher. Writing your own is simple and we encourage you to share your work with the community by submitting a pull request.

pip install 'pymetrics~=1.0'

install_requires=[
    ...
    'pymetrics~=1.0',
    ...
]

pymetrics~=1.0

pymetrics = {version="~=1.0"}