Django App Metrics
.. image:: https://secure.travis-ci.org/frankwiles/django-app-metrics.png :alt: Build Status :target: http://travis-ci.org/frankwiles/django-app-metrics
django-app-metrics allows you to capture and report on various events in your applications. You simply define various named metrics and record when they happen. These might be certain events that may be immediatey useful, for example ‘New User Signups’, ‘Downloads’, etc.
Or they might not prove useful until some point in the future. But if you begin recording them now you’ll have great data later on if you do need it.
For example ‘Total Items Sold’ isn’t an exciting number when you’re just launching when you only care about revenue, but being able to do a contest for the 1 millionth sold item in the future you’ll be glad you were tracking it.
You then group these individual metrics into a MetricSet, where you define how often you want an email report being sent, and to which User(s) it should be sent.
Documentation can be found at ReadTheDocs_.
.. _ReadTheDocs: http://django-app-metrics.readthedocs.org/
django-celery_ must be installed, however if you do not wish to
actually use Celery you can simply set
CELERY_ALWAYS_EAGER = True in your
settings and it will behave as if Celery was not configured.
py-sparkblocks_ is used to add basic ‘sparkblock’ charts to outgoing e-mails.
Django 1.2 or above is required.
from app_metrics.utils import create_metric, create_metric_set, metric, timing, Timer, gauge
# Create a new metric to track my_metric = create_metric(name=‘New User Metric’, slug=‘new_user_signup’)
# Create a MetricSet which ties a metric to an email schedule and sets # who should receive it my_metric_set = create_metric_set(name=‘My Set’, metrics=[my_metric], email_recipients=[user1, user2])
# Increment the metric by one metric(‘new_user_signup’)
# Increment the metric by some other number metric(‘new_user_signup’, 4)
# Aggregate metric items into daily, weekly, monthly, and yearly totals # It’s fairly smart about it, so you’re safe to run this as often as you # like manage.py metrics_aggregate
# Send email reports to users manage.py metrics_send_mail
# Create a timer (only supported in statsd backend currently) with timing(‘mytimer’): for x in some_long_list: call_time_consuming_function(x)
# Or if a context manager doesn’t work for you you can use a Timer class t = Timer() t.start() something_that_takes_forever() t.stop() t.store(‘mytimer’)
# Gauges are current status type dials (think fuel gauge in a car) # These simply store and retrieve a value gauge(‘current_fuel’, ‘30’) guage(‘load_load’, ‘3.14’)
app_metrics.backends.db (Default) - This backend stores all metrics and
aggregations in your database. NOTE: Every call to
metric() generates a
database write, which may decrease your overall performance is you go nuts
with them or have a heavily traffic site.
app_metrics.backends.mixpanel - This backend allows you to pipe all of
your calls to
metric() to Mixpanel. See the
for more information on their API.
Mixpanel documentation: http://mixpanel.com/docs/api-documentation
app_metrics.backends.statsd - This backend allows you to pipe all of your
metric() to a statsd server. See
statsd_ for more information
on their API.
app_metrics.backends.redis - This backend allows you to use the metric() and
gauge() aspects, but not timer aspects of app_metrics.
app_metrics.backends.librato_backend - This backend lets you send metrics to
Librato. See the
Librato documentation_ for more information on their API.
This requires the
Librato library_. It uses use a librato Gauge by default,
although this can be overridden by supplying
metric_type="counter" as a
keyword arg to
app_metrics.backends.composite - This backend lets you compose multiple
backends to which metric-calls are handed. The backends to which the call is
sent can be configured with the
APP_METRICS_COMPOSITE_BACKENDS setting. This
can be overridden in each call by supplying a
backends keyword argument::
metric('signups', 42, backends=['app_metrics.backends.librato', 'app_metrics.backends.db'])
APP_METRICS_BACKEND - Defaults to ‘app_metrics.backends.db’ if not defined.
APP_METRICS_SEND_ZERO_ACTIVITY - Prevent e-mails being sent when there’s been
no activity today (i.e. during testing). Defaults to
APP_METRICS_DISABLED - If
True, do not track metrics, useful for
debugging. Defaults to
APP_METRICS_BACKEND == ‘app_metrics.backends.mixpanel’.
APP_METRICS_MIXPANEL_TOKEN - Your Mixpanel.com API token
APP_METRICS_MIXPANEL_URL - Allow overriding of the API URL end point
APP_METRICS_BACKEND == ‘app_metrics.backends.statsd’.
APP_METRICS_STATSD_HOST - Hostname of statsd server, defaults to ‘localhost’
APP_METRICS_STATSD_PORT - statsd port, defaults to ‘8125’
APP_METRICS_STATSD_SAMPLE_RATE - statsd sample rate, defaults to 1
APP_METRICS_BACKEND == ‘app_metrics.backends.redis’.
APP_METRICS_REDIS_HOST - Hostname of redis server, defaults to ‘localhost’
APP_METRICS_REDIS_PORT - redis port, defaults to ‘6379’
APP_METRICS_REDIS_DB - redis database number to use, defaults to 0
APP_METRICS_BACKEND == ‘app_metrics.backends.librato’.
APP_METRICS_LIBRATO_USER - Librato username
APP_METRICS_LIBRATO_TOKEN - Librato API token
APP_METRICS_LIBRATO_SOURCE - Librato data source (e.g. ‘staging’, ‘dev’…)
Composite Backend Settings
APP_METRICS_BACKEND == ‘app_metrics.backends.composite’.
APP_METRICS_COMPOSITE_BACKENDS - List of backends that are used by default,
APP_METRICS_COMPOSITE_BACKENDS = ('librato', 'db', 'my_custom_backend',)
Running the tests
To run the tests you’ll need some requirements installed, so run::
pip install -r requirements/test.txt
Then simply run::
django-admin.py test --settings=app_metrics.tests.settings
- Include web-visible versions of templates, perhaps with longer timeframes for analysis.