Use the metric shortcut to start recording metrics.

from redis_metrics import metric

# Increment the metric by one

# Increment the metric by some other number
metric('new-user-signup', 4)

Metrics can also be categorized. To record a metric and add it to a category, specify a category keyword parameter

# Increment the metric, and add it to a category
metric('new-user-signup', category="User Metrics")

Metrics can also expire after a specified number of seconds

# The 'foo' metric will expire in 5 minutes
metric('foo', expire=300)

You can also reset a metric with the set_metric function. This will replace any existing values for the metric, rather than incrementing them. It’s api is similar to metric‘s.

from redis_metrics import set_metric

# Reset the Download count.
set_metric("downloads", 0)


There are also gauge‘s. A gauge is great for storing a cumulative value, and when you don’t care about keeping a history for the metric. In other words, a gauge gives you a snapshot of some current value.

from redis_metrics import gauge

# Create a gauge
gauge('total-downloads', 0)

# Update the gauge
gauge('total-downloads', 9999)

The R class

There’s also an R class which is a lightweight wrapper around redis. You can use it directly to set metrics or gauges and to retrieve data.

>>> from redis_metrics.models import R
>>> r = R()
>>> r.metric('new-user-signup')
>>> r.get_metric('new-user-signup')
    'second': 0,
    'minute': 0,
    'hour': 1,
    'day': '29',
    'month': '29',
    'week': '29',
    'year': '29'

# list the slugs you've used to create metrics
>>> r.metric_slugs()
set(['new-user-signup', 'user-logins'])

# Get metrics for multiple slugs
>>> r.get_metrics(['new-user-signup', 'user-logins'])
    {'new-user-signup': {
        'second': '0', 'minute': '0', 'hour': '1',
        'day': '7', 'month': '7', 'week': '7', 'year': '7'}},
        'second': '0', 'minute': '0', 'hour': '1',
        'day': '7', 'month': '7', 'week': '7', 'year': '7'}},

# Delete a metric
>>> r.delete_metric("app-errors")


The included templatetags are useful for visualizing your stored metrics.

Load the templatetags in your template:: {% load redis_metric_tags %}

Viewing your data is possible with the built-in views, but these all make use of a number of templatetags to display metric data and history.

  • metrics_since(slugs, years, link_type="detail", granularity=None) Renders a template with a menu to view a metric (or a list of metrics) for a given number of years. For example:

    {% metrics_since "downloads" 5 %}  {# downloads for the last 5 years #}
  • gauge(slug, maximum=9000, size=200, coerce='float'): Includes a donut chart for the specified gauge. The maximum represents the largest possible value for the gague, while the size is the size of the chart in pixels. The coerce parameter tells the template tag how to coerce numeric data. By default values are converted to floats, but you can include coerce='int' to force values to be listed as integers.:

    {% gauge "tasks-completed" 10 size=300 coerce='int' %}
  • metric_list generates a list of all metrics.

  • metric_detail(slug, with_data_table=False) displays a metric’s current details. This tag will also generate a table of raw data if the with_data_table option is True.

  • metric_history(slug, granularity="daily", since=None, to=None, with_data_table=False) displays a given metric’s history. The granularity option defines the granularity displayed, since is a string or datetime object that specifies the date and/or time from which we start displaying data, the to argument indicates to date or time to which we display data, and with_data_table controls wether or not raw data is displayed in a table. Examples:

    {# dainly signups since Jan 1, 2015 #}
    {% metric_history "signups" "daily" "2015-01-01" %}
    {# daily signups between Jan 1, 2015 & Jan, 1 2016 #}
    {% metric_history "signups" "daily" "2015-01-01" "2016-01-01" %}
    {# monthly signups for a given year #}
    {% metric_history "signups" "monthly" this_year %}
    {# monthly signups for a given year, including data  #}
    {% metric_history "signups" "monthly" this_year with_data_table=True %}
  • aggregate_detail(slug_list, with_data_table=False) is much like metric_detail, but displayes more than one metric on the chart. The slug_list parameter should be a list of metric slugs that you want to display.

  • aggregate_history(slug_list, granularity="daily", since=None, with_data_table=False) is similarly like metric_history, but for multiple metrics on once chart. but displayes more than one metric on the chart. The slug_list parameter should be a list of metric slugs that you want to display.