Welcome to django-trackstats!

Keep track of your statistics.

Source code

http://github.com/pennersr/django-trackstats

Concepts

The following concepts are used:

Metric

A piece of information to keep track of. For example, "Order count", or "Number of users signed up".

Domain

Metrics are organized in groups, each group is called a domain. For example you can have a "shopping" domain with metrics such as "Order count", "Items sold", "Products viewed", and a "users" domain with "Login count", "Signup count". Or, in case you are tracking external statistics from social networks, you may introduce a "Twitter" domain, and metrics "Followers count".

Statistic

Used to store the actual values by date, for a specific metric.

Period

The time period for which the stored value holds. For example, you can keep track of cumulative, all-time, numbers (Period.LIFETIME), store incremental values on a daily basis (Period.DAY), or keep track of a rolling count for the last 7 days (Period.WEEK).

Reference IDs

Domains and metrics must be assigned unique reference IDs (of type string). Rationale: Having a human readable, non PK based, reference is esential as soon as you are going to export statistics.

Usage

First, setup your domains:

from trackstats.models import Domain

Domain.objects.SHOPPING = Domain.objects.register(
    ref='shopping',
    name='Shopping')
Domain.objects.USERS = Domain.object.register(
    ref='users',
    name='Users')
Domain.objects.TWITTER = Domain.object.register(
    ref='twitter',
    name='Twitter')

Define a few metrics:

from trackstats.models import Domain, Metric

Metric.objects.SHOPPING_ORDER_COUNT = Metric.objects.register(
    domain=Domain.objects.SHOPPING,
    ref='order_count',
    name='Number of orders sold')
Metric.objects.USERS_USER_COUNT = Metric.objects.register(
    domain=Domain.objects.USERS,
    ref='user_count',
    name='Number of users signed up')
Metric.objects.TWITTER_FOLLOWER = Metric.objects.register(
    # Matches Twitter API
    ref='followers_count',
    domain=Domain.objects.TWITTER)

Now, let's store some one-off statistics:

from trackstats.models import StatisticByDate, Domain, Metric, Period

# All-time, cumulative, statistic
n = Order.objects.all().count()
StatisticByDate.objects.record(
    metric=Metric.objects.SHOPPING_ORDER_COUNT,
    value=n,
    Period=Period.LIFETIME)

# Users signed up, at a specific date
dt = date.today()
n = User.objects.filter(
    date_joined__day=dt.day,
    date_joined__month=dt.month,
    date_joined__year=dt.year).count()
StatisticByDate.objects.record(
    metric=Metric.objects.USERS_USER_COUNT,
    value=n,
    Period=Period.DAY)

Creating code to store statistics yourself can be a tedious job. Luckily, a few shortcuts are available to track statistics without having to write any code yourself.

Consider you want to keep track of the number of comments created on a daily basis:

from trackstats.trackers import CountObjectsByDateTracker

CountObjectsByDateTracker(
    period=Period.DAY,
    metric=Metric.objects.COMMENT_COUNT,
    date_field='timestamp').track(Comment.objects.all())

Or, in case you want to track the number of comments, per user, on a daily basis:

CountObjectsByDateAndObjectTracker(
    period=Period.DAY,
    metric=Metric.objects.COMMENT_COUNT,
    # comment.user points to a User
    object_model=User,
    object_field='user',
    # Comment.timestamp is used for grouping
    date_field='timestamp').track(Comment.objects.all())

Models

The StatisticByDate model represents statistics grouped by date -- the most common use case.

Another common use case is to group by both date and some other object (e.g. a user, category, site). For this, use StatisticByDateAndObject. It uses a generic foreign key.

If you need to group in a different manner, e.g. by country, province and date, you can use the AbstractStatistic base class to build just that.

Cross-Selling

If you like this, you may also like:

• django-allauth: https://github.com/pennersr/django-allauth
• netwell: https://github.com/pennersr/netwell