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Statistics storage for Django

Project description

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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”.

Subject

When storing statistics we are storing values relating to a specific subject. For example, when keeping track of the “comment count” per user, the subject is clearly the user. Yet, if you are keeping track of the “Order count” globally for the whole webshop, the subject is less clear. You could decide to choose to use the Site instance where your webshop is running at. If not specified, the domain of the metric is used as the subject.

Statistic

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

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 Statistic, Domain, Metric, Period

# All-time, cumulative, statistic
n = Order.objects.all().count()
Statistic.objects.record(
    metric=Metric.objects.SHOPPING_ORDER_COUNT,
    subject=Site.objects.get_current(),
    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()
Statistic.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:

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

Advanced

The Statistic model represents statistics grouped by date, as that is the most common use case. If you need to group in a different manner, e.g. by country and date, you can use the AbstractStatistic base class to build just that.

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