Metadata-Version: 1.1
Name: django-trackstats
Version: 0.5.0
Summary: Statistics storage for Django
Home-page: http://github.com/pennersr/django-trackstats
Author: Raymond Penners
Author-email: raymond.penners@intenct.nl
License: UNKNOWN
Description-Content-Type: UNKNOWN
Description: =============================
        Welcome to django-trackstats!
        =============================
        
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           :alt: Coverage Status
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           :target: https://blockchain.info/address/1AJXuBMPHkaDCNX2rwAy34bGgs7hmrePEr
        
        Keep track of your statistics.
        
        Source code
          http://github.com/pennersr/django-trackstats
        
        
        Use Case
        ========
        
        - You need an elegant solution for storing statistics in a generic and structural fashion.
        
        - You need to denormalize the results of various aggregated queries.
        
        - You require access to the stored statistics within your application layer.
        
        So, the focus is purely on storing statistics for use within your application later
        on. Other features, such as charting, reports, OLAP, query builders, slicing &
        dicing, integration with ``Datadog`` and the likes are all beyond scope.
        
        
        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.objects.register(
                ref='users',
                name='Users')
            Domain.objects.TWITTER = Domain.objects.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
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Framework :: Django
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Classifier: Topic :: Software Development :: Libraries :: Python Modules
