A basic Django app facilitating tracking of certain elementary metrics and statistics - generally just metrics which can be measured in terms of counts and cumulative counts.
This app could be useful for keeping track of registrations, page impressions, sessions, and so on. By default, it allows for tracking of registrations. Adding more metrics is a relatively straightforward task, as explained further on.
Contents
- Save a copy of the
django-analytics
app in your Python path. - Add it to your
INSTALLED_APPS
list in your Django project settings. - Create a
mod_analytics.py
file for each of your apps that require some sort of tracking. See the Creating a mod_analytics Script section below. - Run the following from the command line in order to install the various metrics and automatically make them active:
> python manage.py metrics --install
- Run the following from the command line to update the daily, weekly and monthly statistics for each of the active metrics:
> python manage.py metrics --calculate=ALL
By default, django-analytics
comes with a registrations
metric which counts
the number of users in the system based on their date_joined
timestamp.
If, for example, you have an app called comments
with the following models.py
file:
from django.db import models from django.contrib.auth.models import User class Comment(models.Model): user = models.ForeignKey(User, related_name='comments') timestamp = models.DateTimeField(auto_now_add=True) comment = models.CharField(max_length=300)
and you would like to track the total number of comments, you could
create a mod_analytics.py
script (in the same directory as your app's models)
looking like the following:
from analytics.basemetric import BaseMetric from models import Comment class TotalComments(BaseMetric): uid = "totalcomments" title = "Total comments" def calculate(self, start_datetime, end_datetime): return Comment.objects.filter(timestamp__gte=start_datetime, timestamp__lt=end_datetime).count() def get_earliest_timestamp(self): try: return Comment.objects.all().order_by('timestamp')[0].timestamp except IndexError: return None
In order to allow for Geckoboard integration to allow for
visualisation of your statistics, as well as simple CSV dumping of statistics,
in your project's urls.py
, add the following line:
urlpatterns = patterns('', # ... (r'^analytics/', include('analytics.urls')), # ... )
Note that this project makes use of django-geckoboard
(http://pypi.python.org/pypi/django-geckoboard),
so all of the default django-geckoboard
settings apply.
Geckboard Charts
This will automatically add the following Geckoboard-related URLs to your project:
analytics/geckoboard/numbers
- A numbers widget.
Supported GET variable parameters:
uid
,daysback
,cumulative
,frequency
.daysback
default: 7. analytics/geckoboard/rag
- A RAG widget.
Supported GET variable parameters:
uids
,daysback
,cumulative
,frequency
. analytics/geckoboard/pie
- A pie chart widget.
Supported GET variable parameters:
uids
,daysback
,cumulative
,frequency
. analytics/geckoboard/line
- A line chart widget.
Note that this can only plot a single metric per chart.
Supported GET variable parameters:
uid
,daysback
,cumulative
,frequency
.daysback
default: 7. analytics/geckoboard/geckometer
- A geck-o-meter widget.
Supported GET variable parameters:
uid
,frequency
,cumulative
,min
,max
. analytics/geckoboard/funnel
- A funnel chart widget.
Supported GET variable parameters:
uids
,frequency
,cumulative
,type
,percentage
,sort
.
Geckoboard GET Variable Parameters
uid |
The UID of the metric to display, if a single metric is to be displayed. |
uids |
The UIDs of the metrics to display, if multiple metrics are to be displayed. |
daysback |
The numbers Geckoboard widget shows a single count, and the percentage
change from a previous count. This view returns the most recent count
or cumulative count, as well as the count or cumulative count from
days_back days ago. |
cumulative |
A boolean value (either t or f ) indicating whether the period
count is to be returned, or the cumulative count. Default: t . |
frequency |
The frequency of the statistics to be returned. Can be d , w or
m for daily, weekly or monthly, respectively. Default: d . |
min |
The minimum value of a particular metric - usually for pie charts. Default: 0. |
max |
The maximum value of a particular metric - usually for pie charts. Default: 100. |
type |
Chart type - only applicable to the funnel chart. See the Geckoboard
API for more details. Default: standard . |
percentage |
Whether or not to show a percentage - only applicable to the funnel
chart. See the Geckoboard API for more details. Default: show . |
sort |
A boolean value (either t or f ) indicating whether or not to
sort the statistics - only applicable to the funnel chart. See the
Geckoboard API for more details. Default: f . |
CSV Dump
It will also add the following CSV-related URLs to your project:
analytics/csv/<uid>
- A simple view requiring the UID of the metric as its parameter, returning a CSV dump of all of the statistics for the given metric. By default, this returns the daily statistics for the metric.
CSV Dump GET Variable Parameters
frequency |
The frequency of the statistics to be returned. Can be d , w or
m for daily, weekly or monthly, respectively. Default: d .
For example, analytics/csv/registrations?frequency=w will return all
of the weekly registrations over all time as a CSV dump. |
The django-analytics
module creates Metric
objects for each type of metric that
needs to be tracked, such as registrations, page impressions, etc. Each metric needs to
have a globally unique identifier (UID) so that it can be referenced from the command line
by name, and a title to provide a little more of a description of what that metric
is.
Each metric has a number of Statistic
objects associated with it, each Statistic
only being a simple combination of date/time, a count for that date/time, a cumulative
count, and frequency.
The frequency can currently only be daily, weekly or monthly, and by default, each metric's statistics are calculated for all of those frequencies (so a single metric can have multiple frequencies' statistics).
In general, the cumulative count is automatically calculated for you, and is simply the previous day's/week's/month's cumulative count, added to the current day's/week's/month's count.
The following options are available from the command line for the metrics
management
command:
-l, --list | Lists all of the available metrics, along with some basic information about each. |
-i, --install | Scans the project for available metrics and creates or updates them where necessary. |
-a, --activate | Activates the metric with the specified UID, e.g. --activate=registrations .
If you want to activate all metrics,
simply specify --activate=ALL on the command line. Only active metrics will
be included in a --calculate=ALL execution. |
-d, --deactivate | |
Deactivates the metric with the specified UID. Again, you can specify
--deactivate=ALL to deactivate all metrics. | |
-c, --calculate | |
Calculates the specified metric, e.g. --calculate=registrations . Can
specify --calculate=ALL to calculate all active metrics. | |
-f, --frequency | |
If the --calculate command is specified, this will allow one to force a particular
frequency's statistics to be calculated. Possible values are: d (daily), w (weekly),
m (monthly) and a (all). Default is all. | |
--reset | Deletes all of the Statistic objects associated with the specified metric.
Can specify --reset=ALL to delete all statistics for all metrics, regardless
of whether they are active or not. |
--drop-metric | Deletes the actual Metric with the specified UID. Use --drop-metric=ALL
to drop all metrics (and their statistics) from the database. |
When running the manage.py metrics --install
command, the following happens:
- The script searches through all the installed apps for your project and
attempts to first find a
mod_analytics
module which it can import. - It then searches through all of the classes in each
mod_analytics
module it encounters, and then attempts to find classes derived from theanalytics.basemetric.BaseMetric
class (an abstract class). - For each valid class found which derives from the
BaseMetric
class, the script makes sure it has two functions:calculate
, andget_earliest_timestamp
. It also makes sure the class has two properties:uid
andtitle
. - If the class has these two functions, the script creates a
Metric
instance whose unique identifier and title are set to theuid
andtitle
values of the discovered class.
The calculate
function takes two parameters: start_datetime
and end_datetime
,
and must simply return a count of the relevant metric between those two given dates. You can
perform any calculations you need in this function to get to this final count value.
To understand the reasoning here, the analytics
app has three broad calculation time periods
which it attempts to calculate: daily, weekly and monthly. For a daily calculation,
for example, the start_datetime
parameter supplied will resemble something like
datetime(2011, 5, 1)
and the end_datetime
parameter will resemble something like
datetime(2011, 5, 2)
. The calculate
function must then return a count of the relevant
metric for the time period starting at 2011/05/01 00:00 and ending at 2011/05/02 00:00.
NOTE: You should always return counts starting at exactly the given start_datetime
value (i.e. greater-than-equal-to), but just before the end_datetime
value (i.e.
less-than).
The get_earliest_timestamp
function must simply return a datetime.datetime
object
indicating the earliest data's associated date/time, so that the analytics calculation routine
knows the date at which to start calculating. If there are no entries yet, this function must
return None
.
If you want to create abstract metrics, simply create a separate Python file somewhere
which will contain your "abstract" metrics. For example, create an abstract_metrics.py
file which looks as follows:
from analytics import BaseMetric from django.contrib.auth.models import User class UserBaseMetric(BaseMetric): def calculate(self, start_datetime, end_datetime): return User.objects.filter(date_joined__gte=start_datetime, date_joined__lt=end_datetime).count() def get_earliest_timestamp(self): try: return User.objects.all().order_by('date_joined')[0].date_joined except IndexError: return None
Then, in your mod_analytics.py
file, just import your abstract_metrics
module.
Note: Do not import the UserBaseMetric
, just import the abstract_metrics
module,
as follows:
from analytics import BaseMetric import myapp.abstract_metrics class UserMetric(abstract_metrics.UserBaseMetric): uid = "users" title = "Users"
The following features are planned for future versions of django-analytics
:
- Custom visualisation integrated into Django admin back-end.
- Hourly statistics.
- More complex statistics, such as frequency plots/histograms.
Version | Description |
---|---|
0.0.1 | First version |