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Up until PostgreSQL 9.2, COUNT queries generally required scanning
every row in a database table. With millions of rows, this can become
quite slow. One work-around for this is to query statistics in
PostgreSQL for an approximate row count, which in many cases is an
acceptable trade-off.
Given a table called bigdata, the following query will return an
approximate row count:
SELECT reltuples FROM pg_class WHERE relname = 'bigdata';
What django-postgres-fuzzycount provides is a way of using this
approach directly in your Django model managers. It was originally
built for displaying statistics in the kouio RSS reader, a popular alternative to Google Reader, that acquired over 5 million news articles
in its database during the first week of its launch.
Installation
The easiest way to install django-postgres-fuzzycount is directly
from PyPi using pip by running the following command:
$ pip install -U django-postgres-fuzzycount
Otherwise you can download and install it directly from source:
$ python setup.py install
Usage
By using the fuzzycount.FuzzyCountManager on your Django models,
its count() method will return an approximate value when querying
PostgreSQL tables without any WHERE OR HAVING clauses:
from django.db import models
from fuzzycount import FuzzyCountManager
class BigData(models.Model):
big = models.BooleanField(default=True)
data = models.TextField()
objects = FuzzyCountManager()
BigData.objects.count() # Uses fuzzycount
BigData.objects.filter(id__gt=9000).count() # Doesn't use fuzzycount
The fuzzycount.FuzzyCountManager also checks the database engine
being used, and only applies the approximate count query when using
PostgreSQL, so other database backends can be used and will behave as
usual (for varying definitions of usual, depending on the database :-).