Django’s object-relational mapper (ORM) provides a powerful way to interact with databases in Python code. While the ORM makes common queries easy, getting a random record takes a little extra work. In this post, we’ll explore a few different approaches to selecting a random row from a Django model.
First, let’s look at why retrieving a random record can be useful. For one, random sampling allows creating test data sets for statistical analysis or machine learning. Randomized data also helps ensure better coverage when testing applications. In other cases, you might want to show random content to users – like displaying a random quote or product each time someone visits a page.
Using Django’s Base Manager
Django models include a objects manager by default which supports common queries. However, managers also provide a get_queryset() method allowing us to add custom filters and slicing. By ordering randomly and taking a slice, we can fetch random model instances.
For example:
from random import choice
from myapp.models import Article
articles = Article.objects.all().order_by('?')[:1]
random_article = choice(articles)Here, order_by('?') randomly orders articles before we slice to get just the first one. The choice() function selects a random item from the resulting query set.
This retrieves a properly randomized record. However, it can become slow with large query sets since it orders the entire set before discarding most results.
Using Database Functions- Random record
For better performance, we can use database functions directly with Django’s extra() lookup. Most databases include a function to get a random row – like PostgreSQL’s random() and MySQL’s rand().
For PostgreSQL:
from django.db.models.functions import Random
random_article = Article.objects.all().extra(
select={'random_id': 'random()'}).order_by('random_id')[:1] And MySQL:
from django.db.models import F
random_article = Article.objects.all().extra(
select={'random_id': 'rand()'}).order_by('random_id')[:1]Here, we add a random number column then order and slice results based on that. By letting the database handle randomization, performance improves significantly.
However, watch out for differences between database backends. Functions like `random()` and `rand()` may return different ranges of numbers or even data types.
Alternative Approaches
A few other options can provide random model instances as well.
Django’s `Sample()` exists specifically for taking random samples from query sets. We can use it like:
from django.db.models import Sample
random_article = Article.objects.all().sample()[0]For a simple subset, we could also filter by the row primary key to reduce the candidate set:
import random
from myapp.models import Article
max_id = Article.objects.all().aggregate(max_id=Max('id'))['max_id']
pk = random.randint(1, max_id)
random_article = Article.objects.filter(pk=pk).first()So in summary, Django provides several approaches to getting a random database record, each with different performance implications. Consider the amount of data and database backend when selecting a randomization strategy. With some clever queries, it’s easy to add an element of surprise with Django’s ORM!