This document discusses ways to reduce the number of database queries in a Django application. It recommends monitoring queries, optimizing repeated queries with techniques like select_related, avoiding ORM calls in loops, pre-processing complex data, caching expensive lookups, handling generic relations efficiently, and denormalizing data. Reducing queries from 3000 to 8 in one application had a major performance impact.

1. Reducing the query
count
Daniel Roseman

2. Why look at the query
count?
• Query count is only one factor in
performance
• But an important one
• Number of easy tricks to reduce the count
• Can have a major impact on your app
• In one case, 3000 queries went down to 8

3. Monitoring
• connection.queries
• Debug toolbars
• QueryAnalysis middleware - aggregates
common queries

4. Low hanging fruit
• Go for repeated queries first
• select_related - use sparingly, but can have
a big impact in the right place
• Avoid ORM calls in loops: get all items
first, then loop through them
• Be careful of hidden calls within templates

5. Pre-processing
• Denormalisation
• Save complex data as separate fields

6. ‘Cache’ within instance
• Useful for expensive lookups that will be
called several times on a single instance
def get_assets(self):
if not hasattr(self, '_assets'):
self._assets = self.assets.all()
return self._assets
• Could be a property

7. Generic relations
• select_related doesn’t work
• Get all related instances, and store in a dict
• Again, can stash within model instance

8. Generic relations
shared_objects =
Shared_Objects.objects.select_related('category').filter(
pagemapping__in=article_ids,
content_type=article_type,
)
sharedobj_dict = dict((so.object_id, so)
for so in shared_objects)
articles = Article.objects.filter(
id__in=sharedobj_dict.keys())
for article in articles:
article._shared = sharedobj_dict[article.id]