Optimizing Django QuerySet with Nested Aggregations

I’m working on optimizing a complex Django query where I need to perform nested aggregations and conditional annotations across multiple related models. I want to fetch the top 5 most active users based on their interactions with posts, while also calculating different types of engagement metrics (like views, comments, and likes).

My models:

class User(models.Model):
    name = models.CharField(max_length=100)

class Post(models.Model):
    author = models.ForeignKey(User, on_delete=models.CASCADE)
    title = models.CharField(max_length=255)
    created_at = models.DateTimeField()

class Engagement(models.Model):
    user = models.ForeignKey(User, on_delete=models.CASCADE)
    post = models.ForeignKey(Post, on_delete=models.CASCADE)
    type = models.CharField(max_length=50)  # 'view', 'like', 'comment'
    created_at = models.DateTimeField()

Here is what my code looks like:

from django.db.models import Count, Q

some_date = ...

top_users = (
    User.objects.annotate(
        view_count=Count('engagement__id', filter=Q(engagement__type='view', engagement__created_at__gte=some_date)),
        like_count=Count('engagement__id', filter=Q(engagement__type='like', engagement__created_at__gte=some_date)),
        comment_count=Count('engagement__id', filter=Q(engagement__type='comment', engagement__created_at__gte=some_date)),
        total_engagements=Count('engagement__id', filter=Q(engagement__created_at__gte=some_date))
    )
    .order_by('-total_engagements')[:5]
)

It works, however the query performance is not ideal. With large datasets, this approach leads to slow query execution times and I wonder whether using multiple Count annotations with filter conditions is efficient.

Is there a more optimized way to write this query, or any best practices I should consider for improving performance, especially when dealing with large amounts of data? Any insights or suggestions would be really helpful!

It works, however the query performance is not ideal. With large datasets, this approach leads to slow query execution times and I wonder whether using multiple Count annotations with filter conditions is efficient.

It is not very efficient. The filter=… ^[Django-doc] approach is implemented as a CASE … WHEN …, so that typically means the database will first consider all engagements, and then filter out the ones that do not satisfy the filter in a linear scan.

If however we never want to return users without any engagement after some_date, we can boost efficiency by filtering on the JOIN:

top_users = (
    User.objects.filter(engagement__created_at__gte=some_date)
    .annotate(
        view_count=Count('engagement__id', filter=Q(engagement__type='view')),
        like_count=Count('engagement__id', filter=Q(engagement__type='like')),
        comment_count=Count(
            'engagement__id', filter=Q(engagement__type='comment')
        ),
        total_engagements=Count('engagement__id'),
    )
    .order_by('-total_engagements')[:5]
)

and add a db_index=True ^[Django-doc] on the created_at field:

class Engagement(models.Model):
    user = models.ForeignKey(User, on_delete=models.CASCADE)
    post = models.ForeignKey(Post, on_delete=models.CASCADE)
    type = models.CharField(max_length=50)  # 'view', 'like', 'comment'
    created_at = models.DateTimeField(db_index=True)

Note: It is normally better to make use of the settings.AUTH_USER_MODEL ^[Django-doc] to refer to the user model, than to use the User model ^[Django-doc] directly. For more information you can see the referencing the User model section of the documentation ^[Django-doc].