CODEWITHSUNDEEP
djangodjango-viewsdjango-querysetdjango-q
0xDukis
0xDukis

Reputation: 25

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!

Upvotes: 1

Views: 47

Answers (1)

willeM_ Van Onsem
willeM_ Van Onsem

Reputation: 477607

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].

Upvotes: 2

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