Agent skill

django-orm-mastery

Master Django ORM patterns: QuerySets, select_related, prefetch_related, F/Q objects, annotations, aggregations, and raw SQL safety. Use when the user writes Django database queries, asks about ORM optimization, encounters N+1 queries, or works with complex filtering and aggregation. Trigger when you see inefficient Django queries.

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SKILL.md

Django ORM Mastery

Write efficient, expressive Django ORM queries. The ORM is lazy by default -- QuerySets are not evaluated until iterated. Understanding this is key to avoiding N+1 queries and building performant applications.

When to Use

  • User writes Django model queries
  • User encounters slow queries or N+1 problems
  • User needs complex filtering, aggregation, or annotation
  • User asks about select_related vs prefetch_related
  • User needs database-level operations without pulling data into Python

Core Patterns

QuerySet Basics and Chaining

QuerySets are lazy -- they build SQL without executing until evaluated. Chain freely.

python
# Lazy -- no SQL executed yet
qs = (
    Article.objects
    .filter(status="published")
    .exclude(category__name="draft")
    .order_by("-published_at")
)

# SQL executes when you iterate, slice, call list(), len(), bool(), etc.
articles = list(qs[:10])

# Efficient existence check -- use .exists() not len() or bool()
if Article.objects.filter(slug=slug).exists():
    raise ValidationError("Slug already taken")

# Efficient count -- use .count() not len(qs)
total = Article.objects.filter(status="published").count()

# Get or 404
from django.shortcuts import get_object_or_404
article = get_object_or_404(Article, slug=slug, status="published")

select_related (ForeignKey / OneToOne)

Performs a SQL JOIN. Use for ForeignKey and OneToOneField relationships. Fetches related objects in a single query.

python
# WITHOUT select_related -- N+1 problem
articles = Article.objects.all()
for article in articles:
    print(article.author.name)  # Each access hits the database

# WITH select_related -- single JOIN query
articles = Article.objects.select_related("author", "category").all()
for article in articles:
    print(article.author.name)  # No extra query -- already loaded

# Chain through relationships
comments = (
    Comment.objects
    .select_related("article__author", "article__category")
    .filter(is_approved=True)
)

prefetch_related (ManyToMany / Reverse FK)

Performs a separate query and joins in Python. Use for ManyToManyField and reverse ForeignKey relationships.

python
from django.db.models import Prefetch

# Basic prefetch -- separate query for tags
articles = Article.objects.prefetch_related("tags").all()

# Custom Prefetch with filtering
articles = Article.objects.prefetch_related(
    Prefetch(
        "comments",
        queryset=Comment.objects.filter(is_approved=True).select_related("author"),
        to_attr="approved_comments",  # Access as article.approved_comments
    )
)

for article in articles:
    for comment in article.approved_comments:  # No extra queries
        print(f"{comment.author.name}: {comment.text}")

F Objects (Database-Level Operations)

Use F() to reference field values in queries without pulling data into Python.

python
from django.db.models import F

# Increment without race condition
Article.objects.filter(id=article_id).update(view_count=F("view_count") + 1)

# Compare fields
products = Product.objects.filter(stock__lt=F("reorder_level"))

# Arithmetic between fields
orders = Order.objects.annotate(
    profit=F("revenue") - F("cost")
).filter(profit__gt=0)

# Reference related fields
articles = Article.objects.filter(
    comments_count__gt=F("category__avg_comments") * 2
)

Q Objects (Complex Lookups)

Use Q() for OR conditions, negation, and complex boolean logic.

python
from django.db.models import Q

# OR condition
results = Article.objects.filter(
    Q(status="published") | Q(author=current_user)
)

# AND with OR
results = Article.objects.filter(
    Q(status="published") & (Q(category="tech") | Q(category="science"))
)

# Negation
results = Article.objects.filter(~Q(status="archived"))

# Dynamic query building
filters = Q()
if search_term:
    filters &= Q(title__icontains=search_term) | Q(body__icontains=search_term)
if category:
    filters &= Q(category__slug=category)
if date_from:
    filters &= Q(published_at__gte=date_from)

articles = Article.objects.filter(filters)

Annotations and Aggregations

Push computation to the database instead of doing it in Python.

python
from django.db.models import Count, Avg, Sum, Max, Min, Value, CharField
from django.db.models.functions import Coalesce, Upper, TruncMonth

# Aggregate -- returns a dict
stats = Order.objects.aggregate(
    total_revenue=Sum("amount"),
    avg_order=Avg("amount"),
    order_count=Count("id"),
    max_order=Max("amount"),
)

# Annotate -- adds computed fields to each object in QuerySet
authors = (
    User.objects
    .annotate(
        article_count=Count("articles"),
        avg_rating=Avg("articles__rating"),
        total_views=Sum("articles__view_count"),
    )
    .filter(article_count__gt=5)
    .order_by("-total_views")
)

# Conditional aggregation
from django.db.models import Case, When, IntegerField

articles = Article.objects.annotate(
    engagement_score=Case(
        When(view_count__gt=1000, then=Value(3)),
        When(view_count__gt=100, then=Value(2)),
        default=Value(1),
        output_field=IntegerField(),
    )
)

# Group by month
monthly_revenue = (
    Order.objects
    .annotate(month=TruncMonth("created_at"))
    .values("month")
    .annotate(
        revenue=Sum("amount"),
        count=Count("id"),
    )
    .order_by("month")
)

Raw SQL (When ORM Is Not Enough)

Use parameterized queries to prevent SQL injection. Never interpolate user input.

python
# Safe raw query with parameters
users = User.objects.raw(
    "SELECT * FROM auth_user WHERE email LIKE %s AND is_active = %s",
    [f"%@{domain}", True],
)

# Raw SQL for complex queries that don't map to a model
from django.db import connection

def get_revenue_report(year: int) -> list[dict]:
    with connection.cursor() as cursor:
        cursor.execute(
            """
            SELECT category, SUM(amount) as total, COUNT(*) as count
            FROM orders_order
            WHERE EXTRACT(YEAR FROM created_at) = %s
            GROUP BY category
            ORDER BY total DESC
            """,
            [year],
        )
        columns = [col[0] for col in cursor.description]
        return [dict(zip(columns, row)) for row in cursor.fetchall()]

Anti-Patterns

  • N+1 queries: Accessing related objects in a loop without select_related or prefetch_related. Use django-debug-toolbar to spot these.
  • Python-level filtering: Using [a for a in articles if a.status == "published"] instead of .filter(status="published"). Let the database do the filtering.
  • Counting with len(qs): Use .count() which executes SELECT COUNT(*) instead of fetching all rows.
  • String interpolation in raw SQL: f"WHERE id = {user_id}" is a SQL injection vulnerability. Always use parameterized queries with %s placeholders.
  • Calling .all() before .filter(): .filter() already returns a new QuerySet. Article.objects.filter(...) is cleaner than Article.objects.all().filter(...).

Quick Reference

Operation Method
FK/OneToOne join .select_related("field")
M2M/Reverse FK .prefetch_related("field")
OR conditions Q(a=1) | Q(b=2)
Database-level field ref F("field_name")
Aggregate (single result) .aggregate(total=Sum("field"))
Annotate (per-row) .annotate(count=Count("rel"))
Conditional Case(When(cond, then=val))
Existence check .exists()
Efficient count .count()
Bulk create .bulk_create([obj1, obj2])
Bulk update .bulk_update(objs, ["field"])

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