performance-optimizer

Performance Optimizer

Auto-detects and fixes Django ORM performance issues before they reach production.

Activation Triggers

This skill activates when:

• Writing ORM queries
• Accessing related objects (foreign keys, M2M)
• Creating views that fetch data
• Mentioning "slow", "performance", "optimization"
• Looping over querysets
• Creating list/detail views

Performance Targets

• ✅ NO N+1 queries
• ✅ Appropriate indexes on frequently queried fields
• ✅ Pagination for list endpoints
• ✅ select_related() for foreign keys
• ✅ prefetch_related() for reverse foreign keys and M2M
• ✅ Caching for expensive operations

Auto-Detection Patterns

Pattern 1: N+1 Query Detection

❌ INEFFICIENT (N+1 query):

# Bad: 1 query for users + N queries for organizations
users = User.objects.all()  # 1 query
for user in users:  # N queries
    print(user.organization.name)  # Hits DB each time!

✅ OPTIMIZED:

# Good: 2 queries total (1 for users + 1 JOIN for organizations)
users = User.objects.select_related('organization').all()
for user in users:
    print(user.organization.name)  # No DB hit!

Pattern 2: Reverse Foreign Key N+1

❌ INEFFICIENT:

# Bad: 1 + N queries
organizations = Organization.objects.all()  # 1 query
for org in organizations:  # N queries
    users = org.users.all()  # Hits DB each time!
    print(f"{org.name}: {users.count()} users")

✅ OPTIMIZED:

# Good: 2 queries total
organizations = Organization.objects.prefetch_related('users').all()
for org in organizations:
    users = org.users.all()  # Pre-fetched!
    print(f"{org.name}: {users.count()} users")

Pattern 3: Many-to-Many N+1

❌ INEFFICIENT:

# Bad: 1 + N queries
users = User.objects.all()  # 1 query
for user in users:  # N queries
    roles = user.roles.all()  # Hits DB each time!

✅ OPTIMIZED:

# Good: 2 queries total
users = User.objects.prefetch_related('roles').all()
for user in users:
    roles = user.roles.all()  # Pre-fetched!