If you've ever opened Django Debug Toolbar and seen 200+ queries on a single page load, this post is for you.

Most Django performance problems come down to three things:

1. N+1 Queries

Every time you loop over a queryset and access a related object, Django fires a separate SQL query. A page listing 50 orders with customer names? That's 51 queries instead of 2.

# ❌ 51 queries
for order in Order.objects.all()[:50]:
    print(order.customer.name)

# ✅ 2 queries
for order in Order.objects.select_related('customer')[:50]:
    print(order.customer.name)

2. Python-Level Aggregation

Pulling 100K rows into Python to sum them is orders of magnitude slower than letting PostgreSQL do it:

# ❌ Loads 100K objects into memory
total = sum(o.total for o in Order.objects.all())

# ✅ Single query, instant
total = Order.objects.aggregate(Sum('total'))['total__sum']

3. Missing Indexes

A sequential scan on a 10M row table takes seconds. An index scan takes milliseconds. Most Django developers never check their query plans.

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I built Advanced SQL Query Optimization for Django to fix this. It's a 24-lesson course covering everything from ORM internals to production-scale patterns like PgBouncer, read replicas, and cursor pagination.

The final lesson is a complete case study: taking a real Django dashboard from 12 seconds to 200ms response time. Step by step, with code.

If you're a senior Django developer or backend engineer who's ready to master the performance stack, check it out.