Django Cache Low Level Fix

In this tutorial, you'll learn about Django Cache Low Level Fix. We cover key concepts, practical examples, and best practices.

The Problem

Computing dashboard statistics, API responses, or aggregations on every request is wasteful. These values change infrequently but cost significant CPU and database time.

Quick Fix

Wrong — computing on every request

def dashboard(request):
    # Computed every request — expensive
    stats = compute_dashboard_stats()
    return render(request, 'dashboard.html', {'stats': stats})

Output: Every page load recalculates stats. Under load, this overwhelms the database.

Correct — cache the computed value

from django.core.cache import cache

def dashboard(request):
    stats = cache.get('dashboard_stats')
    if stats is None:
        stats = compute_dashboard_stats()
        cache.set('dashboard_stats', stats, 3600)  # Cache for 1 hour
    return render(request, 'dashboard.html', {'stats': stats})

Output: Stats computed once per hour. Subsequent requests served from cache.

Cache with timeout and version

# Set with version
cache.set('key', value, timeout=300, version=2)

# Get with version
value = cache.get('key', version=2)

# Bump all keys with same prefix
cache.set('key', value, version=3)

Cache API response

def get_cached_data():
    cache_key = f'api_data_{settings.BUILD_NUMBER}'
    data = cache.get(cache_key)
    if data is None:
        data = fetch_from_external_api()
        cache.set(cache_key, data, 300)
    return data

Cache with lock for cache stampede

from django.core.cache import cache

def get_expensive_data():
    data = cache.get('expensive')
    if data is None:
        if cache.add('expensive_lock', 'locked', timeout=10):
            data = compute_expensive()
            cache.set('expensive', data, 3600)
            cache.delete('expensive_lock')
        else:
            time.sleep(0.5)
            data = cache.get('expensive')  # Other thread computed it
    return data

Bulk operations

# Set multiple keys at once
cache.set_many({
    'stats_total': 1000,
    'stats_active': 750,
    'stats_new': 250,
}, timeout=3600)

# Get multiple
result = cache.get_many(['stats_total', 'stats_active', 'stats_new'])

Prevention

• Use cache.get / cache.set pattern for expensive computations.
• Choose appropriate timeouts — too short defeats the purpose, too long serves stale data.
• Use cache.set_many and cache.get_many for batch operations.

Common Mistakes with cache low level

1. Using foldl instead of foldl' causing stack overflow on large lists
2. Forgetting deriving (Show, Eq) on custom data types needed for debugging
3. Placing the wildcard pattern first in case expressions, making all subsequent patterns unreachable

These mistakes appear frequently in real-world DJANGO code. DodaTech's contributors have identified these patterns through analysis of open-source projects and production systems.

Practice Exercise

Write a pure function that safely divides two integers using Maybe, then test it with edge cases like division by zero and negative numbers.

This exercise reinforces the concepts covered in this guide. Try implementing it before checking online solutions.

FAQ