How can I debug Django database queries and identify performance bottlenecks?

Answered
Aug 30, 2025 41 views 5 answers
44

I'm working on a Django project and encountering an issue with Django views. Here's my current implementation:


# models.py
# views.py
from django.shortcuts import render
from .models import Article

def article_list(request):
    articles = Article.objects.all()
    for article in articles:
        print(article.author.username)  # N+1 problem here
    return render(request, 'articles.html', {'articles': articles})

The specific error I'm getting is: "django.urls.exceptions.NoReverseMatch: Reverse for 'article_detail' not found"

I've already tried the following approaches:

  • Checked Django documentation and Stack Overflow
  • Verified my database schema and migrations
  • Added debugging prints to trace the issue
  • Tested with different data inputs

Environment details:

  • Django version: 5.0.1
  • Python version: 3.11.0
  • Database: PostgreSQL 15
  • Operating system: Ubuntu 22.04

Has anyone encountered this before? Any guidance would be greatly appreciated!

M
Newbie 45 rep

Comments

admin: Could you elaborate on the select_related vs prefetch_related usage? When should I use each? 1 week, 4 days ago

5 Answers

16

The difference between threading and multiprocessing in Python is crucial for performance:

Threading (shared memory, GIL limitation):

import threading
import time

def io_bound_task(name):
    print(f'Starting {name}')
    time.sleep(2)  # Simulates I/O operation
    print(f'Finished {name}')

# Good for I/O-bound tasks
threads = []
for i in range(3):
    t = threading.Thread(target=io_bound_task, args=(f'Task-{i}',))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

Multiprocessing (separate memory, no GIL):

import multiprocessing
import time

def cpu_bound_task(name):
    # CPU-intensive calculation
    result = sum(i * i for i in range(1000000))
    return f'{name}: {result}'

# Good for CPU-bound tasks
if __name__ == '__main__':
    with multiprocessing.Pool(processes=4) as pool:
        tasks = [f'Process-{i}' for i in range(4)]
        results = pool.map(cpu_bound_task, tasks)
        print(results)

Concurrent.futures (unified interface):

from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor

# For I/O-bound tasks
with ThreadPoolExecutor(max_workers=4) as executor:
    futures = [executor.submit(io_bound_task, f'Task-{i}') for i in range(4)]
    results = [future.result() for future in futures]

# For CPU-bound tasks
with ProcessPoolExecutor(max_workers=4) as executor:
    futures = [executor.submit(cpu_bound_task, f'Process-{i}') for i in range(4)]
    results = [future.result() for future in futures]
A
Answered by admin 1 week, 4 days ago
Bronze 75 rep

Comments

abadi: Could you provide the requirements.txt for the packages used in this solution? 1 week, 4 days ago

22

Python decorators with arguments require a three-level nested function. Here's the proper implementation:

import functools

# Decorator with arguments
def retry(max_attempts=3, delay=1):
    def decorator(func):
        @functools.wraps(func)  # Preserves function metadata
        def wrapper(*args, **kwargs):
            for attempt in range(max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_attempts - 1:
                        raise e
                    time.sleep(delay)
        return wrapper
    return decorator

# Usage
@retry(max_attempts=5, delay=2)
def unreliable_function():
    # Function that might fail
    pass

Class-based decorator (alternative approach):

class Retry:
    def __init__(self, max_attempts=3, delay=1):
        self.max_attempts = max_attempts
        self.delay = delay
    
    def __call__(self, func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(self.max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == self.max_attempts - 1:
                        raise e
                    time.sleep(self.delay)
        return wrapper

# Usage
@Retry(max_attempts=5, delay=2)
def another_function():
    pass
A
Answered by alex_dev 1 week, 4 days ago
Newbie 30 rep
16

The RecursionError occurs when Python's recursion limit is exceeded. Here are several solutions:

1. Increase recursion limit (temporary fix):

import sys
sys.setrecursionlimit(10000)  # Default is usually 1000

2. Convert to iterative approach (recommended):

# Recursive (problematic for large inputs)
def factorial_recursive(n):
    if n <= 1:
        return 1
    return n * factorial_recursive(n - 1)

# Iterative (better)
def factorial_iterative(n):
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result

3. Use memoization for recursive algorithms:

from functools import lru_cache

@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

4. Tail recursion optimization (manual):

def factorial_tail_recursive(n, accumulator=1):
    if n <= 1:
        return accumulator
    return factorial_tail_recursive(n - 1, n * accumulator)
S
Answered by sarah_tech 1 week, 4 days ago
Newbie 45 rep
10

To handle Django database transactions properly and avoid data inconsistency, use Django's transaction management:

from django.db import transaction

# Method 1: Decorator
@transaction.atomic
def transfer_money(from_account, to_account, amount):
    from_account.balance -= amount
    from_account.save()
    
    to_account.balance += amount
    to_account.save()

# Method 2: Context manager
def complex_operation():
    with transaction.atomic():
        # All operations in this block are atomic
        user = User.objects.create(username='test')
        profile = UserProfile.objects.create(user=user)
        # If any operation fails, all are rolled back

For more complex scenarios with savepoints:

def nested_transactions():
    with transaction.atomic():
        # Outer transaction
        user = User.objects.create(username='test')
        
        try:
            with transaction.atomic():
                # Inner transaction (savepoint)
                risky_operation()
        except Exception:
            # Inner transaction rolled back, outer continues
            handle_error()
E
Answered by emma_programmer 1 week, 4 days ago
Newbie 40 rep
10

The RecursionError occurs when Python's recursion limit is exceeded. Here are several solutions:

1. Increase recursion limit (temporary fix):

import sys
sys.setrecursionlimit(10000)  # Default is usually 1000

2. Convert to iterative approach (recommended):

# Recursive (problematic for large inputs)
def factorial_recursive(n):
    if n <= 1:
        return 1
    return n * factorial_recursive(n - 1)

# Iterative (better)
def factorial_iterative(n):
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result

3. Use memoization for recursive algorithms:

from functools import lru_cache

@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

4. Tail recursion optimization (manual):

def factorial_tail_recursive(n, accumulator=1):
    if n <= 1:
        return accumulator
    return factorial_tail_recursive(n - 1, n * accumulator)
W
Answered by william 1 week, 4 days ago
Newbie 40 rep

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