Django ORM Explained

Django ORM Internals and Query Optimization — What Every Backend Developer Should Understand What is Django ORM Really Doing? The Django ORM is an abstraction layer that converts Python code into SQL queries. When you write: books = Book.objects.all() Django does not immediately hit the database. Instead, it creates a QuerySet — a lazy object that represents the SQL query. The actual database call happens only when the data is evaluated. Examples of evaluation: Iterating over QuerySet Converting to list Accessing elements This concept is called lazy loading. How QuerySets Work Internally A QuerySet goes through multiple steps: Query construction Django builds a SQL query internally using a query compiler Optimization It decides joins, filters, and conditions Execution The query is sent to the database Result caching Results are stored to avoid repeated queries This means: Reusing the same QuerySet can save queries Creating new QuerySets repeatedly can hurt performance The Real Problem: N+1 Queries One of the biggest mistakes developers make: books = Book.objects.all() for book in books: print(book.author.name) This creates: 1 query for books N queries for authors This is inefficient and slows down applications at scale. Optimization Techniques 1.select_related() Used for ForeignKey and OneToOne relationships. books = Book.objects.select_related('author') This performs a SQL JOIN and fetches related data in a single query. 2.prefetch_related() Used for ManyToMany or reverse relationships. authors = Author.objects.prefetch_related('books') This runs separate queries but combines results efficiently in Python. 3.only() and defer() Fetch only required fields: Book.objects.only('title') Reduces data transfer and speeds up queries. 4.values() and values_list() Return dictionaries or tuples instead of full model objects: Book.objects.values('title', 'price') Useful for APIs and data-heavy operations. Why This Matters Poor ORM usage leads to: Slow APIs High database load Bad user experience Optimized queries result in: Faster response times Better scalability Efficient resource usage #Python #Django #ORM #BackendFramework #BackendDevelopment #SoftwareDevelopment #QuerySets #SQL #Optimization #Scalable #Fast_API_Response

🚀 Day 19: Models & Database in Django As I dive deeper into Django, I explored how data is structured and managed using Models. 👉 In Django, a Model defines the structure of your database. It acts as a bridge between your application and the database. 🔹 What is a Model? A model is a Python class that represents a database table. Each attribute in the class corresponds to a column in the table. 💡 Example: from django.db import models class Student(models.Model):     name = models.CharField(max_length=100)     age = models.IntegerField() 🔹 Key Concepts: ✔ Fields → Define data types (CharField, IntegerField, etc.) ✔ Migrations → Apply changes to the database ✔ ORM (Object Relational Mapping) → Interact with DB using Python instead of SQL 🔹 Basic Commands: python manage.py makemigrations python manage.py migrate 📌 Why it matters? ✔ Simplifies database operations ✔ Eliminates the need to write raw SQL ✔ Makes applications scalable and maintainable Django’s ORM is one of its most powerful features for backend development. 💡 Good developers don’t just store data they structure it efficiently. 📈 Step by step, building real-world backend expertise. #Django #Python #Database #BackendDevelopment #ORM #WebDevelopment #LearningJourney #FullStack

Django ORM Explained Like a Pro 😎 Still writing raw SQL in Django? 😳 You’re missing the real power ❌ Content: Django ORM makes database work simple + powerful 👇 ⚡ What is Django ORM? → It lets you interact with database using Python → No need to write raw SQL 🔥 Example: ❌ Raw SQL: SELECT * FROM users WHERE age > 18; ✅ Django ORM: users = User.objects.filter(age__gt=18) Simple, clean, readable 🔥 ⚡ Powerful ORM features: 🚀 Filtering data → .filter(), .exclude() 🚀 Relationships → ForeignKey, ManyToMany 🚀 Aggregations → .count(), .sum() 🚀 Query optimization → select_related, prefetch_related ❌ What beginners do: ❌ Use raw queries everywhere ❌ Write complex SQL ✅ What smart devs do: ✅ Use ORM effectively ✅ Write clean queries ✅ Optimize performance Why this matters: Clean queries = better code 💯 Reality: ORM is not slow… Bad usage is 😳 Pro Tip: Learn ORM deeply… It will save you hours ⏳ CTA: Follow me for Django mastery 🚀 Save this post 💾 Comment "ORM" if you learned something 👇 #Django #Python #Backend #ORM #Programming #Developer #Coding #SoftwareEngineer #Tech #WebDevelopment

Day-120,121 📘 Python Full Stack Journey – Django Forms & User Input Handling Today I learned how to handle user input in Django using models and forms — an important step toward building interactive and data-driven applications. 🚀 🎯 What I learned today: 🗄️ Model Creation (Contact Form) Created a Contact model to store user data: Name Email Phone number Applied migrations and registered the model in Django Admin for easy data management 📝 Django ModelForm Created a form using Django’s built-in ModelForm: class BookingContact(forms.ModelForm): class Meta: model = Contact fields = '__all__' Learned how Django automatically generates form fields from models 🌐 Displaying Forms in Templates Rendered forms in HTML using: {{ form }} {{ form.as_p }} for structured layout 📩 Form Submission (POST Method) Used POST method for secure data submission Added {% csrf_token %} for protection Handled form submission in views.py: if request.method == 'POST': form = BookingContact(request.POST) if form.is_valid(): form.save() 🎨 Custom Form Styling Styled individual form fields manually using labels and inputs Learned how to design forms for better user experience This session helped me understand how Django manages forms, validation, and database storage seamlessly — a key step in building real-world web applications. Excited to keep building more interactive features! 💻✨ #Django #Python #FullStackDevelopment #WebDevelopment #Backend #Forms #Database #CodingJourney #LearningToCode #Upskilling #ContinuousLearning

I just published my first article on "The Django Habits That Hurt You in Go". Coming from Python/Django, a lot of patterns feel natural, with Celery, background jobs, fire and forget tasks, safe retries and queues still feels like "Django". Then you switch to Go after hearing so much about how awesome goroutines are, and naturally, you try to use them like Celery :) Goroutines feel lightweight and easy, so it is tempting to treat them like background workers. But they lack persistence, retry mechanism, fault isolation, and guarantees. This article breaks down: - why goroutines are not a replacement for Celery - the common mistake Django engineers make in Go - what the correct pattern looks like - and what to use instead when you actually need a task queue If you are a Django developer who is giving Go a shot, this will save you from a few painful lessons. https://lnkd.in/ef5BPMYC

💡One small Django detail that can silently break authentication! A lot of beginners assume these 3 methods do the same thing: 1- User.objects.create() 2- User.objects.create_user() 3- User.objects.create_superuser() Actually they don’t. Here’s the real difference: ✅ create() This is the raw ORM method used to create any model object. It simply inserts data into the database. The dangerous part? If you pass a password here, Django stores it as ''plain text'' unless you manually call set_password(). --> That means login will fail because Django expects a hashed password. ✅ create_user() This is the correct method for creating normal authenticated users. It usually: 1- validates required fields 2- hashes the password with set_password() 3- saves the user safely --> It applies any custom business logic from your manager "This should be your default choice for sign-up systems, APIs, and scripts." ✅ create_superuser() This builds on create_user() but also sets the required admin flags: is_staff=True is_superuser=True is_active=True --> This is what allows access to Django admin because of setting is_staff flag to True. 💡Summary: create() = just create a DB row create_user() = create a real login account create_superuser() = create an admin account Understanding this difference early saves hours of debugging “why can’t my user log in?” issues. #Django #Python #WebDevelopment #Backend #SoftwareEngineering #100DaysOfCode

Understanding Flask and Django in My Python Learning Journey 🚀 As I continue building my skills in Python backend development, I’ve been exploring two widely used web frameworks: Flask and Django. Both frameworks are powerful in their own way, but what really stood out to me is how differently they approach web development. Learning both has given me a broader perspective on designing and building applications. Working with Flask Flask gave me a clear understanding of how web applications function at a fundamental level. Its lightweight and minimal design allows developers to build step by step without unnecessary complexity. While working with Flask, I explored: ▸ Routing and request handling ▸ Building simple APIs ▸ Structuring small-scale applications What makes Flask interesting is the level of control it provides. It allows developers to choose how they want to build and scale their applications. Exploring Django Django offered a more structured and feature-rich experience. It comes with many built-in tools that make development faster and more organized. Some key features I found valuable: ▸ Built-in admin panel ▸ ORM for database operations ▸ Predefined project structure ▸ Authentication and security support Django feels like a framework that is ready for larger applications right from the start. Key Takeaway One important difference I noticed: ▸ Flask → Flexibility and simplicity ▸ Django → Structure and built-in functionality This helped me understand that the choice of framework depends on the type and scale of the project. Moving Forward Exploring both Flask and Django is strengthening my backend development skills and helping me understand real-world application design more effectively. Looking forward to building more projects and diving deeper into the Python ecosystem. #Python #Flask #Django #WebDevelopment #BackendDevelopment #SoftwareDevelopment #LearningJourney 🚀 #snsinstitutions #snsdesignthinkers #designthinking

FastAPI vs. Django: Which Framework Should You Choose and When? In Python backend development, a perennial debate exists: FastAPI or Django? Whether you are preparing for an interview or selecting a framework for your next project, this comparison will prove invaluable. Here are 4 major differences between the two: 1. Performance and Speed FastAPI: Built upon Starlette and Pydantic, it ranks among the fastest Python frameworks available. Its asynchronous support (async/await) is superior for handling high-concurrency scenarios. Django: This is a synchronous framework (though it now includes ASGI support). It follows a "Batteries Included" approach, which can make it somewhat heavier. 2. Nature: Micro vs. Monolith FastAPI: A micro-framework. You receive only the essential tools; other functionalities (such as Authentication and Database handling) must be plugged in manually. It is perfect for microservices. Django: A monolith. It comes with a built-in Admin panel, ORM, and Authentication system. It is an excellent choice for full-stack web applications. 3. Data Validation and Documentation FastAPI: It utilizes Pydantic for data validation and automatically generates Swagger UI (OpenAPI) documentation. Developers do not need to write documentation separately. Django: APIs are typically built using the Django REST Framework (DRF), which involves a slightly higher degree of manual configuration. 4. Learning Curve  FastAPI: Being compact and modern, it can be learned quickly—provided you are familiar with modern Python type hints. Django: It possesses a vast ecosystem, so mastering it takes some time; however, once learned, it significantly accelerates the development process. Conclusion: Choose FastAPI if you prioritize high performance, microservices, and rapid API development. Choose Django if you need to build a robust, secure, and feature-rich application quickly. Which one is your favorite? Let us know in the comments! #Python #BackendDevelopment #FastAPI #Django #SoftwareEngineering #WebDevelopment #CodingLife #InterviewPrep

Django Beginner Mistake: get() vs filter() While working with database queries in Django, I learned an important difference between get() and filter() that often confuses beginners. Both are used to retrieve data from the database, but they behave very differently. Using get() student = Student.objects.get(id=1) get() is used when you expect exactly one object. ✔ Returns a single model object ✔ Best used with unique fields like id, email, or username However, it can raise errors: ❌ DoesNotExist → when no object matches the query ❌ MultipleObjectsReturned → when more than one object matches Example: student = Student.objects.get(email="ali@example.com") This works well if email is unique. Using filter() students = Student.objects.filter(age=20) filter() is used when multiple objects may match the query. ✔ Returns a QuerySet (collection of objects) ✔ If no record exists → returns empty QuerySet instead of error Example: students = Student.objects.filter(city="Karachi") This may return: ✔ 5 students ✔ 1 student ✔ or even 0 students But it will not raise an exception. Extra Tip 💡 If you only need one object but want to avoid errors, you can use: student = Student.objects.filter(id=1).first() This returns: ✔ the first object if it exists ✔ None if no object exists 📌 Key Lesson Use: get() → when you are sure only one object should exist filter() → when multiple objects may exist Understanding this small difference can help avoid many common beginner errors while working with Django databases. #Django #Python #BackendDevelopment #LearningInPublic #WebDevelopment