Django bulk_create and bulk_update: trade-offs and implications

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

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

🎭 #The_"#Translator_Who_Tries_Too_Hard": #Understanding #SerializerMethodField: Ever built a Django API and realized your database model is a bit… boring? You have a first_name and a last_name, but your frontend developer is begging for a full_name or a is_vibe_check_passed field that doesn't actually exist in your SQL table. Enter the SerializerMethodField. In the world of Django Rest Framework (DRF), this field is like that one friend who refuses to just repeat what they heard and instead adds their own "flavour" to the story. 🛠️ How it Works When you define a field as a SerializerMethodField(), you are telling Django: "Hey, don't look for this in the database. I’m going to write a custom function to calculate this on the fly." 👨🍳 The Recipe Define the field: Add it to your serializer class. The "Get" Magic: Create a method named get_<field_name>. The Logic: Do your math, string concatenation, or soul-searching inside that method. Python class UserSerializer(serializers.ModelSerializer): days_since_joined = serializers.SerializerMethodField() class Meta: model = User fields = ['username', 'days_since_joined'] def get_days_since_joined(self, obj): # 'obj' is the actual model instance return (timezone.now() - obj.date_joined).days ⚠️ The Catch (Read: Why your Senior Dev is frowning) While powerful, SerializerMethodField is read-only. You can't POST data back to it. Also, if you use it to run heavy database queries for every single item in a list of 1,000 objects, your API performance will disappear faster than free pizza in a tech office. 🍕💨 TL;DR: Use it when you need to transform raw data into something useful for the frontend, but use it sparingly to keep those response times snappy! 🚀 #Django #Python #WebDevelopment #DRF #BackendTips #CodingHumor

Day-117 📘 Python Full Stack Journey – Django Models to UI Rendering Today I worked on a complete flow in Django — from creating database models to displaying dynamic data on a webpage. This felt like a true full-stack experience! 🚀 🎯 What I learned today: 🗄️ Model Creation (Database Table) Defined a model in models.py: class Course(models.Model): course_name = models.CharField() course_description = models.TextField() Learned: CharField → for small text data TextField → for larger content Understood that inheriting from models.Model creates a database table 🔄 Migrations & Admin Integration Applied database changes using: py manage.py makemigrations py manage.py migrate Registered model in admin.py: admin.site.register(Course) Managed data through Django Admin (add, edit, delete) 💡 Also learned that missing migrations can cause errors like “no such table” 🌐 Fetching & Displaying Data Retrieved data in views.py: details = { 'data': Course.objects.all() } Passed data to template and displayed using loop: {% for i in data %} <h1>{{i.course_name}}</h1> <p>{{i.course_description}}</p> {% endfor %} 🎨 Styling & Layout Used Flexbox/Grid to design responsive course cards Created a clean UI layout for displaying multiple records dynamically This session helped me understand how Django connects database → backend → frontend, making applications truly dynamic and data-driven. Excited to build more real-world features using Django! 💻✨ #Django #Python #FullStackDevelopment #WebDevelopment #Backend #Frontend #Database #CodingJourney #LearningToCode #Upskilling #ContinuousLearning

𝐃𝐣𝐚𝐧𝐠𝐨 𝟏𝟎𝟏 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧𝐢𝐬𝐭𝐚𝐬 🐍 | 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐐𝐮𝐞𝐫𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 As a Django application grows, database performance becomes a central topic. One of the most common bottlenecks is the N+1 Query Problem. 💡 𝐓𝐡𝐞 𝐅𝐚𝐜𝐭: By default, Django’s ORM uses "lazy loading." It only fetches related data at the moment it is accessed. While this saves memory, it can lead to an excessive number of database hits during loops. The N+1 Scenario: If you want to display a list of 50 Books and their Authors: One query fetches the 50 books. As you loop through the books to show the author's name, Django performs a new database lookup for each individual author. 👉 This results in 51 database trips for a single list. Technical Solutions: 🚀 select_related() This is used for "one-to-many" or "one-to-one" relationships. It performs an SQL JOIN in the initial query. Book.objects.select_related('author').all() Instead of many trips, Django fetches everything in one single query. 🚀 prefetch_related() This is used for "many-to-many" or reverse relationships. It performs a separate lookup for the related objects and joins the data in Python. This effectively reduces hundreds of queries down to two. 🔍 How to identify it: Tools like django-debug-toolbar help visualize how many queries are fired per request. If you see the same SQL pattern repeating multiple times, it’s a clear indicator that the ORM needs optimization. 𝐓𝐡𝐞 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞: Database "round-trips" are expensive. Using these tools ensures that your application remains performant and scalable, regardless of how much data you are handling. #Python #Django #WebDevelopment #Database #SoftwareEngineering

𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝗻 𝗗𝗷𝗮𝗻𝗴𝗼. 𝗪𝗵𝗶𝗰𝗵 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗗𝗼 𝗬𝗼𝘂 𝗣𝗿𝗲𝗳𝗲𝗿? Caching was one of those topics I kept putting off learning properly. 𝗜𝘁 𝗳𝗲𝗹𝘁 𝗰𝗼𝗺𝗽𝗹𝗲𝘅. 𝗧𝗵𝗲𝗻 𝗜 𝗿𝗲𝗮𝗹𝗶𝘀𝗲𝗱 𝗗𝗷𝗮𝗻𝗴𝗼 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗶𝗻𝗴𝗹𝘆 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗮𝗯𝗹𝗲.   There are two common ways to cache in Django and they solve slightly different problems.   Look at the two options in the image.   ➝ 𝗢𝗽𝘁𝗶𝗼𝗻 𝟭 𝘂𝘀𝗲𝘀 𝘁𝗵𝗲 @𝗰𝗮𝗰𝗵𝗲_𝗽𝗮𝗴𝗲 𝗱𝗲𝗰𝗼𝗿𝗮𝘁𝗼𝗿. You add one line above your view and the entire response gets cached for a set duration. Simple, fast to implement, and works well for public pages that rarely change.   ➝ 𝗢𝗽𝘁𝗶𝗼𝗻 𝟮 𝘂𝘀𝗲𝘀 𝗰𝗮𝗰𝗵𝗲.𝗴𝗲𝘁() 𝗮𝗻𝗱 𝗰𝗮𝗰𝗵𝗲.𝘀𝗲𝘁() 𝗺𝗮𝗻𝘂𝗮𝗹𝗹𝘆. More code, but you get precise control over what gets cached, for how long, and under what key.   Here is where the difference matters in practice:   ➝ @𝗰𝗮𝗰𝗵𝗲_𝗽𝗮𝗴𝗲 caches the full response regardless of content. If the data changes, users still see stale results until the cache expires. ➝ Manual caching lets you cache specific querysets or computed values and invalidate them selectively when data changes. ➝ For user-specific data, @𝗰𝗮𝗰𝗵𝗲_𝗽𝗮𝗴𝗲 can serve the wrong data to the wrong user if not configured carefully.   I started with @𝗰𝗮𝗰𝗵𝗲_𝗽𝗮𝗴𝗲 because it was easy to reach for. Over time I moved to manual caching for anything that needed precise invalidation.   𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗰𝗮𝗰𝗵𝗶𝗻𝗴 𝘀𝗲𝘁𝘂𝗽 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲 𝗮𝗻𝗱 𝘄𝗵𝗶𝗰𝗵 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗵𝗮𝘃𝗲 𝘆𝗼𝘂 𝗳𝗼𝘂𝗻𝗱 𝗺𝗼𝗿𝗲 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗶𝗻 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? #Django #Python #BackendDevelopment #Performance #SoftwareEngineering

Day 130-131 📘 Python Full Stack Journey – Django Query Filtering 🔍 Today I explored how to filter data in Django using QuerySets, which is a powerful way to retrieve specific records from the database. 🚀 🎯 What I learned today: 🔎 Django Filter Queries Used different filtering techniques on the Employee model: startswith → fetch records where a field starts with a value endswith → fetch records where a field ends with a value icontains → case-insensitive search within a field Example: Employee.objects.filter(fullname__startswith='A') 📊 Displaying Filtered Data Passed multiple filtered datasets from views → template Used Django template loops to display results dynamically ⚙️ Multiple Filters in One View Combined multiple queries in a single function Even filtered data from different models in one page 💡 This makes it easy to build features like search, filtering, and categorization in web applications. Django User Profile (One-to-One Relationship) Today I implemented a User Profile system in Django using a One-to-One relationship, taking a big step toward building personalized user experiences. 👤 One-to-One Relationship Created a Profile model linked to Django’s built-in User model Ensured one user → one profile using: user = models.OneToOneField(User, on_delete=models.CASCADE) Understood how Django handles relationships like one-to-one, one-to-many, and many-to-many 🗄️ Profile Model Fields Added fields like: Bio Location Birth date Profile image Used: blank=True and null=True for optional fields ImageField for uploading profile pictures 🌐 Profile Display & Editing Displayed logged-in user details using: {{ request.user.username }} Created a profile page and an edit form page Used get_or_create() to automatically create a profile if it doesn’t exist 🔐 Access Control Used @login_required decorator to restrict access to logged-in users only 📸 Handling File Uploads Used enctype="multipart/form-data" for image uploads Displayed uploaded images dynamically in templates This session helped me understand how to build user-specific features and profiles, along with how Django makes data retrieval flexible and efficient—both of which are essential for real-world applications like social platforms, search systems, and dashboards. Excited to keep building more personalized and dynamic applications while exploring advanced queries next! 💻✨ #Django #Python #FullStackDevelopment #WebDevelopment #Backend #BackendDevelopment #Database #QuerySets #UserProfile #CodingJourney #LearningToCode #Upskilling #ContinuousLearning

From SQL Queries to Pythonic Code Django ORM has completely changed the way I interact with databases. Instead of writing long and complex SQL queries, I now use simple, readable, and powerful Python code to: ✔ Query data efficiently ✔ Manage relationships (ForeignKey, ManyToMany) ✔ Build scalable and maintainable backend systems ✔ Reduce development time and avoid common SQL errors Example: User.objects.filter(is_active=True) What really impressed me is how Django ORM allows chaining queries and performing advanced operations like aggregations, annotations, and joins — all without writing raw SQL. It not only improves productivity but also makes the codebase cleaner and easier to understand for teams. Currently exploring: • Query optimization techniques (select_related, prefetch_related) • Writing efficient database queries • Understanding how ORM translates into SQL under the hood Every day I’m realizing that mastering the basics deeply can unlock powerful capabilities. Excited to keep building, optimizing, and growing as a backend developer #python #django #SQL #webdevelopment #backend

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

Most Django developers hit the database way more than they need to. I see this pattern constantly in codebases: ❌ The slow way: # N+1 query — hits DB once per order orders = Order.objects.all() for order in orders:   print(order.user.name) # separate query every loop This runs 1 + N database queries. With 500 orders, that's 501 queries on a single page load. ✅ The fix — select_related(): # 1 query total using SQL JOIN orders = Order.objects.select_related('user').all() for order in orders:   print(order.user.name) # no extra DB hit Use select_related() for ForeignKey / OneToOne fields. Use prefetch_related() for ManyToMany or reverse FK relations. This single change dropped our API response time by 60% on a production endpoint last month. Django's ORM makes it easy to write code that looks clean but silently destroys performance. Always check your query count with Django Debug Toolbar before shipping a new endpoint. What's your go-to Django optimization? Drop it below 👇 #Django #Python #WebDevelopment #FullStackDeveloper #BackendDevelopment #PythonDeveloper #SoftwareEngineering #DjangoTips