Paolo Melchiorre’s Post

Revisiting Django ORM I reviewed some advanced ORM concepts in Django, and I’m starting to appreciate how powerful it really is when used properly. A few concepts that stood out: Atomic transactions – ensure operations either fully succeed or fully fail. Critical for financial logic and data integrity. F expressions – perform database-level field operations without pulling data into Python. Safer and more efficient. Q objects – build complex queries with AND/OR logic cleanly. Case & When – conditional logic directly in SQL via the ORM. Powerful for annotations and computed fields. Subquery, OuterRef & Exists – write correlated subqueries and advanced filters without raw SQL. Generated fields & annotations – compute values at query time instead of storing redundant data. Multiple databases – route reads/writes strategically when scaling. Using logic closer to the database makes your application faster and more efficient. #Django #BackendEngineering #Python #ORM

Clean code is good. Efficient queries are better. 🚀 One of the biggest lessons in Django backend development: performance is our responsibility, not the database’s problem. Understanding when to use: ✔️ "select_related()" ✔️ "prefetch_related()" ✔️ Query count monitoring ✔️ Proper DB indexes Small ORM decisions → huge production impact. Avoid N+1 queries early, and your future self will thank you. 💻⚡ #Django #Python #BackendDevelopment #PerformanceOptimization #DjangoORM #SoftwareEngineering

🚀 select_related() vs prefetch_related() One of the most common performance issues in Django applications is the N+1 query problem. If you are not careful while fetching related data, your API or page can make dozens (or hundreds) of unnecessary database queries. Two powerful Django ORM tools help solve this: 🔹 select_related() - Uses SQL JOIN - Fetches related objects in a single query - Best for ForeignKey and OneToOne relationships 🔹 prefetch_related() - Executes separate queries - Joins the data in Python - Best for ManyToMany and reverse ForeignKey relationships 💡 Rule of thumb: - Use select_related() for single-valued relationships - Use prefetch_related() for multi-valued relationships Optimizing queries like this can dramatically reduce database load and speed up your Django APIs. I created a simple infographic to visually explain the difference 👇 What other Django performance techniques do you use in production? #django #python #backenddevelopment #webdevelopment #softwareengineering #databaseoptimization #orm #programming #djangoorm #codingtips

Most performance problems are not CPU problems. They are database problems. Early in my career, when something was slow, my instinct was to look at the Python code. Later I learned the real bottleneck was almost always: Too many queries Inefficient joins Missing indexes Loading more data than necessary A single endpoint can look perfectly fine in code and still generate hundreds of database queries. A few habits that made a big difference for me: Always checking query counts during development Using select_related and prefetch_related intentionally Avoiding loading full objects when only a few fields are needed Being careful with nested serializers in APIs Django’s ORM is incredibly productive. But performance comes from understanding what SQL is actually being executed behind the scenes. The ORM abstracts the database. It does not eliminate it. Hashtags #Django #Python #BackendEngineering #PerformanceOptimization #DatabasePerformance #SoftwareEngineering

The Django ORM feels like magic. But how does it work internally? It's actually three layers doing very specific jobs. Layer 1: Query -> When a QuerySet is built, Django creates a Query object internally. -> This object is a structured, database-agnostic representation of the intended operation. -> All this is stored as Python data structures. No SQL yet. This is why QuerySets are chainable. Layer 2: SQL Compiler -> At evaluation time, Django takes the Query object and hands it to a SQLCompiler. -> Compiler translates Python representation into actual SQL. -> This is where database-specific syntax is handled. The same Query object produces different SQL depending on which backend is active. Layer 3: Connection -> The compiled SQL string is handed to the database Connection. -> This manages the actual connection to the database, sends the query, and returns raw results. -> Django then maps those raw results back into model instances. The ORM is not a black box. It's a pipeline and knowing it means knowing exactly where to look when something goes wrong. I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

Django ORM vs Raw SQL ================= Django ORM: * Abstracts database operations into clean Python code (models & QuerySets) * A project typically uses ORM for 90-95% of queries (one primary way to interact) * Faster development, more readable, secure by default (prevents SQL injection) Raw SQL: * Direct SQL queries executed via .raw(), extra(), or cursor * Can be used multiple times when needed * Potentially faster for complex joins, CTEs, window functions, or heavy optimizations (but slower to write & maintain) Use ORM first -> back to raw SQL only when performance is measured #Django #Python #ORM #DjangoORM #SQL #Database #BackendDevelopment #WebDevelopment #SoftwareEngineering #PythonDeveloper

When I first saw our production database hitting 99% utilization, I thought we needed a bigger server. I was wrong. The real problem wasn’t infrastructure. It was one unoptimized query quietly running inside a loop. Development was fast. Production exposed everything. I just published a deep dive on Django Query Optimization — covering N+1 problems, select_related vs prefetch_related, aggregations, indexing strategies, and real-world mistakes that slow apps down. here is my article link https://lnkd.in/gViDwwVd If you’re building APIs in Django, this might save you from a painful production incident. Would love to know: Have you ever faced a performance issue that wasn’t obvious at first? #Django #BackendDevelopment #Python #DatabaseOptimization #WebPerformance

Developer Rewrites Collectible Deal Finder with Flask and MySQL for Multi-Niche Scalability 📌 A developer transformed a clunky collectible deal finder into a scalable, modular system using Flask and MySQL-letting new niches like YuGiOh or One Piece be added in under an hour. No more duplicated code; now one script handles any niche, making expansion fast and maintenance seamless. This rewrite proves Python’s flexibility for real-time, niche-driven data apps. 🔗 Read more: https://lnkd.in/dZRTCUAS #Flask #Mysql #Nichescalability #Collectibledeals #Scriptmodular

🚀 SQL Query Performance Analyzer We developed a powerful backend tool using SQL, Python, Node.js, Jest, Postman, and Streamlit to execute queries, extract EXPLAIN metrics, and deliver REST-based analytics enabling data-driven SQL optimization and performance insights. 👥 In collaboration with: Marwan Essa 🔗 GitHub Repository: https://lnkd.in/dV6V-7Bw 📊 Streamlit App: https://lnkd.in/dpFiUdyD #SQL #NodeJS #Python #BackendDevelopment #DataAnalytics #PerformanceOptimization #Streamlit #Pandas

Python is powerful, but it has one well-known limitation: the Global Interpreter Lock (GIL). Because of the GIL, only one thread can execute Python bytecode at a time inside a single process. This means that traditional multi-threaded web servers in Python often don’t scale efficiently for high-concurrency workloads, especially when thousands of requests are waiting for network or database responses. This is where FastAPI comes in. FastAPI is a modern Python web framework built on top of ASGI (Asynchronous Server Gateway Interface). Instead of relying on heavy threads for every request, FastAPI uses async programming with an event loop. Here’s the simple idea: • A request comes in • If the request is waiting for I/O (database, API call, file read) • The event loop pauses that task and switches to another request So instead of 1 thread per request, a single event loop can handle thousands of concurrent requests. Think of it like a waiter in a restaurant: A traditional threaded server: The waiter stands at one table until the food arrives. FastAPI async model: The waiter takes the order, moves to other tables while the kitchen prepares the food, and comes back when it's ready. Because of this: • Much lower memory usage • Higher concurrency • Faster APIs for I/O heavy applications • Better scalability FastAPI doesn’t remove the GIL, but it works around the limitation by avoiding thread blocking and using non-blocking asynchronous execution. That’s why FastAPI has become one of the fastest growing frameworks for building high-performance Python APIs. #fastapi #python #backenddevelopment #softwareengineering #asyncio #systemdesign #programming #webdevelopment #tech