Optimize Database Performance in Django: Avoiding CPU Bottlenecks

Optimize Queries with only() and defer() When working with Django ORM, it's important to be mindful of the data you retrieve from the database. Fetching all fields when only a few are required can lead to unnecessary overhead and slower performance. ❌ User.objects.all() → Retrieves all fields ✅ User.objects.only('id', 'name') → Retrieves only the specified fields By limiting the data you load, you can significantly improve query efficiency and overall application performance. Key takeaway: Less data fetched = Faster queries ⚡ #Django #Python #WebDevelopment #PerformanceOptimization

🚀Day-19/39: Just Basics of Database loggings.. SQL-Based Logging System with Python (Just Basics) Today I implemented a structured logging system that stores application logs directly into a SQL database. Tech Stack: • Python • SQLAlchemy ORM • SQLite database Key Concepts I Learned: ✔ Designing a log database schema ✔ Using ORM models for SQL tables ✔ Writing logs into a database instead of files ✔ Querying logs for debugging and monitoring Example log fields stored: log level (INFO / ERROR) message timestamp This small code helped me understand how real backend systems track errors, monitor services, and analyse system behavior. Next steps: Exploring scalable logging systems in this SQLite3 into the Intermediate mode Tomorrow ... Always learning and building. #Python #SQL #BackendDevelopment #Logging #SoftwareEngineering #LearningInPublic #Sqlite3 #git #simpleCode #justBasics #GrowEveryDay #ConnectForMore

This article explores the significance of real-time data in modern software and provides insights on implementing WebSockets using Python and FastAPI. I found it interesting how crucial real-time communication has become in various applications from finance to collaborative tools. What are your thoughts on the importance of real-time data in your industry?

Most Data Engineers know SQL. But when they start using Python (Pandas), the same operations suddenly feel confusing. SELECT WHERE GROUP BY JOIN WINDOW FUNCTIONS So I made a simple cheat sheet. SQL → Python equivalents for 10 common data operations. Swipe through the carousel and save it for later. Which one do you use more in your daily work? SQL or Python? #DataEngineering #SQL #Python #Pandas #DataScience

The Python "Gotcha" That Every Developer Hits Once Ever had a function return data from a previous call that you never asked for? You might be falling for the Mutable Default Argument trap. It’s a classic behavior that still catches experienced devs off guard. Take a look at the comparison below 👇 The Issue: Code 1 ❌ When you use a mutable object like a list or dict as a default argument, Python evaluates that expression only once — at the moment the function is defined. It doesn't create a new list for every call. Instead, it reuses the same object in memory. The result? Your data "leaks" from one function call to the next, creating a persistent state you probably didn't want. The Fix: Code 2 ✅ To ensure a clean slate every time, use the Late Binding pattern: Set the default value to None Initialize the mutable object inside the function body This ensures that a brand-new list or dictionary is created only when the function actually runs. ⚡ Key Takeaway Avoid using [], {}, or set() as default arguments. Stick to None or immutable types (strings, ints, tuples) to keep your code predictable and side-effect-free. It’s a small implementation detail, but mastering it saves hours of debugging "ghost data" in your backend. #Python #PythonDeveloper #Programming #SoftwareEngineering #BackendDevelopment #CodingTips #CleanCode #Debugging #ProgrammingTips #DevCommunity #SoftwareDevelopment

 🚀 microvec is a lightweight micro-vector database in pure Python. If you're building RAG pipelines, semantic search, or anything with embeddings and don't want to spin up a full vector DB service, this is for you.  What it does:  - Store vectors alongside document text and metadata  - Search by cosine, euclidean, or dot product similarity  - Filter results with any Python predicate before scoring  - Batch insert, update, and delete nodes  - Persist to disk safely (no pickle — JSON + numpy format)  What's new in v0.1.0:  - Complete rewrite from prototype to production-ready library  - Search results now return the actual document text, not just an index  - Full input validation with descriptive errors  - 107 tests, 98.5% coverage, mypy strict, ruff clean  Install: pip install microvec  No servers. No config. Just embeddings.  GitHub: https://lnkd.in/dnxWycy9

Let us look at the keyword “FROM” in SQL and Python. Sometimes the same word can exist in different environments but serve completely different purposes. Take “FROM” for an example. ● In Python, from is commonly used when importing modules, libraries, or specific functions into your notebook or script. Example: from pandas import DataFrame Here, Python is bringing tools into your workspace so you can perform tasks like data manipulation, analysis, or visualization. ● In SQL, FROM plays a different role. Example: SELECT column_name FROM table_name Here, SQL is specifying where the data is coming from, the table that holds the dataset you want to query. So even though the keyword is the same, the purpose is different. ● In Python, from helps you import tools. ●In SQL, FROM helps you retrieve data from a source. Same keyword. Different environments. Different responsibilities. This reminded me of something beyond tech. Sometimes we compare ourselves with others and wonder why our results look different. Why someone is doing something one way and we are doing it another way. However, like SQL and Python, we may be operating in different environments with different purposes. ● SQL doesn’t compete with Python. ● Python doesn’t compete with SQL. They simply perform different roles in the data ecosystem. So if today you feel like your journey looks different from someone else’s, remember this: You might just be operating in a different environment, and that is perfectly okay. Your uniqueness is part of your design. #DataAnalytics #SQL #Python #TechLearning #WomenInTech #ContinuousLearning #DataJourney #TechMotivation

Mastering strings in Python is one of those “small” skills that quietly powers almost everything we build—APIs, data pipelines, web apps, you name it. This week I revisited the fundamentals of Python strings: - Creating and slicing strings to access and update specific parts - Formatting with `format()` to build clean, readable outputs - Leveraging powerful built-in methods like `strip()`, `split()`, `join()`, `find()`, `replace()`, and case conversions (`lower()`, `upper()`, `title()`) to clean and transform text efficiently What struck me again is how much you can do *just* with string methods before reaching for heavier tools. If you’re starting with Python (or even revisiting basics), investing time in string operations is one of the highest-ROI steps you can take as a developer or data professional.

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