Python N+1 Query Problem: Fix for Slow APIs

Most developers try to optimize code first. Senior backend engineers optimize the database query first. Because: A 20ms faster Python function means little. But one bad query can make your whole app slow. Real examples: • Missing indexes • N+1 queries • SELECT * everywhere • No pagination • Unnecessary joins You don’t always have a slow backend. Sometimes you have a fast backend waiting on a slow database. That’s a different problem. #backenddevelopment #python #databases #softwareengineering #django #systemdesign

🚀 Day 17 – Python API Integration Today I explored the power of Django REST Framework and how it simplifies building RESTful APIs in Python. 🔹 Key takeaways: Understood how Django REST Framework extends Django to build APIs efficiently Created a Django project and app structure (countryapi, countries) Built a Model (Country) to represent data Learned how Serializers convert Django models into JSON Used ModelViewSet to handle CRUD operations automatically Configured DefaultRouter to generate API endpoints 🔹 Implemented API endpoints: GET → Retrieve countries POST → Create new country PUT / PATCH → Update data DELETE → Remove data 💡 What stood out: Django REST Framework reduces a lot of boilerplate by providing built-in tools for serialization, routing, and request handling — making API development faster and more structured. 📌 This is a big step forward in building production-ready backend systems. #Python #DataEngineering #Django #DjangoRESTFramework #APIs #BackendDevelopment #LearningJourney #selfLearning

🔥 Mastering JSON Parsing in Python! 🐍 Ever wondered how to work with JSON in Python? JSON (JavaScript Object Notation) is a popular format for data exchange due to its simplicity and readability. For developers, understanding JSON parsing is essential as it allows you to interact with APIs, handle configuration files, and exchange data between different systems effortlessly. Here are the steps to parse JSON in Python: 1️⃣ Load the JSON data using the `json.loads()` function. 2️⃣ Access the values using keys just like a Python dictionary. 3️⃣ Iterate through JSON arrays to extract multiple values. 👉 Pro Tip: Use `json.dumps()` to convert Python objects back to JSON. ❌ Common Mistake: Forgetting to handle exceptions when parsing JSON can lead to runtime errors. 🚀 Ready to level up your Python skills? Try parsing JSON with this code snippet: ``` import json # Sample JSON data json_data = '{"name": "John", "age": 30, "city": "New York"}' # Parse JSON data = json.loads(json_data) # Access values name = data['name'] age = data['age'] city = data['city'] print(name, age, city) ``` 🤔 What's your favorite way to work with JSON data in Python? 🌐 View my full portfolio and more dev resources at tharindunipun.lk #JSON #Python #APIs #DataExchange #CodingTips #JSONParsing #Programming #DeveloperCommunity #TechSkills

Most Python developers use Flask, FastAPI, or Django… But many still overlook one fundamental concept: HTTP methods. No matter which framework you choose, everything comes down to how your application handles these requests: • GET – Retrieve data • POST – Create a resource • PUT – Replace an entire resource • PATCH – Update specific fields • DELETE – Remove a resource Here’s where it gets interesting 👇 A lot of developers confuse PUT and PATCH. PUT → Replaces the entire resource PATCH → Updates only what’s necessary Why does this matter? Because choosing the right method leads to: ✔ Cleaner API design ✔ Better performance ✔ Easier maintainability Frameworks may differ in style and complexity, but the foundation remains the same: HTTP. Master these basics once, and switching between Flask, FastAPI, and Django becomes much easier. What’s one concept in backend development that took you time to fully understand? #Python #WebDevelopment #APIDesign #BackendDevelopment #Flask #FastAPI #Django #HTTPMethods

As a long-time Java engineer, I continue to be impressed by how much Python has evolved. What once felt like a simple scripting language has grown into a remarkably capable ecosystem: C-backed libraries like NumPy, performance-oriented tooling in Rust, native coroutine support with async and await, and multiple concurrency models for very different workloads. One thing I find especially interesting is Python’s concurrency toolbox. Choosing the right model usually comes down to one question: What is your code actually waiting on? If your program is mostly waiting on the network, a database, or disk, you are likely dealing with an I/O-bound problem. In that case, asyncio can be a strong fit when the surrounding stack is async-native. If your program spends most of its time computing, parsing, or transforming data, you are likely dealing with a CPU-bound problem. In standard CPython, threads usually do not speed up pure Python CPU work because of the GIL. For that, multiprocessing is often the better fit. A few practical rules I keep in mind: • asyncio for high-concurrency I/O with async-native libraries • threads for blocking libraries or simpler concurrency • multiprocessing for CPU-heavy pure Python workloads • threads with native libraries when heavy work runs in C or Rust A good example is PyArrow and PyIceberg. PyArrow’s Parquet reader supports multi-threaded reads. That means you can get parallelism without rewriting everything around asyncio, because the heavy work happens in native code rather than Python bytecode. PyIceberg builds on this ecosystem. From the Python caller’s point of view, the workflow is still synchronous, while file access and data processing can benefit from native parallelism underneath. The key lesson for me: Not every high-performance I/O workflow in Python needs asyncio. If the underlying engine is native and already parallelizes efficiently, threads can be the right tool. If the stack is async-native, asyncio becomes much more compelling. A simple mental model: Async-native I/O → asyncio Native libraries parallelizing outside Python → threads CPU-heavy pure Python → multiprocessing Not sure → profile first That mindset is often more useful than memorizing any specific framework. #Python #Java #Concurrency #AsyncIO #Threading #Multiprocessing #Performance #SoftwareEngineering #DataEngineering

🔍 A difference I recently started noticing: CRUD developer vs Backend developer. CRUD developer: -> Creates models -> Writes basic views -> Makes things "work" Backend developer: -> Thinks about data flow -> Designs API structure -> Handles edge cases & failures Both can build features. But only one builds systems. Made me reflect: Am I just making things work… or actually understanding how they work? Still learning. But now I'm more aware. Which one are you striving to become? #Django #BackendDevelopment #Python #LearningInPublic

💡 𝗧𝗶𝗽 𝗼𝗳 𝘁𝗵𝗲 𝗗𝗮𝘆 — 𝗗𝗷𝗮𝗻𝗴𝗼 𝗗𝗶𝗱 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄? Django’s "annotate()" lets you add 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗳𝗶𝗲𝗹𝗱𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝘆𝘀𝗲𝘁𝘀. Instead of processing data in Python after fetching it, you can compute values at the 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗹𝗲𝘃𝗲𝗹. 🔧 𝗖𝗼𝗺𝗺𝗼𝗻 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: - Counting related objects ("Count") - Calculating averages ("Avg") - Adding computed fields to API responses This reduces data processing in your app and leverages the power of your database. Smarter queries = faster apps. #Django #Python #BackendDevelopment #WebDevelopment #DatabaseOptimization #PerformanceOptimization #SoftwareEngineering #CodingTips #FullstackDeveloper

🐍 Access Modifiers in Python — What Every Developer Should Know! Coming from Java or C++? You might expect Python to have strict private and public keywords. It doesn't — and that's by design. 🎯 Python uses a naming convention to signal access intent: 1️⃣ Public → self.name Accessible from anywhere. The default for all attributes and methods. 2️⃣ Protected → self._name Single underscore. A gentle signal for "internal use" — still accessible, but handle with care. 🔓 3️⃣ Private → self.__name Double underscore triggers name mangling → _ClassName__name. Harder (but not impossible) to access from outside. 🔒 💡 Python's philosophy: "We're all consenting adults here." It trusts developers to respect conventions rather than enforcing hard rules. Understanding this is key to writing clean, maintainable, Pythonic OOP code. #Python #PythonProgramming #OOP #ObjectOrientedProgramming #SoftwareEngineering #CodeNewbie #PythonDeveloper #Programming #TechLearning #CleanCode #100DaysOfCode #LearnPython #BackendDevelopment #DevTips #PythonTips

Day 7 of my Python Full Stack journey. ✅ Today's topic: Dictionaries — the most important data structure in Python. A dictionary stores data as key-value pairs. Think of it like a real dictionary — word (key) and its meaning (value). Here's what I typed today: student = { "name": "Punith", "age": 24, "course": "Python Full Stack" } # Access print(student["name"]) # Punith # Add / Update student["city"] = "Bangalore" # Loop through for key, value in student.items(): print(f"{key}: {value}") Why this matters for Django: Every Django model, every API response, every JSON data — all dictionary-like. If you understand dictionaries, Django will make sense. 60 minutes done. Pushed to GitHub. Day 8 tomorrow. One week and 2 days in. Still showing up. 💪 #PythonFullStack #Day7 #Dictionaries #BuildingInPublic #100DaysOfCode #Bangalore

Your unittest.mock is lying to you. Tests pass in CI, production breaks, and nobody knows why. The problem? Hand-written mocks drift from the real API silently. I've been contributing to the Microcks open-source ecosystem, and I want to share my latest work on Microcks Testcontainers https://lnkd.in/edzprW5k family for Python. Microcks Testcontainers takes a different approach: your OpenAPI spec becomes the mock. Load it into a Microcks container inside your test suite, and it: - Mocks your dependencies using spec-defined examples - Contract tests your implementation against the spec - Catches API drift automatically I also built a demo app with Flask showing the pattern end-to-end. Library: https://lnkd.in/eXyXwpnB Demo app (Flask): https://lnkd.in/eRhXfKeZ Full step-by-step guide: https://lnkd.in/eBvxUJy8 #Python #Testing #Microservices #API #OpenAPI