Optimize Database Queries Before Code for Faster Apps

🔍 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

Node.js vs Python: Which Backend Should You Learn in 2026? In 2026, Node.js excels in real-time, scalable applications with JavaScript ecosystems, while Python dominates AI, data science, and rapid development. Choosing the right backend depends on your project needs, performance goals, and career focus. For a deeper dive, check out the complete blog on our website. https://lnkd.in/dyCrb9Uq #Nodejs #Python #BackendDevelopment #WebDevelopment #FullStackDevelopment #JavaScript #Coding #Programming #TechTrends2026 #BackendFrameworks #LearnToCode #ManifestInfotech #DeveloperCommunity #NodejsVsPython #SoftwareDevelopment #TechBlog #FutureOfCoding #WebDevelopers #CodingLife #ManifestInfotech

While digging deeper into environment setup, I noticed an interesting difference between Python and Node.js: Python installs packages globally by default. Unless you create a virtual environment, all dependencies go into a shared global space. Node.js installs packages locally by default. Every project gets its own node_modules directory. At first, this made me think: → Python focuses more on isolation and optimization → Node.js doesn’t care as much But that’s not completely true. Both ecosystems solve the same problem — dependency management — but in different ways: Python → requires you to explicitly create isolation (virtual environments) Node.js → gives you isolation by default (per-project dependencies) Trade-offs: Python → cleaner environments, but extra setup Node.js → easier start, but larger project size and duplication Key insight: It’s not about which is better — it’s about understanding the design decisions behind each ecosystem. This kind of detail matters when you switch between stacks or design scalable systems. #Python #Nodejs #Backend #SoftwareEngineering #LearningInPublic

🐍 Python Developer Nuggets — Day 17 Retries in APIs Why do APIs fail even when everything looks correct? Because failures happen… and your system doesn’t retry smartly. The problem: Request fails once and stops User sees an error Even though it might have worked on retry The solution: Retry the request Add delay between retries Limit number of retries request = call_api() # retried on failure What changes: First attempt → may fail Retry → succeeds Better user experience Fewer visible failures Don’t retry everything Retry only when: Retry when: Network issues Timeout Temporary failures Don’t retry when: Wrong input Validation errors Golden rule: Retry only when failure is temporary Retry turns small failures into success #Python #Django #BackendEngineering #SystemDesign #CleanCode #Performance #DeveloperTips

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

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

💡 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

14/30 Performance Post 7: Async Processing Nobody likes a loading spinner. If your API is waiting for an email to send or a PDF to generate before responding to the user, you’re creating a bottleneck. Asynchronous Processing is the secret to building "snappy" apps that handle heavy workloads without breaking a sweat. Swipe through to see how to move your slow tasks to the background and keep your main thread fast! #BackendDevelopment #SystemDesign #SoftwareEngineering #Async #RabbitMQ #NodeJS #Python #LearnInPublic

Python Reimplementation of Claude Code Agent Now Available for Local Models 📌 Claw Code Agent lets devs run Claude-style coding agents locally-no cloud subscription needed. Built in pure Python, it strips away enterprise bloat for lean, open-source control over tool calls, file edits, and shell commands. Perfect for Python engineers wanting to experiment with agentic workflows using local models like Qwen3-Coder. 🔗 Read more: https://lnkd.in/d7SNVJ4f #Python #Claudecodeagent #Clawcodeagent #Localmodels #Opensource

20+ years of business in a nutshell: Details matter. In Python, [row] * 2 is a silent trap. Senior developers catch this immediately; juniors spend hours debugging "ghost" data updates. Don't just write code—understand the "game beneath the game." #PythonDeveloper #CleanCode #SoftwareArchitecture #ProgrammingTips #CareerGrowth