Why Python Performance Matters for Developers

Why Performance Matters in Python Development Python is loved for its simplicity and flexibility, but when it comes to performance, it still poses challenges for real-world systems. Here are some key insights from JetBrains’ latest article: • Python’s interpreted nature introduces runtime overhead compared to compiled languages. • The Global Interpreter Lock (GIL) limits multithreading for CPU-bound workloads. • Poor data structures and lack of profiling often hurt performance more than the language itself. • Performance directly affects cost, scalability, and user experience. • Optimization helps developers work faster and keeps the code easier to maintain. Read the full article: https://lnkd.in/dP4WU5PS #Python #Performance #SoftwareEngineering #Optimization

You've been learning Python for a while now. You’ve done loops, classes, functions, and even built a few small scripts. But when someone says, “Build a backend for this app,” you freeze. Here's why this happens to so many Python devs and how to fix it. The Python programming language isn’t the problem. Your mindset is. You’ve been learning language mechanics instead of systems thinking. Backend engineering isn’t about knowing every Python trick — it’s about connecting concepts to solve real-world problems. Example: You know how to make HTTP requests. But do you know how to: - Design REST endpoints? - Handle authentication tokens? - Manage rate limiting and errors? - Version your API? That’s what real backend work looks like. Or take databases. You can write SELECT * FROM users, sure. But backend engineers: - Design efficient schemas - Add constraints and indexes - Handle transactions safely - Think about scaling and caching This is the “invisible work” that makes apps reliable. Same with authentication. Beginners think it’s just login/signup. But engineers design: - Secure password hashing - JWT or OAuth-based sessions - Role-based permissions - Token expiry and refresh logic You can’t skip these if you’re building for real users. The biggest mistake Python learners make? They learn sequentially (syntax → functions → classes) instead of contextually (project → problems → solutions). You don’t grow by memorizing syntax. You grow by building and debugging systems. Real backend engineering looks like this: - Designing APIs that talk to databases - Handling errors gracefully - Writing tests before deployment - Monitoring logs after release In short, you’re building for reliability, not just “it works.” And here’s the secret no one tells you — backend engineers don’t know everything. They just know how to think about trade-offs. - SQL or NoSQL? - Async or sync? - Docker or bare metal? Every decision impacts scalability, cost, and complexity. That’s the skill you’re missing. You can’t learn this from random tutorials. You need structured, real-world projects that simulate actual backend challenges, the kind where every choice matters. That’s how you go from Python developer → backend engineer. That’s exactly what “Become A Python Backend Engineer” was designed for. You’ll master backend architecture, APIs, databases, and deployment — all in Python. Stop coding in fragments. Start building systems. 👉 https://lnkd.in/d5tahN8C

If you’ve been learning Python for months but still can’t build a real backend, the problem isn’t your code. It’s your approach. Let’s talk about the difference between learning Python and learning backend engineering 🧵 Python is a language. Backend engineering is a discipline. You can master every syntax trick, use every library, and still fail to build something that scales. Because backend work isn’t about how to code, it’s about how to design systems. Here’s the truth: backend engineers think in flows, not features They don’t just ask “How do I write this function?” They ask “How will data move from the user → to the API → to the database → back to the user, safely and efficiently?” That’s a different level of thinking A backend engineer’s job is to make things invisible work: - APIs that never break - Servers that scale under pressure - Databases that stay consistent - Logs that tell the truth when something fails If no one notices your system, you’ve done your job well. But here’s the mistake most Python learners make: They focus on tools instead of concepts. You can use Flask, Django, or FastAPI, but if you don’t understand HTTP, caching, security, and data modeling You’re just stitching libraries together without understanding the machine. Backend engineering is a craft of trade-offs - Should you use REST or GraphQL? - SQL or NoSQL? - Async or sync? - Microservices or monolith? There’s no single “right” answer, only design decisions that fit the context And learning that judgment is what makes you valuable So if you feel stuck even after “finishing” Python, you’re not missing knowledge; you’re missing application. You need to stop coding for output and start coding for architecture. - Stop asking “Does it work?” - Start asking “will it scale?” The path forward is simple but not easy: - Pick real backend problems. - Build them end-to-end. - Debug your failures. - Learn from production-like challenges. That’s how you grow from Python learner → backend engineer. That’s exactly why we built “Become A Python Backend Engineer.” It’s not about syntax drills, it’s about building production-level systems step by step APIs, databases, auth, testing, deployment — everything that makes a backend engineer real Stop chasing new frameworks. Start mastering systems. Because the world doesn’t need more Python coders, it needs engineers who can build things that last 👉 https://lnkd.in/d5tahN8C

Python Frontier: What Every Dev Needs to Learn Now Python isn’t just surviving — it’s thriving. The language is rapidly evolving into a more structured, performant, and deeply integrated ecosystem. If you’re a Python developer, standing still means falling behind. The next frontier of Python demands new capabilities — skills that go beyond syntax and scripts, into architecture, performance, and production readiness. Here are the three must-master areas to future-proof your Python career in the coming decade. Master Modern Concurrency If your Python experience is limited to synchronous code, you’re only using half of what the language can offer. Tool Best For Key Concept Read Extra : Here Action Item: Learn the async/await syntax. Experiment with async-native web frameworks like FastAPI or Tornado. Integrate async libraries such as httpx or async-compatible database drivers. Understand when to offload CPU-heavy code using multiprocessing — that’s the mark of a performance-aware Python developer. Embrace Static Typing and Pydantic https://lnkd.in/gaKfW5aw

You’ve mastered Python basics. Variables, loops, functions, etc But when you try to build something real, an API, a user system, a database-backed app, you freeze Why? Because learning Python is not the same as learning Backend Engineering Let’s fix that 👇 Backend engineering is about how data flows through systems, not just writing code that works It’s about: - Structuring databases that scale - Designing APIs others can trust - Managing concurrency & background jobs - Handling authentication & permissions - Deploying to production safely Python is just the language. Backend is the craft. Most tutorials skip the real-world parts. They teach you how to “print(‘Hello World’)” but not how to handle 100k API requests without breaking. They ignore: - Caching layers - Security best practices - Logging & monitoring - Database migrations - Error handling in distributed systems That’s what you actually need for a job. The only way to master backend is by building real systems. When you design an API from scratch, connect it to a database, handle edge cases, and deploy it. That’s when you understand how everything connects. Backend engineering is learned by doing, not watching. That’s why the "Become a Python Backend Engineer" course exists to bridge that gap It teaches you the backend layer behind every successful app -APIs -Databases -Auth -Scaling -Deployment You won’t just “know Python.” You’ll build real backend systems https://lnkd.in/d5tahN8C

Increase your confidence in your Python code's correctness. Tom Reid's article covers 5 real-world examples of using Hypothesis to uncover subtle bugs. Read the full article now without the paywall.

🔍 Python's Hidden Gem: Short-Circuit Evaluation with Operand Return Ever wondered why Python's "or" and "and" operators are more powerful than they seem? Unlike Java or C++, they don't just return True/False – they return the actual values! Short-circuit evaluation means that logical operators like and and or stop evaluating as soon as the result of the expression is known. In Python, these operators don’t just return True or False; they actually return the operand that determined the result — that’s what is meant by operand return. Python's logical operators ("or" and "and") return one of the actual operands, not necessarily True or False. The "or" Operator - - Returns the first truthy value it encounters - If all values are false, returns the last value The "and" Operator in Python - - Returns the first false value it encounters - If all values are truthy, returns the last value ``` # OR examples print(None or [1, 2, 3])   # [1, 2, 3] - returns first truthy print("hello" or "world")   # "hello" - returns first truthy print(False or 0 or [] or {}) # {} - all falsy, returns last print(42 or False)       # 42 - returns first truthy # AND examples   print([1, 2] and "text")    # "text" - all truthy, returns last print("text" and None)     # None - returns first falsy print(5 and 10 and 20)     # 20 - all truthy, returns last print(0 and [1, 2, 3])     # 0 - returns first falsy # Check if user exists AND has valid credentials OR is admin user = get_user(username) access_granted = (user and user.password == password) or is_admin # Returns: user object if password matches, False if not, or True if admin ``` False Values in Python (the complete list) : False None 0 (integer zero) 0.0 (float zero) 0j (complex zero) "" (empty string) [ ] (empty list) ( ) (empty tuple) { } (empty dict) set( ) (empty set) range(0) (empty range) Truth Values in Python (everything else!) : True Any non-zero number: 1, -1, 3.14, 2+3j Any non-empty string: "hello", "0", " " (even a space!) Any non-empty collection: [1, 2], (1,), {"a": 1} Any object or instance (by default) Functions, classes, modules ⚡ Why This Matters: - Reduces code complexity - Eliminates nested if-else pyramids - Makes default value handling elegant - Improves code readability dramatically #Python #CleanCode #CodingTips #SoftwareDevelopment

Do you still think Python is slow? Or do you just want to make your Python code faster? Whether you're processing large datasets, developing real-time systems, or improving computational efficiency, optimizing Python code for speed can make a huge difference in achieving better results. 👉 Read how to do it here: https://lnkd.in/dhei_iXq Thanks to JetBrains, I had the opportunity to write this article, and I hope everyone finds something useful to enhance their code performance! #python #coding #programming #performance

🐍 Cracking the Python Concurrency Code: Threading vs. Multiprocessing & the GIL Navigating concurrency in Python can be tricky, primarily due to the infamous Global Interpreter Lock (GIL). Understanding whether to use the threading or multiprocessing module is crucial for writing efficient code. Here is a quick guide to help you decide: 🧵 Threading: The "Concurrent" Approach Threads share the same memory space, but due to the GIL, only one thread runs Python bytecode at a time within a single process. It’s about concurrency (rapid switching), not true parallelism. ✅ Best for I/O-Bound Tasks: Tasks where your program spends time waiting for external operations. Examples: Network requests (APIs, web scraping), file I/O, database queries. The GIL is released during these wait times, allowing other threads to run. ❌ Not for CPU-Bound Tasks: Won't speed up heavy math or data analysis because the GIL prevents true parallel execution on multiple cores. 💻 Multiprocessing: The "Parallel" Approach Multiprocessing creates entirely separate Python processes, each with its own memory space and its own GIL. ✅ Best for CPU-Bound Tasks: Tasks that rely heavily on computation and can use multiple CPU cores simultaneously. Examples: Data science computations, video rendering, heavy mathematical simulations. ❌ Higher Overhead: Slower to start and use more memory than threads. Communication between processes is more complex (via queues/pipes). 🤔 The Lock Confusion: Shared Memory & The GIL Threading allows memory sharing, but the GIL keeps the internal interpreter state safe. You still need locks for your own application logic to avoid race conditions. Multiprocessing uses isolated memory (no locks needed by default). If you intentionally use shared memory objects, then locks are required for safety. 💡 Key Takeaway: A lock only creates a temporary, synchronous bottleneck (a "critical section") to protect shared data. It doesn't make your entire application synchronous; the rest of your code still runs concurrently/in parallel. If you want to speed up your Python code: 👉 Use threading for latency hiding (I/O). 👉 Use multiprocessing for pure computation (CPU). #Python #Concurrency #Threading #Multiprocessing #GIL #PythonProgramming #CodeTips #WeekendLearning #Learning #Revision #MultipleThreading #ArtificialIntelligence #GenerativeAI