Mahesh Patil’s Post

Stop writing Python like Java/C++. Most tutorials get this wrong. They teach you to build APIs with rigid, verbose structures that feel more at home in compiled languages. Python offers a more fluid and powerful approach. The 'Pythonic' way is about embracing the language's dynamic nature and built-in features. Think about composition over deep inheritance, clear and concise function signatures, and leveraging data structures effectively. For scalable applications, clarity and maintainability are paramount. This means making your API intuitive, easy to understand, and simple to extend without unnecessary complexity. Example: Handling Configuration Okay (Java/C++ mindset): class Config: def init(self): self.db_host = "localhost" self.db_port = 5432 class App: def init(self): self.config = Config() def run(self): print(f"Connecting to {self.config.dbhost}:{self.config.dbport}") app = App() app.run() Best (Pythonic): from dataclasses import dataclass @dataclass class DbConfig: host: str = "localhost" port: int = 5432 def runapp(dbconfig: DbConfig): print(f"Connecting to {dbconfig.host}:{dbconfig.port}") config = DbConfig(host="prod.db.com", port=5433) run_app(config) Insight: * Dataclasses: Offer concise data structures with auto-generated init, repr, etc. * Function Arguments: Pass configuration directly as arguments, promoting loose coupling. * Readability: Much cleaner and easier to understand what data is needed. Designing clean Python APIs for scalable applications means writing code that is idiomatic, readable, and simple to maintain. #Python #CodingTips

🚀 Java vs Python in OOP: Key Differences Every Developer Should Know Object-Oriented Programming (OOP) is core to both Java and Python—but they implement it differently. Here’s a simple comparison 👇 🔹 1. OOP Approach - Java: Pure Object-Oriented (Everything inside classes) - Python: Supports OOP + Procedural + Functional (Flexible) 🔹 2. Class Declaration - Java: Strict structure, requires class keyword and main method - Python: Simple and minimal syntax 🔹 3. Access Modifiers - Java: public, private, protected (Strict enforcement) - Python: public, _protected, __private (Naming convention based) 🔹 4. Inheritance - Java: Supports single inheritance (multiple via interfaces) - Python: Supports multiple inheritance directly 🔹 5. Encapsulation - Java: Strong encapsulation using getters/setters - Python: Easier access, less strict 🔹 Example: Simple OOP Class Python: class Car: def __init__(self, name): self.name = name def display(self): print(self.name) c = Car("BMW") c.display() Java: class Car { String name; Car(String name){ this.name = name; } void display(){ System.out.println(name); } } 💡 Conclusion: ✔ Java → Strict, Secure, Enterprise-level OOP ✔ Python → Flexible, Faster Development, Easy to Use 📌 Java enforces OOP. Python enables OOP. #Java #Python #OOPS #Programming #Coding #Developers

Today’s dev headline: Python just joined the semantic refactoring party. Moderne announced Python support for OpenRewrite — extending its Lossless Semantic Tree (LST) model across Java, JavaScript, TypeScript, and now Python. For GitHub-heavy teams managing multi-repo, multi-language stacks, this is significant. Instead of: ▪️ Manually hunting deprecated APIs ▪️ Writing custom migration scripts ▪️ Fixing dependency drift repo by repo You can model code semantically and apply repeatable “recipes” across projects. Think: 🔄 Python version upgrades 📦 Dependency add/remove/replace 🔐 Cross-language vulnerability remediation 🧹 Consistent formatting and cleanup Modern systems rarely evolve in isolation. A Java service exposes an API. A Python integration consumes it. A shared library touches frontend and backend. Coordinated change is becoming a core competency. If you manage active GitHub orgs, this trend is clear: Refactoring is moving from manual effort to orchestrated campaign. Are your repos ready for multi-language modernization at scale?

For most of my career, I’ve worked extensively with Java. Recently, I decided to properly learn Python — not by watching tutorials, but by building real programs. Over the past few weeks, I worked through topics like: Strings, Lists, Dictionaries Functions, Scope, *args / **kwargs File I/O & Regex Modules & External Libraries OOP, Inheritance, Polymorphism Unit Testing Multithreading MySQL integration And I implemented 8 progressively complex projects — including: • Reading and processing PDFs • Extracting data using regex from config files • Storing structured questions in MySQL • Implementing polymorphism for multiple question types • Parsing RSS feeds using multithreading Coming from a Java background, this was eye-opening. Some reflections: ✔ Python dramatically reduces boilerplate ✔ Development speed is significantly faster ✔ OOP feels more flexible and less rigid ✔ Error handling is simpler and cleaner But at the same time: ✔ Java’s type safety provides strong guarantees ✔ Enterprise architecture patterns feel more mature ✔ Concurrency control is more explicit and powerful The biggest difference isn’t syntax. It’s philosophy. Java says: “Define everything clearly before running.” Python says: “Write it simply. Make it work.” I’ve written a detailed Medium article sharing my hands-on comparison, lessons learned, and when I would choose one over the other. If you’re a Java developer considering Python (or vice versa), this might be useful. https://lnkd.in/gJ5TWCiF Would love to hear from others who’ve worked with both — what differences stood out to you? #Python #Java #SoftwareEngineering #LearningJourney #BackendDevelopment #Programming #TechGrowth

Most tutorials get this wrong. When building high-performance backend services in Python, it's easy to fall into patterns that feel familiar from languages like Java or C++. But Python offers a more elegant and efficient path. The 'Pythonic' way to think about building fast backend services with FastAPI is to embrace its asynchronous nature and leverage Python's built-in concurrency features. Instead of blocking operations, think in terms of non-blocking I/O. This allows your server to handle many requests simultaneously without getting bogged down. "Okay" Approach (Synchronous, Blocking): from fastapi import FastAPI import time app = FastAPI() @app.get("/process") async def processdatasync(): # Simulating a blocking I/O operation time.sleep(5) return {"message": "Data processed (slowly)"} "Best" Approach (Asynchronous, Non-blocking with async/await): from fastapi import FastAPI import asyncio app = FastAPI() @app.get("/process") async def processdataasync(): # Simulating a non-blocking I/O operation await asyncio.sleep(5) return {"message": "Data processed (quickly)"} The key difference is async def and await. This tells Python to run these functions concurrently, meaning the server isn't stuck waiting for one request to finish before starting another. Insight: FastAPI is built around Python's asyncio library. Using async and await for I/O-bound tasks is crucial for unlocking its high-performance potential. Fix: * Define your endpoints with async def. * Use await for any I/O operations (database calls, external API requests, file operations). * Consider using libraries that support asyncio for I/O (e.g., httpx for HTTP requests, asyncpg for PostgreSQL). FastAPI lets you build performant Python backends by thinking asynchronously, not by writing imperative, blocking code. #Python #CodingTips

This is a little project or lab for Java. This is to learn how to define and initialize/assign a value to a variable. Java seems to be more specific than Python. With that said, if you want to define a variable and initialize a value of a string to that variable, you have to spell it out like String title(variable = (initialize) " Bands of Brothers"( a little grammatical error here, a string). Also, int is an integer just like in Python. However, when we get to assigning a float. If you assign a decimal number, it only allows 21 to 21, something like that. If you use the double "method", it gives you more digits, from my understanding. When you create a document, the public class has to match the file name as well. It's like bash, case sensitive, and similar to Python. Practice makes perfect! Happy learning everyone. #python #html #java #softwaredeveloper #softwareengineers #it #programming #softwaredevelopment

Who knew code written in Python could be faster than Rust? SQLGlot's compiled mode (pip install "sqlglot[c]") now matches or beats Rust-based SQL parsers across most benchmarks. Thanks to the amazing mypyc project, SQLGlot can compile down into efficient C, the only thing we needed to do was add type annotations! The speed comes from compiling the core parsing and expression modules with mypyc the same tool mypy uses internally. No Rust, no C, no FFI... just Python that gets compiled to efficient C extensions. The pure Python version (zero dependencies, works everywhere) is already faster than most alternatives. The compiled version closes the gap with native code entirely. Some highlights from the benchmarks (ratio vs pure Python sqlglot): - sqlglot[c]: 0.17x–0.33x (3–6x faster than Python runtime) - sqloxide (Rust): 0.15x–0.58x - polyglot-sql (Rust): 0.17x–0.44x - sqlparse (Python): 3.6x–19x slower - sqlfluff (Python): 40x–177x slower Full benchmark suite with 16 query types is in the repo. Numbers are on Python 3.14. What I find most interesting is that this disproves the common assumption that you need to rewrite in Rust to get fast performance in Python. A well structured Python codebase compiled with mypyc gets you incredibly far.

💡Looking at how different teams perform with Python and Java, one insight stands out: language choice matters less than aligning the technology with the product’s architecture, constraints, and growth trajectory. Java brings structure, predictability, and long-term stability – qualities that matter hugely in enterprise systems, financial platforms, and high-load environments. When consistency under pressure is non-negotiable, Java delivers that reliability every time. Python, on the other hand, accelerates everything around development itself.  • The speed of iteration.   • The pace of experimentation.  • The ability to turn complex ideas into working features with minimal friction. I’ve seen Python unlock rapid progress in data-heavy projects, AI-driven platforms, and fast-evolving products where adaptability is just as important as performance. And I know how Java can anchor critical systems that need to run flawlessly for years. What's interesting is that companies rarely choose “Python or Java.” They choose the language that moves that specific piece of the product forward. ✨ 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘀𝗽𝗲𝗲𝗱.  𝗝𝗮𝘃𝗮 𝗳𝗼𝗿 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲. Different strengths are both essential, depending on the mission. If you want a more detailed breakdown of how they compare in performance, syntax, scalability, and ecosystem maturity, we recently published a full guide: https://lnkd.in/drGmi827 ?utm_source=LinkedIn&utm_medium=post&utm_campaign=LiPostsOct25&utm_id=LiPostsOct25 Curious to hear your thoughts 🤔 Which language has served you better in real projects, and why? Have you seen Python or Java outperform expectations in unexpected ways? 𝗜’𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗿𝗲𝗮𝗱 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀 💬👇

Understanding Python: Interpreted vs. Compiled Languages I've been working with Python lately, and it's made me think about the fundamental differences between interpreted and compiled languages. Compiled Languages (C++, Java, Go): - Code gets translated into machine code before you run it - Faster execution - Catches errors during compilation - Creates platform-specific executable files Interpreted Languages (Python, JavaScript, Ruby): - Code runs line-by-line in real-time - More flexible for development - Works across different platforms without changes - Easier to test and debug quickly What I've learned using Python: The interpreted approach means slightly slower execution, but you gain a lot in return. Development is faster, debugging is straightforward, and you can run the same code on different systems without modification. For most projects, the development time you save far outweighs any performance concerns. And when you do need speed, there are tools like Cython or you can integrate C libraries. It's all about choosing the right tool for the job. Python's flexibility has made it my go-to for most tasks.

🛠️ If you’re troubleshooting Kubernetes pod restarts, understanding the difference between Java Metaspace and Python memory is a game-changer. 🛠️ Here’s the breakdown: ✅ Java (JVM) Pods: They use #Metaspace to store class metadata. If your pods are crashing with #OutOfMemoryError: Metaspace, increasing this limit usually stabilizes them. "Not restarting" is the goal—it means your app is finally stable! 🚫 Python Pods: Python doesn’t have Metaspace. Everything lives in a private heap. If a Python pod restarts, it’s usually because it hit the Kubernetes Memory Limit and got OOMKilled. 💡 Key Takeaway: Java: Check Metaspace + Heap settings. Python: Profile your heap and check K8s limits. Both: CPU limits won’t kill your pod; they just throttle (slow down) your app. Stability isn't just about throwing more RAM at the problem—it's about knowing where that memory is going. 🚀 #Kubernetes #Java #Python #DevOps #SRE #CloudNative