Md. Nur Islam’s Post

🚀 Building scalable Python apps just got easier: PyLine Core v3.0.0 is out! 🐍 Managing business logic shouldn't be a nightmare of tangled dependencies and side effects. That's why I built PyLine Core—a lightweight framework bringing Clean Architecture to Python with zero external dependencies. What's inside? ✨ CQRS & Mediator: Keep your reads (Queries) and writes (Commands) perfectly isolated. 🧱 Pipeline Orchestration: Seamlessly chain operations with shared context. 📻 [NEW] Async Event Bus: Full Publish/Subscribe EDA support! Decouple side-effects naturally using non-blocking background tasks. ⚡️ 100% Async & Type-Safe: Built for modern, high-performance Python. Stop wrestling with spaghetti code and start building modular backends. 📦 Install: pip install pyline-core 🔗 PyPI: https://lnkd.in/dQtqdQKt 💻 GitHub: https://lnkd.in/dNX3sbcG #Python #SoftwareEngineering #CleanArchitecture #CQRS #EventDriven #OpenSource #Asyncio

Pydantic will serialize your dataclass outputs from an MCP tool perfectly. It will not deserialize your dataclass inputs. No warning. No error at import time. Your type hints are valid Python. Your tests pass. Then production throws `AttributeError: 'dict' object has no attribute 'organization'` and you spend several hours wondering what you missed. It's not a bug. It's a deliberate design decision that makes complete sense once you understand it -- and makes no sense at all until you do. Post 2 of my FastMCP debugging series: the asymmetry that bites everyone eventually, and the pattern that fixes it cleanly. 👇 https://lnkd.in/gR5_GxMJ

🚀 What Happens When an API Call Fails? Network instability. Temporary downtime. Unexpected timeouts. API failures are not rare — they’re inevitable. The real question is: Is your application prepared for it? Instead of letting a transient failure break your workflow, a robust retry strategy can make your system more resilient and production-ready. That’s where Tenacity comes in. With tenacity in Python, implementing retries becomes clean, flexible, and powerful — all through an elegant decorator. 🔧 Why Tenacity? It allows you to: ✔ Define custom stop conditions ✔ Configure intelligent wait strategies (fixed, exponential backoff, etc.) ✔ Retry based on specific exceptions ✔ Handle retries for coroutines (async support) In short, it helps you build fault-tolerant and reliable systems with minimal effort. For developers building real-world applications, retry logic isn’t optional — it’s essential. 🔗 Explore it here: 👉 https://lnkd.in/dv8izbVU #Python #APIDesign #SoftwareEngineering #BackendDevelopment #DevTools #CodingBestPractices

Scaling Python testing isn’t just about adding more tests—it’s about keeping feedback loops tight as the codebase grows. In a large production environment with 2.5M+ lines of Python and 10,000+ tests, three metrics quickly become the pressure points: test execution time, reliability (flaky vs. consistent results), and coverage (signal quality). A practical workflow pairs Pytest + coverage reporting with CI gating (PR checks + required thresholds), treating tests as both a quality net and living documentation. When test runs started creeping past 30 minutes, the CI pipeline was optimized with a few high-leverage strategies: 1) parallelism on a single machine via pytest-xdist, and at a bigger scale, splitting across multiple runners using pytest-split—including duration-based balancing so slow tests don’t bottleneck one runner; 2) caching to cut dependency install time (pip cache keyed by requirements hash), plus faster installers like uv and prebuilt Docker images for heavy non-Python deps; 3) skipping unnecessary compute, e.g., only running certain jobs when Python files change, running linters only on touched files, and measuring coverage only for changed paths on PRs (then running full coverage on main); and 4) modern runners, including autoscaled self-hosted runners on Kubernetes/EC2 to improve price/performance. The results were tangible: the pipeline was brought down to <15 minutes, while coverage improved (e.g., moving from ~85% to ~95% over a year) without sacrificing PR safety. The operational reality also showed up: parallelization can surface flaky tests and shared-state conflicts, so retries, reporting to code owners, and quarantining blockers become part of keeping CI reliable as the suite grows. #Python #Testing #Pytest #ContinuousIntegration #CI #TestCoverage #DeveloperExperience

🚀 Built a structured Python engineering project focused on clean architecture, scalability, and real-world extensibility. It’s designed as a solid foundation that can evolve into automation systems, data pipelines, or AI-driven workflows. Check it out: 🔗 https://lnkd.in/dRDCdNW6 #Python #SoftwareEngineering #Automation #Backend #GitHub

Python packaging for binary extensions (C/C++) is finally getting more developer-friendly. The core shift is moving away from ad-hoc setup.py scripts (and the brittle “run python setup.py bdist_wheel and hope” workflow) toward modern, standardized builds where the build requirements are declared up front and tools can reliably produce wheels across environments. The old approach often ran into a classic chicken-and-egg problem: builds would fail because a build dependency wasn’t installed yet, and the build step itself would also “pollute” your current environment because it wasn’t isolated. The newer packaging flow (centered around pyproject.toml and isolated builds) addresses this directly—declare build dependencies, run the build in a clean environment, and get a reproducible wheel output that’s much easier to automate in CI/CD. If you maintain Python projects with compiled components, modern packaging is not just “new syntax”—it’s a structural improvement in reliability, repeatability, and onboarding. It reduces surprise failures, makes builds more deterministic, and sets a clearer foundation for future tooling improvements. #Python #Packaging #DevTools #OpenSource #SoftwareEngineering

Built a benchmark-focused Python logging implementation for high-concurrency workloads. Result (4 threads, 100K logs/thread, structured JSON): - rapidlog: 20,133 logs/sec - structlog: 12,101 logs/sec - stdlib-json: 6,487 logs/sec - loguru: 3,248 logs/sec That is 3.1x vs stdlib-json in this workload. Benchmark conditions: - Identical JSON output shape across libraries - Real output path (no null sink shortcuts) - Multi-threaded producer workload - Same message size profile per run Design that drives the delta: - Per-thread buffering on hot path - Async single writer for JSON serialization + batched writes - Minimal contention in producer threads Scope note: If you log <1K events/sec, stdlib is usually sufficient. This design targets high-throughput, multi-threaded services where logging becomes a measurable bottleneck. Repo + reproducible benchmark code: - GitHub: https://lnkd.in/gRFahDJW - PyPI: https://lnkd.in/gQxjeRSw - DEV.to post: https://lnkd.in/gMsdBT6W Interested in peer review of benchmark methodology and edge cases from engineers running Python services at scale. #Python #PerformanceEngineering #Backend #OpenSource #Benchmarking #SystemsEngineering #DevTools

🚀 #Day09 of 50 Days of Learning #Python through #Automation In Day 09, I built a practical automation project — generating files automatically using CSV data and Python. This project helped me understand how structured data can be used to create dynamic content like reports, emails, and documents. 📌 In this blog, I covered: ✅ What CSV (Comma-Separated Values) is with a simple example ✅ How Python reads CSV files using the built-in csv module ✅ How template-based file generation works ✅ Replacing dynamic values from CSV data into templates ✅ Automatically generating multiple output files ✅ A complete working Python script for CSV-based automation 💡 This automation is extremely useful for generating bulk documents, personalized messages, reports, and data-driven content. This beginner-friendly project shows how Python can automate repetitive tasks using structured data — an essential skill for real-world automation workflows. 👉 Read the full blog here: https://lnkd.in/giBpExzm #Python #Automation #CSV #PythonProjects #100DaysOfCode #PythonLearning #CodingJourney #Developer #DataAutomation

Build a Real-Time Weather Scraper Using Only 3 Lines of Modern Python Most people think scraping real-time data requires complex scripts, APIs, and heavy setup. Not anymore. With modern Python + clean syntax, you can fetch live weather data in just three lines: What this tiny script demonstrates: ✔ Calling a live endpoint ✔ Parsing JSON instantly ✔ Extracting only useful fields Why this matters This isn’t just a trick. It’s proof that modern developers win by combining: the right libraries API awareness clean logic Not by writing longer code. Because in real engineering, efficiency isn’t measured by lines written — it’s measured by problems solved. Minimal code. Real data. Production mindset. Question: What’s the most powerful thing you’ve built in under 10 lines of code? #Python #WebScraping #APIs #Programming #CodingTips #Developers #Automation #SoftwareEngineering

🧠 Python Feature That Makes Functions Pluggable: functools.partial 💻 Pre-fill arguments once… 💻 Reuse the function forever 😌 ❌ Repeating Yourself def power(base, exp): return base ** exp square = lambda x: power(x, 2) cube = lambda x: power(x, 3) Works, but feels hacky 😬 ✅ Pythonic Way from functools import partial power = lambda base, exp: base ** exp square = partial(power, exp=2) cube = partial(power, exp=3) Clean. Reusable. Intentional ✨ 🧒 Simple Explanation Imagine a juice shop 🧃 You decide orange once 🍊 Every glass after that is just “size?” That’s partial. 💡 Why This Is Powerful ✔ Reduces duplicate functions ✔ Cleaner callbacks ✔ Great for functional programming ✔ Used in real frameworks ⚡ Common Use Case from functools import partial import logging debug = partial(logging.log, logging.DEBUG) 🐍 Python lets you lock in decisions early. 🐍 functools.partial turns functions into reusable tools #Python #PythonTips #PythonTricks #Functools #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode