Configuring Python Logging for Third-Party Modules

Virtual environments isolate Python dependencies at the project level, preventing version conflicts and keeping experiments contained without affecting system-wide installations. By Jessica Wachtel

Python virtual environments: isolation without the chaos. Virtual environments isolate Python dependencies at the project level, preventing version conflicts and keeping experiments contained without affecting system-wide installations.

Just read our Python + DCS blog and I finally understand why our engineering team keeps pushing DCS over open source conversion libraries. The reliability angle for production systems makes total sense when you see all the edge cases listed out.

Day 30 of 150: System-Level Automation with Python and Wget Reaching the 20% milestone of this challenge by bridging the gap between Python and system-level utilities. Today’s focus was on using Python as a wrapper for Wget to build a high-performance, versatile downloader capable of "scraping and downloading anything" from the web. Technical Focus: Subprocess Management: Utilizing the subprocess module to execute system-level wget commands directly from within Python scripts. Recursive Mirroring: Implementing wget flags (like -r and -np) to mirror entire directory structures while preventing the scraper from "wandering" to external domains. Dynamic Argument Parsing: Building a logic layer to translate Python variables into shell commands, allowing for flexible file-type filtering (e.g., only downloading .pdf or .iso). Process Monitoring: Handling standard output (stdout) and error (stderr) streams to track download progress and manage network timeouts programmatically. By combining Python’s logic with Wget’s robust network engine, I’ve moved from simple scraping to building industrial-strength data acquisition tools. 120 days to go. #Python #Automation #DevOps #SystemProgramming #150DaysOfCode #SoftwareEngineering

🚀 From print() to Proper Logging in Python While building backend systems and data pipelines, I recently made a small but important improvement in my code. Instead of using print() statements for debugging and tracking execution, I switched to a proper logging system using Python’s built-in logging module. Here’s what I implemented: ✅ Created a reusable get_logger() function ✅ Logs are saved to a file (logs/pipeline.log) ✅ Logs also appear in the console for real-time monitoring ✅ Added structured log format with timestamp, module name, level, and message Why this is better than print(): Helps track issues in production environments Keeps a history of application events Makes debugging easier in complex systems Provides structured and readable logs This small change makes backend applications much more production-ready and maintainable. One thing I’ve learned while building backend systems: Good logging is as important as good code. Do you still use print() for debugging or have you fully switched to logging? #Python #BackendDevelopment #Logging #SoftwareEngineering #CleanCode #PythonTips

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

🚀 API Automation — File Upload & Download using Python File Upload and Download APIs using Python automation. Using httpbin, I automated: ✅ File Upload using multipart/form-data ✅ API Response Validation ✅ File Download using GET API ✅ Binary file handling in Python ✅ Status Code validation 🔧 Tech Stack Python Requests Library REST API Automation Backend Testing 📌 Upload API Automation import requests upload_url = "https://httpbin.org/post" files = {"file": open(r"C:\Users\JC\Pictures\Saved Pictures\2.jpg", "rb")} response = requests.post(upload_url, files=files) print(response.status_code) 📌 Download API Automation download_url = "https://lnkd.in/drJK5Jcu" download_response = requests.get(download_url) with open("download.jpg", "wb") as f: f.write(download_response.content) 💡 Key Learning ✔ Handling multipart file uploads ✔ Working with binary file downloads ✔ API validation using status codes ✔ Real-world backend automation workflow #APIAutomation #Python #BackendTesting #SoftwareTesting #RESTAPI #AutomationTesting #QAEngineer #Playwright #LearningJourney #TestAutomation

Python Conditional Statements Explained: if, elif, else, and Logical Operators Conditional statements are the backbone of decision-making in Python. They allow your code to evaluate conditions and execute different logic paths based on real-time data, user input, or program state. Python relies on indentation, not braces, to define code blocks—making readability and structure critically important. Using if, elif, and else, you can handle multiple scenarios cleanly without deeply nested logic. Comparison operators such as ==, !=, <, <=, >, and >= enable precise condition checks, while logical operators (and, or, not) let you combine or negate conditions for more expressive rules. Well-written conditionals improve code clarity, reduce bugs, and make business logic easier to maintain—especially in real-world applications like validations, workflows, access control, and data processing pipelines. Mastering Python conditionals is essential for progressing into loops, functions, error handling, and advanced application logic. #Python #PythonConditionals #IfElse #ProgrammingBasics #CleanCode #SoftwareDevelopment

Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data Efficiently are so easy to use that it’s also easy to use them the wrong way, like holding a hammer by the head. The same is true for Pydantic, a high-performance data validation library for Python. In Pydantic v2, the core validation engine is implemented in Rust, making it one of the fastest data validation solutions in the Python ecosystem. However, that performance advantage is only realized if you use Pydantic in a way that actually leverages this highly optimized core....

With the addition of Python, Moderne customers will now be able to coordinate their modernization efforts across a greater proportion of their codebases. https://lnkd.in/exNEDt4m SD Times Moderne