Log Analyzer in Python for SRE and DevOps

Improved my Log Analyzer project with more practical SRE features 🚀 After building a basic log analyzer, I enhanced it to make it more flexible and closer to real-world usage. 🔧 What’s new: • Added CLI-based input handling for dynamic log file analysis • Implemented Top N error detection using Python’s Counter • Improved output formatting for better readability 💡 Why this matters: In real systems, logs are large and constantly changing — tools need to be flexible and configurable. This update helped me understand how to make scripts more production-ready. 🔗 Updated Repo: https://lnkd.in/dEZyK7qH Next step: planning to add regex-based parsing and error spike detection. Would love your feedback! #SRE #Python #DevOps #Observability #LearningInPublic #GitHub

I’ve published my first technical article: a walkthrough of the SOLID principles—with Python examples. It started as “I’ve heard these letters everywhere—what do they actually mean in code?” Turning that into something concrete helped me more than skimming another diagram. In the post I break things down into bite-sized pieces, including: • Single Responsibility: One job per module—easier to reason about and change. • Open/Closed: Extend behavior without rewriting existing code. • Liskov Substitution: Subtypes that don’t break expectations. • Interface Segregation: Small, focused contracts instead of fat interfaces. • Dependency Inversion: Depend on abstractions, not concrete details. Beyond the theory, each section includes short Python snippets so the ideas map to something you can run and tweak—not just memorize. The full post is here: https://lnkd.in/gFXSE4d9 #SoftwareEngineering #SOLID #Python #CleanCode #OOP #DesignPatterns

The moment you add a new condition to an if/else chain in your pipeline, you’ve modified existing code! That’s exactly the smell OCP is meant to catch. 🔍 Open for extension, closed for modification means adding new behavior by writing new code, not patching old one! In my latest article, I break down what OCP looks like in Python data projects: strategy patterns, abstract base classes, and the tradeoffs of when the extra structure is actually worth it. https://lnkd.in/eE9yicvC This is Part 2 of my SOLID series. What’s the most painful “just add another elif” you’ve ever had to maintain? 😅 #Python #SoftwareEngineering #SOLID #DataScience #CleanCode

𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗹𝗲𝘀𝘀𝗼𝗻𝘀, 𝘂𝗻𝗳𝗶𝗹𝘁𝗲𝗿𝗲𝗱 I added logging to my Python app and thought I was done. Two lessons later — I wasn't even started. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟭 : 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴 𝗳𝗶𝗹𝗲 𝗶𝘀 𝗮 𝘁𝗶𝗰𝗸𝗶𝗻𝗴 𝗯𝗼𝗺𝗯 No size limit. No rotation. No cleanup. It just grows. Forever. Dev - fine. Production - it silently eats your disk until your app is down at 2AM. One swap in Python's standard library fixes this. Max size. Backup count. Auto-rotates. Auto-deletes oldest. That's it. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟮 : 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴𝘀 𝗮𝗿𝗲 𝘂𝗻𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 🔍 Human-readable text is perfect for dev. In production with thousands of requests ⁉️ Useless. Switch to JSON logs. Every line becomes a structured event : timestamp, level, request_id, user_id. Want all errors from one specific request? One query. Done. No regex. No grepping through walls of text. The difference between logging and logging for production is bigger than I thought. That gap - that's what this series is about #Python #Backend #LearnInPublic #SoftwareEngineering

I built a Python script that organized my entire Downloads folder in under 3 seconds. Here's the embarrassing truth : my Downloads folder had 100+ files. PDFs, random images, zip files, old code. All just sitting there. A complete mess. So instead of cleaning it manually (again), I wrote a Python script to do it for me. What it does: → You point it at any folder → It scans every file → Sorts them into Images, Videos, Documents, Code, Archives → Shows a preview before touching ANYTHING → Logs every move with a timestamp What I learnt building this: → pathlib makes file/folder handling so much cleaner than I expected → sys.exit() is how you kill a program gracefully instead of just crashing → mkdir() creates folders on the fly if they don't exist in a single line The part I'm most proud of is that it asks for confirmation before moving anything, so you never accidentally mess something up. Full code on GitHub: https://lnkd.in/gCwyzJAv 🤘 #Python #Automation #7DaysOfPython #100DaysOfCode

Pydantic is a Python library that helps you: 👉 Validate data automatically 👉 Convert data types safely 👉 Define clear data structures Before Pydantic, these were the problems: - You forget validation → bugs - Wrong types → crashes - Messy code everywhere With Pydantic: - Converts "25" → 25 - Validates data - Throws error if invalid Pydantic is heavily used in: - APIs → with FastAPI - Data pipelines - Backend systems - Config validation These are some of the advantages of Pydantic. #Python #Pydantic #datavalidation #LLMs

𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝗳𝗲𝗲𝗹𝘀 𝘀𝗹𝗼𝘄 𝗱𝗲𝘀𝗽𝗶𝘁𝗲 𝘂𝘀𝗶𝗻𝗴 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗵𝗿𝗲𝗮𝗱𝘀 ? The secret lies in how Python handles execution. I’ve put together a 12-slide deep dive into Python Concurrency, moving from absolute basics to the future of Python 3.13. What’s inside? ✅ Synchronous vs. Async: Why "𝘄𝗮𝗶𝘁𝗶𝗻𝗴" is the biggest bottleneck. ✅ The Event Loop: How 𝗮𝘀𝘆𝗻𝗰𝗶𝗼 manages thousands of tasks on a single thread. ✅ The 𝗚𝗜𝗟 (𝗚𝗹𝗼𝗯𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗲𝗿 𝗟𝗼𝗰𝗸): Why traditional Python threading isn't always "parallel." ✅ The 𝗙𝘂𝘁𝘂𝗿𝗲 (𝗙𝗿𝗲𝗲-𝗧𝗵𝗿𝗲𝗮𝗱𝗶𝗻𝗴): How Python 3.13+ finally enables true multi-core parallelism. 🟪 𝗧𝗵𝗲 "𝗞𝗶𝘁𝗰𝗵𝗲𝗻" 𝗔𝗻𝗮𝗹𝗼𝗴𝘆: Think of a single cook (Thread) multitasking between a gas stove (I/O) and a cutting board. That’s Async. Now imagine a kitchen with multiple cooks and multiple gas stoves. That’s Modern Free-Threading. Whether you're building 𝘄𝗲𝗯 𝘀𝗰𝗿𝗮𝗽𝗲𝗿𝘀 (𝗜/𝗢-𝗯𝗼𝘂𝗻𝗱) or 𝗵𝗲𝗮𝘃𝘆 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 (𝗖𝗣𝗨-𝗯𝗼𝘂𝗻𝗱), choosing the right model is key to performance. Check out the slides below! #Python #Programming #SoftwareEngineering #Concurrency #AsyncIO #Multithreading #Python313 #TechLearning

Most data isn’t behind a paywall… it’s behind a login. 🔐 And that’s where many scraping workflows stop. This video shows how to scrape authenticated pages using Python, with Crawlbase supporting reliable data extraction so you can access what’s actually behind the login, not just what’s publicly visible. What you will learn: 🔹 How to handle login based scraping workflows 🔹 How session management works in real scenarios 🔹 Why authenticated scraping is critical for real data access 🔹 How Crawlbase supports more consistent data extraction If you’re working with protected data sources, this is a practical walkthrough worth watching. 👉 Watch full video: https://lnkd.in/gxfJPxGH #Crawlbase #WebScraping #Python #Automation #DataEngineering #Developers #APIs #TechTools #PythonTutorial

𝐃𝐚𝐲 𝟏𝟏: 𝐒𝐭𝐨𝐩 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠, 𝐒𝐭𝐚𝐫𝐭 𝐅𝐢𝐧𝐝𝐢𝐧𝐠  I’ve noticed as I move deeper into Python, is that how we 𝐬𝐭𝐨𝐫𝐞 𝐝𝐚𝐭𝐚 is just as important as the focusing on the code logic. Up until now, I’ve been reaching for a 𝐋𝐢𝐬𝐭 for almost everything. It’s the go-to structure when you start. But as my data grows,I realized why that can be a trap. 👉 If you have 1,000 items, a List forces you to check every single one until you find a match. • With a 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲, I’m not just piling data into a box; I’m giving every piece of data a unique Key. 👉𝐓𝐡𝐞 𝐋𝐢𝐬𝐭 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 (𝐦𝐲𝐥𝐢𝐬𝐭) : Iterating through the stack until you hit "Bob." 👉𝐓𝐡𝐞 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 (𝐦𝐲𝐝𝐢𝐜𝐭) : Direct access via a unique Key. Instant results✅ A Dictionary is a completely different, and much more efficient, tool for the job. It’s a fundamental shift in how we handle data retrieval. #Python #30DaysOfCode #Day11 #LearningInPublic #SoftwareEngineering

#Learnings File operations are really helpful especially for csv and json files. We perform various operations read, write in normal file manager too, but Python allows multiples diverse logics along with various data structures that make it an effective way to perform such operations. Here is a glimpse of a similar code shared below based on file operations: https://lnkd.in/gjFbX85v