Romulo Thomaz Lima’s Post

Python as a backend isn't a technical decision. It's a decision made by someone who learned Python first and never questioned whether it was the right tool. You get latency, concurrency limited by the GIL, and a web framework ecosystem that holds up because "everyone uses it." That's not architecture — that's collective inertia. If your backend is Python and it's not an ML script or data pipeline, you probably made the wrong call. And the worst part? You never even asked yourself the question. Fight me.

Your Python isn’t slow. Your data model is. Most developers chase faster libraries or rewrite code. But the real bottleneck? Invisible overhead between your code and the machine. I cut a batch job from 10 minutes -> 90 seconds without concurrency. Just by: - replacing a dict with a slots-based structure - pre-allocating a list Less memory churn. Fewer cache misses. CPU finally did real work. Two facts most people ignore: - A Python int isn’t just a number, it’s ~28 bytes of object overhead - A dict lookup is fast, but still far heavier than array-style access In tight loops, that overhead > actual computation. That’s why switching to typed arrays (or minimal C paths) feels like a massive speedup, same logic, different cost model. My rule: Don’t optimize algorithms first. Optimize how data moves. - reduce allocations - batch work - keep data contiguous Measure with real data. Then optimize where it actually hurts. #Python #Performance #Engineering #Optimization

Why and when should we use Python? 🤔 For me, Python is not just a programming language — it’s an ecosystem that turns ideas into real products, fast. The key is understanding where it delivers the most value: 🔹 Data → Insight (when dealing with large datasets) Transforming raw data into real, actionable decisions. 🔹 Machine Learning (when intelligence is a priority) From prototype to production — rapidly building AI-powered systems. 🔹 Web & APIs (when speed matters) FastAPI / Django — for building fast, scalable backends. 🔹 Automation & Scripting (when time = resource) If it can be automated — it should be automated. 🔹 Glue Layer (when systems need to be connected) Bringing different technologies together into a single product. 💡 Python is the right choice when your priorities are speed, flexibility, and fast time-to-market. 🚀 #Python #SoftwareEngineering #MachineLearning #WebDevelopment #Automation #KhvichaDev

Your Python logs are lying to you. 🚩 Most server logs are parsed line-by-line in Python. It’s the industry standard because it's easy. But it’s slow, and more importantly, it can be inaccurate. I just benchmarked a 10M row server log ingestion using standard Python vs. a custom C-Hybrid engine I built. Here are the results: 🚀 Execution Speed: 1.01s (Python) ➡️ 0.20s (Hybrid C) 🛡️ Data Integrity: Detected 180 "Ghost" errors that standard parsing missed. Why the difference? Standard line-by-line readers are "blind" to strings sliced exactly across I/O memory boundaries. If a status code like " 500 " is split between two chunks of data, standard iteration skips it. I solved this by building a Hybrid Engine that uses: 1️⃣ 8KB Binary Buffered I/O: Reading raw bytes directly into RAM. 2️⃣ Boundary Overlap Logic: Ensuring no string is ever "sliced" out of existence. 3️⃣ C-Python Bridge: Bringing C-level speed into a Python workflow using ctypes. The ROI: A 5x speedup and 100% data integrity. At enterprise scale (Netflix/Uber), this is the difference between catching a critical security signal and wasting thousands in unnecessary compute costs. 📂 Source Code: https://lnkd.in/g6Vv7DN2 I’m opening 3 slots for free performance audits on data pipelines this week. If your logs are slow or you suspect your numbers aren't 100% accurate, DM me 'OPTIMIZE'. #Python #CProgramming #DataEngineering #PerformanceOptimization #Backend #SoftwareArchitecture #ZeroLatency

🚨 Most Python developers are using concurrency WRONG. Yes, I said it. If you're still confused between Multithreading and Multiprocessing, you're probably leaving performance on the table. Let’s fix that 👇 🧵 Multithreading → Same process, shared memory → Fast & lightweight → Perfect for I/O tasks (API calls, file handling, DB queries) BUT… Thanks to Python’s GIL, threads DON'T run in true parallel for CPU tasks. 👉 Translation: Your CPU-heavy code is still slow. ⚙️ Multiprocessing → Separate processes, separate memory → Uses multiple CPU cores → TRUE parallel execution 👉 Best for: Heavy computations Data processing ML workloads 💥 The Reality: If you're using: Threads for CPU tasks ❌ (Wrong choice) Processes for simple I/O ❌ (Overkill) You're wasting resources. 🧠 Simple Rule: 👉 I/O-bound → Use Multithreading 👉 CPU-bound → Use Multiprocessing 🔥 Pro Tip: Top developers don’t just write code… They choose the RIGHT execution model. 💬 What are you using more in your projects — Threads or Processes? #Python #Multithreading #Multiprocessing #BackendDevelopment #SystemDesign #CodingTips

How to build a sub-100ms live trading pipeline in Python (without writing C++). ⚡ If your tick-to-trade latency is over 500ms, your alpha is already decaying. Python is often called "too slow" for HFT, but the bottleneck is usually bad architecture, not the language. Here is the exact stack I use to keep execution ultra-fast: 1️⃣ Drop REST APIs: Use ZeroMQ for asynchronous, microsecond-level message passing between microservices. 2️⃣ Kill Disk I/O: Store the live order book and tick data entirely in Redis (in-memory) for zero-latency retrieval. 3️⃣ Stream, Don’t Poll: Use direct WebSocket integrations (XTS / Zerodha) instead of requesting data via REST. 4️⃣ Fast DataFrames: Swap Pandas for Polars on the hot path to crunch rolling spreads and time-series data instantly. Finding the strategy is math. Executing it before everyone else is pure engineering. 🛠️ Question for the developers: Are you team Pandas or team Polars for time-series data? Let me know below! 👇 #QuantDev #Python #SystemDesign #SoftwareArchitecture #HighFrequencyTrading #Redis #ZeroMQ #LowLatency #Polars #BackendEngineering

I recently built a small Python backend service to make one thing easier to inspect: good backend engineering transfers across stacks. Python is not my primary language, so the goal was not to “learn Python in public” or chase source parity with an existing system. The goal was to reimplement the core shape of a real orchestration service in a bounded way: - thin API, - orchestration-owned flow, - persisted job and step state, - provider boundary, - tests, - CI/CD, and a small infrastructure baseline. What mattered most to me was not the language switch itself, but preserving the system design and operational model. That’s usually the part that scales beyond any one stack. https://lnkd.in/eF-j-dn3

The "Shadow" Fix: Python Version Compatibility **Hook:** Building for the "Latest & Greatest" is easy. Building for the "Real World" is where the engineering gets messy. **Body:** While finalizing my Enterprise RAG pipeline, I hit a silent production-breaker: A `TypeError` buried deep in a third-party dependency. The culprit? The `llama-parse` library uses Python 3.10+ type union syntax (`|`), but the production environment was locked to Python 3.9. Result: Immediate crash on boot. Instead of demanding a system-wide upgrade—which isn’t always possible in locked-down enterprise environments—I implemented a **Graceful Fallback Logic**: ✅ **Dynamic Imports**: Wrapped the cloud-parser initialization in a guarded `try-except` block. ✅ **Smart Routing**: If the Python environment is incompatible, the system automatically redirects to a local, high-fidelity `PyMuPDF` parser. ✅ **System Resilience**: The app stays online, the UI remains responsive, and 99% of RAG functionality remains available without a single user noticing a failure. Real Engineering isn't just about using the best tools—it’s about writing code that doesn't break when the environment isn't perfect. #Python #SoftwareEngineering #RAG #AIEngineering #SystemDesign #Resilience

Most Python classes I've seen in DS projects do too much! They load data, clean it, transform it, run the model, and log results... all in one place. It feels efficient until you need to change one thing and have to re-test everything else. That's the cost of ignoring the Single Responsibility Principle. 🐍 In my latest article, I break down what SRP actually means for Python data pipelines: https://lnkd.in/esKz_ARk This is post 1 of 5 in a series on SOLID principles applied to Data Science code. What's the messiest class you've inherited on a DS project? 👇 #Python #DataScience #SoftwareEngineering #SOLID #DataEngineering

I ran `kill -9` on a Python worker processing three tasks. They vanished — no error, no retry, no record. This is the default behavior of most task frameworks: a worker dies mid-execution, and the work disappears. So I built automatic crash recovery into pynenc, an open-source distributed task orchestration framework for Python. Here's what it does: • Every runner emits periodic heartbeats • When heartbeats stop, the recovery service detects the dead runner • Orphaned tasks are automatically re-queued • A healthy runner picks them up and finishes the job No external monitoring. No manual re-queueing scripts. No lost work. I wrote up the full scenario — including a runnable demo you can try locally with zero dependencies (no Docker, no Redis): https://lnkd.in/ehWVK-3p The demo takes about 90 seconds and shows recovery happening end-to-end. How does your team handle crashed workers today? #python #distributedsystems #opensource #backend #reliability