Optimizing API Performance with Query Refactoring

🚀 𝗙𝗿𝗼𝗺 𝗠𝗮𝗻𝘂𝗮𝗹 𝗗𝗕 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝘁𝗼 𝗦𝗮𝗳𝗲 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 ⚙️ While improving my Smart Reminder Backend Project, I ran into an important question: How do you safely change a production database schema without breaking existing data? With 𝗔𝗹𝗲𝗺𝗯𝗶𝗰, database changes become version-controlled, structured, and reproducible — making schema evolution much safer. 🧩 𝗪𝗵𝗮𝘁 𝗜 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 & 𝗟𝗲𝗮𝗿𝗻𝗲𝗱 : ⚡ 𝗔𝘂𝘁𝗼𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Using alembic revision --autogenerate to detect model changes and generate migration scripts. 🔄 Safely Adding New Columns (Production Pattern) 1️⃣ Add the column as nullable 2️⃣ Backfill existing rows with default data 3️⃣ Apply the NOT NULL constraint ⚙️ 𝗗𝗮𝘁𝗮 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Sometimes schema changes also require updating existing records — for example, populating default values for previously stored data. 🛠 𝗞𝗲𝘆 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀 𝗜 𝗨𝘀𝗲𝗱 ✅ alembic init alembic ✅ alembic revision --autogenerate ✅ alembic upgrade head 🐞 𝗦𝗺𝗮𝗹𝗹 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗟𝗲𝘀𝘀𝗼𝗻 I also ran into an issue with column naming. In 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟, mixed-case column names behave differently, which can cause unexpected errors. Using 𝘀𝗻𝗮𝗸𝗲_𝗰𝗮𝘀𝗲 naming is generally safer for database schemas. 💬 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺 𝗼𝘁𝗵𝗲𝗿 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: Do you rely on Alembic --𝗮𝘂𝘁𝗼𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲, or do you prefer writing migration scripts manually? #Python #FastAPI #PostgreSQL #Alembic #BackendDevelopment #DatabaseMigrations #LearningInPublic #DeveloperJourney

Published a new blog post: Disjoint Set (Union-Find). This data structure comes up a lot in graph problems: checking connectivity, clustering, Kruskal's algorithm for Minimum Spanning Trees. It looks simple on the surface, but there are a few non-obvious details worth understanding from the start. In the article I build the implementation from scratch in TypeScript: how to store components, how find, union, and connected work, and what the complexity of each operation is. Plus working tests you can run right away. Path compression and union by rank are coming next, but that's a separate story. https://lnkd.in/dBKvSpUj

Scaling FastAPI for Real-World Data Loads and Handling Millions of Request Building scalable APIs with FastAPI requires more than just leveraging its speed and asynchronous capabilities. While FastAPI performs exceptionally well for small to medium workloads, challenges begin to surface when applications need to handle millions of rows of data without overwhelming server resources. As data volume grows whether dealing with audit logs, transactional records, analytics data, or historical events naïve implementations can lead to increased memory consumption, slow response times, and degraded overall performance. This is where thoughtful architecture and performance optimization become critical. Designing APIs that remain stable at scale requires strategic decisions, such as: - Avoiding loading entire datasets into memory at once - Implementing streaming or chunked responses for large data transfers - Optimizing database queries and serialization processes - Ensuring efficient pagination and resource management Handling high-volume data efficiently is essential for modern backend systems, particularly when building APIs that support analytics platforms, reporting tools, or enterprise-grade applications. Scalability is not just about performance it’s about building systems that remain reliable, responsive, and sustainable as demand grows. #FastAPI #BackendDevelopment #APIDesign #ScalableSystems #Python #digital transformation #APIDevelopment #SystemDesign #PerformanceOptimization #TechInnovation

𝐒𝐜𝐡𝐞𝐦𝐚 𝐨𝐧 𝐖𝐫𝐢𝐭𝐞. Because writing boilerplate is boring. 🥱 You have a dataset. You want to search it. In a standard workflow, your next step is spending 20 minutes writing a CREATE TABLE script or a massive JSON mapping definition just to tell the database what a "string" or an "integer" is. Not with 𝐂𝐚𝐦𝐞𝐨𝐃𝐁. As part of our "single binary, batteries included" philosophy, we built in Schema Detection. Just point the engine at your data. CameoDB reads the file directly from the URL, infers the data types (Text, Integers, Booleans, Dates), and automatically builds the optimal Search and Document schema on the fly. The 2-Step Test Drive: We just published a new dataset to try it out: 4,600+ YouTube TED Talks. 1️⃣ cameodb schema detect ted <url> (Instantly maps viewCount as Integer, videoDescription for Full-Text Search) 2️⃣ cameodb data load ted <url> (Ingests the data into the index). Zero boilerplate. Instant search. Look below to see the auto-generated schema in action 👇 #CameoDB #DataEngineering #RustLang #Database #DevOps #SearchEngine

Excited to share one of the AI projects I recently worked on! I built a Python FastAPI-based LLM application that demonstrates how modern backend services can integrate with Large Language Models to deliver intelligent responses through a simple API. Tech Stack: • Python • FastAPI • LLM Integration • REST APIs • Async backend architecture • Claude (AI-assisted development) What this project demonstrates • Building high-performance APIs using FastAPI • Integrating LLM capabilities into backend services • Designing scalable endpoints for prompt processing and response generation • Structuring clean backend architecture for AI-driven applications • Using Claude to analyze the codebase, identify bottlenecks, and improve implementation efficiency During development, I leveraged Claude to help review the architecture, refine certain implementations, and remove performance bottlenecks. This significantly improved the clarity of the code and the overall efficiency of the service. This project was a great opportunity to explore how AI services can be exposed as production-ready APIs, making them easy to integrate with web applications, chat interfaces, or automation systems. GitHub Repository: https://lnkd.in/ge3zEJb2 Always excited to build systems where AI meets scalable backend engineering. #AI #LLM #FastAPI #Python #ClaudeAI #BackendEngineering #SoftwareEngineering

✨ A Simple Backend Pattern That Made My ML Systems Predictable ✨ One of the most impactful improvements I made to an ML system had nothing to do with the model. It was about how I structured the backend code around JSON, Python classes, and database access. Earlier, everything lived together: • Parse request JSON • Validate inputs • Apply estimation logic • Run SQL queries • Return response It worked — but debugging was painful. A SQL change could look like a model bug. A missing JSON field could silently skew results. The issue wasn’t complexity. It was mixed responsibilities. 🔁 The structure I now follow Request JSON ↓ Validation Layer ↓ Service Layer (Business / Estimation Logic) ↓ DAO Layer (Pure Data Access) ↓ Database Each layer owns one responsibility only. 📊 Clear ownership makes a difference Validator Responsible for: JSON schema + semantic validation Never does: Query the database Service Responsible for: Estimation / business logic Never does: Write SQL DAO Responsible for: SQL execution, data retrieval Never does: Apply formulas Model Responsible for: Structured domain objects Never does: Handle DB connections 💡 What changed after this ✔ Bugs became easier to isolate ✔ SQL changes stopped affecting logic ✔ Estimation rules became explicit ✔ Testing became meaningful ✔ System behavior became predictable The model stayed the same. The architecture didn’t. In applied ML systems, stability often comes from clean backend boundaries, not better algorithms. Antony Pulicken Deepak Y V Pawel Jasionowski Abdul Kareem Lakkundi Pavithra Umapathi Uday Kumar #Python #BackendEngineering #MachineLearning #DataEngineering #CleanCode #SystemDesign #MLOps

Code: https://lnkd.in/dtfG73ir 🚀 Just wrapped up learning FastAPI, and I'm excited to share my journey! Over the past few days, I've been diving deep into backend development with FastAPI, and it's been an incredible learning experience. Here's what I've built and learned: 🔧 What I Built: A complete Patient Management API with full CRUD operations - creating, reading, updating, and deleting patient records. But it wasn't just about basic operations; I implemented features like automatic BMI calculation, health status categorization, and smart data filtering. 📚 Key Concepts I Mastered: API Fundamentals - Understanding how APIs work as bridges between applications, and why they're crucial in modern software development. HTTP Methods - GET for retrieving data, POST for creating new records, PUT for updates, and DELETE for removal. Each serves a specific purpose in RESTful design. Data Validation with Pydantic - This was a game-changer! Learning how Pydantic models automatically validate incoming data, enforce type safety, and even compute fields like BMI based on other inputs saved me from writing tons of validation code. Request/Response Flow - Tracing how data transforms from JSON (what the user sends) → Pydantic models (validated objects) → Python dictionaries (easy manipulation) → back to JSON (response). Understanding these conversions was crucial. Smart Filtering & Sorting - Implemented query parameters for flexible data retrieval, allowing users to search by multiple criteria, sort by different fields, and filter by ranges. Error Handling - Using HTTP status codes properly and providing meaningful error messages makes APIs much more user-friendly. Path vs Query Parameters - Path parameters identify specific resources, while query parameters filter and customize. 💡 Biggest Takeaway: The real understanding was why we convert between different data formats. Converting dictionaries to Pydantic models isn't just busy work - it triggers automatic recalculations of computed fields, ensures data consistency, and catches errors before they reach the database. #FastAPI #BackendDevelopment #Python #API #WebDevelopment #LearningInPublic #SoftwareEngineering