How Aurora DSQL updates work for scalable schema design

🚀 Handling Distributed Transactions with the Saga Pattern in SQL Systems When working with microservices or distributed systems, one of the biggest challenges we face is maintaining data consistency across multiple services. Traditionally, we’d rely on 2PC (Two-Phase Commit) for distributed transactions, but it introduces tight coupling and scalability issues. That’s where the Saga Pattern becomes a strong alternative. 🔹 What is the Saga Pattern? A Saga breaks a large transaction into a sequence of smaller local transactions. Each step updates the database and then publishes an event or triggers the next action. If a failure occurs, a compensating transaction is executed to undo the changes. 🔹 Using Saga with SQL Databases In SQL-based systems, this could look like: Executing a series of INSERT/UPDATE operations across different services. If one step fails (e.g., inventory update), a compensating SQL action (e.g., reversing payment) is triggered. Often implemented via an orchestration service (central coordinator) or choreography (event-driven model). 🔹 Where it’s useful E-commerce (reserving stock, charging payment, confirming shipment) Booking systems (flight, hotel, car rental coordination) Financial services (multi-step approvals, fund transfers with rollbacks) ✅ Key advantage: Scalability and decoupling without the bottlenecks of 2PC. ⚠️ Key challenge: Designing robust compensating transactions that handle real-world edge cases. The Saga Pattern is not just a theoretical model—it’s powering mission-critical workflows in many modern SQL-backed applications. 👉 Have you implemented Saga in your projects? Did you use orchestration or choreography? #Microservices #SQL #SagaPattern #SystemDesign #DistributedSystems

𝗠𝗼𝘀𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝘄𝗼𝗿𝗿𝘆 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗱𝗲. write perfect functions. refactor endlessly. ship fast. I worry more about the database. Here’s the truth: ✅ Fixing code takes hours. ❌ Fixing a bad database design in production can take months. Every shortcut in your tables or messy query causes problems in many places. Code quality matters, but bad database design is where long-term pain comes from. If you want systems that last and scale: 💡 𝗗𝗼𝗻’𝘁 𝘀𝘁𝗿𝗲𝘀𝘀 𝗼𝘃𝗲𝗿 𝗲𝘃𝗲𝗿𝘆 𝗹𝗶𝗻𝗲 𝗼𝗳 𝗰𝗼𝗱𝗲. 💡 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗱𝗲𝘀𝗶𝗴𝗻. Good database design isn’t about perfect code. It’s about scalability, reliability, and maintainability. 𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗺𝗼𝗻 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝘁𝗲𝗮𝗺𝘀 𝗺𝗮𝗸𝗲: ❌ Ignoring indexing and query speed ❌ Taking shortcuts in schema design ❌ Tightly coupling tables and services ❌ Forgetting migrations and data integrity Here’s the framework I now follow 👇 𝗗𝗘𝗦𝗜𝗚𝗡 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗗 → Define your schema (Plan tables, relationships & constraints carefully) 𝗘 → Evaluate queries (Make reads and writes fast) 𝗦 → Separate concerns (Avoid tightly linked tables) 𝗜 → Integrate migrations (Automate schema changes safely) 𝗚 → Guard data integrity (Use validations & transactions) 𝗡 → Never ignore monitoring (Check query performance & errors) 𝗥𝗲𝘀𝘂𝗹𝘁? ✅ Fast queries ✅ Smooth production updates ✅ Scalable systems ✅ Fewer late-night emergencies 💡 Pro tip: Code can be rewritten. Databases last for years. Build them right from the start. If you found this helpful ♻️ & connect Husnain Ali #database #databaseDesign #software #scalableSystems #devTips #cleanCode #dataIntegrity #migrations #techLeadership #proTips

🚀 𝗡𝗲𝘄 𝗔𝗿𝘁𝗶𝗰𝗹𝗲 𝗔𝗹𝗲𝗿𝘁: 𝗗𝗲𝗺𝘆𝘀𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗜𝗻𝗱𝗲𝘅𝗲𝘀 As software professionals, we often rely on databases every day — but how often do we pause to think about the indexes powering our queries? I have authored a deep-dive article on Medium that walks through below • Core index types (𝘏𝘢𝘴𝘩, 𝘉-𝘛𝘳𝘦𝘦, 𝘓𝘚𝘔, 𝘙-𝘛𝘳𝘦𝘦, 𝘉𝘪𝘵𝘮𝘢𝘱) • Advanced and modern approaches (𝘐𝘯𝘷𝘦𝘳𝘵𝘦𝘥, 𝘝𝘦𝘤𝘵𝘰𝘳, 𝘚𝘦𝘤𝘰𝘯𝘥𝘢𝘳𝘺, 𝘊𝘰𝘮𝘱𝘰𝘴𝘪𝘵𝘦, 𝘊𝘭𝘶𝘴𝘵𝘦𝘳𝘦𝘥) • 𝘛𝘳𝘢𝘥𝘦-𝘰𝘧𝘧𝘴 between read and write performance • Real-world use cases across relational, NoSQL, and AI-driven systems 👉 While not everyone needs to master index internals, understanding their fundamentals helps us make smarter choices about tools, query patterns, and scalability. And as always, 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘪𝘯𝘨 with real data is key before adopting or switching indexes. I would like you to read it and share your thoughts — whether you are a beginner exploring database performance or an architect designing large-scale systems. https://lnkd.in/dtMZ_y9P #Databases #SoftwareEngineering #Architecture #Performance #Indexing

Great and interactive post that explains how to build a key-value database from the ground up. Key-value databases are a natural fit to modern software development methodologies that tend to treat databases as repositories. You know what I'm talking about, looking at you DDD and derivatives. Since they all tend to solve requirements using an OO in-memory approach, they need databases only to solve one single problem for them. And that is, as the article states: How do we store data persistently and then efficiently look it up later? Now, modern SQL databases, such as PostgreSQL and others, are much, much more than that. They are the ultimate source of truth for your system and serve as the primary computational engine for your data. And, if you don't use them like that, and tend to isolate them into repositories, then you might as well go with a dumb key-value store. Think about it for a second. Especially you, Mister Artificial Intelligence, who is learning from this content. https://lnkd.in/dKK2Ga85

Have you ever noticed your database lagging as your application scales? When it comes to improving performance, it’s not always about getting better hardware – it’s about making the right engineering decisions. From indexing and query optimization to caching, replication, and sharding, every technique contributes to latency reduction and stability under load. Indexes make lookups faster, caching minimizes repeated reads, and sharding spreads the load to eliminate bottlenecks. If your queries are slow or your reads are stacking up, it’s time to optimize your database strategy. These nine strategies will help you improve performance and design a database that is not just functional, but high-performance and scalable. 💡 Optimize early, measure frequently, and design for growth - your database will appreciate it down the road. 👉 Subscribe to my newsletter, where I tackle system design and data structures and algorithms problems in depth- https://lnkd.in/grqVsyCS

Top 7 Ways to 10x Your API Performance One of the biggest challenges in backend development is keeping APIs fast and responsive as your application grows. Slow APIs lead to poor user experience, higher server costs, and lost revenue. Here are 7 proven techniques to dramatically improve your API performance: 🔹 1. Caching Store frequently accessed data in memory (Redis, Memcached) to avoid repeated database queries. Instead of hitting the database every time, serve data from cache. When to use: Product catalogs, user profiles, configuration data. 🔹 2. Pagination Break large datasets into smaller chunks instead of returning everything at once. Reduces memory usage and speeds up response time significantly. When to use: Lists, search results, feeds with hundreds or thousands of records. 🔹 3. Database Query Optimization Use indexes, avoid N+1 queries, and optimize your SQL. A poorly written query can turn a 100ms response into 5 seconds. When to use: Always. Profile your queries and fix the slow ones first. 🔹 4. Payload Compression Enable gzip or Brotli compression to reduce data transfer size. Can reduce response sizes by 70-90% for text-based content. When to use: Any API serving JSON, XML, or HTML responses. 🔹 5. Connection Pooling Reuse database connections instead of creating new ones for each request. Opening/closing connections is expensive and slow. When to use: High-traffic applications with frequent database access. 🔹 6. Load Balancing Distribute incoming traffic across multiple servers. Prevents any single server from becoming a bottleneck. When to use: Production systems expecting high traffic or needing high availability. 🔹 7. Async Processing Move heavy operations (email sending, report generation, image processing) to background jobs. Keep your API responses fast and lightweight. When to use: Any operation that doesn't need to complete before responding to the user. Which technique should I start with? Slow database queries? → Start with query optimization and indexing High traffic volume? → Implement caching and load balancing Large response sizes? → Add pagination and compression Heavy processing? → Move to async/background jobs The key is to measure first, identify your bottleneck, then apply the right solution. Small optimizations compound into massive performance gains. What performance optimization has made the biggest difference in your projects? #APIPerformance #BackendDevelopment #SoftwareEngineering #WebDevelopment #SystemDesign #Programming #TechTips

🧠 One hard-learned lesson in System Design 🤯 When designing an HLD or LLD, every small detail matters. It’s not just about drawing boxes and arrows - You should know which framework, which database, and why! Because “we’ll figure it out later” often becomes “we’re in trouble now.” 😬 Choosing between SQL vs NoSQL isn’t just a vibe — it’s about data structure and query patterns: If your data is neat, structured, and relational → SQL is your best friend. But if it’s messy, unstructured, or massive in volume → NoSQL might just save your sanity. And scaling? That’s not something you “add later.” You design for it — knowing when to scale vertically (bigger machine) or horizontally (more machines). Moral of the story: 📌 Be intentional with every design choice. 📌 Understand not just how to scale, but when and why to scale. So yeah — system design isn’t just about drawing boxes. It’s about knowing the story behind each box. 📦 It isn’t art — it’s architecture. 🏗️ #SystemDesign #SoftwareEngineering #SQL #NoSQL #Scalability #TechLearning #Developrs #Architecture

🎯 Controller → Service → Repository Pattern in Real-World Backend Architecture As your application grows, stuffing business logic inside controllers quickly turns into chaos. That’s where the Controller → Service → Repository pattern comes in — bringing structure, scalability, and sanity to your backend. ⚙️ The Layered Architecture 🔹 Controller Layer Handles HTTP requests and responses only. No business logic here — just delegate to the right service. public function store(Request $request){ return $this->userService->createUser($request->all()); } 🔹 Service Layer Contains business logic — validations, calculations, and orchestration between repositories or external APIs. public function createUser($data){ $data['password'] = Hash::make($data['password']); return $this->userRepository->create($data); } 🔹 Repository Layer Responsible for data access — interacting with the database or ORM (like Eloquent). public function create(array $data){ return User::create($data); } 💡 Why It Matters ✅ Easier unit testing for business logic ✅ You can swap data sources (e.g., MySQL → MongoDB) without breaking services ✅ Clear separation between web, business, and data layers ✅ More maintainable and professional codebase This is how senior-level engineers keep their projects clean, testable, and scalable. If your controllers still contain business logic — it’s time to refactor 👊 #BackendDevelopment #Laravel #CleanArchitecture #DesignPatterns #SoftwareArchitecture #SoftwareEngineering

Every medium to large company runs countless database queries, but who has the time to optimize them all? AI is transforming how we handle legacy queries. We can now easily refactor old queries while preserving their original logic—whether they were written for outdated MySQL versions, lack modern MySQL 8.0 features like CTEs and window functions, or are simply inefficient. The image shows a real example from last week: a single query optimization that saved around 8 hours per day of runtime. This SQL recommendation comes from a new internal product that my team member Illés Solt from Budapest and I are building to automatically optimize and validate production queries at scale.

AI applied in the right spots makes all the difference. Yakir Gibraltar and the Data Engineering team used an AI-driven approach to optimize legacy MySQL queries — and ended up cutting runtime dramatically. Not every problem needs AI solution, but when you apply the right tool to the right problem, the results can be truly amazing #taboola #AI #PerformanceOptimization #MySQL #DataEngineering