Excessive Logging: A Hidden Performance Killer in Backend Systems

🚫 Why We Should Avoid Excessive Lookups in UAT & Production It’s easy to rely on lookup queries during development — they work fine in small datasets and local environments. But what works in dev can break badly in UAT and Production. 🔍 What changes in UAT & Prod? Real users 👥 Large data volumes 📊 High traffic 🚀 Now, those “simple lookups” start becoming expensive operations. ⚠️ Why excessive lookups are dangerous: ❌ Increased latency Every lookup hits the database → slower response time ❌ Database overload Thousands of repeated queries can exhaust DB connections ❌ Scalability issues System struggles as users increase ❌ N+1 query problem Loop-based queries multiply DB calls drastically ❌ Unnecessary cost More DB usage = higher infrastructure cost 💡 Why We should NEVER rely on repeated lookups: 👉 Databases are not meant for repeated fetching of the same data 👉 Network calls are expensive in distributed systems 👉 Performance issues often appear only in UAT/Prod, not in dev ✅ What to do instead: ✔️ Use caching (store frequently used data in memory) ✔️ Fetch data in bulk (avoid queries inside loops) ✔️ Use joins instead of multiple queries ✔️ Add proper indexing ✔️ Design APIs to minimize DB calls 🚀 Real-world systems focus on reducing database hits to ensure high performance and reliability. 👉 Key takeaway: “Code that works in development is not enough — it must scale in production.” #SystemDesign #BackendDevelopment #Performance #Java #Scalability

How a Simple Query Optimization Improved API Performance by 60%? We often jump to scaling systems with caching, load balancers, etc. But sometimes, the bottleneck is much simpler: bad queries. In one of my projects, API response time was consistently high. 🔍 Root cause: Complex joins Missing indexes Inefficient filtering 💡 What we did: ✅ Added proper indexing on frequently queried columns ✅ Refactored heavy joins ✅ Reduced unnecessary data fetching 🔥 Result: 👉 ~60% reduction in API response time (No infrastructure changes required) ⚙️ Example: Before: Full table scan → slow After: Indexed lookup → fast 📌 Lesson: Before scaling your system, make sure your database is not the bottleneck. #Java #SpringBoot #Microservices #SystemDesign #BackendEngineering #SoftwareArchitecture

💻 Diving deeper into Operating Systems & Distributed Systems Lately, I’ve been exploring core OS concepts like process management, multithreading, and synchronization—and I realized the best way to truly understand them is to build something that depends on them. So, I built my own Distributed File System (DFS) from scratch using Java 🚀 Rather than relying on high-level frameworks, I focused on understanding how real systems handle data distribution, failures, and communication at a low level. 🔧 What’s happening behind the scenes? ⚡ Socket-Based Communication Implemented direct TCP socket communication between nodes to enable fast and efficient data transfer without relying on REST APIs. 🧵 Concurrency & Thread Management Designed the system to handle multiple client requests simultaneously using thread pools and concurrent data structures, ensuring safe and efficient execution. 🛡️ Fault Tolerance & Replication Integrated a replication strategy to ensure data availability. Even if a node fails unexpectedly, the system can recover and continue serving requests seamlessly. 📡 Heartbeat Monitoring System Built a mechanism for continuous health checks of nodes, allowing the system to detect failures in real time and respond accordingly. 📊 Interactive Monitoring Interface Created a lightweight frontend dashboard to visualize file distribution and track node activity dynamically. 🧠 Key Takeaways Working on this project helped me connect theoretical OS concepts with real-world system design challenges—especially around network communication, synchronization, and fault handling. It also gave me a deeper appreciation for how large-scale systems maintain reliability under unpredictable conditions. 🔗 Project Repository: https://lnkd.in/gP-DAtj2 I’d love to hear your thoughts or feedback! #DistributedSystems #OperatingSystems #Java #BackendDevelopment #SystemDesign #ComputerScience #Networking

When the application is slow, the Backend Architect is the only one who can’t look away. A slow application doesn’t just frustrate users; it stops business. Too often, development teams try to solve performance issues by upgrading server hardware or refactoring the Frontend. The bottleneck isn’t the CPU; it’s the Database. At tek Lads, we specialize in the deep-level SQL Server tuning and EF Core optimization that makes data flow instantly. By utilizing developers with top MNC pedigree, we find the deadlocks, optimize the indices, and tune the execution plans so you don’t have to. Our SQL Optimization Blueprint: Advanced Indexing Strategies and Query Tuning. Resolving N+1 problems in Entity Framework Core. Designing for High-Concurrency and Deadlock Avoidance. Schema Modernization without downtime. We don't just write code; we ensure your data is as fast as your ambition. 🏗️🛡️ Is your SQL database driving performance, or holding it back? Let’s optimize the data you already have. 🤜🤛 #TekLads #DotNetCore #SQLServer #DatabaseOptimization #PerformanceTuning #BackendArchitecture #MNCExperience #SoftwareEngineering #DataScaling

#HLD #SystemDesign #Scaling 𝐖𝐞 𝐝𝐢𝐝𝐧’𝐭 𝐡𝐚𝐯𝐞 𝐚 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐩𝐥𝐚𝐧… 𝐮𝐧𝐭𝐢𝐥 𝐭𝐡𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐛𝐫𝐞𝐚𝐤𝐢𝐧𝐠 Most architectures look clean in diagrams. In production, they evolve under pressure. Over the next 8 days, I’m breaking down how systems actually scale from 1 user to 1 million users. No fluff. Only real bottlenecks and production fixes 𝐃𝐚𝐲 𝟏 𝐌𝐨𝐧𝐨𝐥𝐢𝐭𝐡 𝟏 𝐭𝐨 𝟏𝟎𝟎 𝐮𝐬𝐞𝐫𝐬 Everything runs on one machine Simple, fast, fragile 𝐃𝐚𝐲 𝟐 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐒𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧 𝟏𝟎𝟎 𝐭𝐨 𝟏𝐊 App and DB fighting for resources First real bottleneck appears 𝐃𝐚𝐲 𝟑 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠 𝟏𝐊 𝐭𝐨 𝟏𝟎𝐊 One server becomes a risk Horizontal scaling begins 𝐃𝐚𝐲 𝟒 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 𝟏𝟎𝐊 𝐭𝐨 𝟏𝟎𝟎𝐊 Database starts collapsing under reads Caching changes everything 𝐃𝐚𝐲 𝟓 𝐀𝐬𝐲𝐧𝐜 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝟏𝟎𝟎𝐊 𝐭𝐨 𝟓𝟎𝟎𝐊 Sync calls cause timeouts Queues bring stability 𝐃𝐚𝐲 𝟔 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝟓𝟎𝟎𝐊 𝐭𝐨 𝟏𝐌 Writes become the bottleneck Replication and sharding enter 𝐃𝐚𝐲 𝟕 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞 Teams slow down monolith growth Services unlock speed 𝐃𝐚𝐲 𝟖 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 Failures become invisible Monitoring becomes survival 𝐓𝐡𝐢𝐬 𝐬𝐞𝐫𝐢𝐞𝐬 𝐢𝐬 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 • No over engineering from day one • No theoretical diagrams • Only real production problems and fixes • Built from backend engineering experience Follow along for the next 8 days #SystemDesign #BackendEngineering #Scalability #Microservices #Java #SpringBoot #DistributedSystems #BuildInPublic #SoftwareEngineering

Most performance issues are not bugs. They are the result of architecture decisions. Recently, I was debugging a system where APIs were randomly timing out. At first, everything looked fine. The database was working, cache was connected, services were up. But when real traffic came in, things started breaking. The problem was not a slow query or a missing index. It was something deeper. Different types of work were running in the same place. User-facing APIs, background workers, and cron jobs were all running together in one process. Over time, they started competing with each other. - For database connections. - For CPU. - For execution time. And the system became unpredictable. APIs that should respond quickly started taking seconds. Sometimes they failed completely. The interesting part was this. The moment we removed background workers from the API process, everything became stable again. - No major code changes. - No optimizations. - Just removing contention. The lesson is simple. Just because things can run together does not mean they should. Good architecture is not only about writing clean code. It is about separating responsibilities properly. - APIs should stay fast and responsive. - Workers should handle heavy tasks. - Each part should scale independently. Sometimes the real fix isn’t optimization. It’s fixing the architecture first. #SystemDesign #BackendEngineering #Scalability #SoftwareArchitecture

One of our APIs started getting slower over time. At first, nothing looked wrong. It was working fine in dev. But as data increased, response time kept going up 📈 After digging a bit, we found the issue. Inside a loop, we were calling the database for every item. So one request was actually triggering 100+ queries 😅 Turns out, this is a classic N+1 query problem. Didn’t notice it early on because the data was small. But once it grew, it started hurting performance. We fixed it by changing it to a single query (join/batch). Same logic. Way better performance 🚀 Small thing, but big impact. Made me realize how easy it is to miss these issues when things “seem fine”. Now I always keep an eye on how many DB calls an API is making 👀 #BackendEngineering #Java #Performance #Database #Microservice

Task 10 is done in Hiveboard. A lot of workflow APIs become hard to maintain because validation, persistence, and side effects all end up inside one endpoint method. I did not want that here. In the C# API project, I kept the Minimal API surface deliberately thin. The endpoint only accepts the request and delegates to an application service. The application service coordinates: - task loading with EF Core - assignment updates - transition execution - task event creation - notification fan-out - parent task completion logic The actual transition policy lives in a separate TaskStateMachine in the Core layer. That architectural decision matters because it keeps the codebase honest: - Minimal APIs stay transport-focused - application services stay orchestration-focused - core services stay rule-focused - EF Core stays persistence-focused This is the kind of separation I want in a real .NET backend, especially for agent coordination where state transitions are business-critical.

In a monolith, life is simple: One DB transaction → 𝗰𝗼𝗺𝗺𝗶𝘁 𝗼𝗿 𝗿𝗼𝗹𝗹𝗯𝗮𝗰𝗸 But in microservices? Each service owns its database. There is 𝗻𝗼 𝗴𝗹𝗼𝗯𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻. So what happens when something fails midway? ⚠️ Real Problem Imagine an order flow: 1. Order Service → creates order 2. Payment Service → deducts money 3. Inventory Service → reserves stock Now suppose 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗳𝗮𝗶𝗹𝘀 ❌ You’re left with: * Order created * Payment deducted * No inventory 👉 Classic 𝗱𝗮𝘁𝗮 𝗶𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 ✅ 𝗘𝗻𝘁𝗲𝗿: 𝗦𝗮𝗴𝗮 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Saga breaks a distributed transaction into a 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝗼𝗳 𝗹𝗼𝗰𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀, where: - Each step commits independently - Every step has a 𝗰𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗻𝗴 𝗮𝗰𝘁𝗶𝗼𝗻 (𝘂𝗻𝗱𝗼 𝗹𝗼𝗴𝗶𝗰) 🔁 How it works Forward flow:  • Create Order ✅  • Deduct Payment ✅  • Reserve Inventory ❌ Compensation flow:  • Refund Payment 🔄  • Cancel Order 🔄 👉 System reaches 𝗲𝘃𝗲𝗻𝘁𝘂𝗮𝗹 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 🧠 Key Engineering Concepts Local Transactions → Each service updates its own DB Compensation → Undo logic must be defined for every step Idempotency → Critical for retries (no double refunds!) Eventual Consistency → Not immediate, but guaranteed over time #SystemDesign #Microservices #DistributedSystems #BackendEngineering #Java

🚀 New Video: Why @Transactional is Important in Spring Boot What happens if: ✔ Employee is saved ❌ IdCard fails 👉 You get inconsistent data This is where @Transactional saves you. 💡 Simple idea: Either everything succeeds… or nothing is saved. In this video, I show: ✔ Real problem (partial data save) ✔ How rollback works ✔ Why transactions are critical in real systems 🎥 Watch here: https://lnkd.in/dN3Duxnj #SpringBoot #JPA #Java #BackendDevelopment #Hibernate