Distributed Locking Prevents Data Corruption in Distributed Systems

Race Conditions in Backend Systems:- A simple order service where users can place orders and inventory gets updated. Problem I faced :- Everything worked fine in testing. But in production, something weird started happening: Same product got sold more times than available Inventory went negative Duplicate updates started appearing No errors. No exceptions. Just wrong data. How I fixed it:- The issue was a race condition. Multiple requests were updating the same data at the same time. Here’s what helped: Added database-level locking for critical updates Used optimistic locking with version fields Introduced idempotency checks for repeated requests For high contention cases, used Redis distributed locks After that, updates became consistent again. What I learned: Concurrency issues don’t break loudly. They silently corrupt your data. And by the time you notice, it’s already too late. Question? Have you ever faced a bug where everything looked fine in logs… but the data was completely wrong? #Java #SpringBoot #Programming #SoftwareDevelopment #Cloud #AI #Coding #Learning #Tech #Technology #WebDevelopment #Microservices #API #Database #SpringFramework #Hibernate #MySQL #BackendDevelopment #CareerGrowth #ProfessionalDevelopment #RDBMS #PostgreSQL #backend

Stop hiding SQL. Start owning it. After working with different approaches, one thing became clear. sqlc is the cleanest way to handle data in serious backend systems. You write real SQL. What you write is what runs. No guessing, no surprises. Queries are checked at compile time, not in production. There is no hidden behavior. No unexpected joins. No performance issues showing up later. The structure stays clean. SQL, generated code, repository, service. Easy to follow, easy to maintain. ORMs feel fast in the beginning. But as systems grow, they bring hidden complexity and make debugging harder. With sqlc, you stay in control from day one. If you are building APIs, microservices, or anything that needs to scale, this approach just makes more sense. #sqlc #golang #backend #softwareengineering #microservices #postgresql #cleanarchitecture #api #webdevelopment

In databases… “Almost correct” is completely wrong. That's why 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀 matter. This is exactly why databases like PostgreSQL take transactions very seriously. Because in real-world systems, 𝗽𝗮𝗿𝘁𝗶𝗮𝗹 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 = 𝘁𝗼𝘁𝗮𝗹 𝗳𝗮𝗶𝗹𝘂𝗿𝗲. So what is a Transaction? A transaction is a group of operations that either completely succeed or completely fail. No in-between. Example: Transferring ₹1000 from A → B Deduct from A Add to B (failed) Without transaction → Data is inconsistent  With transaction → Everything is rolled back This is powered by 𝗔𝗖𝗜𝗗 properties: 🔹 A – Atomicity (All or Nothing) Either the entire transaction happens… or none of it. 🔹 C – Consistency (Valid State Always) Database always moves from one correct state to another. 🔹 I – Isolation (No Interference) Multiple transactions don’t mess with each other. 🔹 D – Durability (Permanent Changes) Once committed, data stays — even after crashes. Why PostgreSQL stands out:  • Strong ACID compliance  • Reliable transaction handling  • Used in systems where data integrity is critical Real insight Bugs can be fixed. UI can be redesigned. But 𝗰𝗼𝗿𝗿𝘂𝗽𝘁𝗲𝗱 𝗱𝗮𝘁𝗮? That’s a nightmare. Transactions are not just a feature… They’re your safety net. Next time you write a query, don’t just think “does it work?” Think… “what if it 𝗳𝗮𝗶𝗹𝘀 𝗵𝗮𝗹𝗳𝘄𝗮𝘆?” #PostgreSQL #Database #RDBMS #ACID #Transactions #BackendDevelopment #SoftwareEngineering #SQL #DataIntegrity #Developers #CoreJava #SpringFramework #SpringBoot #Hibernate #ORM #MicroServices #aswintech 

Thundering Herd Problem (When Everything Breaks at Once):- A caching layer to reduce database load for frequently accessed data. --- Problem I faced: Everything worked well… until cache expired. Suddenly: Huge spike in database queries CPU usage shot up API latency increased System became unstable All at the same moment. --- How I fixed it:- This was the Thundering Herd Problem. When cache expired, multiple requests tried to fetch fresh data simultaneously. Fixes applied: Added cache locking (single-flight) so only one request refreshes data Introduced randomized cache expiry (TTL jitter) to avoid simultaneous expiration Used stale-while-revalidate approach for smoother refresh Now: Only one request hits DB Others wait or get cached response System stays stable. --- What I learned:-- Caching reduces load… but poorly managed caching can create bigger spikes than no cache at all. --- Question? Have you ever seen your system fail not because of traffic… but because many requests did the same thing at the same time? #Java #SpringBoot #Programming #SoftwareDevelopment #Cloud #AI #Coding #Learning #Tech #Technology #WebDevelopment #Microservices #API #Database #SpringFramework #Hibernate #MySQL #BackendDevelopment #CareerGrowth #ProfessionalDevelopment #RDBMS #PostgreSQL #backend

🚀 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐆𝐞𝐧𝐞𝐫𝐢𝐜 𝐃𝐚𝐭𝐚 𝐏𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐞 𝐔𝐭𝐢𝐥𝐢𝐭𝐲: 𝐀 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 We had a requirement in one of our existing application which is used to take several datapoints from any upstream through API and push the data into the Azure lake space in the form of JSON (transformed) files. But some critical data needs to be stripped off from the JSON file and push it into our on-premises PostgreSQL database. This feature was not existing before and I follow this principle: “𝑰𝒇 𝒚𝒐𝒖’𝒓𝒆 𝒘𝒓𝒊𝒕𝒊𝒏𝒈 𝒕𝒉𝒆 𝒔𝒂𝒎𝒆 𝒍𝒐𝒈𝒊𝒄 𝒕𝒘𝒊𝒄𝒆, 𝒚𝒐𝒖’𝒓𝒆 𝒏𝒐𝒕 𝒄𝒐𝒅𝒊𝒏𝒈—𝒚𝒐𝒖’𝒓𝒆 𝒓𝒆𝒑𝒆𝒂𝒕𝒊𝒏𝒈.” So I came up with a generic solution: 🛠️ 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: I built a generic data persistence function that abstracts all of this complexity into a single reusable component. 🔑 𝗜𝗻𝗽𝘂𝘁𝘀 𝘁𝗼 𝘁𝗵𝗲 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻: 1. A list of data objects to be persisted 2. The target table name 3. A mapping between object fields and database columns 4. The primary key of the table (to handle update of data for that id) With just one function call, the data is persisted—no additional boilerplate required. 🔥 𝗜𝗺𝗽𝗮𝗰𝘁: This approach brought immediate improvements: ✅ Eliminated repetitive code across multiple modules ✅ Improved development speed significantly ✅ Reduced chances of human error in SQL handling ✅ Standardized data persistence logic ✅ Increased maintainability and scalability 🧠 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: As engineers, we often focus on solving complex problems—but sometimes the biggest wins come from simplifying the repetitive ones. By introducing a layer of abstraction for data persistence, I was able to turn a common bottleneck into a streamlined, reusable solution. If you're working in backend systems dealing with frequent database interactions, building such generic utilities can be a game-changer. Would love to hear how others are approaching similar challenges in their systems 👇 #Java #SpringBoot #PostgreSQL #BackendDevelopment #SoftwareEngineering #CleanArchitecture #Productivity

Phillip Merrick made a point in The New Stack (https://hubs.la/Q04dPhph0) worth pausing on: AI agents only stop hallucinating when you give them the actual enterprise data... and that data already lives in Postgres. 🐘 If you're wiring an LLM into a Postgres database, the pgEdge MCP Server is open source and built for that exact job. Postgres 14+, read-only by default, and handles real workflows, like analysts running ad-hoc questions in plain English, developers debugging schemas without leaving their editor, and DBAs pulling index recommendations from inside Claude Code or Cursor. It's coming from the team that maintains pgAdmin, so Postgres knowledge is baked into the server itself - not bolted on after the fact. Token usage is genuinely tuned with TSV output, auto-pagination, and context compaction. 🛠️ ⭐ Star the repo, clone it, point it at any new or existing PostgreSQL database (including Supabase, RDS, & more): https://hubs.la/Q04dPdmC0 #devops #aiengineering #programming #sideprojects #postgres #mcp #opensource #supabase #aws #amazon #rds #cloudsql #heroku #postgresql

If you don’t understand this DBMS concept, your backend will never scale properly ⚠️ Today I explored this while learning Database Management Systems (DBMS). Here’s what I understood: • Physical Level → How data is actually stored (indexes, storage, compression) • Logical Level → Defines what data is stored + relationships (tables, schemas) • View Level → Shows only required data to users (security + simplicity) 💡 How this is used in backend systems: As a backend developer working with Node.js & APIs, we rarely deal with raw storage. Instead: - ORMs (like Prisma / Mongoose) work at the logical level - APIs expose view-level data (filtered responses) - DB engines optimize physical storage internally ⚡ Example: When building an API: - You don’t think about how data is stored on disk - You design schemas (logical level) - And return custom responses (view level) 👉 Meanwhile, DB handles indexing & storage automatically 🔥 Why this matters: Understanding abstraction helps you: - Write better queries - Design scalable APIs - Avoid performance bottlenecks 🛠 Tech stack I’m focusing on: Node.js • Next.js • TypeScript • REST APIs • Databases • Backend Systems #BackendDevelopment #DBMS #Databases #NodeJS #SystemDesign #SoftwareEngineering #APIs #FullStackDeveloper #LearnInPublic

Recently I came across a discussion on query performance that made me rethink a habit most of us have when writing APIs. You build an endpoint in ASP.NET Core, hook it to your database, and everything works fine. Clean code, async calls, repository pattern… all good... until one day the endpoint slows down. Not because of traffic. Not because of infrastructure. But because of data shape. Picture this: You have an endpoint that returns a list of orders with customer info and items. So you write a query using your ORM (like Entity Framework Core):  • Include Orders  • Include Customer  • Include Items Looks fine, right? But under the hood, this often becomes a massive join that multiplies rows: 𝟭 𝗼𝗿𝗱𝗲𝗿 × 𝟭 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 × 𝗡 𝗶𝘁𝗲𝗺𝘀 = 𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝗱 𝗱𝗮𝘁𝗮 𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝘄𝗶𝗿𝗲 I was reading a post from SQLAuthority that reminded of a key principle: The problem is not always the query, it’s what you ask the query to return. Instead of loading everything in one shot, a better approach in many cases is:  • Project only what you need (SELECT specific columns)  • Split queries when relationships explode  • Avoid blindly using .Include() for complex graphs For example:  • First query: Orders (lightweight)  • Second query: Items grouped by OrderId  • Merge in memory Yes, it’s two queries, but often faster, smaller, and more predictable. This becomes even more important when using databases like PostgreSQL or SQL Server in high-scale systems, where:  • Network payload matters  • Execution plans matter  • Memory pressure matters What I like about this is how it challenges a common assumption: “𝘍𝘦𝘸𝘦𝘳 𝘲𝘶𝘦𝘳𝘪𝘦𝘴 = 𝘣𝘦𝘵𝘵𝘦𝘳 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦” In reality, better-shaped data beats fewer queries almost every time. If you’re building APIs today, especially in microservices, it’s worth asking: 𝘈𝘳𝘦 𝘺𝘰𝘶 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘪𝘯𝘨 𝘲𝘶𝘦𝘳𝘺 𝘤𝘰𝘶𝘯𝘵... 𝘰𝘳 𝘥𝘢𝘵𝘢 𝘧𝘭𝘰𝘸? #DotNet #EntityFramework #SQLServer #PostgreSQL #Performance #BackendDevelopment #Microservices #API #CleanArchitecture #SoftwareEngineering #Cloud

Your API works fast locally… But becomes slow in production. Why does this happen? 👉 I’ve seen this multiple times in real systems. --- ❌ Common reasons: 1. N+1 Queries → One request triggers multiple DB calls 2. Blocking operations → Threads waiting unnecessarily 3. No caching → Repeated DB hits for same data 4. Poor database design → Unoptimized queries & indexes --- ✅ What actually helps: ✔️ Use caching (Redis) ✔️ Optimize queries & indexing ✔️ Use async processing where needed ✔️ Monitor performance (logs/metrics) --- 🧠 Reality: Performance issues don’t appear in development… They show up under real traffic. --- 💬 Curious: What’s the biggest performance issue you’ve faced in production? #Java #Backend #Performance #SystemDesign #Microservices #LearningInPublic