Adding Timeouts to Prevent Slow Dependencies in APIs

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

Sometimes your system isn’t slow because of heavy logic. It’s slow because it’s waiting. Waiting for: another service a database an external API And while it waits, threads just sit there doing nothing. --- This is where Async Processing helps The idea is simple: Don’t block. Do the work later. --- What this looks like Instead of doing everything in one request: User places an order System saves order immediately Email is sent later Notification is processed in background The user doesn’t wait for everything. --- How it’s usually done Background jobs Message queues (Kafka, RabbitMQ) @Async in Spring Boot You move non-critical work out of the main flow. --- Why this matters Without async: Requests take longer Threads stay blocked System struggles under load With async: Faster response times Better scalability Smoother user experience --- Real-world example When you upload a file: You don’t wait for processing You get a response quickly Processing happens in background --- Trade-offs Async adds complexity: Harder to debug Requires retry handling Failures are not immediate --- Simple takeaway Not everything needs to happen right now. --- If your system is slow, how much of that work actually needs to be done synchronously? #Java #SpringBoot #Programming #SoftwareDevelopment #Cloud #AI #Coding #Learning #Tech #Technology #WebDevelopment #Microservices #API #Database #SpringFramework #Hibernate #MySQL #BackendDevelopment #CareerGrowth #ProfessionalDevelopment #RDBMS #PostgreSQL #backend

Just shipped: SNIP — A URL Shortener built with Spring Boot & PostgreSQL. Ever wondered how bit.ly or go/links work internally? I built one from scratch to find out. Where it's used in the real world: Shortening links for social media & SMS Email campaigns to track click-through rates. Internal enterprise tools like go/handbook. QR codes on product packaging. What I built: Shorten any URL with custom expiry & click tracking PostgreSQL on Render for persistent production storage . Dockerized deployment with a futuristic terminal UI Lesson learned: H2 works great locally but wipes all data on every cold start in production. Migrating to PostgreSQL fixed it permanently. GitHub: https://lnkd.in/gDbN8n4H Live: https://lnkd.in/g_-D5-dz #SpringBoot #Java #PostgreSQL #Docker #BackendDevelopment

Level Up Your Backend: REST APIs with Spring Boot 4.0.5 & PostgreSQL Building a REST API is easy, but building one the right way is what separates the juniors from the pros. 🛠️ In my latest tutorial on Unshakable with Cliff, I walk through the modern "Clean Code" stack for Java developers. We aren't just writing code; we’re architecting a solution. What we’re implementing in this guide: ✅ Spring Boot 4.0.5: Staying on the bleeding edge of the ecosystem. ✅ Lombok Power: Using @Data, @NoArgsConstructor, and @AllArgsConstructor to eliminate boilerplate. ✅ JPA Magic: Leveraging built-in queries to fetch data from PostgreSQL without writing a single line of SQL. ✅ Clean Controllers: Creating a clean endpoint to fetch users and testing it live. If you’re a developer looking to move from "Junior" to "Intermediate," understanding how these pieces fit together is crucial. 📺 Watch the full tutorial here: https://lnkd.in/d_XJAP3p I’m currently at 935 subscribers and pushing for that 1,000 subscriber milestone! If you find value in these deep dives into the "Cliff Stack," please hit that Subscribe button and join the community. 🤝🔥 #Java #SpringBoot #BackendDevelopment #PostgreSQL #Lombok #SoftwareEngineering #CodingTutorial #UnshakableWithCliff #TechCommunity

Partial Failure (When Only Part of Your System Breaks) --- Built:- A service that aggregates data from multiple services: User service Order service Recommendation service All combined into one response. --- Problem I faced:- Everything worked fine… until one dependency started failing. Then: Entire API failed Even though other services were working Users saw errors for everything One small failure took down the whole response. --- What was really happening:- This was a partial failure. Only one service failed… but the system treated it like a full failure. * No isolation * No fallback * No graceful handling --- How I fixed it:- Instead of failing everything: Added fallback responses for optional services Marked some data as non-critical Used timeouts + circuit breakers Returned partial responses where possible Now: Core data always loads Optional features degrade gracefully System stays usable even during failures --- What I learned:- In distributed systems, failure is normal. The goal is not to avoid failure. It’s to limit its impact. --- Simple mental model:- If one feature breaks, the whole app shouldn’t feel broken. --- Carousel Breakdown :- Slide 1 → One service fails Slide 2 → Entire API fails Slide 3 → Identify partial failure Slide 4 → Add fallbacks Slide 5 → Return partial response Slide 6 → System stays usable --- Question::- If one dependency in your system goes down, does your API fail completely… or degrade gracefully? #Java #SpringBoot #Programming #SoftwareDevelopment #Cloud #AI #Coding #Learning #Tech #Technology #WebDevelopment #Microservices #API #Database #SpringFramework #Hibernate #MySQL #BackendDevelopment #CareerGrowth #ProfessionalDevelopment #RDBMS #PostgreSQL #backend

Sometimes one request needs to touch multiple systems. It looks simple: Save order Update inventory Process payment But what happens if one step fails? In a single database, you’d use a transaction. In distributed systems, that’s not so simple. That’s where Distributed Transactions come in. The problem You’re dealing with multiple services, each with its own database. If one succeeds and another fails, your system becomes inconsistent. The traditional approach (2PC) Two-Phase Commit tries to solve this: 1. Ask all services if they can commit 2. If yes → commit everywhere 3. If not → rollback everywhere Sounds perfect, but: Slow Complex Not scalable Can lock resources That’s why it’s rarely used in modern microservices. The practical approach Instead of strict transactions, systems use: Saga Pattern (you’ve seen this) Eventual Consistency Compensating actions You don’t force everything to succeed together. You handle failures gracefully. Why this matters In distributed systems: Failures are normal Networks are unreliable Systems are independent Trying to make everything perfectly consistent often hurts performance and scalability. Simple takeaway In microservices, consistency is designed — not guaranteed. If multiple services in your system need to update data together, are you using strict transactions — or handling it differently? #Java #SpringBoot #Programming #SoftwareDevelopment #Cloud #AI #Coding #Learning #Tech #Technology #WebDevelopment #Microservices #API #Database #SpringFramework #Hibernate #MySQL #BackendDevelopment #CareerGrowth #ProfessionalDevelopment #RDBMS #PostgreSQL #backend

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

Built a production-grade backend from scratch — here's what I learned. TaskAlloc is an employee and task allocation REST API I built with FastAPI and PostgreSQL. Not a tutorial follow-along — I designed the architecture, made the decisions, and figured out why things break. What's under the hood: → 3-tier role system (Admin / Manager / Employee) with access enforced at the query layer — not just filtered in the response → JWT auth with refresh token rotation. Raw tokens never touch the database, only SHA-256 hashes are stored. If the DB leaks, the tokens are useless. → Task state machine — PENDING → IN_PROGRESS → UNDER_REVIEW → COMPLETED. Invalid transitions are rejected before any database write. → Middleware that auto-logs every mutating request with who did it, what resource they touched, and the HTTP status code → 67 passing tests against SQLite in-memory. No external database needed to run the suite. 35+ endpoints. Soft delete. UUID primary keys. Docker + Docker Compose. Full Swagger docs. The thing that surprised me most was how much I learned from just trying to do things the right way — not "make it work" but "make it work correctly." Things like why audit logs shouldn't have a foreign key to users, or why you write the activity log before the status update commits. GitHub in the comments. #FastAPI #Python #BackendDevelopment #PostgreSQL #SoftwareEngineering #BuildingInPublic #OpenToOpportunities #Development

I thought switching databases was easy. Production disagreed. I recently migrated my backend from MySQL to PostgreSQL (Supabase + Render). On paper: switch the database, update the connection string, run migrations. In reality? Deployment failed instantly. Three times. Here's what actually broke 👇 🔌 Port confusion Direct connections (port 5432) weren't reachable from Render. Fix: switch to Supabase's connection pooler (port 6543). 🌐 IPv6 walls Supabase's direct endpoint resolved to IPv6. Render couldn't reach it. Silent "Network unreachable" errors — until I dug deep into DNS resolution. 🔑 Stale environment variables My app kept connecting to the old database even after I updated everything. A local .env file was silently overriding Render's environment variables. The whole time. ⚙️ Alembic picking the wrong URL Migrations failed because the database URL was being loaded from the wrong config layer at runtime. Traced it across three files before I found it. What this really taught me: → "Works locally" is meaningless in production → Always log your actual database URL at startup — not what you think you configured → Managed services have hidden constraints. Learn them before you deploy → Debugging deployment is a skill. Treat it like one. If you're planning a similar migration: ✅ Use the connection pooler ✅ Enforce SSL ✅ Verify what your app is actually connecting to at runtime ✅ Never trust your local .env in production Three failed deployments. A lot of logs. One stable production app. Worth it. 🚀 What's the most painful deployment bug you've ever chased? Drop it below 👇 #Python #PostgreSQL #BackendDevelopment #SoftwareEngineering #DevOps #BuildingInPublic

DevOps is about solving the "invisible" problems. 🛠️ I just wrapped up a 3-tier React, FastAPI, and PostgreSQL project, and the real victory wasn't just getting it to run—it was handling the hurdles along the way. In this project, I moved beyond simple containers and focused on Infrastructure Resiliency: 🔹 The "Wait for DB" Problem: Implemented custom Python Retry Logic to ensure the backend waits for the PostgreSQL engine to be ready before attempting a connection. 🔹 The Cross-Platform Bug: Diagnosed and fixed a "Line Ending" (CRLF vs. LF) syntax error that occurs when moving SQL initialization scripts from Windows to Linux containers. 🔹 Automated Bootstrapping: Used servers.json configuration injection to auto-register my database server in pgAdmin, eliminating manual GUI setup. The Stack: ✅ Frontend: React (Dark Mode Dashboard) ✅ Backend: Python (FastAPI) ✅ Database: PostgreSQL (SQL) ✅ Infrastructure: Terraform & Docker Compose ✅ Cloud: Amazon ECR (AWS) DevOps isn't just about the tools you use; it’s about how you engineer them to work together seamlessly. Check out the video below to see the full "Triple Threat" workflow! 🚀 #DevOps #AWS #Terraform #Python #React #CloudEngineering #InfrastructureAsCode #PostgreSQL