Caching Bugs: The 90% of 'Data Isn't Updating' Issues

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

A month ago I ran into an interesting (and slightly scary) problem. I had to design an API that runs a heavy background process. During that process, a lot of things could change in the database — multiple users, multiple updates, different operations happening at the same time. And then a thought hit me: “What if multiple users trigger this API at the same time in a horizontally scaled or async system?” That’s where things can get dangerous. You can end up with: race conditions stale or inconsistent data even potential data corruption when multiple operations depend on the same records At first, the obvious solution seems to be: Use a synchronous backend like Django transactions for sensitive operations. And yes, that helps — but only within a single instance. The real challenge starts when your system scales: multiple instances, async workers, FastAPI services, distributed architecture… At that point, you realize: 👉 local locks are not enough anymore That’s where Redis became my “safety net”. Using Redis distributed locks, you can control concurrency across the entire system: lock by user_id + process_name to prevent duplicate execution per user or lock by event_id / shared resource key to prevent conflicting operations ensure only one process can modify a critical dataset at a time So instead of relying on a single app instance, you enforce global consistency across all services. This approach works regardless of: Django FastAPI async workers multiple server instances It’s simple but powerful: 👉 “If the process is running somewhere, no one else can run it.” And honestly, Redis saved me from a lot of potential chaos. Key takeaway: When scaling systems, database safety is not just about transactions — it’s about coordination across processes. #BackendDevelopment #SystemDesign #Redis #Django #FastAPI #DistributedSystems #SoftwareEngineering #Scalability #Databases #Microservices

3/25 — Building Distributed Systems from Scratch 🔧 Built a Rate Limiter from scratch — in Go What this library does in practice: 1. Three rate-limiting algorithms: Fixed Window Counter, Sliding Window Log, Token Bucket 2. Two storage backends: In-memory (single instance) + Redis (distributed) 3. Two middleware adapters: net/http + Gin 4. Per-key limiting: Rate limit by IP, API key, user ID, or any custom key 5. Atomic Redis limiting: Lua script removes the INCR/EXPIRE race condition 6. Fail-open behavior on backend errors: Keeps the service available during Redis issues 7. Prometheus instrumentation: Allowed/denied counters, errors, active keys Throughput benchmarks (Apple M1): Fixed Window 53 ns/op (18M+ ops/sec), Token Bucket 98 ns/op (10M+ ops/sec), Sliding Window 69 ns/op (14M+ ops/sec) CI + quality gates: go vet, unit tests, race detector, benchmark smoke runs, Docker build checks Repo: 🐹 Go → https://lnkd.in/ghGxk3zR Ps :- Can't wait to get over these building blocks and start building architecturally more complex stuff. 😫 #SystemDesign #DistributedSystems #Go #RateLimiting #SoftwareEngineering #OpenSource #Backend #PerformanceEngineering

What's the difference between an app that handles 400 users and one that handles 5,000? Not the language. Not the framework. Not the server size. It's usually one thing: knowing what NOT to ask your database. On Seendr, our video chat matching system was interrogating PostgreSQL on every single match request — in real time, for every user. After user Redis Here's what changed: ✅ Matching pool stored in Redis Sets → no more DB queries for live users ✅ Django Channels backed by Redis → WebSockets synced across all instances ✅ Celery using Redis as broker → async tasks offloaded cleanly ✅ Profile cache with smart TTLs → 94% cache hit rate I wrote a detailed breakdown of every pattern, every mistake, and every number. The article covers: — Cache-Aside, Write-Through, Cache Warming — Real-time matching with Redis Hashes and Sets — The cache stampede problem (and how to fix it) — Why redis.keys() can kill your production app — Sorted Sets for live leaderboards https://lnkd.in/esiSBsSF #Python #Django #Redis #BackendEngineering #SystemDesign #WebDevelopment Rebase Code Camp Redis

🚨 Last week , our one API crashed.... Not because of traffic. Because of one noisy user. And we didn’t even have high traffic. ------------------------------------------ He kept retrying… Again Again Again We’ve all seen this pattern. ----------------------------------- Within minutes: 💥 12K requests/sec 💥 CPU maxed 💥 DB locked 🔒 (no queries going through) ------------------------------------------------- We didn’t need scaling. We needed control. ------------------------------------------------- Next day we added: 👉 Rate Limiting (Bucket4j) 👉 Redis (distributed control) --------------------------------------------- Now: Noisy user → controlled System → stable ✅ -------------------------------------------- 💡 Lesson: Traffic doesn’t kill systems. Lack of control does. #Microservices #SystemDesign #Java #BackendEngineering #RateLimiting #SoftwareEngineering #IndianTech #Developer #JavaDeveloper #Redis #LearnInPublic #Springboot

My Node.js API started slowing down as traffic increased — here’s what actually fixed it. At first, I assumed it was just “Node being single-threaded.” Wrong. After profiling, I found: • Event loop was getting blocked by heavy JSON processing • Some DB queries were unindexed and slow • Repeated API calls were hitting the database unnecessarily Fixes that made the difference: • Moved CPU-heavy work to worker threads • Added Redis caching for repeated queries • Optimized SQL queries and added indexes Result: ~40% reduction in response time under load. Biggest lesson: Performance issues are usually architectural, not language limitations. Where do you usually start when debugging performance issues? #NodeJS #BackendEngineering #WebPerformance #APIDesign #PerformanceOptimization #SystemDesign #Scalability

Two API requests hit at the same time. Both passed the rate limit check. Both incremented the counter. The limit was 100. The counter hit 101. This is not a bug. This is a race condition. And it's embarrassingly easy to write. The naive implementation looks correct: → GET the current count from Redis → Check if it's under the limit → If yes, INCR and allow the request Three operations. Perfectly logical. Completely broken under load. Here's why. Two requests arrive simultaneously. Both GET before either one INCRs. Both see count = 99. Both pass the check. Both increment. Counter hits 101. Limit violated. This is called TOCTOU — Time Of Check To Time Of Use. The state you checked is no longer the state you're acting on. The fix: make the check and the increment a single atomic operation. Redis Lua scripts do exactly this. The entire script runs as one indivisible unit. No other Redis command executes between your first line and your last. My sliding-window.lua does this in a few lines: → GET both window counters → Calculate estimated count using the sliding window formula → If at limit: return 0 + retry-after → Otherwise: INCR, set TTL, return 1 One network round trip. Atomic. No locks. No transaction overhead. The script lives as an embedded resource in the DLL. Readable .lua file during development. Compiled into the assembly at build time. Clean separation, zero file dependencies at runtime. Distributed systems correctness often comes down to one question: Is this operation truly atomic? If not, what breaks when it isn't? What race conditions have you hit in production? Drop it below 👇 Part 3 of my rate limiter build series — follow for more. #dotnet #csharp #redis #distributedsystems #concurrency #backend #softwaredevelopment

🚀 How I Used Redis to Power Real-Time Delivery Systems While working on a delivery partner application, I got hands-on experience with Redis — and honestly, it changed how I think about performance and real-time systems. 💡 Why Redis? Redis is an in-memory data store designed for ultra-fast data access and real-time messaging. 🔧 How I used it in my project: ⚡ Caching (Performance Boost) Stored delivery status and frequently accessed data Reduced database load significantly Achieved faster response times 📡 Pub/Sub (Real-Time Updates) Broadcasted live updates to delivery partners Enabled instant notifications for order status Improved real-time tracking experience 🔥 Key Benefits I Observed: ✔️ Low latency ✔️ High scalability ✔️ Efficient data handling ✔️ Smooth real-time communication This experience gave me deeper insight into building scalable backend systems and handling real-time data flow effectively. Still exploring more advanced system design concepts — exciting journey ahead! 🚀 #Redis #BackendDevelopment #Python #RealTimeSystems #SystemDesign #FastAPI #WebSockets #LearningByDoing #SoftwareEngineer

Part 1 was about the infra. This is Part 2 - what I learned I learned once the agents were actually running. Honestly, the hardest bugs weren't in the model. They were in the plumbing around it. Early on I had agents passing messages to each other. By the time a result reached the final node, nobody could tell where it came from or why. I removed that out and replaced it with a single shared state object, a TypedDict that every agent reads from and writes to. That one change made debugging go from impossible to just hard. Memory was harder than I expected. I assumed I could just stuff everything into the context window and call it done. I ended up with three layers: in-context for the current task, Redis for session state that needed to survive across turns, and a vector DB for long-term retrieval. Each agent has a router that decides which layer to hit. I also started treating prompts like code. Every agent has its own system prompt, versioned in Git, reviewed in PRs, tested before deploy. A prompt is just another file. I don't know why it took me this long to think about it that way. The last thing and maybe the most underrated is the Postgres checkpointer. When an agent workflow fails at step 14 of 20, it doesn't restart from zero. It picks up at step 14. That alone has saved me more times than I can count. If you want to talk through the architecture DMs are open. #AgenticAI #LangGraph #Python #AWS #AIEngineering #MLOps #AIEngineering #SystemDesign #Terraform #Pinecone #Redis #LangGraph #AgenticAI #LLMOps #RAG