Fixing OutOfMemoryError in Java Applications

⚠️ “Why does the app crash after running for a few hours?” Everything looked stable… until it didn’t. The system worked perfectly after deployment. But after a few hours — crash. Restart. Repeat. 🔍 I checked metrics. Memory usage kept increasing… and never came down. 🧠 The issue: A memory leak in the application. • Objects not being released • Improper handling in long-running processes • Increased heap usage over time ⚠️ Impact: • Frequent application crashes • Pod restarts in Kubernetes • Unstable production system 🛠️ What I did: • Analyzed heap dumps • Identified objects not getting garbage collected • Fixed code causing memory retention • Tuned JVM heap size & GC settings • Added monitoring alerts for memory thresholds ⏱️ After fix: Memory stabilized. No more crashes. ✅ System became reliable again ✅ Zero unexpected restarts 💡 Lesson: Not all bugs are visible immediately… some grow silently until they break everything. #Java #Performance #MemoryLeak #Kubernetes #Backend #SoftwareEngineering

I was given a debug exercise. 3 bugs to fix. The codebase was Python and React. Not my primary stack. I had to read every line slowly instead of scanning. That slowness changed everything. I found the 3 bugs. Then I kept reading. Every critical issue had the same shape: right infrastructure, wrong wiring. Built correctly. Never connected. RLS policies: enabled, never enforced. SecureClient: built, never used. TenantCache: imported nowhere. Revenue calculator: correct logic, never called. Auth middleware: wired to nothing. I found 50 issues total. Fixed 14. The assignment asked for 3. Why 14? My engineering manager used to say it in every 1:1: enough is not good enough. But knowing where to draw the line, that is still the job. I drew it at trust. Fixed everything a user would need to trust before they could work. Left the rest. Swipe to see the pattern. Full article in the comments.

Why Executor Framework is a game changer: Thread Pooling: 1. Reuses threads instead of creating new ones 2. Improves performance and resource utilization Better Control: 1.Limit number of threads 2.Manage lifecycle (shutdown, await termination) Async Execution with Futures: Future<String> result = executor.submit(() -> "Task Done"); System.out.println(result.get()); Scalability: Handles high-load systems smoothly Real-world impact: In one of my services, switching to Executor Framework: 1.Reduced CPU spikes 2.Improved response time 3.Made async processing clean and maintainable Lesson learned: Creating threads is easy but Managing them efficiently is where real engineering begins. If you’re working on backend systems, APIs, or microservices: Stop creating threads manually.Start using the Executor Framework. #Java #Multithreading #Concurrency #BackendDevelopment #Performance

You hit “Enter” on a URL… and within milliseconds, you get a response. But here’s the truth most engineers miss 👇 👉 Your API doesn’t start in your controller… 👉 It starts in the OS kernel Before your Spring Boot app even sees the request: • DNS resolves the domain • OS creates a socket (file descriptor) • TCP handshake establishes a connection • TLS secures the channel • Data is split into TCP packets • Kernel buffers and reassembles everything And only then… your application gets a chance to run. --- 💡 The uncomfortable reality: Most developers spend 90% of their time optimizing: ✔ Controllers ✔ Queries ✔ Business logic But ignore the layers that actually control: ❌ Latency ❌ Throughput ❌ Scalability --- ⚙️ Real performance lives in: • Kernel queues (SYN queue, accept queue) • Socket buffers • Syscalls (accept, read, write) • Threading vs event-loop models • TCP/IP behavior --- 🚨 That’s why in production you see: • High latency with “fast” code • Thread exhaustion under load • Random connection drops • Systems that don’t scale --- 🧠 The shift that changed how I design systems: I stopped thinking in terms of “APIs” and started thinking in terms of: 👉 Data moving through layers Browser → OS → Kernel → Network → Server → App → Back --- If you understand this flow, you don’t just write code… 👉 You build systems that scale. --- 👇 I’ve broken this entire flow down (end-to-end) in the carousel Comment “DEEP DIVE” if you want the next post on: ⚡ epoll vs thread-per-request (what actually scales to millions of requests) #SystemDesign #BackendEngineering #DistributedSystems #Java #SpringBoot #Networking #Scalability #SoftwareEngineering #TechDeepDive

🚦 Your API is talking… but are you understanding its language? 💡 Every HTTP Status Code is a hidden message about what really happened behind the request. If you are working with APIs, backend, or frontend — understanding HTTP status codes saves hours of debugging. Here is the simple meaning 👇 🔵 1xx – Informational Request received, continue process Example: 100 Continue 🟢 2xx – Success Everything worked perfectly Example: 200 OK, 201 Created 🟡 3xx – Redirection Resource moved, try another URL Example: 301 Moved Permanently, 302 Found 🔴 4xx – Client Error Problem in request (wrong input, unauthorized etc.) Example: 400 Bad Request, 401 Unauthorized, 404 Not Found 🟣 5xx – Server Error Server failed to process valid request Example: 500 Internal Server Error, 503 Service Unavailable 📌 Most commonly used codes developers should remember: 200 → success 201 → created 400 → bad request 401 → unauthorized 403 → forbidden 404 → not found 500 → server error Understanding status codes helps you: ✔ Debug faster ✔ Build better APIs ✔ Write production-ready backend Follow me for simple backend & system design explanations 🚀 #backend #api #webdevelopment #softwareengineering #programming #developers #coding #fullstack #restapi #http #systemdesign #learncoding #tech #python #java #javascript #100daysofcode #codinglife #developercommunity HP Hewlett Packard Enterprise Walmart Dell Technologies IBM

Day 4: The Logging Mistake Nobody talks about this in backend development… Your logging might be slowing down production. ⸻ ⚠️ The Bug We’ve all written this: log.info("User data: " + user); Looks fine. But under load, this becomes a problem: • String concatenation happens every time • Even when logging level is OFF • Adds unnecessary CPU overhead • Can accidentally log sensitive data 👉 Silent performance + security issue ⸻ ✅ The Fix log.info("User data: {}", user); Why this works: • Lazy evaluation (only logs when needed) • No unnecessary object/string creation • Cleaner and structured logs ⸻ In high-throughput systems (millions of requests): Bad logging ≠ small issue It directly impacts: • Latency • GC pressure • Observability quality Logging should be efficient, not just informative. How do you handle logging in production systems? Structured logging / masking / async logs? #BackendDevelopment #Java #SpringBoot #Microservices #Performance #CleanCode #SoftwareEngineering #Developers #TechTips #Logging

A subtle Spring behavior that causes real production issues: @Transactional propagation. Most people rely on the default propagation without thinking about transaction boundaries. Example: Method A → @Transactional (REQUIRED) calls Method B → @Transactional (REQUIRES_NEW) What actually happens? Method B runs in a NEW transaction. So even if Method A fails and rolls back, Method B can still commit ❌ Result: Partial data committed → inconsistent state Fix: • Use REQUIRED if operations must succeed or fail together • Use REQUIRES_NEW only when you intentionally need an independent transaction (e.g., audit/logging) • Define transaction boundaries clearly at the service layer Seen this during backend development while handling dependent operations. Lesson: Don’t rely on defaults — design your transaction boundaries consciously. #SpringBoot #Java #Transactions #Microservices #Backend #SoftwareEngineering

Lessons from Real Backend Systems Short reflections from building and maintaining real backend systems — focusing on Java, distributed systems, and the tradeoffs we don’t talk about enough. ⸻ We had logs everywhere. Still couldn’t explain the outage. At first, it didn’t make sense. Every service was logging. Errors were captured. Dashboards were green just minutes before the failure. But when the system broke, the answers weren’t there. What we had: [Service A Logs] [Service B Logs] [Service C Logs] What we needed: End-to-end understanding of a single request The issue wasn’t lack of data. It was lack of context. Logs told us what happened inside each service. They didn’t tell us how a request moved across the system. That’s when we realized: Observability is not about collecting signals. It’s about connecting them. At scale, debugging requires three perspectives working together: Logs → What happened? Metrics → When and how often? Traces → Where did it happen across services? Without correlation, each signal is incomplete. The turning point was introducing trace context propagation. [Request ID / Trace ID] ↓ Flows across all services ↓ Reconstruct full execution path Now, instead of guessing: * We could trace a failing request across services * Identify latency bottlenecks precisely * Understand failure propagation Architectural insight: Observability should be designed alongside the system — not added after incidents. If you cannot explain how a request flows through your system, you cannot reliably debug it. Takeaway: Logs help you inspect components. Observability helps you understand systems. Which signal do you rely on most during incidents — logs, metrics, or traces? — Writing weekly about backend systems, architectural tradeoffs, and lessons learned through production systems. Keywords: #Observability #DistributedSystems #SystemDesign #BackendEngineering #SoftwareArchitecture #Microservices #Tracing #Monitoring #ScalableSystems

I sat in a debugging session where the question was embarrassingly  simple: did the dependency recover, or did we serve fallback?  We had retries, a timeout, a fallback path and the dashboard said: clean success.  It took two engineers and forty minutes of log tracing to figure  out that "clean success" meant the fallback had been serving  cached responses for twenty minutes while upstream recovered.  That is the composition problem.  Once timeout, retry, fallback, and breaker checks all live in the  same part of the request path, the code becomes harder to reason  about than the failure itself.  Structured concurrency gives you a cleaner boundary: keep the  request lifecycle separate from the policies around it. So those  policies can be tested, logged, and reviewed independently.  The rule I keep coming back to: if a policy changes what the  caller sees, it should be visible in the code and visible in  the metrics. #Java #StructuredConcurrency #ProjectLoom #BackendEngineering  #DistributedSystems

I increased concurrency to speed up a bulk workflow. It worked… until it didn’t. At higher volumes, things started failing with: PrematureCloseException — connections closing before response That’s when I realized: this wasn’t a performance problem anymore — it was a system pressure problem. What actually fixed it: * reducing unsafe parallelism * treating concurrency as a budget, not a goal * tuning chunk size for stability * adding retry with backoff (not blind retries) * fixing connection pool behavior * preserving partial failures instead of failing everything The biggest lesson? > More concurrency doesn’t always mean more throughput. Full debugging story: https://lnkd.in/g_Mq45kw #SpringBoot #Java #Backend #SystemDesign #DistributedSystems #Debugging