Codekerdos

Most developers know Java code runs on the JVM. But when applications start slowing down, crashing with OutOfMemory errors, or struggling under high traffic, understanding JVM tuning becomes extremely important. So, what exactly is JVM tuning? JVM (Java Virtual Machine) tuning is the process of optimizing JVM settings to improve an application's: • Performance • Memory usage • Garbage Collection behavior • CPU utilization • Response time • Stability under heavy load By default, the JVM uses generic configurations that work “okay” for most applications. But production systems often have very different workloads. For example: - A high-traffic e-commerce system - A real-time trading platform - A microservices-based backend - A streaming or analytics application All of them behave differently in terms of memory allocation, object creation, and thread usage. This is where JVM tuning helps. Some common areas of JVM tuning include: ✅ Heap size configuration (-Xms, -Xmx) ✅ Garbage Collector selection (G1GC, ZGC, Shenandoah, etc.) ✅ Thread stack sizing ✅ Metaspace optimization ✅ GC pause time reduction ✅ Monitoring memory leaks ✅ CPU and thread optimization Why is it used? Because poor JVM configuration can lead to: ❌ Frequent GC pauses ❌ High latency ❌ Application crashes ❌ Increased infrastructure cost ❌ Slow APIs and bad user experience A properly tuned JVM can significantly improve throughput, reduce response times, and make systems more reliable at scale. This is one of the reasons why backend engineers working on large-scale systems spend a lot of time analyzing: - Heap dumps - GC logs - Thread dumps - Memory allocation patterns - Application profiling reports Writing Java code is only one part of backend engineering. Making that code perform efficiently in production is where JVM tuning becomes powerful. #Java #JVM #BackendDevelopment #SystemDesign #PerformanceEngineering #SoftwareEngineering #GarbageCollection #Microservices #DevOps #Programming

Many developers confuse: 👉 Monolithic vs Microservices with 👉 Centralized vs Decentralized Systems But they are completely different concepts. Both monolithic and microservices architectures can run on distributed systems. The real difference between Centralization and De-Centralization is ownership and control. ✅ Centralized Systems: Single authority + single source of truth Examples: Google, Facebook ✅ Decentralized Systems: No single authority + distributed control Examples: Blockchain, Torrent In our latest YouTube video, I explained this concept in a simple and beginner-friendly way. This topic is extremely important for: - System Design - Backend Engineering - Distributed Systems interviews Check out the video and let me know what topic I should cover next 👇 https://lnkd.in/geGVT4KR #SystemDesign #Microservices #DistributedSystems #BackendDevelopment #SoftwareEngineering #Programming

Every software engineer today is scared of AI taking their job 😞 AI can now: • Write code • Debug issues • Generate APIs • Build full-stack apps But nobody is asking the most important question: 👉 Is AI economically sustainable at scale? Big tech companies are already struggling with AI costs. There are discussions around: • Companies reducing expensive AI tooling usage • Moving towards in-house copilots • AI budgets getting exhausted extremely fast Because AI is not “free productivity”. Every prompt costs compute. Every generated line of code costs GPUs. Every AI workflow has inference costs. Now imagine: 20,000 engineers using AI tools all day. The cost becomes massive. And today many AI tools are still heavily subsidized. What happens when subsidies disappear? Another important issue: Can we fully trust AI-generated systems? We are already seeing: • Supply chain attacks • Dependency hacks • Security breaches • Vulnerable generated code Software engineering is not just writing code. It is: • Scalability • Reliability • Security • System Design • Business tradeoffs • Ownership AI will definitely change software engineering. But “AI assisting engineers” and “AI completely replacing engineers” are two very different things. The future probably belongs to: 👉 Engineers who know how to use AI effectively. Not engineers who ignore it. And not AI replacing every engineer entirely. #AI #SoftwareEngineering #SystemDesign #Coding #Tech #ArtificialIntelligence #BackendDevelopment #Developers #CodeKerdos

Last week in Session 6 of our Gen AI Course at CodeKerdos, we covered: • Gen AI Fundamentals • AI Essentials • Modern AI system concepts • And built "Drishti" - an AI Agent 🚀 This Saturday, 30th May at 11 AM, we are continuing the next part of building Drishti in a free live webinar/session. Most people today are using AI tools. Very few actually understand how AI agents and AI systems work underneath. Our goal with these sessions is to help students think like AI engineers - not just AI users. If you want to learn how modern AI applications are actually built, feel free to join us tomorrow 👇 Google Meet Link: https://lnkd.in/gCaqCczr Would love to see passionate developers and students join ❤️ #AI #GenAI #AIAgents #ArtificialIntelligence #LLM #SoftwareEngineering #Developers #Coding #Tech #CodeKerdos

Most engineers look at CPU, memory, and dashboards when something breaks. But that’s only one-third of the story. Modern SRE is built on three pillars of observability: 📈 Metrics — What happened? 📜 Logs — Why did it happen? 🛣️ Traces — Where did it happen? Imagine a production outage. Metrics tell you that latency increased. Logs tell you that a database connection failed. Traces tell you exactly which service, API call, or dependency caused the issue. Without all three, troubleshooting becomes guesswork. This is why observability has become one of the most important skills for SREs, DevOps Engineers, Platform Engineers, and Cloud Engineers working with modern distributed systems. Monitoring tells you something is wrong. Observability tells you why. At CodeKerdos, we are starting a dedicated SRE Bootcamp soon, where we will cover observability, monitoring, incident management, SLIs, SLOs, error budgets, Kubernetes reliability, automation, and real-world production troubleshooting. DM us to get the early bird offer. #SRE #Observability #DevOps #Kubernetes #PlatformEngineering #CloudComputing #Monitoring #OpenTelemetry #Prometheus #Grafana #ReliabilityEngineering #CodeKerdos

Most developers know Redis as a “cache database”. But in real-world scalable systems, Redis solves much bigger problems. Two extremely popular use cases: 1️⃣ Caching for Read-Heavy Features If a feature gets millions of reads but data changes very rarely, directly hitting the database for every request becomes expensive. So we place Redis between the Server and the Main Database. Flow: Client → Server → Redis → Database → Server → Client (if Cache miss) Client → Server → Redis → Server → Client (if Cache hit) Most requests are served directly from Redis (Cache Hit), which: ✅ Reduces DB load ✅ Improves response time ✅ Makes the system scalable 2️⃣ Rate Limiting / DDoS Protection To protect systems from abuse and traffic spikes, we implement Rate Limiting at the Load Balancer or API Gateway layer. Example: “Allow only 100 requests/minute per IP” But where do we store request counts for every IP/User? 👉 Redis. Because Redis: ✅ Is extremely fast ✅ Supports atomic counters ✅ Supports TTL/expiry ✅ Is perfect for real-time tracking If the limit exceeds: 🚫 Reject request (HTTP 429) Redis is one of the most important technologies used in modern backend systems and System Design. #SystemDesign #Redis #BackendDevelopment #SoftwareEngineering #ScalableSystems #Coding #Tech #DistributedSystems #CodeKerdos

Most people are using AI tools. Very few actually understand how AI systems and AI agents work underneath. In Session 6 of our Gen AI Course at Codekerdos, we covered: • Gen AI Fundamentals • AI Essentials • Core concepts behind modern AI systems • And built “Drishti” — an AI Agent 🚀 The goal of this session was not just to teach prompts or tools, but to help students think like AI engineers and understand the architecture behind intelligent systems. One thing we strongly believe: The future belongs to developers who can build with AI, not just use AI. We are trying to make our students industry-ready by teaching practical concepts, real-world implementations, and hands-on AI engineering workflows. Sharing the full session below 👇 https://lnkd.in/gaAjiQBh If you want to learn Gen AI, System Design, DSA, DevOps, and Software Engineering concepts in depth from engineers working at MAANG-level companies, do check out Codekerdos ❤️ #AI #GenAI #ArtificialIntelligence #LLM #AIAgents #MachineLearning #SoftwareEngineering #Developers #Coding #Tech #OpenAI #CodeKerdos

One of the most satisfying parts of building Codekerdos is seeing students genuinely transform their careers. 🚀 A few months ago, one of our students joined the Codekerdos DevOps batch while working at Accenture. Recently, he got placed at Tata Consultancy Services with a 100%+ Salary Hike 🚀 . I recorded a small conversation with him where he shared: ✅ How he prepared for interviews ✅ What made our classes different ✅ How interactive problem-solving sessions helped him think better ✅ How mentorship from engineers working at top product companies made a difference ✅ How our team helped improve his Resume, LinkedIn, and Naukri profile One thing he especially talks about is the teaching style: 4 different mentors were used to teach different topics of DevOps. For every topic, we brought in the SME(Subject Matter Expert) for that topic. This helped students build actual interview thinking ability. All classes are taught by experienced engineers currently working at MAANG companies. If you are preparing for: • DevOps Interviews • Better DevOps roles • Product-Based Companies • Amazon / MAANG Preparation Big congratulations to him on this achievement. Wishing him the very best for his journey at Tata Consultancy Services. 🔥 You can watch the full conversation here 👇 https://lnkd.in/gmmQeP9B #Amazon #SystemDesign #SoftwareEngineering #SDE #TechCareers #InterviewPreparation #CodingInterview #CareerGrowth #CodeKerdos #Learning

Most developers think System Design is difficult. But almost every System Design topic can actually be classified into these 5 major parts. And before appearing for any big tech System Design interview, you should be thorough with each one of them. 🚀 𝗦𝘁𝗲𝗽 𝟭: 𝗕𝘂𝗶𝗹𝗱 𝗦𝘁𝗿𝗼𝗻𝗴 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 → Networking Concepts (HTTP, TCP/IP, DNS, Load Balancing) → API Design Principles (REST, GraphQL, Authentication, Rate Limiting) → Database Fundamentals (SQL, NoSQL, Indexing, Partitioning) → Caching Techniques (Redis, CDN, In-Memory Caching) Without strong fundamentals, advanced architectures become confusing. 𝗦𝘁𝗲𝗽 𝟮: 𝗟𝗲𝗮𝗿𝗻 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Vertical vs Horizontal Scaling → Traffic Distribution Strategies → Replication & Database Sharding → Message Queues & Asynchronous Processing This is where systems start handling millions of users efficiently. 𝗦𝘁𝗲𝗽 𝟯: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗦𝘆𝘀𝘁𝗲𝗺 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 → Monolithic vs Microservices → Event-Driven Architectures → CQRS & Event Sourcing → High Availability & Fault Tolerance Most interview discussions revolve around architectural trade-offs. 𝗦𝘁𝗲𝗽 𝟰: 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 → CAP Theorem & Consistency Trade-offs → Distributed Storage Systems → Data Replication & Partitioning → Query Optimization & Efficient Indexing Good System Design engineers understand data deeply. 𝗦𝘁𝗲𝗽 𝟱: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗗𝗲𝘀𝗶𝗴𝗻 → Architect scalable applications → Explain technical trade-offs clearly → Participate in mock design interviews → Improve designs through continuous feedback Because System Design is not just theory. It is the ability to think, discuss, justify, and optimize systems under constraints. Master these 5 areas properly, and System Design interviews become far less intimidating. 🔥 If you want to learn these concepts from developers who work at companies like Amazon, Google, Microsoft, Nvidia, etc, then do join Codekerdos. #SystemDesign #SoftwareEngineering #BackendDevelopment #Scalability #Microservices #DistributedSystems #TechInterviews #CodingInterview #SDE #Engineering

LLMs are powerful. RAG makes them useful in the real world. Instead of relying only on training data, RAG helps AI systems retrieve real-time relevant context from documents, databases, websites, code repositories, and internal knowledge sources before generating a response. This is one of the core building blocks behind modern AI applications and Agentic AI systems. At CodeKerdos, we have started our Agentic AI and DevOps Bootcamp where we cover practical AI engineering concepts like RAG, MCP, AI agents, Kubernetes AI workflows, observability, and automation. Few seats are still open. DM us to join the batch. #RAG #LLM #AgenticAI #AI #Kubernetes #DevOps #GenerativeAI #MCP #ArtificialIntelligence #CodeKerdos