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🛠️ The Code Hut - Index

🌐 Distributed Systems Series 📖 Distributed Systems with Java (Series Intro) 📖 Intro to Distributed Systems ☁️ Cloud-Native Considerations ⚠️ Distributed System Anti-Patterns 🧪 Testing Strategies for Distributed Systems 🛡️ Advanced Fault Tolerance 🔗 Distributed Transactions Deep Dive 📈 Microservices Scaling Patterns 🔔 Monitoring & Alerting 📡 Event-Driven Architecture 🔐 Security in Distributed Systems 👀 Observability ⚡ Caching Strategies 📊 Consistency Models 🔍 Service Discovery & Load Balancing 💪 Resilience Patterns 💬 Communication Patterns 🧱 Fault Tolerance & Reliability 🔄 Transactions & Sagas 🤝 Consensus & Coordination 🔒 Concurrency & Locking 🏗️ Microservices & Architecture 🧩 Key Microservices Design Patterns 📐 Applying SOLID Principles 🌐 RESTful API Fundamentals — Designing Clean, Resource-Oriented Endpoints ⚡ REST vs gRPC in Java: Choosing the Right Com...
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🗄️ Caching Strategies in Distributed Systems

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore caching strategies that improve performance and reduce load on distributed systems. ⚡💾 🛠️ Why Caching Matters Caching reduces latency, avoids repeated computations, and decreases database load. Choosing the right caching strategy is crucial for performance and consistency. 1. 🏠 Local Caching Local caches store data in the memory of the application instance: ⚡ Very fast, low latency ❌ Not shared across instances 📚 Example libraries: Guava Cache, Caffeine // Using Caffeine for local caching Cache orderCache = Caffeine.newBuilder() .expireAfterWrite(10, TimeUnit.MINUTES) .maximumSize(1000) .build(); Order order = orderCache.get(orderId, id -> orderService.fetchOrder(id)); 2. 🌐 Distributed Caching Distributed caches are shared across multiple application instances: 🔄 Data is consistent across nodes 📈 Supports scaling horizontally 📚 Exam...
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⚖️ Architecture Trade-Offs & System Design Comparisons for Senior Engineers

⚡ Understanding distributed systems is not only about implementation, it’s about making the right architectural trade-offs. Senior backend and system design interviews focus on comparing approaches, identifying constraints, and justifying decisions. 💡 🧠 This guide summarizes the most important architecture comparisons every experienced engineer should confidently explain. 1. 🔄 Synchronous vs Asynchronous Communication 🔹 Synchronous communication (REST, gRPC) follows a blocking request-response model. Immediate response — the caller waits for a reply Simpler flow — easier to reason about Tighter coupling — services must be available 🔹 Asynchronous communication (Kafka, message brokers) is event-driven and decouples services. Decoupled — producers and consumers operate independently Highly scalable — can handle bursts and long-running tasks Eventual consistency — responses may not be immediate For a deeper dive, see: Communication Patterns in Di...
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