The Code Hut

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore cloud-native concepts, best practices for containerized applications, and how to leverage Kubernetes and Docker in distributed systems. Why Cloud-Native Matters Cloud-native design allows your services to be scalable, resilient, and easy to deploy in cloud environments. 1. Containers and Docker Package applications with all dependencies for consistent environments Lightweight and portable compared to virtual machines Example: Dockerfile for a Java microservice FROM openjdk:17-jdk-slim COPY target/order-service.jar /app/order-service.jar WORKDIR /app ENTRYPOINT ["java", "-jar", "order-service.jar"] 2. Kubernetes Basics Automated deployment, scaling, and management of containerized applications Concepts: Pods, Deployments, Services, ConfigMaps, Secrets Example: Deployment for a Java microservice apiVersion: apps/v1 kind: Deployme...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore common anti-patterns that can lead to failures, performance issues, and maintenance nightmares in distributed systems. 1. God Service A single service doing too much becomes a bottleneck and hard to maintain. Solution: Break services into smaller, focused microservices 2. Tight Coupling Between Services Services that rely heavily on each other reduce flexibility and increase failure risk. Solution: Use asynchronous communication, APIs, and events 3. Shared Database Anti-Pattern Multiple services writing to the same database creates coordination issues. Solution: Each service should manage its own database; use events or sagas for consistency 4. Ignoring Failures Not handling partial failures or retries can cause cascading issues. Solution: Implement fault tolerance patterns: retries, circuit breakers, bulkheads 5. Overusing Distributed Transactions Using 2-ph...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore testing strategies that help ensure reliability, correctness, and resilience in complex distributed systems. Why Testing Distributed Systems is Challenging Unlike monolithic applications, distributed systems involve multiple services, networks, and databases. Failures can be partial or intermittent, making testing more complex: Service-to-service communication failures Concurrency and race conditions Network partitions and latency 1. Unit Testing Test individual components in isolation using mocks or stubs: @Test public void testOrderService() { PaymentService paymentService = mock(PaymentService.class); OrderService orderService = new OrderService(paymentService); Order order = new Order(...); orderService.process(order); verify(paymentService).charge(order); } 2. Integration Testing Test multiple components together, often with in-memory or container...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore advanced fault tolerance techniques, including retries, circuit breakers, and recovery strategies for distributed systems. Why Fault Tolerance Matters Distributed systems are prone to partial failures, network issues, and service outages. Fault tolerance ensures your system continues to operate and recover gracefully. 1. Retry Strategies Simple Retry: Retry a failed operation a fixed number of times. Exponential Backoff: Increase the wait time between retries to reduce load. Jitter: Add randomness to backoff to avoid thundering herd problems. Java Example: Retry with Exponential Backoff int retries = 5; long wait = 100; // initial backoff in ms for (int i = 0; i 2. Circuit Breakers Circuit breakers prevent cascading failures by stopping requests to failing services. Closed: Requests flow normally. Open: Requests are blocked due to failures. Half-Open...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll dive deeper into distributed transactions, advanced Saga patterns, and techniques to maintain consistency across microservices. Why Distributed Transactions Are Tricky In a distributed system, each service typically manages its own database. Coordinating multiple services to commit or roll back changes atomically is challenging because: Network failures can occur Partial failures can leave inconsistent state Global locking is expensive and reduces availability 1. Saga Pattern Deep Dive The Saga pattern breaks a transaction into a series of smaller, independent steps with compensating actions if something fails. Choreography: Services emit events that trigger the next step. Orchestration: A central coordinator tells each service what action to take next. 2. Example: Orchestrated Saga in Java // Orchestrator triggers a series of service calls public class OrderSagaOrche...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore how to scale microservices effectively to handle increasing load and maintain performance. Why Scaling Matters Microservices allow independent scaling of services, but improper scaling can lead to bottlenecks, downtime, or excessive costs. Understanding scaling patterns ensures your system remains responsive and resilient. 1. Types of Scaling Vertical Scaling (Scale Up): Add more CPU, memory, or storage to existing instances. Horizontal Scaling (Scale Out): Add more instances of a service to handle increased load. 2. Horizontal Scaling Patterns Load Balancing: Distribute requests across multiple instances using a load balancer. Stateless Services: Design services to be stateless so new instances can be added easily. Partitioning / Sharding: Split data or traffic across multiple instances to reduce contention. 3. Vertical Scaling Considerations Quick and...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll cover how to monitor your distributed systems and set up alerts to catch issues before they impact users. Why Monitoring Matters Distributed systems are complex and failures can propagate quickly. Effective monitoring helps you: Detect anomalies and failures early Understand system behavior Improve reliability and performance 1. Key Metrics to Monitor Latency: Measure response times across services Throughput: Requests processed per second Error rates: Count of failed requests or exceptions Resource usage: CPU, memory, disk, network Queue depth: For messaging systems like Kafka 2. Tools for Monitoring Prometheus: Metrics collection and storage Grafana: Dashboards for visualizing metrics ELK Stack (Elasticsearch, Logstash, Kibana): Centralized logging and analysis Jaeger / Zipkin: Distributed tracing for request flows 3. Exampl...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore Event-Driven Architecture (EDA) and how it enables scalable, decoupled distributed systems. Why Event-Driven Architecture? EDA decouples services through events, allowing asynchronous communication and improving scalability, responsiveness, and fault tolerance. 1. Key Concepts Event: A record of something that happened in the system. Event Producer: Service that publishes events. Event Consumer: Service that reacts to events. Event Broker: Middleware (like Kafka) that routes events from producers to consumers. 2. Kafka Example Kafka is a popular distributed event streaming platform. Here’s a simple Java producer and consumer example: // Producer Properties props = new Properties(); props.put("bootstrap.servers", "localhost:9092"); props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); props.put...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore essential security practices for distributed systems. Why Security Matters Distributed systems are exposed to more attack vectors than monolithic systems. Proper security ensures confidentiality, integrity, and availability of data and services. 1. Authentication Verify the identity of users or services: Common approaches: OAuth2, JWT, API keys Use centralized identity providers for microservices // Spring Security JWT example Authentication authentication = authenticationManager.authenticate( new UsernamePasswordAuthenticationToken(username, password) ); String token = jwtProvider.generateToken(authentication); 2. Authorization Control what authenticated users or services can access: Role-based access control (RBAC) Attribute-based access control (ABAC) // Spring Security role-based example @PreAuthorize("hasRole('ADMIN')") public void dele...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore how to gain insight into distributed systems using logging, metrics, and distributed tracing. Why Observability Matters Distributed systems are complex. Observability allows you to understand system behavior, troubleshoot issues, and improve reliability. 1. Logging Centralized logging helps track events across multiple services: Use structured logs (JSON) for easy querying Centralize logs using tools like ELK Stack (Elasticsearch, Logstash, Kibana) or Graylog // Example with SLF4J private static final Logger logger = LoggerFactory.getLogger(OrderService.class); logger.info("Order created with id {}", orderId); logger.error("Failed to process order {}", orderId, exception); 2. Metrics Metrics provide numerical insight into system performance: Track response times, throughput, error rates Use libraries like Micrometer with Prometheus/Grafana ...

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 Example libraries: Redis, Hazelcas...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore how distributed systems maintain data consistency across nodes and the trade-offs involved. Why Consistency Matters In distributed systems, multiple nodes can store copies of the same data. Without proper consistency, users may see outdated or conflicting data. 1. Strong Consistency Strong consistency ensures that all nodes see the same data at the same time: Any read reflects the latest write Easy for developers to reason about May increase latency due to synchronization across nodes // Example: using a central database transaction @Transactional public void updateOrder(Order order) { orderRepository.save(order); } 2. Eventual Consistency Eventual consistency ensures that all nodes will converge to the same state eventually: Reads may return stale data temporarily Higher availability and performance Common in distributed databases like Cassandra, Dynam...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore how distributed services find each other and balance load efficiently. Why Service Discovery Matters In dynamic distributed systems, services may scale up/down or move across nodes. Hardcoding endpoints isn’t feasible. Service discovery allows services to locate each other automatically. 1. Registry-Based Discovery Services register themselves in a central registry: Examples: Consul, Eureka, Zookeeper Clients query the registry to find service instances Supports dynamic scaling and fault tolerance // Spring Cloud Eureka client example @Service public class OrderClient { @Autowired private RestTemplate restTemplate; public Order getOrder(Long id) { return restTemplate.getForObject("http://ORDER-SERVICE/orders/" + id, Order.class); } } 2. Client-Side Load Balancing The client chooses a service instance, often with a load balancing strategy:...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore patterns that help distributed systems remain resilient under failure conditions. Why Resilience Patterns Matter In distributed systems, failures are inevitable. Resilience patterns help: Prevent cascading failures Maintain availability Improve user experience during partial outages 1. Circuit Breaker Stops requests to a failing service to prevent overwhelming it: // Using Resilience4j circuit breaker CircuitBreaker circuitBreaker = CircuitBreaker.ofDefaults("service"); Supplier decorated = CircuitBreaker .decorateSupplier(circuitBreaker, () -> remoteService.call()); String result = Try.ofSupplier(decorated) .recover(throwable -> "fallback").get(); 2. Bulkhead Isolates resources to prevent one failing part from affecting others: // Using Resilience4j bulkhead Bulkhead bulkhead = Bulkhead.ofDefaults("serviceBulkhead"); Supplie...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore how distributed nodes communicate reliably and efficiently. Why Communication Patterns Matter Distributed systems rely on network communication. Choosing the right pattern affects performance, reliability, and scalability. 1. REST (Representational State Transfer) REST is a popular synchronous HTTP-based communication pattern: Simple, widely supported Stateless requests Easy to debug and monitor // Spring Boot REST client example @RestController public class OrderController { @GetMapping("/orders/{id}") public Order getOrder(@PathVariable Long id) { return orderService.findOrder(id); } } 2. gRPC gRPC is a high-performance RPC framework using Protocol Buffers: Supports streaming Strongly typed contracts Lower latency than REST // gRPC service definition example service OrderService { rpc GetOrder(OrderRequest) returns (O...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore how to design systems that remain reliable even when parts of the system fail. Why Fault Tolerance Matters Distributed systems are prone to failures: network issues, server crashes, or service downtime. Fault tolerance ensures the system continues operating correctly despite these failures. Common Fault Tolerance Patterns 🔁 Retries — automatically retry failed operations. 💡 Idempotency — ensure repeated operations don’t produce duplicate effects. 📦 Replication — duplicate data across nodes to handle failures. 🚪 Circuit Breakers — prevent cascading failures by stopping calls to failing services. 📊 Bulkheads — isolate parts of the system to limit failure impact. Retry Example in Java int retries = 3; boolean success = false; while(!success && retries > 0) { try { externalService.call(); success = true; } catch (Exception e...

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll discuss how to handle transactions that span multiple services or nodes — a common challenge in distributed systems. The Problem with Distributed Transactions In a single database, transactions are simple: ACID guarantees ensure consistency. But in a distributed system: Multiple services may need to update different databases Network failures can occur mid-transaction Traditional two-phase commit (2PC) can be slow and complex The Saga Pattern The Saga pattern is a way to manage distributed transactions as a series of local transactions: Each service performs a local transaction If a step fails, compensating transactions undo previous steps Ensures eventual consistency without blocking resources Example in Java (Simplified) Imagine an e-commerce system where a transaction involves three services: Order Service Payment Service Inventory Service // ...

Welcome back to The Code Hut Distributed Systems series! In this post, we explore how distributed nodes agree on shared state and coordinate their actions — a core challenge in distributed systems. Why Consensus Matters In a distributed system, multiple nodes must agree on decisions such as: Which node is the leader? Which transaction should commit? How to maintain consistent data across nodes? Without proper coordination, you risk data inconsistency, split-brain scenarios, or lost updates. Leader Election Leader election is a common pattern where one node acts as the coordinator for a group of nodes. Popular approaches include: Using Zookeeper to manage ephemeral nodes. Using distributed algorithms like Raft or Paxos . Raft Algorithm (Overview) Raft ensures consensus in distributed systems by: Electing a leader Replicating logs consistently across followers Handling leader failures gracefully In Java, you can use libraries ...

Welcome to The Code Hut Distributed Systems series! In this first post, we’ll introduce the fundamental concepts and give you an overview of what you’ll learn in this series. What is a Distributed System? A distributed system is a collection of independent computers that appear to the user as a single coherent system. They communicate and coordinate to perform tasks, share data, and ensure reliability. Examples of distributed systems you use every day: Google Search Engine (spreads queries across thousands of servers) Apache Kafka (distributed messaging system) Cassandra / DynamoDB (distributed databases) Kubernetes (orchestrates containers across nodes) Why Distributed Systems? Distributed systems exist to solve problems that a single machine cannot handle efficiently: Scalability: Handle millions of users or requests. Fault Tolerance: If one node fails, the system keeps running. High Availability: System remains accessible 24/7. ...

Welcome back to The Code Hut Distributed Systems series! In this post, we explore one of the core challenges in distributed systems: concurrency control . Why Concurrency Control Matters In distributed systems, multiple nodes or services often need to read and write the same data concurrently. Without proper control: Data may become inconsistent Two services may overwrite each other's changes Unexpected errors or crashes can occur Optimistic Locking Optimistic locking assumes that conflicts are rare. Changes are made without locking, but before committing, the system checks if the data was modified by another transaction. In Java (JPA/Hibernate), we use a @Version field: @Entity public class Account { @Id @GeneratedValue private Long id; private double balance; @Version private Long version; // Hibernate checks this on update } If two transactions modify the same row concurrently, Hibernate throws OptimisticLockException . We...

Welcome to The Code Hut Distributed Systems series! This page serves as your central hub to explore all posts in the series. Each article dives into a key concept, mechanism, or pattern with practical Java examples. 📝 Intro to Distributed Systems Overview of distributed systems, why we need them, and what you'll learn in this series. 🔒 Concurrency & Locking Explore optimistic vs pessimistic locking and distributed locks in Java. 🤝 Consensus & Coordination Leader election, Raft, Paxos, and coordination patterns for distributed nodes. 💰 Transactions & Sagas Distributed transactions, two-phase commit, and Saga pattern in Java. ⚡ Fault Tolerance & Reliability Replication, partitioning, idempotency, and error handling in distributed systems. 📡 Communication Patt...

SOLID principles help developers build robust, maintainable, and scalable microservices . Here's how they apply: 1. Single Responsibility Principle (SRP) ✅ Each microservice should have one responsibility and reason to change. 2. Open/Closed Principle (OCP) 💡 Microservices should be open for extension but closed for modification. Use abstractions for future changes. 3. Liskov Substitution Principle (LSP) ✅ Derived classes or microservices should be substitutable without breaking system behavior. 4. Interface Segregation Principle (ISP) 💡 Expose only relevant endpoints/interfaces per service; avoid fat interfaces that force clients to depend on unused methods. 5. Dependency Inversion Principle (DIP) ✅ High-level modules should not depend on low-level modules directly; use abstractions for decoupling and flexibility. Conclusion Applying SOLID principles in microservices ensures your architecture remains clean, modular, and maintainable as the system grows. Labe...

Microservices architecture introduces complexity. Design patterns help solve recurring problems in scalability, resilience, and communication . 1. API Gateway 💡 Acts as a single entry point for all clients, routing requests to appropriate services. 2. Circuit Breaker ⚠️ Prevents cascading failures by stopping requests to failing services and providing fallback responses. 3. Saga Pattern 💡 Manages distributed transactions by breaking them into local transactions with compensating actions. 4. Event Sourcing 💡 Stores state changes as a sequence of events to rebuild the current state. 5. CQRS (Command Query Responsibility Segregation) ✅ Separates read and write operations to improve performance and scalability. Conclusion Using these microservices design patterns can make your architecture more resilient, maintainable, and scalable . Labels: Microservices, Design Patterns, Architecture, Resilience

Design patterns provide reusable solutions to common software problems. Here are the most important patterns every Java developer should know. 1. Creational Patterns Singleton ✅ Ensures a class has only one instance. Useful for logging, configs Factory 💡 Creates objects without exposing instantiation logic. Builder 💡 Helps construct complex objects step by step. 2. Structural Patterns Adapter 💡 Allows incompatible interfaces to work together. Decorator 💡 Adds behavior to objects dynamically. Facade ✅ Provides a simplified interface to a complex subsystem. 3. Behavioral Patterns Observer ✅ Notifies dependent objects automatically of state changes. Strategy 💡 Defines a family of algorithms and makes them interchangeable. Command 💡 Encapsulates a request as an object. Conclusion Understanding Java design patterns is crucial for writing clean, maintainable, and reusable code . Start by implementing a few of these in your projects. ...

Whether you are preparing for a Java interview or brushing up your knowledge, these are the most common core Java questions you should know. Each question includes a short explanation or code example. 1. What is the difference between JDK, JRE, and JVM? JVM: Runs Java bytecode and provides platform independence. JRE: JVM + libraries required to run Java programs. JDK: JRE + development tools like compiler and debugger. 2. Explain Java memory model (Heap vs Stack) Heap: Stores objects and instance variables; garbage-collected. Stack: Stores method calls, local variables; follows LIFO. 3. What are Java access modifiers? Access levels: private , default , protected , public . They control visibility of classes, methods, and fields. 4. Difference between == and equals() String a = new String("Java"); String b = new String("Java"); System.out.println(a == b); // false, compares references System.out.println(a.equals(b)); // true, compares va...

Java 17 is a long-term support (LTS) release that introduces several modern features to improve code readability, performance, and safety. In this post, we’ll cover the most important features every Java developer should know, with examples. 1. Sealed Classes Sealed classes allow you to restrict which classes can extend or implement a class/interface , making your hierarchy more controlled and safe. // Define a sealed class public sealed class Shape permits Circle, Rectangle { } public final class Circle extends Shape { } public final class Rectangle extends Shape { } ✅ Use Case: When you want to control all possible subclasses , useful in modeling finite hierarchies. 2. Pattern Matching for instanceof Pattern matching simplifies type checks and casting. Object obj = "Hello Java 17"; if (obj instanceof String s) { System.out.println(s.toUpperCase()); } ✅ Benefit: No more explicit casting, cleaner and safer code. 3. Records Records are compact, i...