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Microservices Design Patterns 💡 Microservices architecture is a design approach that structures an application as a collection of loosely coupled services. Each service is self-contained and focuses on a specific business capability. 1. API Gateway 🔦 An API Gateway acts as a single entry point for all client requests to backend services. It handles requests by routing them to the appropriate microservices, aggregating results, and returning them to the client. This pattern helps to: 📌 Simplify the client interface by providing a unified API. 📌 Manage cross-cutting concerns like authentication, logging, and rate limiting. 📌 Reduce the number of round trips between the client and the server, improving performance. 2. Service Discovery 🔦 Service Discovery is a mechanism that allows microservices to find each other dynamically, without hardcoding service locations. It can be implemented using: 📌 Client-side discovery: The client is responsible for determining the location of the service. 📌 Server-side discovery: A load balancer or API Gateway queries a service registry to route requests. Benefits include enhanced flexibility and scalability, as services can be added or removed without impacting the clients. 3. CQRS (Command Query Responsibility Segregation) 🔦 CQRS is a pattern that separates the reading (query) and writing (command) of data into different models. This allows for: 📌 Optimized performance by scaling read and write operations independently. 📌 Different data storage mechanisms for reads and writes, which can be tailored to their specific needs. 📌 Improved security by allowing different permissions for read and write operations. CQRS is often used in conjunction with event sourcing, where state changes are stored as a sequence of events. 4. Backends for Frontends (BFF) 🔦 The BFF pattern involves creating separate backend services tailored to the needs of different front-end applications (e.g., web, mobile). This approach allows: 📌 Frontends to have a dedicated backend that optimizes data fetching and processing for their specific requirements. 📌 Simplified API responses, as the BFF can aggregate and format data as needed for the frontend. 📌 Faster development cycles, as changes can be made independently for each frontend without affecting others. 5. Event-Driven Architecture 🔦 In an event-driven architecture, services communicate through events, which are messages that indicate a change in state. This pattern supports: 📌 Loose coupling between services, as they do not need to know about each other directly. 📌 Asynchronous processing, which can improve performance and responsiveness. 📌 Enhanced scalability, as services can independently scale based on event load. Common implementations include message brokers (e.g., Kafka, RabbitMQ) that facilitate event distribution. Want to know more? Follow me or connect🥂 Please don't forget to like❤️ and comment💭 and repost♻️, thank you🌹🙏

8 Must-Used Distributed System Patterns 💡Distributed systems are complex architectures that require careful design to ensure reliability, scalability, and maintainability. 💡Event Sourcing: This pattern involves storing the state of a system as a sequence of events. Instead of persisting the current state, every change is recorded as an event, which can be replayed to reconstruct the state at any point in time. This provides a robust audit trail and allows for flexibility in how the system evolves. 💡Sharding: Sharding is the practice of partitioning data across multiple databases or servers to distribute load and improve performance. Each shard holds a subset of the data, allowing for parallel processing and reducing the strain on any single database or server. 💡Leader Election: This pattern is used to designate a single node as the coordinator among a group of nodes in a distributed system. The leader is responsible for managing tasks, making decisions, and coordinating activities. Leader election algorithms ensure that a leader is chosen and that a new leader can be elected if the current one fails. 💡Pub/Sub (Publish/Subscribe): In this messaging pattern, publishers send messages to a topic without knowledge of subscribers, and subscribers receive messages from topics they are interested in without knowledge of publishers. This decouples the components of the system, allowing for more scalable and flexible architectures. 💡Sidecar Pattern: The sidecar pattern involves deploying a secondary application alongside a primary application to extend its capabilities. This pattern is often used in microservices architectures, where the sidecar can handle cross-cutting concerns such as logging, monitoring, and service discovery without modifying the primary application. 💡Ambassador Pattern: Similar to the sidecar pattern, the ambassador pattern involves deploying a proxy service alongside a primary service. However, the ambassador is primarily used to manage communication between a service and external systems, such as APIs or databases, handling tasks like authentication, routing, or data transformations. 💡CQRS (Command Query Responsibility Segregation): This pattern separates the read and write operations of a system. Commands modify the state, while queries retrieve data. This allows for optimization of each side independently and can improve performance and scalability, especially in systems with complex business logic. Want to know more? Follow me or connect🥂 Please don't forget to like❤️ and comment💭 and repost♻️ x.com/sina_riyahi medium.com/@Sina-Riyahi Instagram.com/Cna_Riyahi github.com/sinariyahi