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Caching in a Typical Architecture: A Multi-Layered Approach

Caching in a Typical Architecture Caching in a Typical Architecture: A Multi-Layered Approach Data is cached at multiple levels across a system, from the front end to the back end , to improve performance, scalability, and reliability . This guide explains the different caching layers in a standard architecture. Multiple Layers of Caching 1. Client Apps HTTP responses can be cached locally by the browser , reducing unnecessary network requests. When a request is made for the first time, the server responds with data and an expiry policy in the HTTP headers. Future requests for the same data are served from the browser cache , improving response times. Service Workers enable offline caching and background synchronization. 2. Content Delivery Network (CDN) CDNs cache static web resources like images, JavaScript files, and CSS in geographically distributed servers. ...
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Mastering Data Consistency Across Microservices

Mastering Data Consistency Across Microservices Mastering Data Consistency Across Microservices Microservices architecture is a software design pattern where an application is built as a collection of small, independent services, each responsible for a specific function. These services communicate with each other using APIs (Application Programming Interfaces) and operate independently, allowing for greater flexibility, scalability, and ease of maintenance. Think of a food delivery app with the following services: The order service manages customer orders. The payment service handles transactions. The restaurant service updates menu availability. The delivery service assigns and tracks deliveries. Each service operates independently, allowing teams to update or scale them separately. Challenges of Data Consistency Due to the separation of services, a major challen...
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Event Sourcing: Choosing the Right Approach for Scalability and Consistency

Event Sourcing: Choosing the Right Approach Event Sourcing: Choosing the Right Approach for Scalability and Consistency Introduction Event Sourcing (ES) is a powerful architectural pattern where system state changes are captured as a sequence of immutable events rather than directly modifying database records. This approach offers benefits such as auditability, replayability, and improved system resilience. However, implementing ES effectively depends on business requirements, scalability needs, and consistency considerations. Direct Database Writes: When ACID Works (and When It Doesn't) A simple way to implement ES is to write events directly to a database, ensuring transactional consistency ( ACID compliance ). This approach is feasible if: Events occur infrequently. Data must be strongly consistent. Scalability is not a primary concern. Why ACID Doesn't Scale for ES ...
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Understanding the C4 Model for Software Architecture

  Understanding the C4 Model for Software Architecture The C4 Model is a structured approach to visualizing and documenting software architecture. Created by Simon Brown , it organizes system design into four hierarchical levels: Context, Container, Component, and Code . This model helps both technical and non-technical stakeholders understand the structure of a system at varying levels of detail. Why Use the C4 Model? Traditional UML (Unified Modeling Language) diagrams can be complex and difficult to maintain. The C4 Model simplifies architectural descriptions, making them more accessible and easier to manage. It allows teams to: ✅ Clearly communicate architecture across different roles. ✅ Maintain flexibility across monolithic and microservices-based architectures. ✅ Focus on essential details without unnecessary complexity. The Four Levels of the C4 Model with Sample Diagrams 1. Context Diagram (High-Level Overview) Purpose : Shows how the system fits within its e...
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Cell-Based Architecture: A Scalable Approach to System Design

Cell-Based Architecture: A Scalable Approach to System Design Introduction In modern computing, system architecture plays a crucial role in ensuring efficiency, scalability, and reliability. One such architectural paradigm that has gained attention is Cell-Based Architecture (CBA). This approach enhances modularity, fault tolerance, and performance, making it ideal for complex distributed systems. This article explores the principles, benefits, and applications of Cell-Based Architecture, providing insights into how it improves system design in various industries. What is Cell-Based Architecture? Cell-Based Architecture is a decentralized system design approach in which a system is divided into independent, self-contained units called cells. Each cell functions autonomously, handling a subset of the system’s overall workload. Cells can communicate with each other but do not share dependencies that could create bottlenecks or single points of failure. Each cell typically includes: Proce...
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Building Resilient .NET Applications with Resilience

  Building Resilient .NET Applications with Resilience Pipelines In modern software development, applications must be resilient to failures, network issues, and unexpected downtimes. The Resilience Pipelines introduced in .NET offer a structured way to implement fault tolerance and improve system stability. This article explores the concept of resilience pipelines, their benefits, and how to implement them using the Polly resilience library in .NET. What Are Resilience Pipelines? A resilience pipeline is a structured approach to handling transient failures in distributed systems. It consists of multiple resilience strategies , such as: Retry: Automatically retrying failed requests. Circuit Breaker: Preventing excessive failures by stopping requests for a period. Timeouts: Limiting the time a request can take before failing. Fallbacks: Providing alternative responses in case of failure. These strategies are combined into a pipeline to ensure robust failure handling...
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Polling vs. SignalR: A Detailed Comparison

  Polling vs. SignalR: A Detailed Comparison Both Polling and SignalR are techniques used to fetch or receive real-time updates from a server, but they work differently. Here’s a comprehensive comparison to help you choose the right approach. 1. Overview 2. How They Work Polling The client repeatedly sends requests at fixed intervals (e.g., every 5 seconds). The server responds with data (whether there are changes or not). If no new data, the response is wasted network traffic . 🔹 Example (Polling in .NET Core) public async Task<List<Message>> PollMessagesAsync() { using var httpClient = new HttpClient(); var response = await httpClient.GetAsync("https://api.example.com/messages"); return await response.Content.ReadAsAsync<List<Message>>(); } Problem: If data updates once every minute , but polling occurs every 5 seconds , that’s 11 wasted requests before useful data arrives. SignalR The client establishes a pers...
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