Hany Ouf

  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...

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...

  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...

  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...

  Polling in Software Systems Polling is a technique where a system repeatedly checks for changes or new data at regular intervals . It is often used when event-driven notifications (e.g., WebSockets, SQL Server notifications) are not available or feasible. 1. Types of Polling a) Regular Polling (Fixed Interval) The system checks for updates at a fixed time interval (e.g., every 10 seconds). Simple to implement but can cause unnecessary load if updates are infrequent. 🔹 Example: A background service in .NET Core checking for new database records every minute. public class PollingService : BackgroundService { private readonly ILogger<PollingService> _logger; public PollingService(ILogger<PollingService> logger) { _logger = logger; } protected override async Task ExecuteAsync(CancellationToken stoppingToken) { while (!stoppingToken.IsCancellationRequested) { _logger.LogInformation("Checking fo...

  If you cache a SQL Server table in .NET Core , you need a way to refresh the cache when data changes. There are several approaches to achieve this: 1. Using SQL Server Query Notifications (SqlDependency) Best for: Applications that need real-time cache updates with minimal overhead. How It Works SQL Server notifies your application when data changes, so you can refresh the cache. Uses SqlDependency to listen for changes. Requires Service Broker to be enabled in SQL Server. Implementation Steps Step 1: Enable Service Broker in SQL Server Run this SQL command: ALTER DATABASE YourDatabase SET ENABLE_BROKER; Step 2: Configure SQL Dependency Install the necessary package: dotnet add package Microsoft.Data.SqlClient Step 3: Implement Caching with Notifications using Microsoft.Data.SqlClient; using Microsoft.Extensions.Caching.Memory; public class ProductCacheService { private readonly IMemoryCache _cache; private readonly string _connectionString; ...

 Decorator Design Pattern The Decorator Pattern is a structural design pattern that allows behavior to be dynamically added to individual objects without modifying their code. It achieves this by wrapping the object inside another object (the decorator) that provides additional functionality. --- When to Use the Decorator Pattern When you need to extend the behavior of an object without modifying its original code. When subclassing is not feasible due to class explosion or maintenance issues. When different combinations of behaviors are required dynamically at runtime. --- Key Components of the Decorator Pattern 1. Component (Interface or Abstract Class) – Defines the common contract. 2. Concrete Component – The original implementation of the component. 3. Decorator (Abstract Class) – Wraps the component and can modify its behavior. 4. Concrete Decorator – Implements additional functionality while delegating to the wrapped component. --- Example: Coffee Shop (Without the Decorator ...

Optimizing Database Performance in .NET Core with Delta Library Introduction Efficient database operations are crucial for performance in .NET Core applications. Frequent and unnecessary updates to the database can lead to high latency, increased resource usage, and degraded application responsiveness. The Delta library, developed by Simon Cropp, provides an elegant solution by implementing change tracking, ensuring that only modified properties are persisted. This approach significantly reduces database load and improves overall efficiency. What is Delta Library? The Delta library is a lightweight .NET library designed to track changes in objects and persist only the modified properties, rather than updating entire database records. This leads to improved performance by minimizing the amount of data being transmitted and processed. Key Features 1. Change Tracking – Detects and persists only modified properties. 2. Optimized Database Updates – Reduces the amount of data being sent to t...

  Understanding Data Lakes: A Comprehensive Guide In the modern digital landscape, organizations generate massive volumes of data from diverse sources such as social media, IoT devices, business applications, and customer interactions. Managing, storing, and analyzing this data efficiently is crucial for deriving actionable insights. This is where data lakes come into play. What is a Data Lake? A data lake is a centralized repository that allows organizations to store structured, semi-structured, and unstructured data at scale. Unlike traditional databases and data warehouses, data lakes do not require pre-defined schemas, making them highly flexible and cost-effective for big data storage and analytics. Key Features of a Data Lake Scalability – Data lakes can store petabytes of data, scaling seamlessly as data volume grows. Flexibility – They support all data types, including text, images, videos, logs, and sensor data. Cost-effectiveness – Data lakes typically leverage...

  Webhooks: How They Work and Why They Matter in .NET Core Introduction Webhooks are a powerful mechanism for real-time communication between systems. They enable applications to send automated notifications or data updates whenever specific events occur. Unlike traditional APIs, which require polling, webhooks push data to the recipient system instantly, making them more efficient and scalable. What Are Webhooks? A webhook is a user-defined HTTP callback that is triggered by an event in a source application. When the event occurs, the source system makes an HTTP request (usually a POST request) to a pre-configured URL (the webhook endpoint) with relevant data. How Webhooks Work Event Occurs – An event happens in the source application (e.g., a new order is placed in an e-commerce system). Trigger Execution – The system detects the event and prepares a data payload. HTTP Request Sent – The system sends an HTTP POST request to the webhook URL with the event data. Receiv...

What is Dapper? What is Dapper? Dapper is a high-performance, lightweight Object-Relational Mapper (ORM) for .NET applications. It is developed by Stack Overflow and is known for its speed and simplicity in executing SQL queries directly against a database while mapping the results to .NET objects. Unlike Entity Framework (EF Core), which provides automatic change tracking and complex data handling, Dapper focuses on raw SQL execution and fast data retrieval. Key Features of Dapper ✔ Fast Performance: Dapper is almost as fast as using raw ADO.NET because it minimizes overhead. ✔ Lightweight & Simple: It’s a single-file library that works with existing ADO.NET connections. ✔ Supports Parameterized Queries: Helps prevent SQL injection. ✔ Automatic Object Mapping: Maps query results to .NET objects with minimal configuration. ✔ Supports Multiple Databases: Works w...

 Best Practices for Storing and Loading JSON Objects from a Large SQL Server Table Using .NET Core Introduction JSON (JavaScript Object Notation) is widely used for storing structured data in SQL Server. However, when dealing with large tables, inefficient handling of JSON data can lead to performance bottlenecks, high storage costs, and slow queries. In this article, we will cover best practices for saving and loading JSON objects from large SQL Server tables using .NET Core. We will focus on storage strategies, indexing, efficient querying, and optimized data retrieval using Entity Framework Core (EF Core) and Dapper. --- 1. Choosing the Right Storage Strategy SQL Server supports JSON natively, but choosing the correct storage method depends on your use case. Option 1: Store JSON as NVARCHAR(MAX) (Best for Flexibility & Simplicity) ✔ Ideal for semi-structured or frequently changing data. ✔ Suitable when you need to store the entire JSON object but rarely query individual fiel...

Building High-Performance APIs with gRPC in .NET Core 9 Introduction gRPC (Google Remote Procedure Call) is a high-performance, open-source RPC framework designed for efficient communication between services. It offers faster response times, low bandwidth usage, and strongly typed contracts, making it a great alternative to REST APIs for microservices and real-time applications. In this article, we will explore how to set up and use gRPC in .NET Core 9, covering key concepts, implementation steps, and best practices. --- Why Use gRPC? ✔ High Performance: Uses HTTP/2 for multiplexing, compression, and lower latency. ✔ Strong Typing: Uses Protocol Buffers (Protobuf) instead of JSON, ensuring data integrity. ✔ Streaming Support: Supports unary, client-streaming, server-streaming, and bi-directional streaming. ✔ Better Efficiency: Uses binary serialization (Protobuf), reducing payload size compared to JSON. ✔ Language Agnostic: Supports multiple languages like C#, Java, Python, and Go. ---...

  Efficient Data Pagination Using OFFSET-FETCH in SQL Stored Procedures with Complex Joins Introduction When dealing with large datasets in SQL Server, fetching all records at once can lead to performance issues. Instead, we can implement pagination using OFFSET-FETCH, especially when working with complex joins. This method allows fetching a subset of data efficiently, improving both performance and scalability. This article will guide you through implementing OFFSET-FETCH in a SQL stored procedure with complex joins. --- Understanding OFFSET-FETCH in SQL Server The OFFSET and FETCH clauses are used with ORDER BY to skip a specified number of rows and retrieve only a limited set of records. Syntax SELECT columns FROM TableName ORDER BY ColumnName OFFSET @Offset ROWS FETCH NEXT @PageSize ROWS ONLY; OFFSET @Offset ROWS: Skips the specified number of rows. FETCH NEXT @PageSize ROWS ONLY: Fetches the next batch of records. --- Implementing Pagination in a Stored Procedure with Complex ...