NimblePros’ Post

🥊 DAO vs DTO DAO - Design pattern - Separates data access logic from business logic. - Handles connection to a DB (CRUD operations). - Allows data access implementation to be changed without affecting the business logic. - Encapsulation: centralizes data access (hides database details). - Reusability and maintainability: promotes efficiency in managing data, facilitates updates and maintenance. DTO - Design pattern - Transfer data between processes. - Object that contains only data, without business logic - Is used to transport data between layers of an app or between different applications. - Simplicity: no logic or complex behavior. - Plain: Basic properties and accessible with getters and setters. - Serializable: Can be transformed into JSON or XML formats for transmission between processes or services. 🚀 Simple Example: DAO: You have a BookDAO class responsible for all database operations related to books. It has methods like getAllBooks, getBookById, addBook, updateBook, and deleteBook. So, if you need to fetch all books or add a new one, you interact with BookDAO, and it handles the database communication for you. DTO: Now, let's say you want to send book information from your server to a client application. You create a BookDTO that includes only the necessary fields, like title, author, and publicationYear, without any database-related details. The client receives this DTO and uses it to display book information to the user. 🚧 Why Are These Patterns Important? Applying DAO and DTO patterns helps in creating a robust architecture that separates concerns, promotes code reuse, and enhances scalability. #DesignPatterns #BackendDevelopment #CodingBestPractices

"Where can repository pattern be preferred over other patterns???" Data Access Abstraction: There's a need to abstract data access logic from the business logic of the application. The Repository pattern provides a clear separation between these concerns, making it easier to maintain and evolve the codebase. Multiple Data Sources: The application needs to support multiple data sources such as databases, web services, or external APIs. The Repository pattern allows for a unified interface to interact with different data sources, abstracting away the specific implementation details. Testability: Testability is a priority, and the application requires unit testing of business logic without relying on actual data access mechanisms. Repositories can be easily mocked or stubbed in unit tests, facilitating isolated testing of components. Consistent Query Logic: There's a need for consistent query logic across the application. Repositories encapsulate query logic, promoting code reuse and maintaining a centralized location for data retrieval operations. Domain-Driven Design (DDD): The application follows DDD principles, where repositories serve as a bridge between domain entities and data persistence. Repositories help in modeling domain concepts in a more expressive and domain-centric way. Complex Data Access Operations: Data access operations involve complex queries, transactions, or caching strategies. Repositories provide a layer of abstraction to handle such complexities while keeping the business logic clean and focused. In summary, the Repository pattern is preferred in C# when there's a need for abstraction of data access logic, support for multiple data sources, emphasis on testability, consistent query logic, alignment with DDD principles, handling complex data access operations, promoting modularity and maintainability, and enforcing consistency and standardization in data access patterns." #softwaredeveloper #dotnet #Melbourne #dev

There are many different ways to implement the Repository pattern. Let's consider a few of them, and their merits and drawbacks: https://bit.ly/3GNWMuj #DesignPatterns #DevIQ

8 Design Patterns for Low-Latency Applications 🚀 In the age of real-time data, low-latency apps are crucial. From trading platforms to gaming, minimizing latency is key. Here are essential design patterns to ensure efficient, rapid response times. 1- Event-Driven Architecture 📅 - Components react to events rather than being called. - Event Producers, Consumers, and Brokers (Kafka, RabbitMQ) ensure real-time, asynchronous communication. - Decouples components for scalable, maintainable systems. 2- Microservices Architecture 🏗️ - Break down applications into smaller, loosely coupled services. - Independent deployment, scaling, and updating. - Enhances scalability and fault isolation. 3- Caching 🗄️ - Store frequently accessed data in high-speed storage. - Use In-Memory Caching (Redis, Memcached) and CDNs. - Significantly reduces data retrieval time and offloads database queries. 4- Asynchronous Processing ⏳ - Decouple tasks for asynchronous processing. - Utilize Message Queues (RabbitMQ, Kafka) and Job Queues. - Improves responsiveness by offloading time-consuming tasks. 5- Load Balancing ⚖️ - Distribute network traffic across multiple servers. - Use Hardware and Software Load Balancers (HAProxy, NGINX). - Ensures high availability, reliability, and optimal resource utilization. 6- Data Partitioning and Sharding 📊 - Divide large datasets for independent processing. - Horizontal Partitioning (Sharding) and Vertical Partitioning. - Improves performance and scalability. 7- Edge Computing 🌐 - Process data closer to the source. - Deploy edge servers or devices for real-time processing. - Reduces latency and enhances real-time decision-making. 8- Optimized Data Structures and Algorithms 📈 - Use efficient data structures and algorithms. - Choose lower time complexity algorithms and fast-access data structures. - Ensures faster operations and minimal computational overhead. 🏁 By leveraging these design patterns, developers can build systems that meet the demands of modern, latency-sensitive applications. Optimize your architecture for the best performance! 💡 #SoftwareDevelopment #RealTime #Programming https://lnkd.in/dEEATfzd

.𝑵𝑬𝑻 𝑵𝒂𝒎𝒊𝒏𝒈 𝑩𝒆𝒔𝒕 𝑷𝒓𝒂𝒄𝒕𝒊𝒔𝒆𝒔: 𝑫𝑻𝑶𝒔 Often seen, often used incorrectly: DTOs. 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐎𝐛𝐣𝐞𝐜𝐭𝐬. In principle, DTO is an umbrella term, a design pattern that is used to transfer data between. In reality, however, the umbrella term is used directly as an implementation, which has disadvantages in all cases. https://lnkd.in/eaQT27AF #dotnet #softwarearchitecture #aspnetcore

𝗥𝗘𝗦𝗧 𝘃𝘀 𝗚𝗿𝗮𝗽𝗵𝗤𝗟: 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲? Choosing the right API architecture is critical for building robust applications. Let’s break down the key differences between REST and GraphQL to help you decide. 𝗥𝗘𝗦𝗧: REST follows a straightforward approach where each request is self-contained and contains all the data required for processing. • Resource-based: Exposes resources as endpoints (e.g., /users, /orders) with unique URLs. • Standard methods: Utilizes HTTP methods (GET, POST, PUT, DELETE) and status codes (200, 404, etc.). • Decoupled design: Promotes a clean separation between client and server, simplifying caching and reducing complexity. However, REST can lead to issues like underfetching, where you retrieve more data than needed. For instance, a /users endpoint might return an entire user profile when all you need is their username and email. 𝗚𝗿𝗮𝗽𝗵𝗤𝗟: GraphQL adopts a flexible and efficient approach by letting the client dictate the data it needs through a single endpoint. • Schema-driven: APIs are defined using schemas that specify data types and relationships. • Precise data fetching: Clients request only the data they need, avoiding overfetching or underfetching. • Nested queries: Enables retrieval of related data in a single request, improving efficiency. While GraphQL offers greater flexibility, it introduces added complexity with schema management, resolvers, and query optimization. 𝗦𝗶𝗺𝗽𝗹𝗲 𝗔𝗻𝗮𝗹𝗼𝗴𝘆: • REST: Like ordering a pre-set meal – you get everything on the plate, even if you only wanted a few items. • GraphQL: Like visiting a buffet – you choose exactly what you want, but you need to know the available options. Each has its strengths and trade-offs, so your choice depends on your application’s requirements and team expertise.

Do you use CQRS (Command-Query Responsibility Segregation) pattern? 👏= Yes 💡= No, but will consider it 😆= However, no You may find this post very useful and it could help you improve the architecture of your application.

10 years old article about REST resources modeling, but a must read before being locked in the CRUD world of "low level, chatty, business logic leaking everywhere in the clients" APIs. PUTting or PATChing your business entities is like having setters for all your Object's attribute in OOP, you have broken encapsulation. You are exposing your internals to your clients and you might as well give them direct acces to your database. https://lnkd.in/dSYwQ6Fq by Prakash Subramaniam

𝐀 𝐌𝐨𝐝𝐞𝐫𝐧 𝐖𝐚𝐲 𝐭𝐨 𝐒𝐨𝐥𝐯𝐞 𝐏𝐫𝐢𝐦𝐢𝐭𝐢𝐯𝐞 𝐎𝐛𝐬𝐞𝐬𝐬𝐢𝐨𝐧 𝐢𝐧 .𝐍𝐄𝐓 𝐏𝐫𝐢𝐦𝐢𝐭𝐢𝐯𝐞 𝐨𝐛𝐬𝐞𝐬𝐬𝐢𝐨𝐧 is a tendency to use basic data types to represent more complex concepts. It is a common anti-pattern that can lead to unclear code and harder-to-maintain systems. Today you will learn why primitive obsession can lead to problems in your applications and how to address this issue using Value Objects in .NET. 𝐏𝐫𝐢𝐦𝐢𝐭𝐢𝐯𝐞 𝐨𝐛𝐬𝐞𝐬𝐬𝐢𝐨𝐧 occurs when basic data types (such as int, string, or DateTime) are overused to represent complex concepts in your domain. This practice can lead to unclear code, bugs, and difficulty in maintaining and extending your system. The solution to this problem is to use 𝐕𝐚𝐥𝐮𝐞 𝐎𝐛𝐣𝐞𝐜𝐭𝐬, which encapsulate related data and behavior into a single, meaningful unit. 𝐕𝐚𝐥𝐮𝐞 𝐎𝐛𝐣𝐞𝐜𝐭𝐬 represent a value in your domain that has no identity but is defined by its attributes. For example, an Address might be a Value Object, as it is defined by its properties (Street, City, Zip). Value Objects are a key concept in Domain-Driven Design (DDD). 𝐊𝐞𝐲 𝐜𝐡𝐚𝐫𝐚𝐜𝐭𝐞𝐫𝐢𝐬𝐭𝐢𝐜𝐬 𝐨𝐟 𝐕𝐚𝐥𝐮𝐞 𝐎𝐛𝐣𝐞𝐜𝐭𝐬: - 𝐈𝐦𝐦𝐮𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: once created, a Value Object cannot be changed. Any modification results in a new instance. - 𝐄𝐪𝐮𝐚𝐥𝐢𝐭𝐲: value Objects are compared based on their properties, not by reference. - 𝐒𝐞𝐥𝐟-𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: they ensure that their state is always valid by enforcing constraints on their properties. I can list the following most popular options for creating value objects: - using ValueOf library - using C# Records - using C# Record Structs Records are a wonderful choice for value objects, but they are reference types. If you care about memory allocations, you can use readonly record structs for Value Objects. They behave the same as records but they are value types and not allocated on the heap. This is my personal choice for creating Value Objects. If you want to learn how to implement a 𝐕𝐚𝐥𝐮𝐞 𝐎𝐛𝐣𝐞𝐜𝐭 pattern, make sure to check my full blog post on this topic: https://lnkd.in/eWdwc69d 😊 Join 𝟔𝟎𝟎+ software engineers that are already 𝐫𝐞𝐚𝐝𝐢𝐧𝐠 my newsletter and are 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 their .NET and architectural skills: https://bit.ly/4c6Jy9e

Low coupling and high cohesion That is the ideal. Cohesion is a measure of the degree to which the elements inside a module are related. It is the degree to which all elements are directed towards performing a single task. There is no such thing as bad cohesion, cohesion is always desirable. But there are different types of cohesion, and some of those types are just more desirable then others. Before I dive into the different types, I do want to mention that I will always sacrifice cohesion to lower coupling. High coupling is detrimental for an architecture in the long term, where less cohesion, of a certain type, is usually just a minor distraction. But when I need to sacrifice, I will sacrifice less desirable cohesion types first. So without further ado, here are the different types of cohesion ordered from less desireable to very desirable. Layer (Logical) Cohesion: Layer cohesion occurs when elements in a module are grouped together based on their level of abstraction or responsibility, such as a layer that handles only low-level hardware interactions or a layer that handles only business logic. All the code for these interactions is then located in the same layer. Operations are related, but the functions are significantly different. Informational Cohesion: Informational cohesion occurs when elements are grouped together in a module based on their relationship to a specific data structure or object, such as a module that operates on a specific data entity. Communicational Cohesion: Communicational cohesion occurs when elements are grouped together in a module based on their interactions with a specific external system or module. Temporal Cohesion: Temporal cohesion occurs when elements are grouped together in a module based on their timing or frequency of execution, such as a module that handles all periodic or scheduled tasks in a system. Procedural Cohesion: This type of cohesion occurs when elements are grouped together in a module based on their sequence of execution, such as a module that performs a set of related procedures in a specific order. Sequential Cohesion: An element outputs some data that becomes the input for other element, i.e., data flow between the parts. Functional Cohesion: This type of cohesion occurs when all elements in a module contribute to a single well-defined business function or purpose, and there is little or no coupling with other modules. Functional cohesion is considered the most desirable type of cohesion as it leads to more maintainable and reusable code.