Data-Function-Template Architecture - an Alternative to MVC

DFT Architecture

For developing complex, network based application, I propose using “Data Function Template” architecture - a programming paradigm that combines functional programming, data-oriented design, and template-based presentation concepts. It emphasizes the use of functions as the primary building blocks of a program, treats data as immutable, and leverages templates to define reusable and composable view templates. Here the role of data and pure functions is segeregated. A more technical discussion is in this paper: Functional Augmented State Transfer (FAST) Architecture for Computationally Intensive Network Applications

In the Data Function Template paradigm, functions play a central role in defining the behavior of the program. Functions are used to transform data, perform calculations, and produce results. They are designed to be pure and side-effect free, meaning that their behavior is solely determined by their inputs, and they don’t modify any external state.

Data in this paradigm is treated as immutable. Once created, data cannot be modified directly. Instead, functions are used to transform and manipulate data, producing new data structures as a result. Immutable data provides benefits such as improved code reliability, easier testing, and better concurrency support.

Templates are used to define the presentation layer. The Data Function Template paradigm promotes the separation of concerns and encourages a declarative programming style. It emphasizes writing code that is easy to understand, reason about, and maintain. By focusing on functions, immutability, and templates, this paradigm aims to improve code quality, scalability, and maintainability.

Languages like Haskell, Clojure, and Elm embrace principles of the Data Function Template paradigm to varying degrees. These languages provide powerful functional programming features, immutable data structures.

DFT Vs. MVC

The Model-View-Controller (MVC) paradigm is a widely used architectural pattern in software development that separates the concerns of data management, user interface, and application logic. It provides a structured and modular approach to building software systems.

The MVC pattern consists of three main components:

• Model: The Model represents the data and the business logic of the application. It encapsulates the data structures, state, and behavior of the application. It is responsible for managing data persistence, retrieval, and manipulation. The Model notifies the View and the Controller of any changes in the data, ensuring synchronization between the components.

• View: The View is responsible for rendering the user interface and presenting the data to the user. It displays the data from the Model and provides a visual representation of the application’s state. The View receives user input and communicates it to the Controller for further processing.

• Controller: The Controller acts as an intermediary between the Model and the View. It handles user input, processes requests, and updates the Model accordingly. The Controller receives input from the View, performs the necessary operations on the Model, and updates the View with any changes in the data.

The Data Function Template paradigm and the Model-View-Controller (MVC) architectural pattern are different in their focus and goals.

Focus

Data Function Template: The Data Function Template paradigm focuses on the composition and transformation of data using functions. It emphasizes immutability and the use of pure functions to manipulate data.

MVC: The MVC pattern focuses on the separation of concerns in software development, specifically the separation of data (model), user interface (view), and business logic (controller).

Data Manipulation:

Data Function Template: In this paradigm, data is treated as immutable, and functions are used to transform and manipulate data to produce new data structures. Functions act as the primary means of transforming data. MVC: In MVC, the model represents the data and its associated operations. The controller interacts with the model to update or retrieve data, and the view is responsible for displaying the data to the user.

Code Organization:

Data Function Template: This paradigm promotes a modular approach where functions and templates are used to define reusable and composable code structures. It focuses on writing functions that are independent and self-contained. The functions and the data can be used in various templates without any strong coupling. MVC: MVC separates code into three distinct components: model, view, and controller. Each component has its own responsibilities and interacts with the others through predefined interfaces.

Purpose:

Data Function Template: The Data Function Template paradigm aims to provide a clear and concise way of transforming data using functions. It emphasizes code reuse, modularity, and immutability. MVC: MVC is an architectural pattern that provides a structured way of organizing code and separating concerns. It promotes maintainability, reusability, and flexibility in software development.

In summary, the Data Function Template paradigm focuses on data transformation using functions and immutability, while MVC focuses on separating concerns in software development. While they share some similarities, they have different goals and approaches to code organization and data manipulation.

DFT and Decentralized Computing

The Data Function Template (DFT) architecturex can be advantageous for decentralized computing due to its inherent characteristics:

1. Modularity and Code Reusability: DFT promotes a modular approach to building software by using functions and templates. This allows developers to create self-contained and reusable code blocks that can be easily shared and distributed across a decentralized network. Modularity enhances the flexibility and scalability of decentralized applications (DApps) by enabling the composition and combination of different functions to build complex systems.

2. Immutability and Data Integrity: In decentralized computing, where multiple nodes participate in data processing, ensuring data integrity becomes crucial. DFT emphasizes immutability, treating data as immutable and using pure functions for data transformation. Immutable data ensures that the original data remains unchanged, reducing the risk of data corruption or unauthorized modifications in a decentralized environment.

3. Interoperability and Compatibility: DFT promotes the use of standardized function signatures and data templates. This standardized approach enables interoperability and compatibility between different decentralized applications and systems. It allows components built using DFT to seamlessly interact and exchange data, fostering collaboration and integration in a decentralized ecosystem.

4. Transparent and Verifiable Execution: DFT emphasizes the use of pure functions, which are deterministic and produce consistent results. This transparency enables easy verification and validation of function execution across different nodes in a decentralized network. By ensuring that the same input always produces the same output, DFT enhances trust and reliability in decentralized computing environments.

5. Developer Empowerment and Flexibility: DFT provides developers with the freedom to choose and compose functions based on specific use cases. It allows them to leverage the power of decentralized networks by utilizing different functions and templates available in the ecosystem. This flexibility empowers developers to build decentralized applications tailored to their unique requirements, without being restricted to a specific programming paradigm or framework.