Skip to main content

Architecture Styles & Patterns

 One thing is very clear: Design Patterns are design patterns not Architecture Styles or Architecture Patterns. What is not clearly standardized is Architecture Style versus Architecture Patterns. Even if you try to define them based on the fundamental difference between style and pattern, you see same name is used in Architecture Style and Architecture Pattern as well. However to make things easier let us consider following:

  • The distinction between architectural styles and patterns is often context-dependent rather than strictly defined.
  • Styles focus on structural organization, while patterns offer problem-solving approaches within that structure.
  • Many of the fundamental architectural styles from Shaw & Garlan are later categorized as patterns by Bass et al.

  • Architectural Style

    An architectural style defines a high-level structural organization of a software system. It provides a general framework for system design, including common component types and their interactions. Architectural styles help guide the system's structure but do not specify detailed implementation.

    Historical Context:

    • The concept of architectural styles was discussed extensively in the 1990s by Mary Shaw and David Garlan in Software Architecture: Perspectives on an Emerging Discipline.
    • Architectural styles represent recurring, well-known ways to organize software systems.

    🔹 Examples of Architectural Styles:

    1. Layered Architecture – Used in OS design, enterprise applications, web applications (e.g., MVC).
    2. Client-Server – Found in database systems, email, and networked applications.
    3. Microservices – Modern cloud-based applications (Netflix, Amazon, Uber).
    4. Event-Driven Architecture – Used in IoT systems, real-time analytics, messaging platforms.

    Architectural Pattern

    An architectural pattern provides a solution to common architectural problems by defining how different components should interact. Unlike architectural styles, which focus on system structure, architectural patterns define specific design decisions to address challenges like scalability, security, or fault tolerance.

    Historical Context:

    • Len Bass et al. introduced architectural patterns in Software Architecture in Practice.
    • Architectural patterns emerged to standardize and solve architectural problems that arise in different contexts.
    • Unlike GOF (Gang of Four) design patterns, which apply to low-level software design, architectural patterns address system-wide concerns.

    🔹 Examples of Architectural Patterns:

    1. Model-View-Controller (MVC) – Used in web frameworks (Django, Spring MVC, Angular).
    2. Broker Pattern – Found in middleware, message brokers (RabbitMQ, Apache Kafka).
    3. Microkernel Pattern – Used in extensible applications (Eclipse, VS Code).
    4. Pipe and Filter – Found in Unix shell pipelines, ETL processes.

    Architectural Styles from Shaw & Garlan (1996)

    In Software Architecture: Perspectives on an Emerging Discipline, Shaw & Garlan outlined common software architectural styles, including:

    1. Layered Architecture
    2. Pipe and Filter
    3. Client-Server
    4. Event-Based (Implicit Invocation)
    5. Repository (Blackboard)
    6. Interpreter (Virtual Machine)
    7. Process Control

    Architectural Patterns from Bass et al. (1998, 2003, 2012 editions of Software Architecture in Practice)

    Len Bass et al. define architectural patterns that overlap with the above styles, including:

    1. Layered Pattern (equivalent to Layered Architecture)
    2. Microkernel Pattern (similar to Interpreter Architecture)
    3. Client-Server Pattern (same as Client-Server Architecture)
    4. Event-Bus Pattern (matches Event-Based Architecture)
    5. Blackboard Pattern (similar to Repository Architecture)
    6. Pipe-and-Filter Pattern (directly corresponds to Pipe-and-Filter Architecture)

    Comments

    Popular posts from this blog

    Virtual environments in python

     Creating virtual environments is essential for isolating dependencies and ensuring consistency across different projects. Here are the main methods and tools available, along with their pros, cons, and recommendations : 1. venv (Built-in Python Virtual Environment) Overview: venv is a lightweight virtual environment module included in Python (since Python 3.3). It allows you to create isolated environments without additional dependencies. How to Use: python -m venv myenv source myenv/bin/activate # On macOS/Linux myenv\Scripts\activate # On Windows Pros: ✅ Built-in – No need to install anything extra. ✅ Lightweight – Minimal overhead compared to other tools. ✅ Works across all platforms . ✅ Good for simple projects . Cons: ❌ No dependency management – You still need pip and requirements.txt . ❌ Not as feature-rich as other tools . ❌ No package isolation per project directory (requires manual activation). Recommendation: Use venv if you need a simple, lightweight solut...

    Building a Simple Text Generator: A Hands-on Introduction

    Introduction Text generation is one of the most exciting applications of Natural Language Processing (NLP) . From autocorrect and chatbots to AI-generated stories and news articles , text generation models help machines produce human-like text. In this blog post, we’ll introduce a simple yet effective text generation method using Markov Chains . Unlike deep learning models like GPT, this approach doesn’t require complex neural networks—it relies on probability-based word transitions to create text. We’ll walk through: ✅ The concept of Markov Chains and how they apply to text generation. ✅ A step-by-step implementation , fetching Wikipedia text and training a basic text generator. ✅ Example outputs and future improvements. The Concept of Markov Chains in Text Generation A Markov Chain is a probabilistic model that predicts future states (or words) based only on the current state (or word), rather than the full sentence history. How it works in text generation: 1️⃣ We analyze a gi...

    Mastering Trade-Off Analysis in System Architecture: A Strategic Guide for Architects

     In system architecture and design, balancing conflicting system qualities is both an art and a science. Trade-off analysis is a strategic evaluation process that enables architects to make informed decisions that align with business goals and technical constraints. By prioritizing essential system attributes while acknowledging inevitable compromises, architects can craft resilient and efficient solutions. This enhanced guide provides actionable insights and recommendations for architects aiming to master trade-off analysis for impactful architectural decisions. 1. Understanding Trade-Off Analysis Trade-off analysis involves identifying and evaluating the conflicting requirements and design decisions within a system. Architects must balance critical aspects like performance, scalability, cost, security, and maintainability. Since no system can be optimized for every quality simultaneously, prioritization based on project goals is essential. Actionable Insights: Define key quality ...