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00 Intro to software architecture

Defining Software Architecture

Software architecture is often considered the blueprint of the system, but it’s more than just a set of drawings. It is the structural framework that holds the concept of the intended system and facilitates the understanding and development of its functional and non-functional requirements. In Python, this means not only defining modules, classes, and functions but also deciding on higher-level structures like services, packages, and the interactions between them.

It determines the systems’ scalability, reliability, and performance, laying out how these components interact with one another. For instance, in a Python application, choosing a microservices architecture over a monolithic one can have profound implications on how the application scales and how independently services can be developed and deployed.

Furthermore, a well-defined architecture ensures that the system meets both current and future needs. With Python’s philosophy of simplicity and readability, the architecture must also reflect these values, leading to a system that is easier to understand, maintain, and extend.

Software Architecture versus Design

The distinction between software architecture and design is somewhat analogous to the difference between urban planning and building design. While urban planning (architecture) outlines the general layout of the city, major roadways, and zoning regulations, building design (software design) focuses on the detailed structure and functionality of individual buildings within that city.

In the context of Python, software architecture might involve deciding on a framework like Django for a web application, which dictates certain architectural patterns and structures. Software design, however, would involve the specific implementation within that framework: the models, views, controllers, how they interact, and how the data flows through them.

The dynamic nature of Python influences both architecture and design significantly. Architecturally, Python allows for a more fluid and adaptable system structure. For example, its dynamic typing means that the system can be more flexible in how components communicate with each other. Design-wise, Python’s extensive libraries and frameworks mean that many design decisions are influenced by the idiomatic use of these tools. Decisions on how to structure the code, error handling, and even concurrency models are shaped by the chosen libraries and the Pythonic way of leveraging them.

Python’s ability to prototype rapidly also blurs the lines between architecture and design. What starts as a design decision can quickly become an architectural standard within a project if it proves successful. This fluidity requires a disciplined approach to ensure that the system remains maintainable and doesn’t accrue technical debt due to ad-hoc design decisions becoming de facto architectural standards.

⇒ Defining software architecture in the realm of Python development is about setting a high-level framework for the project that reflects Python’s strengths and philosophies. It’s about ensuring that the system’s structure can evolve and adapt while meeting the needs of the stakeholders. On the other hand, design is about the detailed implementation within this framework, fleshing out the architecture with Pythonic code that is as elegant as it is functional.

Aspects of Software Architecture

Software architecture isn’t just about the components and their interactions; it encompasses several aspects such as functional requirements, structural design, and the dynamic behavior of the system. Let’s dissect these aspects, explaining how each contributes to a well-rounded architectural strategy:

An Architecture Defines a Structure

Python’s dynamic typing and interpreted nature allow for a flexible structure where components can be added or modified with relative ease. Pythonic structures often favor readability and simplicity, allowing for a more iterative and evolutionary approach to architectural design. In a Python project, the structure may be loosely coupled, with components interacting through interfaces or protocols like WSGI for web applications.

An Architecture Picks a Core Set of Elements

The selection of core elements in Python is heavily influenced by the vast array of modules available in the standard library, known for its “batteries-included” philosophy. Further, Python’s extensive ecosystem of third-party packages allows architects to select high-quality elements for their core functionalities without having to reinvent the wheel, from network communication to data processing.

An Architecture Captures Early Design Decisions

In Python, early design decisions often revolve around the choice between performance and productivity, the adoption of synchronous or asynchronous paradigms, or the level of adherence to Pythonic idioms. Python’s design philosophy, outlined in PEP 20, known as “The Zen of Python,” emphasizes simplicity and the importance of readability, which can guide these early decisions.

An Architecture Manages Stakeholder Requirements

Python’s readability and the straightforward syntax make it an excellent choice for involving stakeholders who may not be deeply technical. It is easier to demonstrate prototypes or explain code behavior to non-technical stakeholders, thereby ensuring their requirements are met and understood in the context of the system’s capabilities.

An Architecture Influences the Organizational Structure

Python’s culture and development model encourage open communication, community contributions, and a flat hierarchy, often mirroring the organizational structures of companies that use it. The language’s philosophy may influence the organization to adopt a more collaborative and less rigid approach to project management and team structuring.

An Architecture Is Influenced by Its Environment

Python’s versatility makes it well-suited for a range of environments, from small-scale scripts to large web applications. Its cross-platform nature and the ability to interface with other languages and systems mean that Python architectures must be cognizant of the environment they operate in, including the integration with other systems and adherence to platform-specific best practices.

An Architecture Documents the System

Good documentation is crucial for maintaining and scaling Python projects. Python’s own documentation is extensive and serves as a model for how to document software architecture. Python’s docstrings and tools like Sphinx can be used to create comprehensive documentation, ensuring that the architecture is well understood and maintainable.

An Architecture Often Conforms to a Pattern

Python developers often employ design patterns that are well-suited to the language’s features, such as the MVC pattern for web applications or the publisher-subscriber pattern for event-driven systems. Python’s object-oriented features and its support for functional programming concepts allow it to adapt well to various architectural patterns.

In each characteristic, Python’s specific features and philosophy can significantly influence the decisions made during the architectural design process. The language’s emphasis on simplicity, readability, and a rich ecosystem of tools and libraries play into how robust, scalable, and adaptable architectures are defined and implemented.

Importance of Software Architecture

Software architecture is critically important for the success of a project. In this section, we’ll discuss how a good architecture facilitates the development process, promotes quality, and helps manage complexity, particularly in Python projects.

System architecture and enterprise architecture are both crucial in organizing and managing technology, but they operate at different levels and scopes within an organization.

System Architecture

System architecture is concerned with the design and functionality of a specific system. In the context of a Python application, this would involve the arrangement of code modules, classes, functions, and the interaction between different parts of the software. It focuses on the following aspects:

  • Internal Structure: How the code is organized, such as the use of object-oriented principles, design patterns, and data models.
  • Technological Choices: Decisions about frameworks, libraries, and the Python version used.
  • Performance Considerations: Ensuring that the system meets performance requirements, which can include optimizing algorithms and resource management.
  • Scalability: Designing components so that they can handle increased loads or be easily extended.
  • Maintainability: Writing clean, well-documented code that follows PEP 8 and other Python coding standards.

Enterprise Architecture

Enterprise architecture, on the other hand, takes a broader view. It’s about aligning the organization’s IT strategy with its business goals. It encompasses not just one system but the entire ecosystem of applications, data, and technology within the organization. Key areas include:

  • Strategic Alignment: Ensuring that all IT systems and projects support the overarching business objectives and strategies.
  • Standardization: Defining standards and policies for procurement, development, and integration of IT systems across the enterprise.
  • Data Governance: Managing data as an asset across the enterprise, ensuring data quality, security, and compliance.
  • Integration: Designing how different systems within the organization will interact, share data, and provide cohesive services to the end-users.
  • Investment Decisions: Guiding the selection and prioritization of IT projects and initiatives based on their alignment with business goals.

Interplay between System and Enterprise Architecture

In large Python applications, and indeed any substantial software endeavor, system architecture must align with enterprise architecture to ensure that individual systems contribute effectively to the organization’s needs.

  • Compliance: System architects need to ensure that their designs comply with the standards and practices set out by the enterprise architecture.
  • Interoperability: Systems must be designed to interface seamlessly with other applications within the organization, which often involves adhering to enterprise-defined communication protocols and data formats.
  • Scalability and Performance: While system architecture will define how an individual system scales and performs, it must do so within the context of the entire enterprise’s resource management and service level agreements.
  • Change Management: When systems require updates or modifications, both system and enterprise architects must coordinate to ensure that changes are beneficial from both the system and the enterprise perspectives.

For Python applications, especially large-scale ones, the distinction and interaction between system and enterprise architecture become very clear. While system architects will focus on Pythonic ways of building scalable and maintainable code, enterprise architects will be more focused on how the Python application fits into the larger IT ecosystem, interacts with other systems, adheres to security policies, and supports the business processes.

To achieve harmony between these two levels, organizations often employ governance processes, where decisions about system architecture are reviewed in the context of enterprise goals and standards. This ensures that while each Python application may be designed to best utilize the language’s features and the ecosystem’s tools, it also fits into the larger picture of the enterprise’s architecture.

Architectural quality attributes

Architectural quality attributes are non-functional requirements that affect the overall behavior of a system. They represent the criteria to which a system’s architecture must conform and often define the system’s performance, reliability, and maintainability. Here are some of the key architectural quality attributes:


This attribute indicates how easy it is to change the system, whether it’s for corrections, improvements, or adaptations. A highly modifiable system can evolve to meet changing needs without a complete overhaul. This is achieved through good design practices that minimize dependencies and maximize cohesion.


A system is testable if its components can be easily tested for defects. This attribute is crucial for identifying problems early on in the development cycle, which can save time and resources. Designing the architecture to allow for components to be tested in isolation (e.g., through the use of interfaces and dependency injection) enhances testability.


Scalability refers to a system’s ability to handle growth, whether in data volume, traffic, or complexity. An architecture that is designed to scale can accommodate growth without significant changes to the system. This can involve strategies like load balancing, microservices, and data partitioning.


This attribute covers how efficiently a system uses its resources (such as CPU, memory) and how quickly it responds to inputs. Performance considerations often influence the choice of algorithms, data structures, and the layout of components across hardware resources.


Availability measures the proportion of time a system is operational and accessible when it is required for use. High availability can be ensured through fault tolerance and redundancy, meaning the system can continue to operate even if some components fail.


Security is about protecting a system against unauthorized access and ensuring confidentiality, integrity, and availability of the system’s data and services. This involves encryption, secure communication protocols, authentication and authorization mechanisms, and more.


Deployability denotes how easily a system can be transferred from a development environment to a production environment. This involves aspects like automation of deployment processes, containerization, and the ability to roll back to previous versions if needed.

Balancing the architectural design process

Each of these attributes must be carefully considered during the architectural design process. They often interact and may even conflict with one another; for example, improving security may impact performance, or enhancing scalability may reduce availability if not done correctly. Balancing these attributes according to the specific needs and constraints of a project is a key skill for software architects.

Understanding and prioritizing these quality attributes is critical because they have a profound impact on the system’s architecture, maintenance, and overall success. They should be clearly defined early in the design process and consistently referred to during development, deployment, and maintenance phases.



  • “Architecture Patterns with Python: Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices”, aka Cosmic Python


  • pytest-archon: a little tool that helps you structure (large) Python projects. This tool allows you to define architectural boundaries in your code, also known as forbidden dependencies.

Page last modified: 2024-02-13 10:02:46