Top 5 Software Architecture Patterns

Top 5 Software Architecture Patterns

What are the plots in Hollywood movies? Critics say that there are five. There are many ways in which you can structure a program. Most programs use one of these five software architecture patterns.

Mark Richards, a Boston-based software architect has been thinking about how data should flow through software for more than 30 years. His new book Software Architecture Patterns is free. It focuses on five common architectures for organizing software systems. Studying existing programs and understanding their strengths and weaknesses is the best way of software architecture planning new software programs.

This article will provide a quick overview of the strengths and weaknesses of each software development architectures, along with the best use cases. To optimize every section of code, you can use multiple patterns within a single system. It’s sometimes called computer science but it’s actually an art.

Top 5 Software Architecture Patterns

1. Layered (n-tier) architecture

This is the most popular approach because it is built around databases, and many business applications naturally support the storage of information in tables.

This is a self-fulfilling prophecy. This structure is what inspired many of the most popular software frameworks, such as Java EE, Drupal and Express. As a result, many applications created with them are naturally layered.

The code is structured so that the data starts at the top and then works its way down to the bottom. This is often a database. Each layer performs a specific task along the way. This includes checking for consistency and reformatting values to ensure they are consistent. Different programmers may work on different layers independently.

The Model-View-Controller (MVC) structure, which is the standard software development architecture approach proposed by most popular web frameworks, is clearly a layered architecture. The model layer is located just above the database. This layer often includes business logic and information about data types in the database. The view layer is usually CSS, JavaScript or HTML with dynamic embedded codes. The controller is located in the middle. It has many rules and methods to transform the data that moves between the model and view.

A layered architecture has the advantage of a separation of concerns. This allows each layer to focus on its specific role. It is:

  • Maintainable
  • Testable
  • Easy to assign separate “roles”
  • Easy to update and enhance layers separately

Layered architectures that are properly layered will have layers that are isolated and not affected by changes in other layers. This allows for easier refactoring. Additional layers can be added to this architecture, such as a service layer. This layer can access shared services in the business layer and can be bypassed for speed. enterprise architecture patterns

The biggest challenge for architects is to break down the tasks and define separate layers. If the requirements match the pattern, it will be simple to seperate the layers and assign them to different programmers.

Caveats:

  • If source code is not well-organized, and the modules lack clear roles or relationships, it can become a “big lump of mud”.
  • Slow code can lead to slow code, which developers refer to as the “sinkhole-anti-pattern”. This is a technique that allows data to be passed through layers of code without any logic.
  • It can be difficult to understand the architecture without knowing every module, even though layer isolation is an important goal of architecture.
  • Coders are able to skip layers and create tight coupling, creating a logical mess with complex interdependencies.
  • A monolithic deployment can sometimes be unavoidable. This means that small changes may require a complete redeployment.

Best for:

  • Need to quickly build new applications
  • Business applications and enterprise application architectures that must mirror traditional IT processes and departments
  • Teams that have inexperienced developers and don’t know how to understand other architectures
  • Application that require strict standards of maintainability and testing

2. Event-driven architecture

Programs spend a lot of time waiting for things to happen. This is particularly true for computer software architecture that interact with people, but it also happens in other areas such as networks. Sometimes data needs to be processed, but sometimes there aren’t.

This is possible by using event-driven architecture. It creates a central unit that receives all data, and then delegated it to the modules that deal with that type. This handoff generates an “event” and is then delegated to the appropriate code.

JavaScript is used to program a web page. JavaScript allows you to create small modules that respond to keystrokes and mouse clicks. The browser orchestrates all input and ensures that the correct code is able to see the right events. The browser can handle many different types of events, but only the modules will interact with those events. This is very different to the layered architecture, where all data will usually pass through all layers. Event-driven architectures are:

  • They are adaptable to chaotic, complex environments
  • Scale easily
  • They can be easily extended when new types of events occur

Caveats:

  • Testing can become complicated when modules interact with each other. Although individual modules can be tested, interactions between them cannot be tested without a system.
  • It can be hard to structure errors handling, especially when multiple modules are required to handle the same events.
  • The central unit must be able to provide a backup plan in case of failure.
  • Overhead messaging can slow down processing speed, particularly when the central unit must buffer messages arriving in bursts.
  • It can be difficult to develop a systemwide event data structure when events have different requirements.
  • Because the modules are so independent and decoupled, it is difficult to maintain a transaction-based system for consistency.

Best for:

  • Asynchronous systems with asynchronous information flow
  • Application where individual data blocks are interconnected with a small number of modules
  • User interfaces

3. Microkernel architecture

Many applications use a core set operations which are repeated in various patterns depending on the data and task at hand. Eclipse is a popular development tool that can open files and edit them. It also allows you to start background processors. This tool is well-known for its ability to open files, annotate them, edit them, and then compile the code and run it when you push a button.

The microkernel contains the basic functions for editing and displaying files. The java architectures compiler is an additional component that’s added to the microkernel’s basic features. Eclipse has been extended by other programmers to allow them to create code for different languages using other compilers. Although many don’t use Java, they all use the basic routines to edit and annotate files.

Plug-ins are the extra features added to an existing plug-in. This extensible approach is often called a plug-in architecture.

Richards loves to illustrate this by using an example from the insurance industry: “Claims processing may be complex, but the actual steps of the process are not.” It is all the rules that make it complicated.

Pushing basic tasks, such as asking for a name and checking on payment, into the microkernel is one way to solve this problem. By combining the rules and calling the basic functions of the kernel, the various business units can create plug-ins to address the different types claims.

Caveats:

  • It is often a difficult task to decide what should go in the microkernel. It should contain the code that is most frequently used.
  • Plug-ins should include some handshaking code to ensure that the microkernel knows that the plug is installed and is ready to go.
  • Modifying the microkernel is difficult, if not impossible, if a lot of plug-ins are dependent upon it. Modifying the plug-ins is the only way to solve this problem.
  • It is hard to choose the right granularity to use for kernel functions in advance, but it is almost impossible to change later.

Best for:

  • A wide range of tools are used by many people
  • Applications that clearly divide basic routines from higher-order rules
  • Software application architect that have a fixed set core routines and dynamic rules that need to be updated often will have applications.

4. Microservices architecture

Software can look like a baby elephant. It’s cute and playful when it’s small, but it becomes difficult to control and resistant to change once it grows up. Microservice architecture was created to prevent developers from allowing their children to become unwieldy and rigid. Instead of creating one large program, the idea is to create many smaller programs. Then, each time someone adds a feature to a program, a new program will be created. Imagine a group of guinea-pigs.

Richards points out that “everything on Netflix’s UI comes from a separate service.” Your favorites list, ratings and accounting information are delivered by different services in separate batches. It’s almost as though Netflix is a collection of many smaller websites that only happens to be one service.

This approach is similar in concept to microkernel and event-driven approaches. However, it’s only used when tasks can be separated. Different tasks may require different processing times and different use cases. Netflix servers are pushed harder on Fridays and Saturdays, so they need to be able to scale up. The servers that track DVD return data, on the other side, work the majority of the week after the post office delivers the mail. These services can be implemented separately by the Netflix cloud, allowing them to scale up or down as demand changes.

Caveats:

  • Services must be independent otherwise interaction could cause the cloud to become unbalanced.
  • Not all applications have tasks that are not easily divided into separate units.
  • When tasks are distributed among different microservices, performance can be affected. Communication costs can be high.
  • Microservices that are too many can cause confusion for users, as some parts of the website appear later than others.

Best for:

  • web architecture design that use small components
  • Data centers for corporate data with clearly defined boundaries
  • Rapidly growing new businesses and web apps
  • Teams of developers that are distributed, often around the world

5. Space-based architecture

Many websites are built around databases. They work well so long as the database can handle the load. The website will fail if the database is unable to keep up with high usage and transactions logs.

Space-based architecture prevents functional collapse under high loads by dividing the processing and storage across multiple servers. Just like web application architecture design service calls, data is distributed across nodes.

Others use the less definite term “cloud architecture” while others use the more abstract “cloud architecture.” Space-based refers to the “tuple” of users. This space is used to divide the work among the nodes. Richards says, “It’s all memory objects.” “The space-based architecture supports things with unpredictable spikes by eliminating databases.”

The RAM storage makes it possible to do many tasks faster and the storage can be spread out with the processing, which can simplify basic tasks. However, some types of analysis can be more complicated due to the distributed architecture. If you have calculations that need to be distributed across the entire data set, such as finding an average or performing statistical analysis, then it is necessary to break them up into subjobs and spread them across all the nodes. Once the job is done, the aggregated results can be gathered.

Caveats:

  • RAM databases make transactional support more difficult.
  • Although it can be difficult to generate enough load to test the system, each node can be independently tested.
  • It is not easy to develop the skills necessary to cache data at speed, without destroying multiple copies.

Best for:

  • Data with high volumes, such as click streams or user logs,
  • Data of low value that can be lost without major consequences – in other words, bank transactions are not applicable
  • Social networks

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