Which Software Architecture Patterns do you use?

What software architecture patterns do you use? If I asked this question, what would be your answer? I’d probably get a lot of responses that say Clean Architecture, and some people would say Microservices or a Monolith. But really, your software architecture is usually unique. In this video, I will cover how you buffet architecture to mix and match different architectural styles that fit together to make your specific architecture.

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Architectural Styles & Patterns

When you think of going to a buffet-style restaurant, you have all these different types of food available, and you likely pick a few different dishes to make your plate. The same is true with your software architecture. It’s not a single architecture but a composition of different architectural styles and patterns.

Here’s a menu of the most popular/familiar options. What are your needs and what are you choosing?

Software Architecture Patterns

Maybe you’re using a microservices architecture because of organizational concerns and the need for independently deployable and scalable services. You also have a lot of complex domain logic and you want to apply a clean architecture to manage coupling.

Software Architecture Patterns Microservices

Or perhaps you’re creating a monolith with well-defined logical boundaries. You’re using an event-driven architecture to handle asynchronous workflows between logical boundaries. Some logical boundaries might be using a clean architecture and also focusing on features, and using a vertical slice architecture as way to organize code.

Software Architecture Patterns Monolith

Monolith

That last example might be unfamiliar to some. You can have a monolith that isn’t a big ball of mud. It can be a combination of software architecture patterns. As mentioned, you can define explicit boundaries and loosely couple between them. Because not all boundaries have the same domain complexity, they might not all need to be organized the same way. Some might use vertical slices, and others might be more CRUD driven.

Loosely Coupled Monolith

I refer to this combination of architectural patterns as the Loosely Coupled Monolith.

Instead of coupling between types or making calls in-process between logical boundaries, you’re communicating via events. Your monolith is the producer and consumer of events.

One logical boundary can produce an event and send it to a topic on the broker when a particular business event occurs.

Producer

Other logical boundaries within the monolith can consume and react to that event asynchronously. This might be a part of a business process or used for communication.

Consumer

4+1 Architectural View Model

It’s important to remember there are different ways to look at your system. The 4+1 architectural view model illustrates this.

4+1 Architectural View Model

The fallacy in the current industry is thinking a logical view and a physical view are always the same.

Meaning a logical boundary (or service) must be independently deployable. This is not the case. Physical boundaries aren’t logical boundaries.

In my monolith example, multiple logical boundaries are hosted within the same process (physical). There are advantages and also disadvantages to this. But the point is they don’t have to be the same. They don’t need to be one-to-one.

This is important because if you’re loosely coupling between boundaries, then you could decide to host them all together (physical), or you may decide to carve off a logical boundary and deploy it independently.

Logical vs Physical Boundaries

Logical boundaries are also important because they aren’t all created equally. Not every logical boundary will have the same value to the overall system. Some boundaries might be the core of your domain and contain a lot of complexity. Other boundaries might be simpler with no actual domain logic and can be purely CRUD-driven. These are often more in a supporting role. Defining logical boundaries allows us to understand how we want to handle coupling and cohesion within a single boundary.

Logical Boundaries

In one boundary, we might use a vertical slice architecture with a more task-based UI that uses event sourcing and an event store. Another logical boundary might use an entirely different way of persisting state.

Mix and Match

When someone asks you what software architecture patterns you use, your answer likely is a mix and match of different architectural styles and patterns that make it unique based on your requirements.

I hear people shouting: “Just make it simple. Adding all these architectural patterns make things overly complex!”.

I’m not advocating making a heaping plate of architectural patterns for no reason! Be pragmatic and understand your needs. What is often perceived as “simplicity” can also lead to complexity, typically by forcing all use cases into the same mold and not adopting the approach that fits best.

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Why is Clean Architecture so Popular?

You’ve probably noticed many videos and blogs that somewhat explain what Clean Architecture is and show you how to use it. So its Clean Architecture is popular, but should it be? Should you be using it? Here’s why I think it’s popular, the problems it addresses, and some aspects that almost nobody ever mentions.

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Clean Architecture

As a quick primer, what is clean architecture? Well, it’s a way to manage coupling. Specifically, in this diagram, you can see how the outer parts of the circle reference the inner parts of the circle. The dependencies between layers are pointing in a single direction inward.

It’s about managing coupling.

As an example, with the Clean Architecture template for .NET/C#, the project structure and dependencies are as follows.

Clean Architecture Direction of Dependencies

The top (outer layer), called WebUI, is ASP.NET Core. It references an Infrastructure project that contains the entity framework DBContext and other concerns. The WebUI and Infrastructure reference the Application project, which contains the interfaces for implementations in the infrastructure and any application-level code, such as commands, queries, and handlers. Finally, the application project references the Domain project, which contains (or should) your domain models and business logic.

Sounds great. Separation of technical concerns. But why?

Coupling

degree of interdependence between software modules

ISO/IEC/IEEE 24765:2010
Systems and software engineering — Vocabulary
Big ball of Mud

There are two forms of coupling Clean Architecture addresses. Afferent and Efferent.

Efferent Coupling: Who do you depend on? From the perspective of the Domain project, who does it depend on? Nothing.

Afferent Coupling: Who depends on you? From the same perspective of the domain project, which projects depends on it? The Application Project.

This is about stability. Because the Domain project has no dependencies, nothing can force it to change. All our business logic is isolated and cannot be forced to change because of a change within the infrastructure project or any other project. The reverse is true for WebUI. Changes we make in the infrastructure or Application could force us to make changes in the WebUI.

Do you need Clean Architecture?

It would be best if you asked yourself a few questions. What is the size of the application? Do I have complex domain logic? Do I need to control coupling?

Clean architecture is about forcing a direction of dependencies. In .NET, projects were used in the template above to force the separation. However, you do not need separate projects. Coupling is the dependence between types. If you merged the template into one project, you still have the same degree of coupling.

Prescription

Do not use Clean Architecture as a prescription or template. Understand that you’re trying to manage coupling. It doesn’t need to be by projects. However, it can be to help with physical separation. It doesn’t need to be those exact layers. It’s not a prescription.

Large System

You should consider decomposing it into logical boundaries if you have a large system. What’s a large system? Something that takes a team of developers, possibly years to develop. I’ve covered this in many different blog posts and videos. Check out my post Microservices gets it WRONG defining Service Boundaries and Should you use Domain Driven Design? where I talk about logical boundaries. Logical boundaries are about grouping a cohesive set of capabilities within your system. It allows you to decompose a large system into smaller subsystems.

Logical Boundaries

Why does this matter? When you break up a large system into smaller parts, you’ll realize that not all parts provide the same value. While all the boundaries are important, some are more in a supporting role and often built around CRUD (Create-Read-Update-Delete). This is also very similar if you’re creating a smaller app that may take a couple of weeks or months to develop.

If you have no domain logic, do you need to all the same layers as another part of your system that is at the core of the solution space and contains complex business logic? No.

Clean Architecture within logical boundaries

This is why it’s not a prescription or template. Each boundary within a system has different concerns. If you don’t have any business rules, you have an underlying data model. Or perhaps you only have a dozen or so routes/endpoints that have data access. Do you need to add an abstraction to data access in that case? What if your database changes? Then change the 12 or so routes/endpoints!

Clean Architecture

Clean architecture is about coupling. There’s no prescription for the layers you define or how you define the coupling. You don’t need to define layers by projects. It’s about the direction of dependencies between types. Afferent and Efferent coupling are what define the stability of each layer. Do you need stability in a particular layer? Then maybe consider isolating it.

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McDonald’s Journey to Event-Driven Architecture

McDonald’s uses Event-Driven Architecture! Luckily for us, they’ve written a couple of blog posts providing some details of their journey into event-driven architecture. I’m going to go a bit deeper by providing my thoughts on how their system works and why they are doing it so that it can give you some ideas about your systems.

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McDonald’s Event-Driven Architecture

It’s always interesting to see companies post details of the architecture of various systems they have. It can be insightful to see what they are doing, why, and their challenges. McDonald’s posted behind-the-scenes and how-it-works blog posts detailing their journey to event-driven architecture. More specifically, it’s not that they are new to event-driven architecture but rather have a standardized way to implement it with distributed teams of developers with different skill levels.

McDonald's Event-Driven Architecture

There are many different components to their platform. Their infrastructure is within AWS, and they use MSK (Managed Streaming for Kafka) along with ECS, DynamocDB, and API Gateway.

Here’s how everything works together.

Schema Registry

One of their challenges was related to data quality. Likely because there was no set definition (schema) for data within events. If multiple producers produce the same event type, they might not be composing them exactly the same. I believe an event should have a single publisher, the owner of that schema, to avoid this issue. However, this could be applicable in a message-driven architecture that’s also using queues and commands.

Producers at startup use a custom SDK that retrieves all the event schemas from the registry. This allows the producer to validate the event being produced against the schema.

Schema Registry and Producer SDK

If validation passes, the producer can publish this event to the appropriate Kafka topic using the SDK at this point.

Producer SDK to Kafka

As you can expect, on the consumer side, the same thing occurs. Consumers at startup use a custom SDK that retrieves all the schemas from the registry, just like the producers do.

Consumer SDK to Registry

Then the consumers can process messages from the Kafka topics and understand how to deserialize them from the schema and version of the schema.

Consumer SDK to Kafka

Everything within any Kafka topic should be valid based on all the schemas (versioned) within the registry. Data quality issues are solved!

Validation

Of course, not everything goes through the happy path. What if a producer tries to publish an event, but it fails to validate against the schema? The producer then publishes the message to a Dead Letter Queue. Kafka isn’t a queue, so this is a Dead Letter Topic.

Producer to DLQ

Once a message is in the “DLQ” there needs to be a way to view, modify and fix the event so it can be re-published to the correct topic.

For this, an Admin/Utility UI provides this functionality for them.

Admin Utility

Reliable Publishing

The second failure that can occur is failing to publish to Kafka (MSK). Anyone getting involved in Event-Driven Architecture is bound to run into this. It would be best if you had consistency between making state changes to your business data and publishing your event. When events become critical to your system and possibly workflows, you need guarantees that you publish the relevant events when you make some state change to business data.

Mcdonald’s chose to use DynamoDB to persist any events that cannot be published to Kafka. This means their Publisher SDK will fallback to storing the event data within DynamoDB if it cannot publish to Kafka.

Fallback to DynamoDB

Using a fallback to some durable storage is a common approach. However, the Outbox Pattern is another common solution. I discussed this and other common issues in a post about the 5 Pitfalls of EDA.

Once the event data is in DynamoDB, they use Lambda to pull it from DynamoDB and then retry and publish it to Kafka. I’d assume they have different retry intervals/backoffs.

Lambda Retry

Gateway

Lastly, if you’re integrating with 3rd parties or even within a large organization, you’ll need to have them publish events. However, they won’t have direct access to your SDK and Kafka. For this, they use API Gateway as an HTTP interface to convert HTTP requests that will communicate with the Producer that has the SDK and can publish to Kafka.

Event Gateway

That way, we go through the same validation against the schema in the registry just as if any of our client code is using the producer SDK. This allows external 3rd parties to publish events without using our SDK directly. We can instead have them use our Event Gateway (HTTP API).

Technical Blog Posts

I love when companies have technical blog posts that give insights into their architecture and design. It’s hard to know the full context, but seeing how they solve these issues they run into is interesting. Companies face many common issues when using Event-Driven Architecture, but all have unique constraints.

If you have any recommendations for other technical blog post analyses, please let a comment!

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