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Hello, everyone! My name is Sławek Sobótka, and I'm going to talk about two topics today.
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The first topic will be basic techniques of Domain Driven Design modeling.
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The second topic will be Ports and Adapters architecture, according to the language topic from the last presentation.
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As you can hear, English is not my native language, so I will try to use simple syntax and tenses to make it easier for everyone.
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Let's start with a short introduction and a little bit of history.
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My first name is a concatenation of two words from an ancient Slavic language. In Polish, 'slav' means 'slavo', which means someone who cherishes peace.
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So, I'm a peaceful guy. My last name in this ancient language relates to 'Sabbath', something like Black Sabbath, which was a holiday celebrating the shortest night of the year in June.
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That's enough about me. Have you ever been in a situation where you had a meeting with IT and business people, and the business side wanted to introduce just a minor change?
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They need just one little checkbox on the screen, and you find it to be a total disaster from a technical perspective.
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Have you ever experienced this? It’s a strange phenomenon where despite having the best language and being professional, you find yourself estimating an overly optimistic 100 story points.
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What's the usual reaction from the business side? They just can't understand the significance of the change. Maybe the mental models of the problem in your head and in theirs are completely different.
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Have you ever tried using the technical debt metaphor? You should read about it in books and conference papers.
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Using the technical debt metaphor is useful because business people understand money.
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Have you ever tried using it? Did anyone share their success stories?
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What about technical debt? It works; why? Because business people often have a different mental model regarding the current state of the system.
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In result, you might end up working over the weekend to fix issues.
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In my career, I have worked on many different types of systems, frameworks, and embedded systems.
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However, I have spent most of my time in business software as a developer, architect, and team leader.
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Now, I’m trying to help teams as an outside observer and coach.
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I've seen many situations where IT people meet with business people, and I can observe the same patterns over and over again.
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I assume most of you create business software tools that support specific business cases, right?
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Business software is not rocket science; it’s really not.
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The only challenge is understanding the business problem.
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And you don’t need fancy languages. I work in Java, and it’s enough.
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The issue really lies in understanding what’s happening.
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That's why I started looking for solutions to this problem about six years ago.
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I became interested in Domain Driven Design (DDD). In my opinion, it is currently the only complete and coherent approach to handling business problems.
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DDD focuses specifically on business software, making it a no-brainer for business cases.
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How many people here have encountered DDD techniques? And how many actively use DDD?
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What is DDD? Let’s break it down.
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Our goal is to model business problems, not just the vision or nouns.
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Have you seen entity-relationship diagrams? They're often just about data structures.
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It's not just a database schema; it's about understanding what’s happening in the business.
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In DDD, our model will be implemented directly in the source code: one to one.
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It’s about driving your design based on the business model.
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Sometimes, design can be user interface driven, which can lead to unexpected changes.
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In DDD, we also invest our best resources in the core domains where we see competitive advantages.
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The domain is basically the sphere of business knowledge.
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In DDD, there's a process called knowledge crunching, which involves iteratively delving deeper into understanding the problem.
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You cannot grasp non-trivial problems in one go, like in a waterfall approach.
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We focus on the core domain, which usually constitutes about 5% of your source code.
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This is why we create systems: to address supporting domains, which are mundane tasks necessary to add value.
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For instance, you wouldn’t want to implement invoicing repeatedly across different systems.
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We assume the core domain is the main source of system complexity.
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According to complexity theory, there are two kinds of complexity: essential and accidental.
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Essential complexity is the natural complexity of the problem, and you cannot reduce it.
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Accidental complexity comes from your approach to solutions; it’s something you can manage.
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In DDD, we focus on managing essential complexity because we aren’t building trivial operations.
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The main goal of DDD is to manage essential complexity while avoiding accidental complexity.
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So, DDD is all about the model.
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Let's take a look at a sample model of a garage.
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In DDD, a model is produced from conversations between two roles: the domain expert and the modeler.
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The domain expert is someone who understands what is happening in the business.
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You recognize them because when there is a problem, all the business people turn to the domain expert.
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The modeler should be a hands-on developer since the model needs to be implemented one to one with the source code.
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The model is the product of the collaboration between these two roles.
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This process is not an ineffective one where business people write documents and hand them off to IT.
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That often leads to misunderstandings.
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Instead, we meet in a modeling session and use a process called modeling workshops.
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In these workshops, the domain expert shares their knowledge with the modeler.
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The modeler presents the model back to the domain expert and asks if it reflects their understanding.
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The domain expert then can confirm or correct any discrepancies in the model.
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What’s exciting about DDD is that knowledge also flows from the model back to the domain expert.
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This allows business people to learn more about their own business through the model.
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For instance, imagine a business that currently only uses phones to place orders.
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Once you introduce new software, it fundamentally changes how the business operates.
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The business will need to learn how to navigate this new environment.
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Another key point from the Agile Manifesto is productive collaboration.
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Now imagine a scenario where you want to put a landing pad for a helicopter.
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Even non-technical stakeholders will realize it doesn’t make much sense.
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When you have a common understanding of the problem through a shared model, it's easier to discuss technical depths.
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This common model helps clarify what is possible and helps bridge the gap between technical and non-technical people.
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So in DDD, we often split the application into layers.
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We have a presentation layer, logic layer, and infrastructure data layer.
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In DDD, the logic layer is divided into two: application logic and domain logic.
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Application logic knows the 'what' of the scenario, while domain logic knows the 'how'.
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Let's look at a sample model based on an order system.
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In this model, an order has various line items.
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Each line includes a product identifier, quantity, and pricing details.
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It's important to communicate this model clearly, especially to those with traumatic experiences regarding UML.
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A simple visual representation can often convey much more than complex technical specifications.
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Throughout this presentation, I will be demonstrating Java, which should be familiar to most software developers.
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When creating a model in DDD, you utilize standard building blocks. For example, aggregates represent groups of related objects.
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An aggregate's focus should be on behavior rather than inner structure.
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For instance, consider a sample aggregate of an order where behavior is prioritized.
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A domain expert might state, 'I want to add a product to the order with a specified quantity.'
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Here, 'add product' is a technical detail relevant to the aggregate.
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While adding, you have to consider invariants: conditions that must hold true.
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For example, you cannot have two lines in an order referring to the same product.
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If an already added product is included, you would increase the quantity on the existing line.
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Another invariant could dictate that each time you add a product, you must recalculate the total cost of the order.
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Moreover, you cannot add a product to an order that is already closed.
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So we need to model these invariants and protect them within a coherent aggregate.
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This approach is basic but effective for defining the boundaries of an aggregate.
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As the complexity of aggregates increases, we may utilize factories.
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We can query our domain model to determine what is needed to create an order.
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The domain expert may indicate that we need a client. Thus, we create the order using the client.
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We also need to consider constraints such as age or residence, as well as financial requirements.
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Next, we need to create an invoice, which can involve a bookkeeper.
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Let’s take a look at a model for invoicing.
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In this model, you can invoice a single order.
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But what if requirements change and you need to invoice multiple orders?
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Or what if you need to invoice only selected items from the order?
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It's tempting to create an adapter class that composes these orders, but that may complicate your model.
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Your domain expert might say, 'Don’t hack your model; change it instead.'
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Adapting your model without re-evaluating its assumptions can lead to 'model hell'.
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So it would be wise to ask the business people how they would handle various scenarios.
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We could design an entirely new building block, known as a pricing plan.
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A pricing plan could serve as a snapshot of a specific point in time.
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You can present this pricing plan to the user, allowing them to accept the prices.
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Once accepted, you can copy the state of the pricing plan into a separate aggregate, such as 'purchase'.
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This can be used as proof of purchase.
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This way, we’re changing the model instead of compromising it.
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If we don’t hack our model with state fields, we maintain clarity.
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For more complex situations, we can implement additional structure through further levels.
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In DDD, we can conceptualize different levels of model capability.
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At the foundational level, we have general business resources that remain stable.
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For example, dealing with money is fundamental in many projects.
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Many projects might not have a class for money in their standard libraries.
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Instead, developers often use primitive data types.
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This approach neglects the complexities that arise with financial calculations.
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This is why creating a dedicated 'Money' class can be effective for encapsulating such mechanics.
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This class can help model meaningful operations such as addition or comparison.
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In DDD, policies can be introduced to model goals, rules, or constraints.
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For example, a tax policy could be written as part of the invoicing process.
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The invoicing algorithm could be standardized, while tax calculations might differ by jurisdiction.
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This design can be implemented as a strategy pattern, allowing for flexible policy management.
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This policy contains pertinent details required for tax calculation and integrates the feedback from domain experts.
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In some domains, there might also be a decision support level.
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For instance, if a product is unavailable, the system can suggest alternatives based on past purchases.
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This represents a simplified model of how to handle intelligent system behavior.
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So we outline core rules of the business, and on top of that, we have application scenarios.
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Many developers prefer to use user stories to convey requirements.
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While others may favor use cases.
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Which is better? It often depends on the audience and their understanding.
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Some prefer concrete examples, while others enjoy abstract discussions.
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Both story and use case strategies are valid and useful in software development.
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The application logic acts as an animator, directing domain actors to execute business processes.
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The flow of requests is specific and structured.
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This separation means domain knowledge doesn’t leak into application logic.
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You can publish this as a web service without compromising the structure.
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Services load aggregates from a repository, avoiding unnecessary complexity from architectural patterns.
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Keep in mind that we must focus on usable solutions that serve business requirements.
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Consider a scenario where you need to approve an order.
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You load aggregates and check whether they meet the preset specifications.
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If the order cannot be submitted, you might trigger an exception.
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Then, you can generate an invoice according to your application logic.
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Your application also decides the applicable tax based on context.
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Now, let's switch the topic and discuss architecture.
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What does architecture mean to you? Sometimes it can be jargon that adds no real value.
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In our domain, architecture is akin to a general style that underpins software design.
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For instance, hexagonal architecture is a well-known paradigm.
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Originally, it was designed to support six ports, hence the name.
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However, with advancements, we now support multiple ports, leading to the renaming of the concept to Ports and Adapters.
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In this architecture, the kernel serves as a protective shell for the model.
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Ports are designed to interact with the kernel; we differentiate between offered ports and required ports.
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Offered ports allow users to operate based on the model, while required ports handle external data operations.
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Your application can have diverse contexts where varying models may be necessary.
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Some ports can be adapted to different environments, such as web applications.
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We can have outer ports that act as repositories, providing flexibility with data storage.
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For example, data can originate from a traditional database, API, or even other services.
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These are robust ways to ensure your application is adaptable and efficiently integrated.
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Within this architecture, an event-publishing mechanism can enhance communication across components.
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Applying command-query separation helps define how data is handled in the architecture.
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When we have our hexagonal model, we can easily introduce more modules.
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In DDD, this is known as bounded context, an idea where the meaning of terms depends on their context.
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For instance, 'product' may mean something entirely different in inventory management than in sales.
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That's the reason we categorize these contexts to ensure clarity in communication.
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This categorization also paves the way for module integration without confusion.
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In a sample use case, we can have a client relationships management module.
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Here, we manage events that signify changes in the client’s status.
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These events are published, and other modules can react accordingly.
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We also ensure that our domain model remains protected within its context.
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When events travel across the system, we use lightweight objects to carry data, maintaining separation.
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Adapters help to glue different contexts together accordingly.
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Despite the integration, each domain can maintain its integrity and purpose.
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Moving on next, let's discuss testing and its strategies.
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Imagine a testing pyramid that suggests having many unit tests and few end-to-end tests.
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Investing in unit tests is crucial for the kernel of your application.
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To validate the accuracy of core logic, extensive tests are often necessary.
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However, achieving high coverage by targeting upper layers might lead to various complexities.
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Consequently, it's essential to prioritize your domain logic in tests.
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End-to-end tests serve their purpose for general requirements but may not ensure logical perfection.
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To summarize, business software isn’t rocket science; comprehension is the primary obstacle.
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The crucial thing is establishing a common understanding of the problem.
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Utilizing a shared model helps teams find alignment and clarity.
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Thank you for your attention!
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Are there any specific rules or techniques for gathering knowledge from the domain?
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Yes, DDD encompasses a wealth of techniques, though many of these would take a few more hours to cover comprehensively.
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Recommended reading is 'Domain Driven Design' by Eric Evans. Even after ten years, it's seminal work.
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One more question before we conclude.
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How do you manage persistence with events? Are your domain models aware of the persistence layer?
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You raised a few important issues. Persisting events is a technique known as event sourcing.
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However, it’s not suitable for most systems.
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Typically, persistence utilizes a repository pattern.
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For example, when creating an invoice, you call the repository to store it.
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Underlying systems may use an object relational mapping framework.
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In many scenarios, we encapsulate this in an infrastructure component.
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I recommend looking into the book 'Implementing Domain Driven Design' by Vernon.
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It reviews many of these concepts comprehensively.
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Event sourcing stores a sequence of events that transpired on an aggregate.
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This allows querying specific states of the aggregate at various points in time.
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However, it is important to note that event sourcing has its own set of challenges.
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Indeed, using it for business applications may not be necessary.
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Thank you all for your engagement! I appreciate the opportunity to discuss these topics.
