Golang

When beginners embark on their programming journey, the initial focus is typically on algorithms and adapting to a new way of thinking. After some time, they delve into Object-Oriented Programming (OOP). If this transition is delayed, it can be challenging to shift from a functional programming mindset. However, eventually, they embrace the use of objects and incorporate them into their code where necessary, sometimes even where they’re not needed. As they learn about abstractions and strive to make their code more reusable, they may overgeneralize, resulting in abstractions applied everywhere, which can hinder future development. At some point, they come to realize the importance of setting boundaries for excessive generalization. Fortunately, The Interface Segregation Principle has already provided a guideline for this, representing the “I” in the word SOLID. When we do not respect The Interface Segregation # Maintain small interfaces to prevent users from relying on unnecessary features. Uncle Bob introduced this principle, and you can find more details about it on his blog. This principle clearly states its requirement, perhaps better than any other SOLID principle. Its straightforward advice to keep interfaces as small as possible should not be interpreted as merely advocating one-method interfaces. Instead, we should consider the cohesion of features that an interface encompasses. Let’s analyze the code below: User interface type User interface { AddToShoppingCart(product Product) IsLoggedIn() bool Pay(money Money) error HasPremium() bool HasDiscountFor(product Product) bool // // some additional methods // } Let’s assume we want to create an application for shopping. One approach is to define an interface User, as demonstrated in the code example. This interface includes various features that a user can have. On our platform, a User can add a Product to the ShoppingCart, make a purchase, and receive discounts on specific Products. However, the challenge is that only specific types of Users can perform all of these actions. Guest struct type Guest struct { cart ShoppingCart // // some additional fields // } func (g *Guest) AddToShoppingCart(product Product) { g.cart.Add(product) } func (g *Guest) IsLoggedIn() bool { return false } func (g *Guest) Pay(Money) error { return errors.New("user is not logged in") } func (g *Guest) HasPremium() bool { return false } func (g *Guest) HasDiscountFor(Product) bool { return false } We have implemented this interface with three structs. The first one is the Guest struct, representing a user who is not logged in but can still add a Product to the ShoppingCart. The second implementation is the NormalCustomer, which can do everything a Guest can, plus make a purchase. The third implementation is the PremiumCustomer, which can use all features of our system.

I’m not really a fan of reading. Often, when I do read, I find myself losing track of the text’s topic for the past few minutes. Many times, I’ll go through an entire chapter without really grasping what it was all about in the end. It can be frustrating when I’m trying to focus on the content, but I keep realizing I need to backtrack. That’s when I turn to various types of media to learn about a topic. The first time I encountered this reading issue was with the SOLID principle, specifically the Liskov Substitution Principle. Its definition was (and still is) too complicated for my taste, especially in its formal format. As you can guess, LSP represents the letter “L” in the word SOLID. It’s not difficult to understand, although a less mathematical definition would be appreciated. When we do not respect The Liskov Substitution # The first time we encountered this principle was in 1988, thanks to Barbara Liskov. Later, Uncle Bob shared his perspective on this topic in a paper and eventually included it as one of the SOLID principles. Let’s take a look at what it says: Let Φ(x) be a property provable about objects x of type T. Then Φ(y) should be true for objects y of type S where S is a subtype of T. Well, good luck with that definition. No, seriously, what kind of definition is this? Even as I write this article, I’m still struggling to fully grasp this definition, despite my fundamental understanding of LSP. Let’s give it another shot: If S is a subtype of T, then objects of type T in a program may be replaced with objects of type S without altering any of the desirable properties of that program. Okay, this is a bit clearer now. If ObjectA is an instance of ClassA, and ObjectB is an instance of ClassB, and ClassB is a subtype of ClassA – if we use ObjectB instead of ObjectA somewhere in the code, the application’s functionality must not break. We’re talking about classes and inheritance here, two concepts that aren’t prominent in Go. However, we can still apply this principle using interfaces and polymorphism. Wrong implementation of Update method type User struct { ID uuid.UUID // // some fields // } type UserRepository interface { Update(ctx context.Context, user User) error } type DBUserRepository struct { db *gorm.DB } func (r *DBUserRepository) Update(ctx context.Context, user User) error { return r.db.WithContext(ctx).Delete(user).Error } In this code example, we can see one that’s quite absurd and far from best practices. Instead of updating the User in the database, as the Update method claims, it actually deletes it. But that’s precisely the point here. We have an interface, UserRepository, followed by a struct, DBUserRepository. While this struct technically implements the interface, it completely diverges from what the interface is supposed to do. In fact, it breaks the functionality of the interface rather than fulfilling its expectations. This highlights the essence of the Liskov Substitution Principle (LSP) in Go: a struct must not violate the intended behavior of the interface.

Many different approaches and principles can lead to long-term improvements in our code. Some of them are well-known in the software development community, while others remain somewhat under the radar. In my opinion, this is the case with The Open/Closed Principle, represented by the letter O in the word SOLID. In my experience, only those genuinely interested in SOLID principles tend to understand what this principle means. We may have applied this principle without even realizing it in some instances, such as when working with the Strategy pattern. However, the Strategy pattern is just one application of the Open/Closed Principle. In this article, we will delve into the full purpose of this principle, with all examples provided in Go. When we do not respect the Open/Closed Principle # You should be able to extend the behavior of a system without having to modify that system. The requirement for the Open/Closed Principle, as seen above, was provided by Uncle Bob in his blog. I prefer this way of defining The Open/Closed Principle because it highlights its full brilliance. At first glance, it may seem like an absurd requirement. Seriously, how can we extend something without modifying it? I mean, is it possible to change something without changing it? By examining the code example below, we can see what it means for certain structures not to adhere to this principle and the potential consequences. The bad Authentication Service type AuthenticationService struct { // // some fields // } func (s *AuthenticationService) Authenticate(ctx *gin.Context) (*User, error) { switch ctx.GetString("authType") { case "bearer": return c.authenticateWithBearerToken(ctx.Request.Header) case "basic": return c.authenticateWithBasicAuth(ctx.Request.Header) case "applicationKey": return c.authenticateWithApplicationKey(ctx.Query("applicationKey")) } return nil, errors.New("unrecognized authentication type") } func (s *AuthenticationService) authenticateWithApplicationKey(key string) (*User, error) { // // authenticate User from Application Key // } func (s *AuthenticationService) authenticateWithBasicAuth(h http.Header) (*User, error) { // // authenticate User from Basic Auth // } func (s *AuthenticationService) authenticateWithBearerToken(h http.Header) (*User, error) { // // validate JWT token from the request header // } The example presents a single struct, AuthenticationService. Its purpose is to authenticate a User from the web application’s Context, supported by the Gin package. Here, we have the main method, Authenticate, which checks for specific authentication type associated with the data within the Context. How User is retrieved from the Context may vary based on whether the User uses a bearer JWT token, basic authentication, or an application key.

There aren’t too many opportunities for a breakthrough in software development. They usually arise from either rewiring our logic after initial misunderstandings or filling in gaps in our knowledge. I appreciate that feeling of deeper understanding. It can happen during a coding session, while reading a book or an online article, or even while sitting on a bus. An internal voice follows, saying, “Ah, yes, that’s how it works.” Suddenly, all past mistakes seem to have a logical reason, and future requirements take shape. I experienced such a breakthrough with the SOLID principles, which were first introduced in a document by Uncle Bob and later expounded upon in his book, “Clean Architecture.” In this article, I intend to embark on a journey through all the SOLID principles, providing examples in Go. The first principle on the list, representing the letter ‘S’ in SOLID, is the Single Responsibility Principle. When we do not respect Single Responsibility # The Single Responsibility Principle (SRP) asserts that each software module should serve a single, specific purpose that could lead to change. The sentence above comes directly from Uncle Bob himself. Initially, its application was linked to modules and the practice of segregating responsibilities based on the organization’s daily tasks. Nowadays, SRP has a broader scope, influencing various aspects of software development. We can apply its principles to classes, functions, modules, and naturally, in Go, even to structs. Some Frankenstein of EmailService type EmailService struct { db *gorm.DB smtpHost string smtpPassword string smtpPort int } func NewEmailService(db *gorm.DB, smtpHost string, smtpPassword string, smtpPort int) *EmailService { return &EmailService{ db: db, smtpHost: smtpHost, smtpPassword: smtpPassword, smtpPort: smtpPort, } } func (s *EmailService) Send(from string, to string, subject string, message string) error { email := EmailGorm{ From: from, To: to, Subject: subject, Message: message, } err := s.db.Create(&email).Error if err != nil { log.Println(err) return err } auth := smtp.PlainAuth("", from, s.smtpPassword, s.smtpHost) server := fmt.Sprintf("%s:%d", s.smtpHost, s.smtpPort) err = smtp.SendMail(server, auth, from, []string{to}, []byte(message)) if err != nil { log.Println(err) return err } return nil } Let’s analyze the code block above. In this code, we have a struct called EmailService, which contains only one method, Send. This service is intended for sending emails. Although it may seem okay at first glance, upon closer inspection, we realize that this code violates the Single Responsibility Principle (SRP) in several ways.

There are not many code structures that bring me joy whenever I need to write them. The first time I implemented such code was with a lightweight ORM in Go, back when we didn’t have one. However, I used ORM for many years, and at some point, when you rely on ORM, using QueryBuilder becomes inevitable. Here, you may notice terms like “predicates”, and that’s where we can find the Specification pattern. It’s hard to find any pattern we use as Specification, yet we do not hear its name. I think the only thing harder is to write an application without using this pattern. The Specification has many applications. We can use it for querying, creation, or validation. We may provide a unique code that can do all this work or provide different implementations for different use cases. For Validation # The first use case for the Specification pattern is validation. Typically, we validate data in forms, but this is at the presentation level. Sometimes, we perform validation during creation, such as for Value Objects. In the context of the domain layer, we can use Specifications to validate the states of Entities and filter them from a collection. So, validation at the domain layer has a broader meaning than for user inputs. Base Product Specification type Product struct { ID uuid.UUID Material MaterialType IsDeliverable bool Quantity int } type ProductSpecification interface { IsValid(product Product) bool } A simple Product Specification type HasAtLeast struct { pieces int } func NewHasAtLeast(pieces int) ProductSpecification { return HasAtLeast{ pieces: pieces, } } func (h HasAtLeast) IsValid(product Product) bool { return product.Quantity >= h.pieces } In the example above, there is an interface called ProductSpecification. It defines only one method, IsValid, which expects instances of Product and returns a boolean value as a result if the Product passes validation rules. A simple implementation of this interface is HasAtLeast, which verifies the minimum quantity of the Product. Function as a Product Specification type FunctionSpecification func(product Product) bool func (fs FunctionSpecification) IsValid(product Product) bool { return fs(product) } func IsPlastic(product Product) bool { return product.Material == Plastic } func IsDeliverable(product Product) bool { return product.IsDeliverable } More interesting validators are two functions, IsPlastic and IsDeliverable. We can wrap those functions with a specific type, FunctionSpecification. This type embeds a function with the same signature as the two mentioned. Besides that, it provides methods that respect the ProductSpecification interface. This example is a nice feature of Go, where we can define a function as a type and attach a method to it so that it can implicitly implement some interface. In this case, it exposes the method IsValid, which executes the embedded function.

Today, it is hard to imagine writing an application without accessing some form of storage at runtime. This includes not only writing application code but also deployment scripts, which often need to access configuration files, which are also a type of storage in a sense. When developing applications to solve real-world business problems, connecting to databases, external APIs, caching systems, or other forms of storage is practically unavoidable. It’s no surprise, then, that Domain-Driven Design (DDD) includes patterns like the Repository pattern to address these needs. While DDD didn’t invent the Repository pattern, it added more clarity and context to its usage. The Anti-Corruption Layer # Domain-Driven Design (DDD) is a principle that can be applied to various aspects of software development and in different parts of a software system. However, its primary focus is on the domain layer, which is where our core business logic resides. While the Repository pattern is responsible for handling technical details related to external data storage and doesn’t inherently belong to the business logic, there are situations where we need to access the Repository from within the domain layer. Since the domain layer is typically isolated from other layers and doesn’t directly communicate with them, we define the Repository within the domain layer, but we define it as an interface. This interface serves as an abstraction that allows us to interact with external data storage without tightly coupling the domain layer to specific technical details or implementations. A Simple Repository example type Customer struct { ID uuid.UUID // // some fields // } type Customers []Customer type CustomerRepository interface { GetCustomer(ctx context.Context, ID uuid.UUID) (*Customer, error) SearchCustomers(ctx context.Context, specification CustomerSpecification) (Customers, int, error) SaveCustomer(ctx context.Context, customer Customer) (*Customer, error) UpdateCustomer(ctx context.Context, customer Customer) (*Customer, error) DeleteCustomer(ctx context.Context, ID uuid.UUID) (*Customer, error) } The interface that defines method signatures within our domain layer is referred to as a “Contract.” In the example provided, we have a simple Contract interface that specifies CRUD (Create, Read, Update, Delete) methods. By defining the Repository as this interface, we can use it throughout the domain layer. The Repository interface always expects and returns our Entities, such as Customer and Customers (collections with specific methods attached to them, as defined in Go). It’s important to note that the Entity Customer doesn’t contain any information about the underlying storage type, such as Go tags for defining JSON structures, Gorm columns, or anything of that sort. This kind of low-level storage configuration is typically handled in the infrastructure layer.

When I wrote the title of this article, I was trying to remember the first design pattern I had learned from “The Gang of Four”. I think it was one of the following: Factory Method, Singleton, or Decorator. I am sure that other software engineers have a similar story. When they started learning design patterns, either Factory Method or Abstract Factory was one of the first three they encountered. Today, any derivative of the Factory pattern is essential in Domain-Driven Design, and its purpose remains the same, even after many decades. Complex Creations # We use the Factory pattern for any complex object creation or to isolate the creation process from other business logic. Having a dedicated place in the code for such scenarios makes it much easier to test separately. In most cases, when I provide a Factory, it is part of the domain layer, allowing me to use it throughout the application. Below, you can see a simple example of a Factory. Simple example type Loan struct { ID uuid.UUID // // some fields // } type LoanFactory interface { CreateShortTermLoan(specification LoanSpecification) Loan CreateLongTermLoan(specification LoanSpecification) Loan } The Factory pattern goes hand-in-hand with the Specification pattern. Here, we have a small example with LoanFactory, LoanSpecification, and Loan. LoanFactory represents the Factory pattern in DDD, and more specifically, the Factory Method. It is responsible for creating and returning new instances of Loan that can vary depending on the payment period. Variations # As mentioned, we can implement the Factory pattern in many different ways. The most common form, at least for me, is the Factory Method. In this case, we provide some creational methods to our Factory struct. Loan Entity const ( LongTerm = iota ShortTerm ) type Loan struct { ID uuid.UUID Type int BankAccountID uuid.UUID Amount Money RequiredLifeInsurance bool } Loan Factory type LoanFactory struct{} func (f *LoanFactory) CreateShortTermLoan(bankAccountID uuid.UUID, amount Money) Loan { return Loan{ Type: ShortTerm, BankAccountID: bankAccountID, Amount: amount, } } func (f *LoanFactory) CreateLongTermLoan(bankAccountID uuid.UUID, amount Money) Loan { return Loan{ Type: LongTerm, BankAccountID: bankAccountID, Amount: amount, RequiredLifeInsurance: true, } } In the code snippet from above, LoanFactory is now a concrete implementation of the Factory Method. It provides two methods for creating instances of the Loan Entity. In this case, we create the same object, but it can have differences depending on whether the loan is long-term or short-term. The distinctions between the two cases can be even more complex, and each additional complexity is a new reason for the existence of this pattern.

I have spent years understanding and practicing the DDD approach. Most of the principles were easy to understand and implement in the code. However, there was one that particularly caught my attention. I must say that the Aggregate pattern is the most critical one in DDD, and perhaps the entire Tactical Domain-Driven Design doesn’t make sense without it. It serves to bind business logic together. While reading, you might think that the Aggregate resembles a cluster of patterns, but that is a misconception. The Aggregate is the central point of the domain layer. Without it, there is no reason to use DDD. Business Invariants # In the real business world, some rules are flexible. For example, when you take a loan from a bank, you need to pay some interest over time. The overall amount of interest is adjustable and depends on your invested capital and the period you will spend to pay the debt. In some cases, the bank may grant you a grace period, offer you a better overall credit deal due to your loyalty in the past, provide you with a once-in-a-lifetime offer, or require you to place a mortgage on a house. All of these flexible rules from the business world are implemented in DDD using the Policy pattern. They depend on many specific cases and, as a result, require more complex code structures. In the real business world, there are also some immutable rules. Regardless of what we try, we cannot change these rules or their application in our business. These rules are known as Business Invariants. For example, nobody should be allowed to delete a customer account in a bank if any of the bank accounts associated with the customer has money or is in debt. In many banks, one customer may have multiple bank accounts with the same currency. However, in some of them, the customer is not allowed to have any foreign currency accounts or multiple accounts with the same currency. When such business rules exist, they become Business Invariants. They are present from the moment we create the object until the moment we delete it. Breaking them means breaking the whole purpose of the application. Currency Entity type Currency struct { id uuid.UUID // // some fields // } func (c Currency) Equal(other Currency) bool { return c.id == other.id } BankAccount Entity

At first glance, Modules may not seem like a typical software development pattern, especially when we often associate patterns with specific code structures or behaviors. This can be particularly confusing when considering Go Modules. These modules consist of closely related Go Packages, are versioned, and released together, serving as a form of dependency management in Go. Since both Go Modules and Packages impact the project’s structure, it raises the question of their relationship with the DDD pattern known as Module. Indeed, there is a connection between them. The Structure # In Go, we use Packages to organize and group our code. Packages are closely tied to the folder structure within our projects, although there can be variations in naming. These variations arise because we have the flexibility to name our package differently than the actual folder it resides in. Folder pkg/access/domain/model package access_model import ( "github.com/google/uuid" ) type User struct { ID uuid.UUID // // some fields // } Folder pkg/access/domain/service package access_service import ( "project/pkg/access/domain/model" ) type UserService interface { Create(user access_model.User) error // // some methods // } In the example above, you can observe slight differences between folder and package naming. In some cases, when dealing with multiple model packages, I add prefixes from my DDD Modules to facilitate referencing these packages within the same file. Now, we can start to gain a better understanding of what a DDD Module would be in the previous example. In this context, the Module encompasses the access package along with all its child packages. project ├── cmd │ ├── main.go ├── internal │ ├── module1 │ │ ├── infrastructure │ │ ├── presentation │ │ ├── application │ │ ├── domain │ │ │ ├── service │ │ │ ├── factory │ │ │ ├── repository │ │ │ └── model │ │ └── module1.go │ ├── module2 │ │ └── ... │ └── ... ├── pkg │ ├── module3 │ │ └── ... │ ├── module4 │ │ └── ... │ └── ... ├── go.mod └── ... The folder structure in the diagram above represents my preferred project structure for implementing Domain-Driven Design in Go. While I may create different variations of certain folders, I strive to maintain consistent DDD Modules. In my projects, each Module typically consists of no more than four base packages: infrastructure, presentation, application, and domain. As you can see, I adhere to the principles of Hexagonal Architecture. In this structure, I place the infrastructure package at the top. This is because, by following Uncle Bob’s Dependency Inversion Principle, my low-level Services from the infrastructure layer implement high-level interfaces from other layers. With this approach, I ensure that I define a Port, such as the UserRepository interface, in the domain layer, while the actual implementation resides in the infrastructure layer. There can be multiple Adapters for this implementation, like UserDBRepository or UserFakeRepository.

In many cases, Entities are the most effective means of representing elements in Domain-Driven Design. Together with Value Objects, they can provide a precise reflection of our Problem Domain. However, sometimes, the most apt way to depict a Problem Domain is by employing events that transpire within it. In my experience, I increasingly attempt to identify events and then discern the Entities associated with them. Although Eric Evans didn’t cover the Domain Event pattern in the first edition of his book, today, it’s challenging to fully develop the domain layer without incorporating events. The Domain Event pattern serves as a representation of such occurrences within our code. We employ it to elucidate any real-world event that holds relevance for our business logic. In the contemporary business landscape, virtually everything is connected to some form of event. It can be anything # Domain Events can encompass a wide range of occurrences, but they must adhere to certain rules. The first rule is that they are immutable. To support this characteristic, I consistently utilize private fields within Event structs, even though I’m not particularly fond of private fields and getters in Go. However, Events typically don’t require many getters. Additionally, a specific Event can only occur once. This implies that we can create an Order Entity with a particular Identity only once, and consequently, our code can only trigger the Event that describes the creation of that Order once. Any other Event related to that Order would be a different type of Event, pertaining to a distinct Order. Each Event essentially narrates something that has already taken place, representing the past. This means we trigger the OrderCreated Event after we have already created the Order, not before. Global Events type Event interface { Name() string } type GeneralError string func (e GeneralError) Name() string { return "event.general.error" } Order Event type OrderEvent interface { Event OrderID() uuid.UUID } type OrderDispatched struct { orderID uuid.UUID } func (e OrderDispatched) Name() string { return "event.order.dispatched" } func (e OrderDispatched) OrderID() uuid.UUID { return e.orderID } type OrderDelivered struct { orderID uuid.UUID } func (e OrderDelivered) Name() string { return "event.order.delivery.success" } func (e OrderDelivered) OrderID() uuid.UUID { return e.orderID } type OrderDeliveryFailed struct { orderID uuid.UUID } func (e OrderDeliveryFailed) Name() string { return "event.order.delivery.failed" } func (e OrderDeliveryFailed) OrderID() uuid.UUID { return e.orderID } The code example provided above demonstrates simple Domain Events. This code represents just one of countless ways to implement them in Go. In certain situations, such as with GeneralError, I have employed straightforward strings as Event representations. However, there are instances when I’ve utilized more complex objects or extended the base Event interface with a more specific one to introduce additional methods, as seen with OrderEvent.