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Domain-Driven Hexagon

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The main emphasis of this project is to provide recommendations on how to design software applications. This readme includes techniques, tools, best practices, architectural patterns and guidelines gathered from different sources.

Code examples are written using NodeJS, TypeScript, NestJS framework and Slonik for the database access.

Patterns and principles presented here are framework/language agnostic. Therefore, the above technologies can be easily replaced with any alternative. No matter what language or framework is used, any application can benefit from principles described below.

Note: code examples are adapted to TypeScript and frameworks mentioned above. <br/> (Implementations in other languages will look differently)

Everything below is provided as a recommendation, not a rule. Different projects have different requirements, so any pattern mentioned in this readme should be adjusted to project needs or even skipped entirely if it doesn't fit. In real world production applications, you will most likely only need a fraction of those patterns depending on your use cases. More info in this section.

Architecture

This is an attempt to combine multiple architectural patterns and styles together, such as:

And many others (more links below in every chapter).

Before we begin, here are the PROS and CONS of using a complete architecture like this:

Pros

Cons

Diagram

Domain-Driven Hexagon <sup>Diagram is mostly based on this one + others found online</sup>

In short, data flow looks like this (from left to right):

Each layer is in charge of its own logic and has building blocks that usually should follow a Single-responsibility principle when possible and when it makes sense (for example, using Repositories only for database access, using Entities for business logic, etc.).

Keep in mind that different projects can have more or less steps/layers/building blocks than described here. Add more if the application requires it, and skip some if the application is not that complex and doesn't need all that abstraction.

General recommendation for any project: analyze how big/complex the application will be, find a compromise and use as many layers/building blocks as needed for the project and skip ones that may over-complicate things.

More in details on each step below.

Modules

This project's code examples use separation by modules (also called components). Each module's name should reflect an important concept from the Domain and have its own folder with a dedicated codebase. Each business use case inside that module gets its own folder to store most of the things it needs (this is also called Vertical Slicing). It's easier to work on things that change together if those things are gathered relatively close to each other. Think of a module as a "box" that groups together related business logic.

Using modules is a great way to encapsulate parts of highly cohesive business domain rules.

Try to make every module independent and keep interactions between modules minimal. Think of each module as a mini application bounded by a single context. Consider module internals private and try to avoid direct imports between modules (like importing a class import SomeClass from '../SomeOtherModule') since this creates tight coupling and can turn your code into a spaghetti and application into a big ball of mud.

Few advices to avoid coupling:

This ensures loose coupling, refactoring of a module internals can be done easier because outside world only depends on module's public interface, and if bounded contexts are defined and designed properly each module can be easily separated into a microservice if needed without touching any domain logic or major refactoring.

Keep your modules small. You should be able to rewrite a module in a relatively short period of time. This applies not only to modules pattern, but to software development in general: objects, functions, microservices, processes, etc. Keep them small and composable. This is incredibly powerful in a constantly changing environments of software development, since when your requirements change, changing small modules is much easier than changing a big program. You can just delete a module and rewrite it from scratch in a matter of days. This idea is further described in this talk: Greg Young - The art of destroying software.

Code Examples:

Read more:

Each module consists of layers described below.

Application Core

This is the core of the system which is built using DDD building blocks:

Domain layer:

Application layer:

Note: different implementations may have slightly different layer structures depending on applications needs. Also, more layers and building blocks may be added if needed.

Application layer

Application Services

Application Services (also called "Workflow Services", "Use Cases", "Interactors", etc.) are used to orchestrate the steps required to fulfill the commands imposed by the client.

Application services:

One service per use case is considered a good practice.

<details> <summary>What are "Use Cases"?</summary>

wiki:

In software and systems engineering, a use case is a list of actions or event steps typically defining the interactions between a role (known in the Unified Modeling Language as an actor) and a system to achieve a goal.

Use cases are, simply said, list of actions required from an application.

</details>

Example file: create-user.service.ts

More about services:

Commands and Queries

This principle is called Command–Query Separation(CQS). When possible, methods should be separated into Commands (state-changing operations) and Queries (data-retrieval operations). To make a clear distinction between those two types of operations, input objects can be represented as Commands and Queries. Before DTO reaches the domain, it's converted into a Command/Query object.

Commands

Command is an object that signals user intent, for example CreateUserCommand. It describes a single action (but does not perform it).

Commands are used for state-changing actions, like creating new user and saving it to the database. Create, Update and Delete operations are considered as state-changing.

Data retrieval is responsibility of Queries, so Command methods should not return business data.

Some CQS purists may say that a Command shouldn't return anything at all. But you will need at least an ID of a created item to access it later. To achieve that you can let clients generate a UUID (more info here: CQS versus server generated IDs).

Though, violating this rule and returning some metadata, like ID of a created item, redirect link, confirmation message, status, or other metadata is a more practical approach than following dogmas.

Note: Command is similar but not the same as described here: Command Pattern. There are multiple definitions across the internet with similar but slightly different implementations.

To execute a command you can use a Command Bus instead of importing a service directly. This will decouple a command Invoker from a Receiver, so you can send your commands from anywhere without creating coupling.

Avoid command handlers executing other commands in this fashion: Command → Command. Instead, use events for that purpose, and execute next commands in a chain in an Event handler: Command → Event → Command.

Example files:

Read more:

Queries

Query is similar to a Command. It belongs to a read model and signals user intent to find something and describes how to do it.

Query is just a data retrieval operation and should not make any state changes (like writes to the database, files, third party APIs, etc.). For this reason, in read model we can bypass a domain and repository layers completely and query database directly from a query handler.

Similarly to Commands, Queries can use a Query Bus if needed. This way you can query anything from anywhere without importing classes directly and avoid coupling.

Example files:

By enforcing Command and Query separation, the code becomes simpler to understand. One changes something, another just retrieves data.

Also, following CQS from the start will facilitate separating write and read models into different databases if someday in the future the need for it arises.

Note: this repo uses NestJS CQRS package that provides a command/query bus.

Read more about CQS and CQRS:

Ports

Ports are interfaces that define contracts that should be implemented by adapters. For example, a port can abstract technology details (like what type of database is used to retrieve some data), and infrastructure layer can implement an adapter in order to execute some action more related to technology details rather than business logic. Ports act like abstractions for technology details that business logic does not care about. Name "port" most actively is used in Hexagonal Architecture.

In Application Core dependencies point inwards. Outer layers can depend on inner layers, but inner layers never depend on outer layers. Application Core shouldn't depend on frameworks or access external resources directly. Any external calls to out-of-process resources/retrieval of data from remote processes should be done through ports (interfaces), with class implementations created somewhere in infrastructure layer and injected into application's core (Dependency Injection and Dependency Inversion). This makes business logic independent of technology, facilitates testing, allows to plug/unplug/swap any external resources easily making application modular and loosely coupled.

Note: since most ports implementations are injected and executed in application service, Application Layer can be a good place to keep those ports. But there are times when the Domain Layer's business logic depends on executing some external resource, in such cases those ports can be put in a Domain Layer.

Note: abusing ports/interfaces may lead to unnecessary abstractions and overcomplicate your application. In a lot of cases it's totally fine to depend on a concrete implementation instead of abstracting it with an interface. Think carefully if you really need an abstraction before using it.

Example files:

Read more:

Domain Layer

This layer contains the application's business rules.

Domain should operate using domain objects described by ubiquitous language. Most important domain building blocks are described below.

Entities

Entities are the core of the domain. They encapsulate Enterprise-wide business rules and attributes. An entity can be an object with properties and methods, or it can be a set of data structures and functions.

Entities represent business models and express what properties a particular model has, what it can do, when and at what conditions it can do it. An example of business model can be a User, Product, Booking, Ticket, Wallet etc.

Entities must always protect their invariant:

Domain entities should always be valid entities. There are a certain number of invariants for an object that should always be true. For example, an order item object always has to have a quantity that must be a positive integer, plus an article name and price. Therefore, invariants enforcement is the responsibility of the domain entities (especially of the aggregate root) and an entity object should not be able to exist without being valid.

Entities:

Note: A lot of people tend to create one module per entity, but this approach is not very good. Each module may have multiple entities. One thing to keep in mind is that putting entities in a single module requires those entities to have related business logic, don't group unrelated entities in one module.

Example files:

Read more:

Aggregates

Aggregate is a cluster of domain objects that can be treated as a single unit. It encapsulates entities and value objects which conceptually belong together. It also contains a set of operations which those domain objects can be operated on.

All of these rules just come from the idea of creating a boundary around Aggregates. The boundary simplifies business model, as it forces us to consider each relationship very carefully, and within a well-defined set of rules.

In summary, if you combine multiple related entities and value objects inside one root Entity, this root Entity becomes an Aggregate Root, and this cluster of related entities and value objects becomes an Aggregate.

Example files:

Read more:

Domain Events

Domain Event indicates that something happened in a domain that you want other parts of the same domain (in-process) to be aware of. Domain events are just messages pushed to an in-memory Domain Event dispatcher.

For example, if a user buys something, you may want to:

The typical approach involves executing all this logic in a service that performs a "buy" operation. However, this creates coupling between different subdomains.

An alternative approach would be publishing a Domain Event. If executing a command related to one aggregate instance requires additional domain rules to be run on one or more additional aggregates, you can design and implement those side effects to be triggered by Domain Events. Propagation of state changes across multiple aggregates within the same domain model can be performed by subscribing to a concrete Domain Event and creating as many event handlers as needed. This prevents coupling between aggregates.

Domain Events may be useful for creating an audit log to track all changes to important entities by saving each event to the database. Read more on why audit logs may be useful: Why soft deletes are evil and what to do instead.

All changes caused by Domain Events across multiple aggregates in a single process can be saved in a single database transaction. This approach ensures consistency and integrity of your data. Wrapping an entire flow in a transaction or using patterns like Unit of Work or similar can help with that. Keep in mind that abusing transactions can create bottlenecks when multiple users try to modify single record concurrently. Use it only when you can afford it, otherwise go for other approaches (like eventual consistency).

There are multiple ways on implementing an event bus for Domain Events, for example by using ideas from patterns like Mediator or Observer.

Examples:

To have a better understanding on domain events and implementation read this:

Additional notes:

Integration Events

Out-of-process communications (calling microservices, external APIs) are called Integration Events. If sending a Domain Event to external process is needed then domain event handler should send an Integration Event.

Integration Events usually should be published only after all Domain Events finished executing and saving all changes to the database.

To handle integration events in microservices you may need an external message broker / event bus like RabbitMQ or Kafka together with patterns like Transactional outbox, Change Data Capture, Sagas or a Process Manager to maintain eventual consistency.

Read more:

For integration events in distributed systems here are some patterns that may be useful:

Domain Services

Eric Evans, Domain-Driven Design:

Domain services are used for "a significant process or transformation in the domain that is not a natural responsibility of an ENTITY or VALUE OBJECT"

Value objects

Some Attributes and behaviors can be moved out of the entity itself and put into Value Objects.

Value Objects:

Value object shouldn’t be just a convenient grouping of attributes but should form a well-defined concept in the domain model. This is true even if it contains only one attribute. When modeled as a conceptual whole, it carries meaning when passed around, and it can uphold its constraints.

Imagine you have a User entity which needs to have an address of a user. Usually an address is simply a complex value that has no identity in the domain and is composed of multiple other values, like country, street, postalCode etc., so it can be modeled and treated as a Value Object with its own business logic.

Value object isn’t just a data structure that holds values. It can also encapsulate logic associated with the concept it represents.

Example files:

Read more about Value Objects:

Domain Invariants

Domain invariants are the policies and conditions that are always met for the Domain in particular context. Invariants determine what is possible or what is prohibited in the context.

Invariants enforcement is the responsibility of domain objects (especially of the entities and aggregate roots).

There are a certain number of invariants for an object that should always be true. For example:

If the business has some rules similar to described above, the domain object should not be able to exist without following those rules.

Below we will discuss some validation techniques for your domain objects.

Example files:

Read more:

Replacing primitives with Value Objects

Most of the code bases operate on primitive types – strings, numbers etc. In the Domain Model, this level of abstraction may be too low.

Significant business concepts can be expressed using specific types and classes. Value Objects can be used instead primitives to avoid primitives obsession. So, for example, email of type string:

const email: string = 'john@gmail.com';

could be represented as a Value Object instead:

export class Email extends ValueObject<string> {
  constructor(value: string) {
    super({ value });
  }

  get value(): string {
    return this.props.value;
  }
}

const email: Email = new Email('john@gmail.com');

Now the only way to make an email is to create a new instance of Email class first, this ensures it will be validated on creation and a wrong value won't get into Entities.

Also, an important behavior of the domain primitive is encapsulated in one place. By having the domain primitive own and control domain operations, you reduce the risk of bugs caused by lack of detailed domain knowledge of the concepts involved in the operation.

Creating an object for primitive values may be cumbersome, but it somewhat forces a developer to study domain more in details instead of just throwing a primitive type without even thinking what that value represents in domain.

Using Value Objects for primitive types is also called a domain primitive. The concept and naming are proposed in the book "Secure by Design".

Using Value Objects instead of primitives:

Value Object can represent a typed value in domain (a domain primitive). The goal here is to encapsulate validations and business logic related only to the represented fields and make it impossible to pass around raw values by forcing a creation of valid Value Objects first. This object only accepts values which make sense in its context.

If every argument and return value of a method is valid by definition, you’ll have input and output validation in every single method in your codebase without any extra effort. This will make application more resilient to errors and will protect it from a whole class of bugs and security vulnerabilities caused by invalid input data.

Without domain primitives, the remaining code needs to take care of validation, formatting, comparing, and lots of other details. Entities represent long-lived objects with a distinguished identity, such as articles in a news feed, rooms in a hotel, and shopping carts in online sales. The functionality in a system often centers around changing the state of these objects: hotel rooms are booked, shopping cart contents are paid for, and so on. Sooner or later the flow of control will be guided to some code representing these entities. And if all the data is transmitted as generic types such as int or String , responsibilities fall on the entity code to validate, compare, and format the data, among other tasks. The entity code will be burdened with a lot of tasks, rather than focusing on the central business flow-of-state changes that it models. Using domain primitives can counteract the tendency for entities to grow overly complex.

Quote from: Secure by design: Chapter 5.3 Standing on the shoulders of domain primitives

Also, an alternative for creating an object may be a type alias (ideally using nominal types) just to give this primitive a semantic meaning.

Warning: Don't include Value Objects in objects that can be sent to other processes, like dtos, events, database models etc. Serialize them to primitive types first.

Note: In languages like TypeScript, creating value objects for single values/primitives adds some extra complexity and boilerplate code, since you need to access an underlying value by doing something like email.value. Also, it can have performance penalties due to creation of so many objects. This technique works best in languages like Scala with its value classes that represents such classes as primitives at runtime, meaning that object Email will be represented as String at runtime.

Note: if you are using nodejs, Runtypes is a nice library that you can use instead of creating your own value objects for primitives.

Note: Some people say that primitive obsession is a code smell, some people consider making a class/object for every primitive may be overengineering (unless you are using Scala with its value classes). For less complex and smaller projects it's definitely an overkill. For bigger projects, there are people who advocate for and against this approach. If you notice that creating a class for every primitive doesn't give you much benefit, create classes just for those primitives that have specific rules or behavior, or just validate only outside of domain using some validation framework. Here are some thoughts on this topic: From Primitive Obsession to Domain Modelling - Over-engineering?.

Recommended reading:

Make illegal states unrepresentable

Use Value Objects/Domain Primitives and Types (Algebraic Data Types (ADT)) to make illegal states impossible to represent in your program.

Some people recommend using objects for every value:

Quote from John A De Goes:

Making illegal states unrepresentable is all about statically proving that all runtime values (without exception) correspond to valid objects in the business domain. The effect of this technique on eliminating meaningless runtime states is astounding and cannot be overstated.

Let's distinguish two types of protection from illegal states: at compile time and at runtime.

Validation at compile time

Types give useful semantic information to a developer. Good code should be easy to use correctly, and hard to use incorrectly. Types system can be a good help for that. It can prevent some nasty errors at compile time, so IDE will show type errors right away.

The simplest example may be using enums instead of constants, and use those enums as input type for something. When passing anything that is not intended the IDE will show a type error:

export enum UserRoles {
  admin = 'admin',
  moderator = 'moderator',
  guest = 'guest',
}

const userRole: UserRoles = 'some string'; // <-- error

Or, for example, imagine that business logic requires to have contact info of a person by either having email, or phone, or both. Both email and phone could be represented as optional, for example:

interface ContactInfo {
  email?: Email;
  phone?: Phone;
}

But what happens if both are not provided by a programmer? Business rule violated. Illegal state allowed.

Solution: this could be presented as a union type

type ContactInfo = Email | Phone | [Email, Phone];

Now only either Email, or Phone, or both must be provided. If nothing is provided, the IDE will show a type error right away. Now business rule validation is moved from runtime to compile time, which makes the application more secure and gives a faster feedback when something is not used as intended.

This is called a typestate pattern.

The typestate pattern is an API design pattern that encodes information about an object’s run-time state in its compile-time type.

Read more:

Validation at runtime

Data should not be trusted. There are a lot of cases when invalid data may end up in a domain. For example, if data comes from external API, database, or if it's just a programmer error.

Things that can't be validated at compile time (like user input) are validated at runtime.

First line of defense is validation of user input DTOs.

Second line of defense are Domain Objects. Entities and value objects have to protect their invariants. Having some validation rules here will protect their state from corruption. You can use techniques like Design by contract by defining preconditions in object constructors and checking postconditions and invariants before saving an object to the database.

Enforcing self-validation of your domain objects will inform immediately when data is corrupted. Not validating domain objects allows them to be in an incorrect state, this leads to problems.

By combining compile and runtime validations, using objects instead of primitives, enforcing self-validation and invariants of your domain objects, using Design by contract, Algebraic Data Types (ADT) and typestate pattern, and other similar techniques, you can achieve an architecture where it's hard, or even impossible, to end up in illegal states, thus improving security and robustness of your application dramatically (at a cost of extra boilerplate code).

Recommended to read:

Guarding vs validating

You may have noticed that we do validation in multiple places:

  1. First when user input is sent to our application. In our example we use DTO decorators: create-user.request-dto.ts.
  2. Second time in domain objects, for example: address.value-object.ts.

So, why are we validating things twice? Let's call a second validation "guarding", and distinguish between guarding and validating:

This difference leads to different treatment of violations of these business rules. An invariant violation in the domain model is an exceptional situation and should be met with throwing an exception. On the other hand, there’s nothing exceptional in external input being incorrect.

The input coming from the outside world should be filtered out before passing it further to the domain model. It’s the first line of defense against data inconsistency. At this stage, any incorrect data is denied with corresponding error messages. Once the filtration has confirmed that the incoming data is valid it's passed to a domain. When the data enters the always-valid domain boundary, it's assumed to be valid and any violation of this assumption means that you’ve introduced a bug. Guards help to reveal those bugs. They are the failsafe mechanism, the last line of defense that ensures data in the always-valid boundary is indeed valid. Guards comply with the Fail Fast principle by throwing runtime exceptions.

Domain classes should always guard themselves against becoming invalid.

For preventing null/undefined values, empty objects and arrays, incorrect input length etc. a library of guards can be created.

Example file: guard.ts

Keep in mind that not all validations/guarding can be done in a single domain object, it should validate only rules shared by all contexts. There are cases when validation may be different depending on a context, or one field may involve another field, or even a different entity. Handle those cases accordingly.

Read more:

<details> <summary><b>Note</b>: Using validation library instead of custom guards</summary>

Instead of using custom guards you could use an external validation library, but it's not a good practice to tie domain to external libraries and is not usually recommended.

Although exceptions can be made if needed, especially for very specific validation libraries that validate only one thing (like specific IDs, for example bitcoin wallet address). Tying only one or just few Value Objects to such a specific library won't cause any harm. Unlike general purpose validation libraries which will be tied to domain everywhere, and it will be troublesome to change it in every Value Object in case when old library is no longer maintained, contains critical bugs or is compromised by hackers etc.

Though, it's fine to do full sanity checks using validation framework or library outside the domain (for example class-validator decorators in DTOs), and do only some basic checks (guarding) inside of domain objects (besides business rules), like checking for null or undefined, checking length, matching against simple regexp etc. to check if value makes sense and for extra security.

<details> <summary>Note about using regexp</summary>

Be careful with custom regexp validations for things like validating email, only use custom regexp for some very simple rules and, if possible, let validation library do its job on more difficult ones to avoid problems in case your regexp is not good enough.

Also, keep in mind that custom regexp that does same type of validation that is already done by validation library outside of domain may create conflicts between your regexp and the one used by a validation library.

For example, value can be accepted as valid by a validation library, but Value Object may throw an error because custom regexp is not good enough (validating email is more complex than just copy - pasting a regular expression found in google. Though, it can be validated by a simple rule that is true all the time and won't cause any conflicts, like every email must contain an @). Try finding and validating only patterns that won't cause conflicts.

</details>

Although there are other strategies on how to do validation inside domain, like passing validation schema as a dependency when creating new Value Object, but this creates extra complexity.

Either to use external library/framework for validation inside domain or not is a tradeoff, analyze all the pros and cons and choose what is more appropriate for current application.

For some projects, especially smaller ones, it might be easier and more appropriate to just use validation library/framework.

</details>

Domain Errors

Application's core and domain layers shouldn't throw HTTP exceptions or statuses since it shouldn't know in what context it's used, since it can be used by anything: HTTP controller, Microservice event handler, Command Line Interface etc. A better approach is to create custom error classes with appropriate error codes.

Exceptions are for exceptional situations. Complex domains usually have a lot of errors that are not exceptional, but a part of a business logic (like "seat already booked, choose another one"). Those errors may need special handling. In those cases returning explicit error types can be a better approach than throwing.

Returning an error instead of throwing explicitly shows a type of each exception that a method can return so you can handle it accordingly. It can make an error handling and tracing easier.

To help with that you can create an Algebraic Data Types (ADT) for your errors and use some kind of Result object type with a Success or a Failure condition (a monad like Either from functional languages similar to Haskell or Scala). Unlike throwing exceptions, this approach allows defining types (ADTs) for every error and will let you see and handle them explicitly instead of using try/catch and avoid throwing exceptions that are invisible at compile time. For example:

// User errors:
class UserError extends Error {
  /* ... */
}

class UserAlreadyExistsError extends UserError {
  /* ... */
}

class IncorrectUserAddressError extends UserError {
  /* ... */
}

// ... other user errors

// Sum type for user errors
type CreateUserError = UserAlreadyExistsError | IncorrectUserAddressError;

function createUser(
  command: CreateUserCommand,
): Result<UserEntity, CreateUserError> {
  // ^ explicitly showing what function returns
  if (await userRepo.exists(command.email)) {
    return Err(new UserAlreadyExistsError()); // <- returning an Error
  }
  if (!validate(command.address)) {
    return Err(new IncorrectUserAddressError());
  }
  // else
  const user = UserEntity.create(command);
  await this.userRepo.save(user);
  return Ok(user);
}

This approach gives us a fixed set of expected error types, so we can decide what to do with each:

/* in HTTP context we want to convert each error to an 
error with a corresponding HTTP status code: 409, 400 or 500 */
const result = await this.commandBus.execute(command);
return match(result, {
  Ok: (id: string) => new IdResponse(id),
  Err: (error: Error) => {
    if (error instanceof UserAlreadyExistsError)
      throw new ConflictHttpException(error.message);
    if (error instanceof IncorrectUserAddressError)
      throw new BadRequestException(error.message);
    throw error;
  },
});

Throwing makes errors invisible for the consumer of your functions/methods (until those errors happen at runtime, or until you dig deeply into the source code and find them). This means those errors are less likely to be handled properly.

Returning errors instead of throwing them adds some extra boilerplate code, but can make your application robust and secure since errors are now explicitly documented and visible as return types. You decide what to do with each error: propagate it further, transform it, add extra metadata, or try to recover from it (for example, by retrying the operation).

Warning: Some errors/exceptions are non-recoverable and should be thrown, not returned. If you return technical Exceptions (like connection failed, process out of memory, etc.), It may cause some security issues and goes against Fail-fast principle. Instead of terminating a program flow immediately and logging the error, returning an exception continues program execution and allows it to run in an incorrect state, which may lead to more unexpected errors, so it's generally better to throw an Exception in those cases rather than returning it. Analyze if the error is "likely recoverable" or "likely unrecoverable". If an error is most likely a recoverable error, it's a great candidate for using it in a Result object. If an error is most likely unrecoverable, throw it.

Libraries you can use:

Example files:

Read more:

Using libraries inside Application's core

Whether to use libraries in application core and especially domain layer is a subject of a lot of debates. In real world, injecting every library instead of importing it directly is not always practical, so exceptions can be made for some single responsibility libraries that help to implement domain logic (like working with numbers).

Main recommendations to keep in mind is that libraries imported in application's core shouldn't expose:

To use such libraries consider creating an anti-corruption layer by using adapter or facade patterns.

We sometimes tolerate libraries in the center, but be careful with general purpose libraries that may scatter across many domain objects. It will be hard to replace those libraries if needed. Tying only one or just a few domain objects to some single-responsibility library should be fine. It's way easier to replace a specific library that is tied to one or few objects than a general purpose library that is everywhere.

In addition to different libraries there are Frameworks. Frameworks can be a real nuisance, because by definition they want to be in control, and it's hard to replace a Framework later when your entire application is glued to it. It's fine to use Frameworks in outside layers (like infrastructure), but keep your domain clean of them when possible. You should be able to extract your domain layer and build a new infrastructure around it using any other framework without breaking your business logic.

NestJS does a good job, as it uses decorators which are not very intrusive, so you could use decorators like @Inject() without affecting your business logic at all, and it's relatively easy to remove or replace it when needed. Don't give up on frameworks completely, but keep them in boundaries and don't let them affect your business logic.

Offload as much of irrelevant responsibilities as possible from the core, especially from domain layer. In addition, try to minimize usage of dependencies in general. More dependencies your software has means more potential errors and security holes. One technique for making software more robust is to minimize what your software depends on - the less that can go wrong, the less will go wrong. On the other hand, removing all dependencies would be counterproductive as replicating that functionality would require huge amount of work and would be less reliable than just using a popular, battle-tested library. Finding a good balance is important, this skill requires experience.

Read more:

Interface Adapters

Interface adapters (also called driving/primary adapters) are user-facing interfaces that take input data from the user and repackage it in a form that is convenient for the use cases(services/command handlers) and entities. Then they take the output from those use cases and entities and repackage it in a form that is convenient for displaying it back for the user. User can be either a person using an application or another server.

Contains Controllers and Request/Response DTOs (can also contain Views, like backend-generated HTML templates, if required).

Controllers

One controller per trigger type can be used to have a clearer separation. For example:

Resolvers

If you are using GraphQL instead of controllers, you will use Resolvers.

One of the main benefits of a layered architecture is separation of concerns. As you can see, it doesn't matter if you use REST or GraphQL, the only thing that changes is user-facing API layer (interface-adapters). All the application Core stays the same since it doesn't depend on technology you are using.

Example files:

DTOs

Data that comes from external applications should be represented by a special type of classes - Data Transfer Objects (DTO for short). Data Transfer Object is an object that carries data between processes. It defines a contract between your API and clients.

Request DTOs

Input data sent by a user.

Examples:

Response DTOs

Output data returned to a user.

Examples:

DTO contracts protect your clients from internal data structure changes that may happen in your API. When internal data models change (like renaming variables or splitting tables), they can still be mapped to match a corresponding DTO to maintain compatibility for anyone using your API.

When updating DTO interfaces, a new version of API can be created by prefixing an endpoint with a version number, for example: v2/users. This will make transition painless by preventing breaking compatibility for users that are slow to update their apps that uses your API.

You may have noticed that our create-user.command.ts contains the same properties as create-user.request.dto.ts. So why do we need DTOs if we already have Command objects that carry properties? Shouldn't we just have one class to avoid duplication?

Because commands and DTOs are different things, they tackle different problems. Commands are serializable method calls - calls of the methods in the domain model. Whereas DTOs are the data contracts. The main reason to introduce this separate layer with data contracts is to provide backward compatibility for the clients of your API. Without the DTOs, the API will have breaking changes with every modification of the domain model.

More info on this subject here: Are CQRS commands part of the domain model? (read "Commands vs DTOs" section).

Additional recommendations

Local DTOs

Another thing that can be seen in some projects is local DTOs. Some people prefer to never use domain objects (like entities) outside its domain (in controllers, for example) and return a plain DTO object instead. This project doesn't use this technique, to avoid extra complexity and boilerplate code like interfaces and data mapping.

Here are Martin Fowler's thoughts on local DTOs, in short (quote):

Some people argue for them (DTOs) as part of a Service Layer API because they ensure that service layer clients aren't dependent upon an underlying Domain Model. While that may be handy, I don't think it's worth the cost of all of that data mapping.

Though you may want to introduce Local DTOs when you need to decouple modules properly. For example, when querying from one module to another you don't want to leak your entities between modules. In that case using a Local DTO may be justified.

Infrastructure layer

The Infrastructure layer is responsible for encapsulating technology. You can find there the implementations of database repositories for storing/retrieving business entities, message brokers to emit messages/events, I/O services to access external resources, framework related code and any other code that represents a replaceable detail for the architecture.

It's the most volatile layer. Since the things in this layer are so likely to change, they are kept as far away as possible from the more stable domain layers. Because they are kept separate, it's relatively easy to make changes or swap one component for another.

Infrastructure layer can contain Adapters, database related files like Repositories, ORM entities/Schemas, framework related files etc.

Adapters

Read more on ACL: Anti-Corruption Layer: How to Keep Legacy Support from Breaking New Systems

Adapters should have:

Repositories

Repositories are abstractions over collections of entities that are living in a database. They centralize common data access functionality and encapsulate the logic required to access that data. Entities/aggregates can be put into a repository and then retrieved at a later time without domain even knowing where data is saved: in a database, in a file, or some other source.

We use repositories to decouple the infrastructure or technology used to access databases from the domain model layer.

Martin Fowler describes a repository as follows:

A repository performs the tasks of an intermediary between the domain model layers and data mapping, acting similarly to a set of domain objects in memory. Client objects declaratively build queries and send them to the repositories for answers. Conceptually, a repository encapsulates a set of objects stored in the database and operations that can be performed on them, providing a way that is closer to the persistence layer. Repositories, also, support the purpose of separating, clearly and in one direction, the dependency between the work domain and the data allocation or mapping.

The data flow here looks something like this: repository receives a domain Entity from application service, maps it to database schema/ORM format, does required operations (saving/updating/retrieving etc), then maps it back to domain Entity format and returns it back to service.

Application's core usually is not allowed to depend on repositories directly, instead it depends on abstractions (ports/interfaces). This makes data retrieval technology-agnostic.

Note: in theory, most publications out there recommend abstracting a database with interfaces. In practice, it's not always useful. Most of the projects out there never change database technology (or rewrite most of the code anyway if they do). Another downside is that if you abstract a database you are more likely not using its full potential. This project abstracts repositories with a generic port to make a practical example repository.port.ts, but this doesn't mean you should do that too. Think carefully before using abstractions. More info on this topic: Should you Abstract the Database?

Example files:

This project contains abstract repository class that allows to make basic CRUD operations: sql-repository.base.ts. This base class is then extended by a specific repository, and all specific operations that an entity may need are implemented in that specific repo: user.repository.ts.

Read more:

Persistence models

Using a single entity for domain logic and database concerns leads to a database-centric architecture. In DDD world domain model and persistence model should be separated.

Since domain Entities have their data modeled so that it best accommodates domain logic, it may be not in the best shape to save in a database. For that purpose Persistence models can be created that have a shape that is better represented in a particular database that is used. Domain layer should not know anything about persistence models, and it should not care.

There can be multiple models optimized for different purposes, for example:

Over time, when the amount of data grows, there may be a need to make some changes in the database like improving performance or data integrity by re-designing some tables or even changing the database entirely. Without an explicit separation between Domain and Persistance models any change to the database will lead to change in your domain Entities or Aggregates. For example, when performing a database normalization data can spread across multiple tables rather than being in one table, or vice-versa for denormalization. This may force a team to do a complete refactoring of a domain layer which may cause unexpected bugs and challenges. Separating Domain and Persistence models prevents that.

Note: separating domain and persistence models may be overkill for smaller applications. It requires a lot of effort creating and maintaining boilerplate code like mappers and abstractions. Consider all pros and cons before making this decision.

Example files:

For smaller projects you could use ORM libraries like Typeorm for simplicity. But for projects with more complexity ORMs are not flexible and performant enough. For this reason, this project uses raw queries with a Slonik client library.

Read more:

Other things that can be a part of Infrastructure layer

Other recommendations

General recommendations on architectures, best practices, design patterns and principles

Different projects most likely will have different requirements. Some principles/patterns in such projects can be implemented in a simplified form, some can be skipped. Follow YAGNI principle and don't overengineer.

Sometimes complex architecture and principles like SOLID can be incompatible with YAGNI and KISS. A good programmer should be pragmatic and has to be able to combine his skills and knowledge with a common sense to choose the best solution for the problem.

You need some experience with object-oriented software development in real world projects before they are of any use to you. Furthermore, they don’t tell you when you have found a good solution and when you went too far. Going too far means that you are outside the “scope” of a principle and the expected advantages don’t appear. Principles, Heuristics, ‘laws of engineering’ are like hint signs, they are helpful when you know where they are pointing to and you know when you have gone too far. Applying them requires experience, that is trying things out, failing, analyzing, talking to people, failing again, fixing, learning and failing some more. There is no shortcut as far as I know.

Before implementing any pattern always analyze if benefit given by using it worth extra code complexity.

Effective design argues that we need to know the price of a pattern is worth paying - that's its own skill.

Don't blindly follow practices, patterns and architectures just because books and articles say so. Sometimes rewriting a software from scratch is the best solution, and all your efforts to fit in all the patterns and architectural styles you know into the project will be a waste of time. Try to evaluate the cost and benefit of every pattern you implement and avoid overengineering. Remember that architectures, patterns and principles are your tools that may be useful in certain situations, not dogmas that you have to follow blindly.

However, remember:

It's easier to refactor over-design than it's to refactor no design.

Read more:

Recommendations for smaller APIs

Be careful when implementing any complex architecture in small-medium sized projects with not a lot of business logic. Some building blocks/patterns/principles may fit well, but others may be an overengineering.

For example:

DDD and other practices described here are mostly about creating software with complex business logic. But what would be a better approach for simpler applications?

For applications with not a lot of business logic, where code mostly exists as a glue between database and a client, consider other architectures. The most popular is probably MVC. Model-View-Controller is better suited for CRUD applications with little business logic since it tends to favor designs where software is mostly the view of the database.

Additional resources:

Behavioral Testing

Behavioral Testing (and also BDD) is a testing of the external behavior of the program, also known as black box testing.

Domain-Driven Design with its ubiquitous language plays nicely with Behavioral tests.

For BDD tests Cucumber with Gherkin syntax can give a structure and meaning to your tests. This way even people not involved in a development can define steps needed for testing. In node.js world cucumber or jest-cucumber are nice packages to achieve that.

Example files:

Read more:

Folder and File Structure

Some typical approaches are:

- Controllers
  - UserController
  - WalletController
  - OtherControllers...
- Services
  - UserService
  - WalletService
  - OtherServices...
- Repositories
  - ...

This approach makes navigation harder. Every time you need to change some feature, instead of having all related files in the same place (in a module), you have to jump multiple directories to find all related files. This approach usually leads to tight coupling and spaghetti code.

- User
  - UserController
  - UserService
  - UserRepository
- Wallet
  - WalletController
  - WalletService
  - WalletRepository
  ...

This looks better. With this approach each module is encapsulated and only contains its own business logic. The only downside is: over time those controllers and services can end up hundreds of lines long, making it difficult to navigate and merge conflicts harder to manage.

- User
  - CreateUser
    - CreateUserController
    - CreateUserService
    - CreateUserDTO
  - UpdateUser
    - UpdateUserController
    - UpdateUserService
    - UpdateUserDTO
  - UserRepository
  - UserEntity
- Wallet
  - CreateWallet
    - CreateWalletController
    - CreateWalletService
    - CreateWalletDto
  ...

This way each module is further split into highly cohesive subcomponents (by feature). Now when you open the project, instead of just seeing directories like controllers, services, repositories, etc. you can see right away what features application has from just reading directory names.

This approach makes navigation and maintaining easier since all related files are close to each other. It also makes every feature properly encapsulated and gives you an ability to make localized decisions per component, based on each particular feature's needs.

Shared files like domain objects (entities/aggregates), repositories, shared DTOs, interfaces, etc. can be stored outside of feature directory since they are usually reused by multiple subcomponents.

This is called The Common Closure Principle (CCP). Folder/file structure in this project uses this principle. Related files that usually change together (and are not used by anything else outside that component) are stored close together.

The aim here should be to be strategic and place classes that we, from experience, know often changes together into the same component.

Keep in mind that this project's folder/file structure is an example and might not work for everyone. The main recommendations here are:

There are different approaches to file/folder structuring, choose what suits better for the project/personal preference.

Examples:

Read more:

File names

Consider giving a descriptive type names to files after a dot ".", like *.service.ts or *.entity.ts. This makes it easier to differentiate what files do what and makes it easier to find those files using fuzzy search (CTRL+P for Windows/Linux and ⌘+P for MacOS in VSCode to try it out).

Alternatively you could use class names as file names, but consider adding descriptive suffixes like Service or Controller, etc.

Read more:

Enforcing architecture

To make sure everyone in the team adheres to defined architectural practices, use tools and libraries that can analyze and validate dependencies between files and layers.

For example:

  // Dependency cruiser example
  {
    name: 'no-domain-deps',
    comment: 'Domain layer cannot depend on api or database layers',
    severity: 'error',
    from: { path: ['domain', 'entity', 'aggregate', 'value-object'] },
    to: { path: ['api', 'controller', 'dtos', 'database', 'repository'] },
  },

Snippet of code above will prevent your domain layer to depend on the API layer or database layer. Example config: .dependency-cruiser.js

You can also generate graphs like this:

<details> <summary>Click to see dependency graph</summary> <img src="assets/dependency-graph.svg" alt="Dependency graph"> </details> <br>

Example tools:

Read more:

Prevent massive inheritance chains

Classes that can be extended should be designed for extensibility and usually should be abstract. If class is not designed to be extended, prevent extending it by making class final. Don't create inheritance more than 1-2 levels deep since this makes refactoring harder and leads to a bad design. You can use composition instead.

Note: in TypeScript, unlike other languages, there is no default way to make class final. But there is a way around it using a custom decorator.

Example file: final.decorator.ts

Read more:

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