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Clean Hexagon

Main empasis of this project is to provide a guide on how to design complex applications. In this readme are presented some of the techniques, tools, best practices, architectural patterns and guidelines gathered from different sources.

Everything below should be seen as a recomendation. Keep in mind that different projects have different requirements, so any pattern mentioned in this readme can be replaced or skipped if needed.

This project uses TypeScript language, NestJS framework and Typeorm for the database access. Keep in mind that code examples are adapted to TypeScript and mentioned above frameworks so may not fit well for other languages.

Though patterns and principles presented here are framework/language agnostic, so 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.

Architecture

Mainly based on:

Clean, Hexagonal and Onion architectures are not incompatible as some people think and are not competing with each other to see which one is better. All of them provide useful patterns that can be used in combination to achieve a better result.

DDD principles are all about building application domain;
Hexagonal Architecture is all about how application domain interacts with the rest of the world using Ports and Adapters;
Onion architecture proposed separation of concerns by dividing application in layers;
And Clean architecture is somewhat a combination of the above.

This architectures are very similar and pretty much compatible.

Before we begin, here are the PROS and CONS of using this approach:

Pros:

Independent of external frameworks, technologies, databases, etc. Frameworks and external resources can be plugged/unplugged with much less effort.
Easily testable and scalable.
The solution can be worked on and maintained by different teams, without stepping on each other's toes.
Easier to add new features. As the system grows over time, the difficulty in adding new features remains constant and relatively small.
If the solution is properly broken apart along bounded context lines, it becomes easy to convert pieces of it into microservices if needed.

Cons:

This is a sophisticated architecture which requires a firm understanding of quality software principles, such as SOLID, Clean/Hexagonal Architecture, Domain-Driven Design, etc. Any team implementing such a solution will almost certainly require an expert to drive the solution and keep it from evolving the wrong way and accumulating technical debt.
This approach is not recommended for small applications. There is added up-front complexity to support the architecture, such as more boilerplate code, abstractions, etc. thus this architecture is generally ill-suited to simple CRUD applications and could over-complicate such solutions.

Diagram

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

Request/CLI commmand/event is sent to the controller using plain DTO;
Controller parses this DTO, converts it to a Command/Query and passes it to a Application service;
Application service handles this Command/Query; it executes business logic using domain services and/or entities and uses the infrastructure layer through ports;
Infrastructure layer maps data to format that it needs, uses repositories to fetch/persist data and adapters to send events or do other I/O communications, maps data back to domain format and returns it back to Application service;
After application service fishes doing it's job, it returns data/confirmation back to Controllers;
Controllers return data back to the user (if application has presenters, presenters are returned instead).

Modules

This project's code examples use separation by modules (also called components). Each module is separated in parts and layers; Each module gets its own folder with a dedicated codebase; and each use case inside that module gets it's own folder to store most of the things it needs (this is also called Vertical Slicing). It is easier to work on things that change together if those things are gathered relatively close to each other. Try not to create dependencies between modules or use cases, move shared logic into a separate files and make both depend on that instead of depending on each other.

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. Interactions between modules can be done using events or public interfaces, try to avoid direct imports of other module's internal files. This approach ensures loose coupling, and, if bounded contexts are defined and designed properly, each module can be easily separated into a microservice if needed.

A lof 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.

Read more about modular programming benefits:

Modular programming: Beyond the spaghetti mess.

Application Core

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

Dependencies point inwards. Outter layers can depend on inner layers, but inner layers never depend on outter layers.

Core layers shouldn't depend on frameworks or access external resources. 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).

Note: Whether or not to use libraries in a core/domain 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 number converting libraries etc). Read more: referencing external libs.

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

Functionality to access any out-of-precess resources (http calls, database access etc);
Functionality that brings randomness (generating random IDs, timestamps etc) since this makes tests unpredictable;
Functionality not relevant to domain (frameworks, technology details like ORMs etc).

Offload as much of irrelevant responsibilities as possible from the core.

Application's Core consists of:

Domain layer:

Entities
Domain Services
Value Objects

Application layer:

Application Services
Commands and Queries
Ports

Application layer

Application Services

Are also called "Workflow Services", "User Cases", "Interactors" etc. These services orchestrate the steps required to fulfill the commands imposed by the client.

Typically used to orchestrate how the outside world interacts with your application and performs tasks required by the end users.
Contain no domain-specific business logic;
Operate on scalar types, transforming them into Domain types. A scalar type can be considered any type that's unknown to the Domain Model. This includes primitive types and types that don't belong to the Domain.
Application services declare dependencies on infrastructural services required to execute domain logic (by using ports).
Are used in order to fetch domain Entities (or anything else) from database/outside world through ports;
Execute other out-of-process communications through Ports (like event emits, sending emails etc);
In case of interacting with one Entity/Aggregate, executes its methods directly;
In case of working with multiple Entities/Aggregates, uses a Domain Service to orchestrate them;
Are basically a Command/Query handlers;
Should not depend on other application services since it may cause problems (like cyclic dependencies);

One service per use case is considered a good practice.

What are "Use Cases"?

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.

More about services:

Notes: Interfaces for each Use Case and Local DTOs

Interfaces for each use case

Some people prefer having an interface for each use case (Driving Port), which Application Service implements and a Controller depends on. This is a viable option, but this project doesn't use interfaces for every use case for simplicity: it makes sense using interfaces when there are multiple implementations of a workflow, but use cases are too specific and should not have multiple implementations of the same workflow (one service per use case rule mentioned above). Controllers naturally depend on a concrete implementation thus making interfaces redundant. More on this topic here.

Local DTOs

Another thing that can be seen in some projects is local DTOs. Some people prefer never use domain objects (like entities) outside of core (in controllers, for example), and are using DTOs instead. This project doesn't use this technique to avoid extra interfaces and data mapping. Either to use local DTOs or not is a matter of taste.

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.

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 is converted into a Command/Query object. This are simple classes with data.

Commands

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 anything. Though, if needed, returning a bare minimum (like ID of a created item or a confirmation message) may not be a bad idea.

Example of command object: create-user.command.ts

Queries

Query is used for retrieving data and should not make any state changes (like writes to the database, files etc).

Queries are usually just a data retrieval operation and have no business logic involved; so, if needed, application and domain layers can be bypassed completely. Though, if some additional non-state changing logic has to be applied before returning a query response (like calculating something), it should be done in a corresponding application service.

Validation also can be skipped, since no input is persisted in query operations. But, if needed, it can be validated to tell the user that query format is incorrect (when using enums for example).

Example of query bypassing application/domain layers completely: find-user-by-email.http.controller.ts

Note: Some simple cases may not need a Command/Query object, like find query or delete command may only need an ID so there is no point in creating an object for that.

Ports

Ports (for Driven Adapters) are interfaces that define contracts which must be implemented by infrastructure adapters in order to execute some action more related to technology details rather then business logic. Ports act like abstractions for technology details that business logic does not care about.

Ports should be created to fit the Domain needs, not simply mimic the tools APIs.
Mock implementations can be passed to ports while testing. Mocking makes your tests faster and independent from the environment.

Example file: event-emitter.port.ts

Domain Layer

This layer contains application's business rules.

Domain should only operate using domain objects, most important ones 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 methods, or it can be a set of data structures and functions.

Domain business logic goes here. Avoid having business logic in your services when possible, this leads to Anemic Domain Model (domain services are exception for business logic that can't be put in a single entity).

Entities must have an identity. We determine equality between two entities by comparing their identificators (usually its id field).

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 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 know nothing about other layers;
Domain entities data should be modelled to accomodate business logic, not some database schema;
Entities must protect their invariants, try to avoid public setters and update state using methods;
Validate Entities and other domain objects on creation and throw an error on first failure. Fail Fast.

Example files:

user.entity.ts

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.

Aggregates help to simplify the domain model by gathering multiple domain objects under a single abstraction.

Aggregates should not be influenced by data model. Associations between domain objects are not the same as database relationships.

Aggregate root is a gateway to entire aggregate. Any references from outside the aggregate should only go to the aggregate root.

Example files: // TODO

Understanding Aggregates in Domain-Driven Design

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"

Domain Service is a specific type of domain layer class that is used to execute domain logic that relies on two or more Entities.
Domain Services are used when putting the logic on a particular Entity would break encapsulation and require the Entity to know about things it really shouldn't be concerned with.
Domain services are very granular where as application services are a facade purposed with providing an API.
Domain services operate only on types belonging to the Domain. They contain meaningful concepts that can be found within the Ubiquitous Language. They hold operations that don't fit well into Value Objects or Entities.

Value objects

Another important concept is Value Objects from DDD. Unlike entities, Value Objects have no identity. We determine their equality through their structrual property.

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 it's own business logic.

Equality of Value Objects can be checked using equals method:

user1.address.equals(user2.address);

Example files:

address.value-object.ts

Validation of Domain Objects

Replacing primitives with Value Objects

Most of the codebases operate on primitive types – strings, numbers etc. In the Domain Model, this level of abstraction is definitely too low.

Significant business concepts should 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:

email: string;

can be represented as a Value Object instead:

email: Email;

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.

It also makes code easier to understand since it's using ubiquitous language instead of just string.

To avoid having duplicate Value Objects for primitives, some generic ones can be created if needed, like ShortString, LongString, SmallNumber, BigNumber etc.

Example files:

email.value-object.ts

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 a compile time, so IDE will show type errors right away.

The simplest example may be using enums instead of constants, for example: events.ts. This file has enums of events that can occur in a program. Now, event emitter port event-emitter.port.ts uses that events type to prevent illegal types pass. If you try to pass anything that is not intended it will show type error.

More importantly, this approach can be used to make business logic safer.

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 IDE will show a type error right away. This is a business rule validation used at compile time.

At runtime

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

Domain objects have to protect their invariants. Having some validation rules here will protect their state from corruption.

Value Object represent a typed value in domain. The goal here is to incapsulate 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. This will make application more resilient to errors and will protect it from a whole class of bugs.

There are a lot of cases when invalid data may end up in a domain. For example, if data comes from database, from external API, or if it's just a programmer error.

Database may return incorrect data when someone modifies it manually (especially when there are some admin panels that allow to do that);
External APIs may have errors and return corrupted data;
Programmer may create objects with incorrect input by accident: new Email(someRandomString).

Validating will inform immediately when Value Object is created with corrupted data. Data should not be trusted, either it comes from a database or it's a user input. Not validating domain objects allows them to be in an incorrect state, this leads to problems.

To avoid repeating same validation code between different domain objects consider using guards.

Example file: guard.ts

Keep in mind that not all validations can be done in a single Value 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 invole another field, or even a different entity. Handle those cases accordingly.

Note about validation

There are a lot of debates on how data sanity should be validated. There are a few options:

only outside, before data enters the domain (like validation decorators in DTOs)
only in domain's Value Objects, maybe also exposing validate method to be able to gather errors outside of domain
in both: outside of domain and inside domain.

After some research it seems that third option may be the most optimal. Outside validation may do full sanity checks using some well tested validation framework, and validation inside domain may be some simple sanity validations like matching regex and checking if value is not empty.

So, what exactly should Value Object validate?

First of all, business rules.
Checking if value is not empty/null/undefined is important;
Basic sanity validations. For example, it makes no sense for a Phone number to be one digit long, so it can be validated like this: if (phone.toString().length >= 7 && phone.toString().length <= 10), or test a string to check if it has a correct format, like regex.test(email). Even if it duplicates validation in upper layers, it still can be re-validated in a Value Object since it's a simple one-liner and doesn't require much effort to make. It won't be as good as validation framework, but it will be good enough to add extra security and avoid a wh1ole class of possible errors, like preventing programmer creating invalid objects by accident: new Email(someString) or new PhoneNumber(1.1);
if some value is already validated somewhere in upper layers by validation library and it's implementation in Value Object would be too complex, its probably not worth it to replicate this validation here (unless some important business rule depends on this validation);

Recommended to read:

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) 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

Controllers

Controllers are used for triggering use cases and presenting the result.

One controller per use case is considered a good practice.

Also, one controller per trigger type can be used. For example:

create-user.http.controller.ts for http requests (NestJS Controllers),
create-user.cli.controller.ts for command line interface access (NestJS Console)
events controller for external events (NetJS Microservices).
etc.

DTOs

Data Transfer Object (DTO) is an object that carries data between processes.

Request DTOs

Input data sent by a user. May consists of request classes and interfaces.

Examples:

Response DTOs

Output data returned to a user. May consist of Response object and a corresponding interface. Response class can be used to map returned data, and interface may be shared between processes that receive that response.

Additional recommendations:

When returning a Response prefer whitelisting properties over blacklisting. This ensures that no sensitive data will leak in case if programmer forgets to blacklist newly added properties that shouldn't be returned to the user.
Set optional properties to null instead of undefined to maintain consistency.
Interfaces for response objects should be kept somewhere in shared directory instead of module directory since they may be used by a different application (like front-end page, mobile app or microservice). Consider creating submodule for sharing interfaces.
Request/Response DTO classes may be a good place to use validation and sanitization decorators like class-validator and class-sanitizer (make sure that all validation errors are gathered first and only then return them to the user, this is called Notification pattern. Class-validator does this by default).
Request/Response DTO classes may also be a good place to use Swagger/OpenAPI library decorators that NestJS provides.

Examples:

Infrastructure

The Infrastructure is responsible strictly to keep technology. You can find there the implementations of database repositories for business entities, message brokers, I/O components, dependency injection, frameworks and any other thing that represents a detail for the architecture, mostly framework dependent, external dependencies, and so on.

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 make changes or swap one component for another.

Infrastructure adapters (also called driven/secondary adapters) enable a software system to interact with external systems by receiving, storing and providing data when requested (like persistence, message brokers, sending emails or messages, requesting 3rd party APIs etc).

Adapters are essentially an implementation of ports. They are not supposed to be called directly in any point in code, only through ports(interfaces).

Database

This folder contains all database related files: Repositories, Orm Entities, Migrations, Seeds and Seeders etc.

Repositories

Repositories are classes or components that encapsulate the logic required to access data sources. They centralize common data access functionality, providing better maintainability and decoupling 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 in a similar way 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 and maps it back to domain Entity and returns it back to service.

Keep in mind that application's core is not allowed to depend on repositories directly, instead it depends on abstractions (ports/interfaces).

Examples

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

ORM Entities

Using a single entity for domain logic and database concerns leads to a database-centric architecture. In DDD world those two should be separated. If ORM frameworks are used, ORM Entities can be created to represent domain entities in a database.

Since domain Entities have their data modeled so that it best accomodates domain logic, it may be not in the best shape to save in database. For that purpose ORM Entities are used that have shape that is better represented in a particular database that is used.

For simplicity ORM Entities may also contain mapping methods to map from domain to persistence and back. Though a separate Mapper class can be created if preferred.

Example: user.orm-entity.ts

Seeds

To avoid manually creating data in the database, seeding is a great solution to populate database with data for development and testing purposes (e2e testing). Wiki description

This project uses typeorm-seeding package. Files like: Seeds, Seeders and Factories are used by this package to seed the database.

Migrations

Migrations are used for database table/schema changes:

Database migration refers to the management of incremental, reversible changes and version control to relational database schemas. A schema migration is performed on a database whenever it is necessary to update or revert that database's schema to some newer or older version.

Wiki

This project uses Typeorm Migrations as an example.

Adapters

Adapters implementations for accessing external APIs.

Adapters should have:

a port somewhere in domain layer that it implements;
a mapper that maps data from and to domain (if it's needed);
a DTO/interface for received data;
a validator to make sure incoming data is not corrupted (validation can reside in DTO class using decorators).

Other

Other infrastructure related things:

Framework related files;
Application logger implemention;
Periodic cron jobs or tasks (NestJS Schedule);
Internal events handlers for infrastructure operations (Domain Event pattern, Nest-event);
Other technology related files.

Error Handling

Exception types

Consider making different exception types for different situations. For example, an ValidationException, NotFoundException etc.

Keep in mind that your application's core shouldn't throw HTTP exceptions or statuses since it shouldn't know anything about where it is used, since core can be used by anything: HTTP, Microservice, CLI etc. To return proper HTTP code back to user an instanceof check can be used in exception interceptor and appropriate HTTP exception can be returned depending on exception type.

Exception interceptor example: exception.interceptor.ts

Adding a name string with type name for every exception is a good practice, since when that exception is tranferred to another process instanceof check cannot be made anymore so a name string is used instead. Store exception name enum types in a separate file so they can be reused on a receiving side.

When using microservices, all exceptions can be packed as a submodule and reused in each microservice for consistency.

Differentiate between programmer errors and user input errors

For example:

When validation error is thrown by validating user input, it means that input is incorrect and something like InputValidationException type may be thrown containing all the incorrect fields and later converted into 400 Bad Request Exception for the user in exception interceptor.
When validation exception happens on a new Value Object creation that means a programmer did a mistake by assigning an incorrect value to a constructor, so a different type of error should be thrown here, something like DomainValidationException which is later converted into 500 Internal Server Error for the user.

Application should be protected not only from incorrect user input but from a programmer errors as well by throwing exceptions when something is not used as intended. No details should be returned to the user in case of programmer errors since those details may contain some sensitive information about the program, those details should only be shown in logs for a programmer to see so he can fix them.

Exception details

There are times when there is a need to return some extra info about the error (like all the incorrect fields provided when validation exception happens). For this purpose something like details array may be a good option. In this project details is an array of key: string and message: string pairs. An example of validation details may be something like this:

details: [
  { key: 'email', message: 'Incorrect format provided' },
  { key: 'password', message: 'Must be at least 8 characters long' },
];

Why not just an object like { email: "Incorrect email provided" }? This is because objects like this create inconsistency. Clients will never know what fields are returned from exceptions. Also an array is easier to map.
Why include key and not just an array of strings? Because there may be a need to asociate an error with something. For example, frontend page may have an "email" field in a form and will need to show an error right below that field, without a key it would be harder.
Details array may have any other structure, just keep in mind that it should be consistent and easy to use.

Error Serialization

By default, Error objects seralize to JSON with output like this:

{
  name: 'ValidationError';
}

Consider serializing errors by creating a toJSON() method so it can be easily sent to other processes as a plain object.

Exception abstract base class example: exception.base.ts
Domain Validation Exception class example: domain-validation.exception.ts

Testing

Testing each file and method separately is called White Box testing (like entity tests, domain services tests etc). It creates coupling to implementation details, so every time you decide to refactor something this may also cause refactoring corresponding tests.

To solve this and get the most out of your tests, prefer Black Box testing (also called Behavioral Testing). This means that tests should focus on testing user-facing behavior users care about (your code's public API, for example createUser() method in Application Service), not the implementation details of individual units it has inside. This avoids coupling, protects tests from changes that may happen while refactoring, makes tests easier to understand and maintain thus saving time.

Try to avoid White Box testing when possible. Though, there are cases when White Box testing may be needed, like:

There is a need to increase code coverage;
High complexity in implementation details that are hard to cover using Black Box testing;
There is a suspicion that some area of the program may be under-tested, etc.

Use White Box testing only when it is really needed and as an addition to Black Box testing, not the other way around.

It's all about investing only in the tests that yield the biggest return on your effort.

Example files: // TODO

Spec file for a use case in isolation: TODO;
e2e testing a use case from end-user standpoint (with all the infrastructure up, like API routes, databases etc): TODO.

Folder/File Structure

So instead of using typical layered style when all application is divided into services, controllers etc, we divide everything by modules. Now, how to structure files inside those modules?

A lot of people tend to do the same thing as before: create a separate folders for services, controllers etc and keep all the use-cases there. This is the same approach that makes navigation harder.

Using this approach, every time something in service changes, we might have to go to another folder to change controllers, and then go to dtos folder to change the corresponding dto etc.

It would be more logical to separate every module by components and have all the related files close together. Now if a use-case changes, those changes are usually made in a single use-case component, not everywhere across the module.

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 of that component) are stored close together, in a single use-case folder.

And shared files (like domain objects, repositories etc) are stored apart since those are reused by multiple use-cases. Domain layer is isolated, and use-cases which are essentially wrappers around business logic are treated as components. This approach makes navigation and maintaining easier.

The aim here should 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. Main recommendations here are:

Separate you application into modules;
Keep files that change together close to each other (Common Closure Principle);
Group files by their behavior that changes together, not by type of functionality that file provides;
Keep files that are reused by multiple components apart;
Respect boundaries in your code, keeping files together doesn't mean lower layers can import upper layers;
Try to avoid a lot of nested folders;
Move files around until it feels right.

There are different approaches to file/folder structuring, like explicitly separating each layer into a corresponding folder. This defines boundaries more clearly but is harder to navigate. Choose what suits better for the project/personal preference.

Files in this project are named according to Angular Style Guides.

Tools

Linting

Eslint with typescript-eslint plugin and some rules (like airbnb) can be a great tool to enforce writing better code. Consider disallowing things like any type and other things that may cause problems. Also, enabling strict mode in tsconfig is recommended.

Code formatting

Consider using code formatters like Prettier to maintain same code styles in the project.

Pre-push/pre-commit hooks

Consider launching tests/code formatting/linting every time you do git push or git commit. This prevents bad code getting in your repo. Husky is a great tool for that.

Code Generation

Code generation can be important when using complex architectures to avoid typing boilerplate code manually.

Hygen is a great example. This tool can generate building blocks (or entire modules) by using custom templates. Templates can be designed to follow best practices and concepts based on Clean/Hexagonal Architecture, DDD, SOLID etc.

Main advantages of automatic code generation are:

Avoid manual typing or copy-pasting of boilerplate code.
No hand-coding means less errors and faster implementations. Simple CRUD module can be generated and used right away in seconds without any manual code writing.
Using auto-generated code templates ensures that everyone in the team uses the same folder/file structures, name conventions, architectural and code styles.

Note:

To really understand and work with generated templates you need to understand what is being generated and why, so full understanding of an architecture and patterns used is required.

Additional resources

Articles

Videos

More Testable Code with the Hexagonal Architecture

Books

"Domain-Driven Design: Tackling Complexity in the Heart of Software" by Eric Evans
"Implementing Domain-Driven Design" by Vaughn Vernon
"Clean Architecture: A Craftsman's Guide to Software Structure and Design" by Robert Martin

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Clean Hexagon

Architecture

Pros:

Cons:

Diagram

Modules

Application Core

Domain layer:

Application layer:

Application layer

Application Services

Interfaces for each use case

Local DTOs

Commands and Queries

Commands

Queries

Ports

Domain Layer

Entities

Aggregates

Domain Services

Value objects

Validation of Domain Objects

Replacing primitives with Value Objects

At compile time

At runtime

Interface Adapters

Controllers

DTOs

Request DTOs

Response DTOs

Additional recommendations:

Infrastructure

Database

Repositories

Examples

ORM Entities

Seeds

Migrations

Adapters

Other

Error Handling

Exception types

Differentiate between programmer errors and user input errors

Exception details

Error Serialization

Testing

Folder/File Structure

Tools

Linting

Code formatting

Pre-push/pre-commit hooks

Code Generation

Additional resources

Articles

Videos

Books

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