A foundational project for any technology venture
A kotlin-based Spring Boot application using Reactive webflux APIs. The project uses a hexagonal architecture, along with DDD principals to create a clean design that helps enforce good programming practice.
Modulith is essentially a microservice architecture designed and intended to be deployed as a monolith. There is an operational complexity that comes with a microservices architecture. One can argue that microservices are a solution to a people problem, rather than something that arose due to an inherent technical problem. Put simply, there is a point at which the number of people working in a monolith becomes unwieldy.
This project is intended for smaller teams that don't want to take on the operational complexities that come with microservices, but do want to benefit from the microservice advantages.
One of the primary benefits of microservices is that it forces an organization to think deeply about the business domain, and how to split it. In the parlance of DDD, each microservice typically contains a single Bounded Context which addresses a specific area of concern for the business.
Microservices contain various mechanisms to communicate with each other. In order to allow for this, patterns have emerged. In essence, each microservice defines a contract for how other services can speak to it.
In Modulith, we've created an architecture that simulates the communication by contract that separates, but relates each separate microservice (or Bounded Context). Every Bounded Context is packaged in its own maven artifact. This gives us compile time isolation, but we can go further. At runtime, each Bounded Context runs in its own Spring Application Context.
What this does is to give us an application architecture that attempts to combine the best of both monolithic and microservice architectures.
Writing high quality server-side software applications that stand the test of time is a hard problem. Over time, applications become increasingly complex and suffer from a variety of problems, some of which are outlined below.
As illustrated in Figure 1, there are a bevy of concerns, across multiple dimensions that software engineers must be mindful of at all times.
High quality software addresses most, if not all, of these. A good software architecture will either solve these concerns, make it difficult to violate them, or at the very least guide the software engineer to produce high quality software.
(Figure 1)
This application contains influences from a large variety of sources, but there a few sources that are important to highlight as their influence played a much larger role.
- Get Your Hands Dirty on Clean Architecture - Each Bounded Context in a modulith application closely follows the packing structured and architectural constraints outlined in this book.
- Domain Modeling Made Functional - Although this book targets F#, the concepts apply to many other modern languages, including Kotlin. This is by far the best book on functional programming and functional domain modeling that I've come across and I highly recommend it to everyone.
- https://github.com/ddd-by-examples/library - This application influenced two big decisions in this project. The first being that each bounded context is contained in its own Spring context. The second idea is that persistence operations are modelled as event handlers. More on both of these below.
If I've missed something/someone, contact me. I'm happy to give you credit.
Traditionally, Spring Boot applications use a layered architecture that looks something like this:
| Spring Boot Application Layers |
|---|
| Web - Controllers |
| Domain - Business Logic |
| Persistence - Data Access Objects |
Nothing is inherently wrong with this, but a layered architecture has too many open flanks that allow bad habits to creep in and make the software increasingly harder to modify over time.
Here are just some of the problems that arise:
- It promotes DB-driven design
- By its very definition, the foundation of a conventional layered architecture is the database.
- This creates a strong coupling between the persistence layer, the domain layer, and often the web layer as well.
- The persistence code is virtually fused into the domain code, thus it’s hard to change one without the other. That’s the opposite of being flexible and keeping options open.
- It's prone to shortcuts
- The only global rule is that from a certain layer, we can only access components in the same layer or a layer below
- if we need access to a certain component in a layer above ours, we can just push the component down a layer and we’re allowed to access it. Problem solved? Over time, the persistence layer will grow fat as we push components down through the layers.
- It grows hard to test
- A common evolution within a layered architecture is that layers are being skipped. We access the persistence layer directly from the web layer.
- We’re implementing domain logic in the web layer, even if it’s only manipulating a single field. What if the use case expands in the future?
- In the tests of our web layer, we not only have to mock away the domain layer, but also the persistence layer.
- It hides the use cases
- In a layered architecture it easily happens that domain logic is scattered throughout the layers.
- A layered architecture does not impose rules on the “width” of domain services. Over time, this often leads to very broad services that serve multiple use cases.
- A broad service often has MANY dependencies on other Spring beans from the context, at varying layers.
- A broad service is also often used by many components in the web layer.
- The end result is "The Big Ball of Mud".
- It makes parallel work difficult
- Working on different use cases will cause the same service to be edited in parallel which leads to merge conflicts and potentially regressions.
The architecture of this project, although still a Spring Boot application, takes a different approach. None of the individual ideas implemented in this project are new, but the way in which these ideas are combined into a single architecture is new.
All the following concepts dovetail together in an elegant way, working in concert to provide for a powerful system architecture. Here is a high level overview of each of the major the design elements of this architecture:
- Kotlin
- The use of kotlin was a strategic choice. This language provides a number of capabilities not available to Java without which this architecture would not be possible.
- Specifically, the features of kotlin that are heavily leveraged:
- Coroutines
- Support for algebraic data types and exhaustive pattern matching
- Functional Programming support
- DDD - Domain Driven Design
- Not limited to just a set of human procesess for requirements discovery, the output of those processes translates directly into the design of the domain of the system
- In
modulith, each Bounded Context is isolated at compile time by packing them in maven artifacts. - In addition to this, each Bounded Context is isolated at runtime as well. Each runs in its own Spring Application Context.
- CQRS - Command Query Responsibility Segregation
- Inspired by: https://medium.com/swlh/cqrs-and-application-pipelines-in-kotlin-441d8f7fe427
- By separating out how Queries (reading data) of the system are performed from the Commands (writing data), we can optimize for each independently.
- This mechanism also provides the mechanism for how a workflow in one Bounded Context can communicate with another Bounded Context.
- Hexagonal Architecture
- Also knows as a Ports and Adapters architecture
- An alternative to the 3-layer pancake architecture, this inverts the dependencies so that the domain of the application sits at the center, and depends on nothing.
- Algebraic Data Types (ADTs)
- An alternative to the traditional use of Object Oriented (OO) techniques to model a domain.
- The gist is that these allow you to move business invariants from runtime checks to the type system, the effect of which is to move runtime errors to compile time errors. A huge win.
- They also inherently provide state machines for each domain in your system
- These are enabled by kotlin natively, along with exhaustive pattern matching and destructuring
- These objects are immutable, which lend themselves to
- Functional Programming (FP)
- Follow the link for a brief outline of the benefits
- This project introduces the concept of a
Workflow. This is a generalized form of what you might call a "Use Case". There is a 1:1 relationship between each API (e.g. Controller method) and aWorkflow. - Every
Workflowis kicked off with either aCommandorQuery(see: CQRS), and results in anEvent. Workflownever throws exceptions. Instead, the result of the workflow execution is contained in a discriminated union type containing either the desiredEventoutput, or anErrortype.- FP concepts extend beyond just the
Workflowconcept, and are embodied throughout.
- Asynchronous / Non-Blocking
- This project makes use of Spring Webflux, elegantly combined with Kotlin Coroutines.
- A given server can handle a much higher request volume because requests aren't bound to threads, as they are in traditional servers.
- Coroutine support is weaved throughout such that every operation that's performed, from each web request down to each database call are all performed using non-blocking operations.
- Coroutines are a mechanism that makes parallel programming easy, even fun. In Java, you are forced to use primitives (e.g. Thread) that are exceedingly hard to get correct.
- Event Driven
- Direct relation to DDD, which defines a system by its Events.
- Events generated by each workflow, are published using Spring's event system.
- As an example, domain objects are packaged in events, and sent to the event system. Event listeners in the database Adapters translated these into DB Entities for storage. The logic that persists the system state changes is decoupled from the business logic that generated those changes. This reduces a rather large class of bugs.
user: Name of your bounded context (in this case, "user")
domain: The core. Depends on nothing. All Business Logic. Built using ADTs
adapter: Adapters connect to outside systems
in: Input adapters "drive" the system, causing the system to act
web: Typically Spring Controllers
out: Output adapters are "driven" by the system
persistence: Typically DB Entity classes and DAOs (e.g. Spring Repositories)
application: Comprised of Ports and Workflows, these define the interface to our app.
workflows: Workflows with defined input/output types
port: Allows communication between the app core and the adapters
in: Defines Commands/Events for "driving" operations
out: Typically interfaces called by the core for "driven" operationsName it Clean Architecture, Hexagonal Architecture or Ports and Adapters Architecture - by inverting our dependencies so that the domain code has no dependencies to the outside we can decouple our domain logic from all those persistence and UI specific problems and reduce the number of reasons to change throughout the codebase. And fewer reasons to change means better maintainability.
The domain code is free to be modelled as best fits the business problems while the persistence and UI code are free to be modelled as best fits the persistence and UI problems.
The architecture closely follows a template outlined in Get Your Hands Dirty on Clean Architecture with some notable changes, outlined here
-
Create a new maven module for any new bounded context
Out of the box, a
Userbounded context is created for you that includes basic registration and login capability. This module also forms a pattern for any new bounded context. -
The Domain should be built using Algebraic Data Types
More on this below, but for now read these very carefully:
-
Enforce Architecture with ArchUnit
This project enforces that the structure of each bounded context strictly enforces the purity of the hexagonal architecture through the use of ArchUnit testing. Specifically, it ensures that classes cannot import classes from other packages that it should NOT have access to.
It's always preferable to have compile time issues rather than runtime errors. With the use of Algebraic Data Types (ADTs) we can do just that. The effect is to move your business invariants from run-time to compile-time checks. Consider the following.
// Java
class LibraryBook {
String title;
CheckoutStatus status; // Available, CheckedOut, Overdue
Date dueDate;
}
class BookService {
public LibraryBook checkoutBook(LibraryBook book) {
if (CheckoutStatus.Available != book.getStatus()) {
throw Exception();
}
book.setStatus(CheckoutStatus.CheckedOut);
// ...
}
}Versus
sealed class LibraryBook {
class AvailableBook(val title: String) : LibraryBook()
class CheckedOutBook(val title: String, dueDate: Date) : LibraryBook()
}
class BookService {
fun checkoutBook(book: AvailableBook): CheckedOutBook {
return CheckedOutBook(book.title, getCheckoutDate())
}
}In the first example, it's possible you can try to checkout a book in the wrong status. You can't checkout a book if it's already checkedout. Furthermore, we could have logic bugs where the returned instance is actually incorrect.
In the second example, that error simply can't happen. By moving the status into the type system, we can construct APIs that ensure we get books in the correct statuses. Not only that, but we ensure the return type is also exactly correct.
In the first example, the dueDate property has no meaning if a book is available
and should probably be null. Anytime null is a possibility, you leave yourself open to
NullPointerException.
In the 2nd example, each type of LibraryBook only contains the state relevant for that state.
No null fields at all. No non-relevant fields ever.
In modulith, domain objects enforce their internal consistency. That is, if you have an instance of a domain type
it is guaranteed it is in a valid state. Here's an example for a simple type:
class NonEmptyString private constructor(override val value: String)
: SimpleType<String>() {
companion object {
fun of(value: String): ValidatedNel<ValidationError, NonEmptyString> = ensure {
validate(NonEmptyString(value)) {
validate(NonEmptyString::value).isNotEmpty()
}
}
}
}This domain type is a String that guarantees it is non-null and non-empty. No more
if (StringUtils.isBlank(string)) {...}You can use this type to build more complex types. As with the other domain ADTs,
you use the factory method of() to create an instance from primitive values.
If the validation succeeds, you have your instance, otherwise you get a list
of all validation errors. NonEmptyString has a single validation, but more complex
types will have multiple validations. The error case will return all validation errors to you,
not just the first.
There are other types like this one, and you can create your own. Again, these concepts are a practical implementation of the prefer compile time to runtime errors.
As outlined above, one of the inherent problems of the traditional layered architecture is that use cases are implicit. If someone were to ask you what use cases your application supports, would you be able to tell them? Where are they? What are they?
One possible answer, and one that isn't necessarily incorrect, is that the use cases are derived from the various API endpoints your application supports. Look at all the REST API endopints (or GraphQL, etc), viola, you have your answer.
However, look just beneath the surface of the API and then where's the logic? The logic for each use case is typically spread out across a variety of services, and even across layers. To answer the question "What does API do?", engineers have to go spelunking into the code, opening a variety of services, following call hierarchies, to eventually build a picture to say, "X, Y, and Z happens".
Modulith solves this by providing a mechanism to make every use case explicit. In so doing, it changes how
typical Spring services should be constructed.
A workflow is a generalization of a use case. All use cases are workflows, but not all workflows are use cases. This generalization was intentional and allows for engineers to construct complex workflows from simpler workflows. Or, as an example, to use a workflow when it's a sub-process of a use case.
The base class:
interface SafeWorkflow<R: Request, E : Event> : Workflow<E> {
suspend fun invoke(request: R): Result<E>
}Every workflow takes a Request object, typically characterized by either a Command or Query (see CQRS).
Every workflow results in an event object, wrapped by a Result. The invoke() method never throws.
Thanks to Result, the return value is a discriminated value of either an error that occurred, or an Event type
that contains information about the result of the workflow.
We'll focus here on using a Workflow as a Use Case. When building a use case using Workflow, you implement
a single method. We recommend you build from the BaseSafeWorkflow class. When using that, you override the execute()
method.
The guiding principle around implementation is that a Workflow contains the "WHAT"s, not the "HOW"s. What this means is that from the Workflow, you invoke all the service methods / sub workflows required to satisy a particular use case. The workflow contains only the invocations (the WHAT), but the implementations for those methods (the HOW) exist in Services.
To wit, say you have a use case for user registration. To satisfy that use case the following must occur:
- Validate the input
- Save a new User Entity to the DB
- Send a welcome email to the new user
The Workflow would invoke methods to do the above, but the logic to accomplish these steps exists outside the Workflow, typically in Services.
In contrast to how Spring Services are typically built, under this architecture, methods on a service should be small and single purpose without side effects (to the extent that's possible). This takes inspiration from functional programming, where in a pure FP application, methods are referentially transparent. That is, given an input you will always get the same output, with no side effects.
Inside this architecture, you compose a Workflow by assembling small single purpose Service methods. Because service methods are small and single purpose, they are more easily tested, more easily composed. When you have a new use case that is similar to an existing workflow, you create a new Workflow instance, but combine the service methods in a slightly different way.
- Keep your service methods small.
- Keep your service methods of single purpose. It should do one thing.
- A service method name should say what the method does. It should do nothing else.
This helps solve the problems of ever-growing complexity of services. Service methods tend to get more complex over time. Service dependencies (via injection) tend to grow over time. Service methods tend to get more complex over time.
The Workflow pattern gives you a place to assemble Service methods like Lego's to create full a full Use Case, or just a sub-process.