Modular Monolith: Integration Styles

This post is part of articles series about Modular Monolith architecture:

1. Modular Monolith: A Primer
2. Modular Monolith: Architectural Drivers
3. Modular Monolith: Architecture Enforcement
4. Modular Monolith: Integration Styles (this)

Introduction

No module or application in a larger system works in 100% isolation. In order to deliver business value, individual elements must somehow integrate with each other. Let me write here a quote from the book “Thinking in Systems: A Primer”, where Donella H. Meadows defines the system concept in general:

A system is an interconnected set of elements that is coherently organized in a way that achieves something. If you look at that definition closely for a minute, you can see that a system must consist of three kinds of things: elements, interconnections, and a function or purpose.

The concept of systems integration is defined as follows (wiki):

process of linking together different computing systems and software applications physically or functionally, to act as a coordinated whole.

As you can see from the definitions above, in order to provide a system that fulfills its purpose, we must integrate elements to form a whole. In previous articles in this series, we discussed the attributes of these elements which are, in our terminology, called modules.

In this post, I would just like to discuss the missing part – Integration Styles for modules in Modular Monolith architecture.

Enterprise Integration Patterns book

The title of this post is not accidental. It sounds exactly like Chapter 2 of Gregor Hohpe and Bobby Wolf great book Enterprise Integration Patterns. This book is considered a bible of information about systems integration and messaging. This article takes some knowledge from this chapter and relates it to the monolithic and modular architecture.

In any case, everyone interested in the topic of integration, I invite you to read the book or materials that are available online at https://www.enterpriseintegrationpatterns.com/ site.

Integration Styles

Integration Criteria

Like everything in nature, each Integration Style has its pros and cons. Therefore, we must define criteria on the basis of which we will compare all styles. Then, based on that criteria, we will decide on the method of integration in the future.

We can distinguish the following criteria: Coupling, Complexity, Data Timeliness.

1. Coupling

Coupling is a measure of the degree to which 2 modules are dependent on each other (wiki):

coupling is the degree of interdependence between software modules; a measure of how closely connected two routines or modules are; the strength of the relationships between modules.

If you’ve read the previous posts in the series, you already know that one of the most important attributes of modular design is independence. Therefore, it will be easy to guess that coupling is one of the more important criteria in terms of integration style.

2. Complexity

The second criterion for evaluating the Integration Style is its level of complexity. Some integration methods are simple – require little work, are easy to understand and use. However, others are more complicated, require more commitment, knowledge, and discipline.

3. Data Timeliness

The last criterion is the length of time between when one module decides to share some data and other modules have that data. This means how soon after a state change in a given module, the rest of the modules concerned will take this change into account. Of course, the shorter this time, the better.

Now that we know all the most important criteria, let’s move on to the ways of integrating our modules. Let’s discuss 4 Integration Styles: File Transfer, Shared Database Data, Direct Call and Messaging.

File Transfer

The first option is to integrate our modules using a regular file. Such a file must be exported from the source module and imported into the target module. This can happen in 3 ways:
– manual, where the user manually imports/exports
– automatic, where files are imported and exported automatically by systems
– hybrid, where the file is imported/exported automatically on one side and vice versa on the other

Modular Monolith Integration Styles - File Transfer
File Transfer

One of the main tasks of this type of integration is to determine the format of a given file. What is important, it is the only dependency that two modules integrating in this way have. You can visualize it as a really huge message that is carried over by the filesystem. For this reason, it can be assumed that the coupling is very low in this case.

As for the level of complexity of this approach, it can be evaluated as an average. On the one hand, generating a file in a specific format is not difficult in these times. On the other hand, uploading to a shared resource, managing files, handling duplicates, and so on is more complicated and time-consuming.

From a timeliness point of view, modules integration via files is slow (not to mention manual export/import). Most often it is performed in larger batches at some time intervals (so-called batches), often at night. For this reason, the delay can be a day, a week or more.

To be honest, I’ve seen file-sharing integration many, many times between systems, and probably never in a monolith – which is rather understandable. This Integration Style I have described for the sake of completeness of this topic. The most popular integration method for monoliths is the Shared Database Data.

Shared Database Data

In the EIP book, this method of integration is called Shared Database, but I believe that it is not quite the right name. Sharing the database does not always have to mean sharing data, because modules can store its data in separate tables (most often it is done through database schemas). Therefore, in my opinion, Shared Database Data is a better term.

Modular Monolith Integration Styles - Shared Data
Integration Style: Shared Data

In Shared Database Data modules share a certain set of data in the database. In this way, the data are always integrated and consistent with each other because, generally speaking, they are the same data. If module A writes data to table X, module B can read the data immediately after the database transaction is completed.

The level of complexity of such a solution is very small. Nowadays every application/module needs a database, so there is no need to add anything extra with this approach.

The solution looks perfect at first glance. However, its biggest disadvantage is a very high coupling. By sharing data, modules share their state which couples them together. The high coupling means no autonomy for the module. In addition, one little change to database structure or even data itself can break another module without notice. It implies that each change to the database must be consulted and coordinated. This way database becomes bottle-neck of changes. The whole solution is not evolutionary anymore.

The shared state has another significant disadvantage – it is very hard or even impossible to create one, unified data model which will ensure that the requirements of all modules are met. The attempt to unify most often ends with a very weak, ambiguous model that is difficult to understand, develop and maintain.

To reduce coupling while still maintaining the same level of data timeliness we can use Direct Call.

Direct Call

The third option is to directly call the method of the module we’re integrating with. In this case, we use the encapsulation mechanism. The module exposes only what is needed. The whole behavior is closed in a method. In this way, the state of our module is not exposed to the outside as it is in the case of the Shared Database Data approach. Thanks to this, the caller is not able to break anything from the outside.

Modular Monolith Integration Styles - Direct Call
Integration Styles – Direct Call

Not sharing the data implies that each module has its own data set. It can be the same database broken down by schemas or each module can have even a separate database created in different technology. In a scenario with one database, it is important to keep the data really in isolation. It means no constrains between tables from separate modules and no transactions between them.

Both the caller and the callee should treat each other as external. Both modules will use a different language and have different concepts modeled. Therefore, the Anti-Corruption Layer (ACL) should be applied. On the caller side, it could just be a gateway, on the callee side a facade. Thanks to this, modules encapsulation are kept.

In the case of a distributed system, the Direct Call is known as Remote Procedure Invocation/Call (RPI/RPC). Unfortunately, this technique is very often used in Microservice architecture and can lead to the so-called Distributed Monolith anti-pattern architecture. As the call is always synchronous, we are dealing with temporal coupling. Both caller and calle must be available in the same time. In the case of a monolith, it is not a problem because this is its nature, in the case of microservices it is much worse – it reduces architecture quality attributes like autonomous development and deployment. Read other article in this series about architectural drivers for more details.

The Direct Call Integration Style seems to be a very good choice when it comes to integrating our modules, but has some drawbacks too. First, the call is synchronous, so the caller has to wait for the result. Second, the calling module needs to know about the module it is calling, it has to have a direct dependency. Moreover, it has to know the intent of what it wants to do. Coupling is lower than in Shared Database Data, but still exists. If we want to avoid these drawbacks, we can use the last integration style: Messaging

Messaging

The File Transfer integration style has a great advantage – it does not create dependencies between modules. However, it has a big drawback – the data timeliness in most cases is unacceptable. Messaging does not have this disadvantage. The data timeliness is not so good as in the case of Direct Call because it is asynchronous communication, but it can be safely said that it is very good and acceptable in most cases.

Modular Monolith Integration Styles - Messaging - in memory
Integration Styles – Messaging (in memory)
Modular Monolith Integration Styles - Messaging - separate process
Integration Styles – Messaging (separate process)

Appropriate use of Messaging for the implementation of Event-Driven Architecture causes no dependency between modules. Modules integrate through events. However, these are not Domain Events because a domain event is local and should be encapsulated in a given Bounded Context. Integration Events contain only as much as needed to avoid the so-called Fat Events. Integration Events should be as small as possible, as they are part of the contract made available by the given module. As you know, the smaller the contract, the more stable it is -> the less frequently other modules have to change.

In addition, asynchronous processing, that causes Eventual Consistency, on the other hand supports performance advantages, scales better and it is more reliable.

What are the disadvantages of Messaging? First, due to the nature of asynchronicity, the state of our entire system can be eventually consistent as described above. That is why it is so important to have the boundaries of the modules well defined. This is almost as important as in Microservices architecture, but the advantage of the Modular Monolith architecture is that it is much easier to change these boundaries.

The second disadvantage of Messaging is that it is more complex. To provide asynchronous processing and Event-Driven Architecture, we’re going to need some sort of Event Bus. It can be an in-memory broker or a separate component (eg RabbitMQ). In addition, we will also need job processing mechanisms for internal processing – outbox, inbox, internal commands messages. There is need to write some of this infrastructure code. Fortunately, this is a generic problem – we do it only once and there are a lot of libraries and frameworks which support this.

Comparison

Below I present a comparison of all 3 Integration Styles taking into account 3 criteria – Coupling, Data Timeliness and Complexity.

Comparison - Coupling vs Complexity
Comparison – Coupling vs Complexity
Comparison - Coupling vs Data Timeliness
Comparison – Coupling vs Data Timeliness

What can we deduce from these diagrams? First of all, there is no one perfect style. Fortunately, we can see some heuristics:

1. If the system is very, very simple (essential complexity is low) and you do not care about modularity: choose simplicity and use Shared Database Data style.
2. If the system is complex (essential complexity is high) you must care about modularity. Options are:
– choose Messaging if you prefer highest level of autonomy and eventual consistency between modules is acceptable
– choose Messaging if you care a lot about performance, reliability, scalability
– choose Direct Call if you must have strong consistency, don’t need maximum level of modules autonomy or Data Timeliness is the primary factor.

Additionally, you can mix different styles. Most often, when we are talking about modular architecture, it is best to use Direct Call and Messaging together. Some modules can communicate synchronously and some asynchronously, depending on the need.

Summary

As I mentioned at the beginning, no module, component, or system lives in complete isolation. We must follow the “Divide and Conquer” principle so we divide our solution into smaller parts, but finally – we have to integrate these parts together to create a system.

Let’s summarize all 4 styles again:

File transfer – provides low coupling but has almost always unacceptable data timeliness so it is impractical in the monolith
Shared Database Data – the simplest, quick, but couples modules together
Direct Call – provides lower coupling than Shared Database Data, encapsulates modules, relatively simple
Messaging – ensures the lowest coupling, modularity, autonomy but at the cost of complexity

Which Integration Style to choose? Everything, as usual, “it depends”. However, I hope that I managed to at least to some extent explain what it depends on. No silver bullet, again.

Related Posts

1. Modular Monolith: A Primer
2. Modular Monolith: Architectural Drivers
3. Modular Monolith: Architecture Enforcement
4. How to publish and handle Domain Events
5. Handling Domain Events: Missing Part
6. Processing multiple aggregates – transactional vs eventual consistency

Modular Monolith: Architecture Enforcement

This post is part of articles series about Modular Monolith architecture:

1. Modular Monolith: A Primer
2. Modular Monolith: Architectural Drivers

3. Modular Monolith: Architecture Enforcement (this)
4. Modular Monolith: Integration Styles

Introduction

In previous posts we discussed what is the architecture of Modular Monolith and architectural drivers that can affect its choice. In this post, I would like to focus on ways to enforce chosen architecture.

The methods described below are not just about the modular monolith architecture, it can be said that there are universal. Nevertheless, due to the monolithic nature, the size of the codebase and the ease of its changes, they are particularly important in enforcing architecture.

Model-code gap

Let’s assume that based on current architectural drivers, you decided on the architecture of a Modular Monolith. Let’s also assume that you have predefined your module boundaries and solution architecture. You chose the technology, approach, way of communication between modules, way of persistence.

Everything has been documented in the form of a solution architecture document/description (SAD) or you made just a few diagrams (using UML, C4 model or simply arrows and boxes). You’ve done enough up-front design, you can start the first iterations of implementation.

At the beginning it is very simple. It does not have much functionality, there is little code, it is easily maintained and consistent with modeled architecture. There is a lot of time and even if something goes wrong it is easy to refactor. So far so good.

However, at some point it is not easy anymore. Functionalities and code are increasing, requirements changes are starting to appear, deadlines are chasing. We start making shortcuts and our implementation begins to differ significantly from the design. In the case of the Modular Monolith architecture, we most often lose in this way modularity, independence and everything begins to communicate with everything. Another Big Ball Of Mud made:

It was supposed to be like never before, it ended as always

George Fairbanks in his book Just Enough Software Architecture: A Risk-Driven Approach the phenomenon described above defines as follows:

Your architecture models and your source code will not show the same things. The difference between them is the model-code gap.

and later:

Whether you start with source code and build a model, or do the reverse, you must manage two representations of your solution. Initially, the code and models might correspond perfectly, but over time they tend to diverge. Code evolves as features are added and bugs are fixed. Models evolve in response to challenges or planning needs. Divergence happens when the evolution of one or the other yields inconsistencies.

Are we always doomed to such an end in the long run? Well no. It certainly requires a lot of discipline from ourselves, but discipline is not everything. We need to apply appropriate practices and approaches that keep our architecture in check. What are these approaches then?

Architecture enforcement

When describing the tools to check whether our implementation is consistent with the assumed design, we must take into account 2 aspects.

The first aspect is the possibilities that the tool gives us. As we know architecture is a set of rules at different levels of abstraction, sometimes hard to define. Not to mention that we have to check them out.

The second aspect is how quickly we get feedback. Of course, the sooner the better because we are able to fix something faster. The faster we fix something, the less impact this error has on our architecture later.

Considering the following assumptions, when it comes to architectural enforcement we can do it on 3 different levels: through the compiler, automated tests and code review.

Architecture enforcement approaches
Architecture enforcement approaches

Compile-time

The compiler is your best friend. It is able to quickly check for you many things that would take you a long time. In addition, the compiler cannot be wrong, people can. In that case, why so rarely do we use the compiler to take responsibility for compliance with our chosen architecture? Why we do not want to use its possibilities to the maximum?

The first main sin is the everything is public principle. According to the definition of modularity, modules should communicate through well-defined interfaces, which means they should be encapsulated. If everything is public, there is no encapsulation.

Unfortunately, the programming community favors this phenomenon by:

– tutorials
– sample projects
– IDE (creating public classes by default)

We should definitely change the approach to private by default. If something cannot be private, let it be available within the module’s range, but still inaccessible to others.

How to do it? Unfortunately, we have limited options in .NET. The only thing we can do is to separate the module into a separate assembly and use the “internal” access modifier. There is almost a war between supporters of all code in one project (assembly) and supporters of splitting into many projects.

The former say assembly is an implementation unit. Yes, but since we have no other way to encapsulate our modules, the division into projects seems to be a sensible solution. Additionally, thanks to checking references, adding incorrect dependencies (e.g. from the domain to the infrastructure) will be difficult or even impossible.

Lack of encapsulation is one of the most common sins I see, but not the only one. Others are not using immutability (unnecessary setters) or strong typing (primitive obsession) for example.

Generally speaking, we should use our language in such a way that the compiler can catch as many mistakes for us. It is the most efficient approach to enforce architecture of system.

Architecture enforcement - compile-time
Architecture enforcement – compile-time

Automated tests

Not everything can be checked using a compiler. This does not mean, however, that we must check it manually. On the contrary, the computer can still do it for us. In that case, we can use 2 mechanisms – static code analysis and automated tests.

Static code analysis

I will start with a more familiar and common method – a static code analyzer. Certainly, most have heard about such tools as SonarQube or NDepend. These are tools that automatically perform static analysis of our code and based on they provide metrics information that may be very useful to us. Of course, static code analyzers we can connect to CI process and get feedback on a regular basis.

Architecture Enforcement - static analysis
Architecture Enforcement – static analysis

Architecture tests

Architecture tests are another way less known but gaining popularity. These are unit tests, but instead of testing business functionalities they test our codebase in the context of architecture. Most often, such tests are written based on a library dedicated to this type of tests. Such a test may look like this:

We can check many things with these tests. Libraries for this (such as NetArchTests or ArchUnit) allow a lot and writing something different is not a difficult task. A complete example of using such tests can be found here.

Architecture Enforcement – architecture tests
Architecture Enforcement – architecture tests

Code review

If we are not able to check the compliance of our solution with the chosen architecture using a computer (compiler, automated tests), we have the last tool – code review. Thanks to the code review, we can check everything that a computer cannot do for us, but it has some disadvantages.

The first disadvantage is that people can be wrong, so the probability of missing an architectural decision break attempt is relatively high.

The second drawback, of course, is the large amount of time we need to spend on code-review. Of course, this is not a waste of time and we can not give it up, but it must always be included in the estimates of the project.

The conclusion is obvious – to enforce architecture we should use the computer as much as possible and treat code-review as the last line of defense. The question is how to strengthen this line of defense, i.e. how to reduce the time and probability of missing something during code-review? We can use Architecture Decisions Records (ADR).

Architecture Decisions Records (ADR)

What is the Architecture Decisions Record? Let me quote a definition from the most popular GitHub repository related to this topic:

An architectural decision record (ADR) is a document that captures an important architectural decision made along with its context and consequences.

Such a document is usually stored in the version control system, which is also recommended (as the approach itself) by the popular technology radar from ThoughtWorks company.

My advice is to start by describing your decisions as simply and quickly as possible. Without unnecessary ceremonies, choose a simple template (e.g. the one proposed by Michael Nygard), where are the most important elements – context, decision, and consequences. But how does this relate to the discussed code-review?

First, all decisions become public, everyone has access to them and they are described. There is no such thing that someone says “I did not know”. As such decisions are by definition important, everyone must know them and follow them.

The second thing is it speeds up the code-review process, because instead of writing why something is wrong, you can just paste the link to the appropriate ADR instead of explaining why we do it like that, what was the decision, when and in what context.

Summary

Each system has some architecture. The question is: whether you will shape the architecture of your system or whether it will shape itself? Certainly, the first option is better because the second one may condemn us to big failure.

Architecture enforcement is the responsibility of every team member (not just the architect), that’s why the way we do it is so important. It’s a process that requires commitment. The techniques I mentioned can significantly facilitate and improve the process of architectural enforcement while maintaining the quality of our system at the appropriate level.

Additional Resources

1. Unit Test Your Architecture with ArchUnit – Jonas Havers, article
2. Architecture Decision Records in Action presentation – Michael Keeling, Joe Runde, presentation
3. Design It! – Michael Keeling, book
4. Modular Monolith with DDD – Kamil Grzybek, GitHub repository
5. “Modular Monoliths” – Simon Brown, video

Related Posts

1. Modular Monolith: A Primer
2. Modular Monolith: Architectural Drivers
3. Domain Model Encapsulation and PI with Entity Framework 2.2
4. Attributes of Clean Domain Model

Image credits: nanibystudio

Modular Monolith: Architectural Drivers

This post is part of articles series about Modular Monolith architecture:

1. Modular Monolith: A Primer
2. Modular Monolith: Architectural Drivers (this)
3. Modular Monolith: Architecture Enforcement
4. Modular Monolith: Integration Styles

Introduction

In the first post about the architecture of Modular Monolith I focused on the definition of this architecture and the description of modularity. As a reminder, the Modular Monolith:

  • is a system that has exactly one deployment unit
  • is a explicit name for a Monolith system designed in a modular way
  • modularization means that module:
    – must be independent, autonomous
    – has everything necessary to provide desired functionality (separation by business area)
    – is encapsulated and has a well-defined interface/contract

In this post I would like to discuss some of the most popular, in my opinion, Architectural Drivers which can lead to either a Modular Monolith or Microservices architecture.

But what exactly are Architectural Drivers?

Architectural Drivers

In general, you can’t say that X architecture is better than the other. You can’t say that Monolith is better than Microservices, Clean Architecture is better than Layered Architecture, 3 layers are better/worse than 4 layers and so on.

The same rule applies to other considerations such as ORM vs raw SQL, “Current State” Persistence vs Event Sourcing, Anemic Domain Model vs Rich Domain Model, Object-Oriented Design vs Functional Programming… and a lot of more.

So how we can choose the architecture/approach/paradigm/tool/library if there is, unfortunately, no best?

Context is king

Each of our decisions are made in a given context. Each project is different (it results from the project definition) so each context is different. It implies that the same decision made in one context can bring great results, while in another it can cause devastating failure. For this reason, using other people’s/companies approaches without critical thinking can cause a lot of pain, wasted money and finally – the end of the project.

Project Contexts
Every Project is different and has different Context

However, context is a too general concept and we need something more specified to put into practice. That’s why the Architectural Drivers concept was defined. Michael Keeling writes about them in his blog article in the following way:

Architectural drivers are formally defined as the set of requirements that have significant influence over your architecture.

Simon Brown in the book Software Architecture for Developers describes Architectural Drivers similarly:

Regardless of the process that you follow (traditional and plan-driven vs lightweight and adaptive), there’s a set of common things that really drive, influence and shape the resulting software architecture.

Architectural Drivers have their categorization. The main categories are:

  • Fuctional Requirements – what and how problems does the system solve
  • Quality Attributes – a set of attributes that determine the quality of architecture like maintainability or scalability.
  • Technical Constraints – technology standards, tools limitations, team experience
  • Business Constraints – budget, hard deadline
Architectural Drivers
Architectural Drivers

Most importantly, all Architectural Drivers are connected to each other and often focus on one causes loss of another (trade-offs everywhere, unfortunately). Let’s consider this example.

You have some service that calculates some important thing (Functional Requirement) in 3 seconds (Quality Attribute – performance). A new requirement appears, calculation is more complex and takes now 5 seconds (Performance decreased). To go back to 3 seconds another technology could be used, but there is no time for it (Business Constraint – hard deadline) and nobody has used it in the company yet (Technical Constraint – team experience). The only option to increase performance is to move the calculation to the stored procedure, which decreases maintainability and readability (Quality Attributes).

Architectural Drivers example
Architectural Drivers example

As you can see, the software architecture is a continuous choice between one driver and another. There is no one “right” solution. There is No Silver Bullet.

With this in mind, let’s see some of the popular Architectural Drivers and attributes which are discussed during the considerations of Modular Monolith and Microservices architectures

Level of Complexity

At the beginning, let’s consider one of the greatest advantages of a Modular Monolith compared to distributed architectures – Complexity. The definition of complexity on the Wiki is as follows:

Complexity characterizes the behavior of a system or model whose components interact in multiple ways and follow local rules, meaning there is no reasonable higher instruction to define the various possible interactions. The term is generally used to characterize something with many parts where those parts interact with each other in multiple ways, culminating in a higher order of emergence greater than the sum of its parts.

As you see above, Complexity is about components and their interactions. In Modular Monolith architecture interactions between modules are simple because each module is located in the same process. This means that the module that wants to interact with another module:

  • Knows the exact address where he will direct the request and is sure that this address will not change
  • The request is just a method call, no network needed
  • The target module is always available
  • Security issue is not a concern
Modular Monolith Complexity
Complexity – Modular Monolith

On the other hand, consider distributed system architecture. In this architecture, the modules / services are located on other servers and communicate via the network. This means that when a service wants to communicate with another, it must deal with the following concerns:

  • It needs to get somehow address of target module, because it may be changed
  • Communication takes place via the network, which necessitates the use of special protocols like HTTP and serialization.
  • Network may be unavailable (CAP theorem)
  • Secure communication between modules must be ensured

Of course, You can find solutions for these issues. For example, to solve the addressing issue you can add Service Registry and implement Service Discovery pattern. However, it means adding more components and algorithms to the system so complexity rapidly increases.

To be aware of the scale of the problems generated by the Microservices architecture, I recommend that you familiarize yourself with the patterns that are used to solve them. The list is large, and most of them are not needed at all in the Monolith architecture.

Complexity - Distributed System
Complexity – Distributed System

In summary, the architecture of the Modular Monolith is definitely less complex than that of distributed systems. High complexity reduces maintainability, readability, observability. It needs an experienced team, advanced infrastructure, specific organizational culture and so on. If simplicity is your key architectural driver then consider Monolith First.

Productivity

The team’s productivity in delivering changes can be measured in two dimensions: in the context of the entire system and the single module.

In the context of the whole system, the matter is clear. The architecture of the Modular Monolith is less complex => the less complex the easier to understand => the productivity is higher.

From the point of view of the ease of running the entire system, the Modular Monolith ensures productivity at the maximum level – just download the code and run it on the local machine. In a distributed architecture, the matter is not so simple despite the technologies and tools (like Docker and Kubernetes) that facilitate this process.

Running entire system – Monolith vs Distributed

On the other hand, we have productivity related to the development of a single module. In this case, the microservice architecture will be better, because we do not have to run the entire system to test one specific module.

Which architecture, then, supports the team’s productivity? In my opinion, for most systems, the Modular Monolith, but for really large projects (tens or hundreds of modules) are microservices. If your architectural driver is development speed and the system is not huge, a better choice will be a Modular Monolith and in case of system expansion, a possible transition to microservices could be right move to do.

Deployability

The deployability of a software system is the ease with which it can be taken from development to production. However, we must consider 2 situations here. The deployment of the entire system and the single module.

In the context of the entire system, is it easier to deploy one application of several applications? Of course, one application is easier to deploy so it seems that Modular Monolith is better option.

Deployment - Modular Monolith
Deployment – Modular Monolith

On the other hand, in a Modular Monolith, we always have to deploy the whole system. We can not deploy one particular module and this is one of the most important disadvantages. In this architecture, we do not have deployment autonomy so deployment process must be coordinated and may be more difficult.

Deployability - Distribiuted System
Deployability – Distribiuted System

In summary, if you do not mind the deployment of the whole system and you do not care about the autonomy of deployment- this is the point behind the Modular Monolith. Otherwise, consider distributed architecture.

Performance

Performance is about how fast something is, usually in terms of response time, duration of processing or latency.

Assuming the scenario that all requests are processed in a sequential manner, Monolith architecture will always be more efficient than a distributed system. All modules operate in the same process, so there is no overhead on communication between them.

The distributed system has overhead caused by communication over the network – serialization and deserialization, cryptography and speed of sending packets.

Even in real scenarios, the Monolith will be more efficient, but only for some time. With the increase in users, requests, data, and complexity of calculations, it may turn out that performance decreases. Then we come to one of the main drivers of the Microservices architecture: scalability.

Scalability

What is scalability? Wikipedia says:

Scalability is the property of a system to handle a growing amount of work by adding resources to the system

In other words, scalability is about the ability for software to deal with more requests or data.

It’s best to show this by example. Let’s assume that one of our modules must now handle more requests than we initially assumed. To do this, we must increase the resources that are responsible for the operation of this module.

We can always do it in two ways. Increase node computing power (called Vertical Scaling) or add new nodes (called Horizontal Scaling). Let’s see how it looks from the point of view of Monolith and Microservices architecture:

Scaling
Scaling

As can be seen above, both architectures can be scaled. Monolith can be scaled too. Vertical Scaling is the same, but the difference is in Horizontal Scaling. Using this approach, we can scale the Modular Monolith only as a whole, which leads to inefficient resource utilization. In the Microservices architecture, we scale only those modules that we need to scale, which leads to better utilization of resources. This is the main difference.

The more instances of the modules must work, the more significant the difference. On the other hand, if you don’t have to scale a lot, maybe you better accept less efficient resource utilization and stay with the Monolith and take its other advantages? This is a good question that we should ask ourselves in such a situation.

Failure impact

Sometimes our architectural driver may be limiting the impact of failure. Let’s say we have a very unstable module that crashes the entire process once in a while.

In the case of a Modular Monolith, as the whole system works in one process, the whole system suddenly stops working and our availability decreases.

In the case of Microservices architecture, the “risky” module can be moved into a separate process and if it is stopped the rest of the system will work properly.

Failure impact
Failure impact

To increase the availability of the Modular Monolith, you can increase the number of nodes, but as with scalability, resource utilization will not be at the highest level compared to Microservices architecture.

Heterogeneous Technology

One of the attributes of a Modular Monolith that cannot be bypassed in any way is the inability to use heterogeneous technology. The whole system is in the same process, which means that it must be running in the same runtime environment. This does not mean that it must be written in the same language because some platforms support multiple languages (for example .NET CLR or JAVA JVM). However, the use of completely separate technologies is not possible.

Heterogeneous Technology
Heterogeneous Technology

A feature of heterogeneous technology can be decisive to switch to Microservices architecture, but it doesn’t have to be. Often, companies use one technology stack and no one even thinks about the implementation of components in different technologies because team competence or software license does not allow it.

On the other hand, larger companies and projects more often use different technologies to maximize productivity using tailor-made tools to solve specific problems.

A common case associated with heterogeneous technology is the maintenance and development of the legacy system. The legacy system is often written in old technology (and often in a very bad way). To use the new technology, a new service/system is often created that implements new functionalities and the old system only delegates requests to the new one. Thanks to this, the development of the legacy system can be faster and it is easier to find people willing to work with it. The disadvantage here is that because of two systems instead of one – the whole system becomes distributed – with all of cons this architecture.

Summary

This post was not intended to describe all architectural drivers in favor of a Modular Monolith or Microservices. Separate books are being created on this topic.

In this post, I wanted to describe the most common discussed architectural drivers (in my opinion) and make it very clear that the shape of the architecture of our system is influenced by many factors and everything depends on our context.

Summarizing:

  • There is no better or worse architecture – it all depends on the context and Architectural Drivers
  • Architectural Drivers have their categorization – Functional Requirements, Quality Attributes, Technical Constraints, Business Constraints
  • Monolith architecture is less complex than a distributed system. Microservices architecture requires much more tools, libraries, components, team experience, infrastructure management and so on
  • At the beginning, the Monolith implementation will be more productive (Monolith first approach). Later, migration to Microservices architecture can be considered but only if architectural driver for that migration exists
  • Deployment of Monolith is easier but does not support autonomous deployment.
  • Both architectures supports scalability, but Microservices are way more efficient (resources utilization)
  • Monolith has better performance than Microservices until the need for scaling appears – then it depends on scaling possibilities
  • Failure impact is greater in Monolith because everything works in the same process. Risk can be mitigated by duplication but it will cost more than in Microservices architecture
  • Monolith from definition does not support heterogeneous technology

Additional Resources

1. Architectural Drivers – chapter from Designing Software Architectures: A Practical Approach book – Humberto Cervantes, Rick Kazman
2. Software Architecture for Developers book – Simon Brown
3. Design It! book – Michael Keeling
4. Collection of articles about Monolith and Microservices architectures named “When microservices fail…”
5. Modular Monolith with DDD – GitHub repository

Related Posts

1. Modular Monolith: A Primer

Modular Monolith: A Primer

This post is part of articles series about Modular Monolith architecture:

1. Modular Monolith: A Primer (this)
2. Modular Monolith: Architectural Drivers
3. Modular Monolith: Architecture Enforcement
4. Modular Monolith: Integration Styles

Introduction

Many years have passed since the rise of the popularity of microservice architecture and it is still one of the main topics discussed in the context of the system architecture. The popularity of cloud solutions, containerization and advanced tools supporting the development and maintenance of distributed systems (such as Kubernetes) is even more conducive to this phenomenon.

Observing what is happening in the community, companies and during conversations with programmers, it can be concluded that most of the new projects are implemented using the microservice architecture. Moreover, some legacy systems are also moving towards this approach.

Ok, the subject of the post is Modular Monolith and I dwell on microservices, the question is why? Namely, because I think that as an IT industry we have made a false start adopting microservice architecture to such an extent. Instead of focusing on architectural drivers, we believed that microservices are medicine for all the evil that sits in monolithic applications. If you have participated in the development of a system that consists of more than one deployment unit, you already know that this is not the case. Each architecture has its pros and cons – microservices are no exception. They solve some problems by generating others in return.

With this entry, I would like to start a series of articles on the architecture of Modular Monolith. I do it for several reasons.

First of all, I would like to refute the myth that you cannot make a high-class system in monolithic architecture. Secondly, I would like to dispel doubts about the definition of this architecture and its appearance – many people interpret it differently. Thirdly, I treat this series of posts as an extension and addition to my implementation of Modular Monolith with DDD architecture, which I shared a few months ago on GitHub and which was very well received (1k stars a month after publication).

In this introductory post, I will focus on the definition of a Modular Monolith architecture.

What is Modular Monolith?

I always try to be precise when I talk or write about technical and business issues, especially when it comes to architecture. I believe that a clear and coherent message is very important. That is why I would like to clearly define what the architecture of the Modular Monolith means to me and how I perceive it.

Let’s start with the simpler concept, what is Monolith?

Monolith

Wikipedia describes “monolithic architecture” in terms of building construction and not computer science as follows:

Monolithic architecture describes buildings which are carved, cast or excavated from a single piece of material, historically from rock.

In terms of computer science, building is the system and the material is our executable code. So in Monolith Architecture, our system consists of exactly one piece of executable code and nothing more.

Let’s see 2 technical definitions: first one about Monolith System:

A software system is called “monolithic” if it has a monolithic architecture, in which functionally distinguishable aspects (for example data input and output, data processing, error handling, and the user interface) are all interwoven, rather than containing architecturally separate components.

Second one about Monolithic Architecture:

A monolithic architecture is the traditional unified model for the design of a software program. Monolithic, in this context, means composed all in one piece. Monolithic software is designed to be self-contained; components of the program are interconnected and interdependent rather than loosely coupled as is the case with modular software programs

These 2 definitions above (one of the first results in Google) have 2 shared assumptions.

First, they define that this architecture assumes that all parts of the system form one deployment unit – I will agree with that.

The second shared assumption of these definitions is that they assume a lack of modularity in such architecture and I will definitely disagree with that. The phrases “interwoven, rather than containing architecturally separate components” and “components of the program are interconnected and interdependent rather than loosely coupled” very negatively characterize this architecture, assuming that everything is mixed in them. It may be so, but it doesn’t have to be. It is not the ultimate attribute of the Monolith.

To sum up, Monolith is nothing more than a system that has exactly one deployment unit. No less no more.

Modularization

I’ve defined what Monolith means, let’s get to second aspect: Modularity.

What does it mean that something is modular according to the English Dictionary?

Consisting of separate parts that, when combined, form a complete whole/made from a set of separate parts that can be joined together to form a larger object

and Modularization itself:

The design or production of something in separate sections

Because it is a general definition, it is not enough for the programming world. Let’s use a more specific technical one about Modular programming:

Modular programming is a software design technique that emphasizes separating the functionality of a program into independent, interchangeable modules, such that each contains everything necessary to execute only one aspect of the desired functionality. A module interface expresses the elements that are provided and required by the module. The elements defined in the interface are detectable by other modules. The implementation contains the working code that corresponds to the elements declared in the interface.

Several important issues have been raised here. In order to have modular architecture, you must have modules and these modules:

  • a) must be independent and interchangeable and
  • b) must have everything necessary to provide desired functionality and
  • c) must have defined interface

Let’s see what these assumptions mean.

Module must be independent and interchangeable

For the module to meet these assumptions, as the name implies, it should be independent. Of course, it is impossible for it to be completely independent because then it means that it does not integrate with other modules. The module will always depend on something, but dependencies should be kept to a minimum. According to the principle: Loose Coupling, Strong Cohesion.

In the diagram below on the left we have a module that has a lot of dependencies and you can definitely not say that it is independent. On the other hand, on the right, the situation is the opposite – the module contains a minimum of dependencies and they are more loose, it is finally more independent:

Module independence
Module independence

However, the number of dependencies is just one measure of how well our module is independent. The second measure is how strong the dependency is. In other words, do we call it very often using multiple methods or occasionally using one or a few methods?

Strong/Weak dependency
Strong/Weak dependency

In the first case, it is possible that we have defined the boundaries of our modules incorrectly and we should merge both modules if they are closely related:

Modules merged
Modules merged

The last attribute affecting the independence of the module is the frequency of changes of the modules on which it depends on. As you can guess – the less often they are changed, the more the module is independent. On the other hand, if changes are frequent – we must change our module often and it loses its independence:

Module stability
Module stability

To sum up, the module’s independence is determined by three main factors:

  • number of dependencies
  • strength of dependenies
  • stability of the modules on which the module depends on

Module must have everything necessary to provide desired functionality

The module is a very overloaded word and can be used in many contexts with different meanings. A common case here is to call logical layers as modules, e.g. GUI module, application logic module, database access module. Yes, in this context these are also modules but they provide technical, not business functionality.

Thinking about a module in a technical context, only technical changes cause exactly one module to change:

Technical modules and technical change
Technical modules and technical change

Adding or changing business functionality usually goes through all layers causing changes in each technical module:

Technical modules - new/change business feature
Technical modules – new/change business feature

The question we have to ask ourselves is: do we more often make changes related to the technical part of our system or changes in business functionality? In my opinion – definitely more often the latter. We rarely exchange the database access layer, logging library or GUI framework. For this reason, the module in the Modular Monolith is a business module that is able to fully provide a set of desired features. This kind of design is called “Vertical Slices” and we group these slices in the module:

Business modules and vertical slices
Business modules and vertical slices

In this way, frequent changes affect only one module – it becomes more independent, autonomous and is able to provide functionality by itself.

Module must have defined interface

The last attribute of modularity is a well-defined interface. We can’t talk about modular architecture if our modules don’t have a Contract:

Modules without contract (interface)
Modules without contract (interface)

A Contract is what we make available outside so it is very important. It is an “entry point” to our module. Good Contract should be unambiguous and contain only what clients of a given contract need. We should keep it stable (to not break our clients) and hide everything else behind it (Encapsulation):

Modules with contract
Modules with contract

As you can see in the diagram above, the contract of our module can take different forms. Sometimes it is some kind of facade for synchronous calls (e.g. public method or REST service), sometimes it can be an published event for asynchronous communication. In any case, everything that we share outside becomes the public API of the module. Therefore, encapsulation is an inseparable element of modularity.

Summary

1. Monolith is a system that has exactly one deployment unit.
2. Monolith architecture does not imply that the system is poor designed, not modular or bad. It does not say anything about quality.
3. Modular Monolith architecture is a explicit name for a Monolith system designed in a modular way.
4. To achieve a high level of modularization each module must be independent, has everything necessary to provide desired functionality (separation by business area), encapsulated and have a well-defined interface/contract.

In the next post I will discuss the pros and cons of Modular Monolith architecture comparing it to the microservices.

Additional resources

1. Modular Monoliths Video – Simon Brown
2. Majestic Modular Monliths – Axel Fontaine
3. Modular programming – Wikipedia
4. Monolithic application – Wikipedia
5. Modular Monolith with DDD – GitHub repository
6. Vertical Slice Architecture – Jimmy Bogard

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2. Attributes of Clean Domain Model
3. Domain Model Encapsulation and PI with Entity Framework 2.2
4. Simple CQRS implementation with raw SQL and DDD

Image credits: Magnasoma