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One of the goals of this book is to cut through all that confusion and to define, once and for all, what design and architecture are. For starters, I’ll assert that there is no difference between them. None at all.
The word “architecture” is often used in the context of something at a high level that is divorced from the lower-level details, whereas “design” more often seems to imply structures and decisions at a lower level.
The goal of software architecture is to minimize the human resources required to build and maintain the required system.
The measure of design quality is simply the measure of the effort required to meet the needs of the customer. If that effort is low, and stays low throughout the lifetime of the system, the design is good. If that effort grows with each new release, the design is bad. It’s as simple as that.
Every software system provides two different values to the stakeholders: behavior and structure.
If you give me a program that works perfectly but is impossible to change, then it won’t work when the requirements change, and I won’t be able to make it work. Therefore the program will become useless.
If you give me a program that does not work but is easy to change, then I can make it work, and keep it working as requirements change. Therefore the program will remain continually useful.
Notice the pattern that I’ve quite deliberately set up in introducing these three programming paradigms: Each of the paradigms removes capabilities from the programmer. None of them adds new capabilities. Each imposes some kind of extra discipline that is negative in its intent. The paradigms tell us what not to do, more than they tell us what to do.
Another way to look at this issue is to recognize that each paradigm takes something away from us. The three paradigms together remove goto statements, function pointers, and assignment. Is there anything left to take away?
The problem that Dijkstra recognized, early on, was that programming is hard, and that programmers don’t do it very well. A program of any complexity contains too many details for a human brain to manage without help. Overlooking just one small detail results in programs that may seem to work, but fail in surprising ways.
Dijkstra’s solution was to apply the mathematical discipline of proof. His vision was the construction of a Euclidian hierarchy of postulates, theorems, corollaries, and lemmas. Dijkstra thought that programmers could use that hierarchy the way mathematicians do. In other words, programmers would use proven structures, and tie them together with code that they would then prove correct themselves.
But the proofs never came. The Euclidean hierarchy of theorems was never built. And programmers at large never saw the benefits of working through the laborious process of formally proving each and every little function correct. In the end, Dijkstra’s dream faded and died. Few of today’s programmers believe that formal proofs are an appropriate way to produce high-quality software.
Of course, formal, Euclidian style, mathematical proofs are not the only strategy for proving something correct. Another highly successful strategy is the scientific method.
Science is fundamentally different from mathematics, in that scientific theories and laws cannot be proven correct.
That is the nature of scientific theories and laws: They are falsifiable but not provable.
Science does not work by proving statements true, but rather by proving statements false.
Dijkstra once said, “Testing shows the presence, not the absence, of bugs.” In other words, a program can be proven incorrect by a test, but it cannot be proven correct. All that tests can do, after sufficient testing effort, is allow us to deem a program to be correct enough for our purposes.
The implications of this fact are stunning. Software development is not a mathematical endeavor, even though it seems to manipulate mathematical constructs. Rather, software is like a science. We show correctness by failing to prove incorrectness, despite our best efforts.
Software architects strive to define modules, components, and services that are easily falsifiable (testable). To do so, they employ restrictive disciplines similar to structured programming, albeit at a much higher level.
The fact that OO languages provide safe and convenient polymorphism means that any source code dependency, no matter where it is, can be inverted.
In short, when the source code in a component changes, only that component needs to be redeployed. This is independent deployability.
If the modules in your system can be deployed independently, then they can be developed independently by different teams. That’s independent developability.
What is OO? There are many opinions and many answers to this question. To the software architect, however, the answer is clear: OO is the ability, through the use of polymorphism, to gain absolute control over every source code dependency in the system. It allows the architect to create a plugin architecture, in which modules that contain high-level policies are independent of modules that contain low-level details. The low-level details are relegated to plugin modules that can be deployed and developed independently from the modules that contain high-level policies.
Why would an architect be concerned with the mutability of variables? The answer is absurdly simple: All race conditions, deadlock conditions, and concurrent update problems are due to mutable variables.
Architects would be wise to push as much processing as possible into the immutable components, and to drive as much code as possible out of those components that must allow mutation.
To summarize:
• Structured programming is discipline imposed upon direct transfer of control.
• Object-oriented programming is discipline imposed upon indirect transfer of control.
• Functional programming is discipline imposed upon variable assignment.
What we have learned over the last half-century is what not to do.
Good software systems begin with clean code.
The goal of the principles is the creation of mid-level software structures that:
• Tolerate change,
• Are easy to understand, and
• Are the basis of components that can be used in many software systems.
• SRP: The Single Responsibility Principle
An active corollary to Conway’s law: The best structure for a software system is heavily influenced by the social structure of the organization that uses it so that each software module has one, and only one, reason to change.
• OCP: The Open-Closed Principle
Bertrand Meyer made this principle famous in the 1980s. The gist is that for software systems to be easy to change, they must be designed to allow the behavior of those systems to be changed by adding new code, rather than changing existing code.
• LSP: The Liskov Substitution Principle
Barbara Liskov’s famous definition of subtypes, from 1988. In short, this principle says that to build software systems from interchangeable parts, those parts must adhere to a contract that allows those parts to be substituted one for another.
• ISP: The Interface Segregation Principle
This principle advises software designers to avoid depending on things that they don’t use.
• DIP: The Dependency Inversion Principle
The code that implements high-level policy should not depend on the code that implements low-level details. Rather, details should depend on policies.
Components are the units of deployment. They are the smallest entities that can be deployed as part of a system. In Java, they are jar files. In Ruby, they are gem files. In .Net, they are DLLs. In compiled languages, they are aggregations of binary files. In interpreted languages, they are aggregations of source files. In all languages, they are the granule of deployment.
• REP: The Reuse/Release Equivalence Principle
The granule of reuse is the granule of release.
This is not simply because, without release numbers, there would be no way to ensure that all the reused components are compatible with each other.
• CCP: The Common Closure Principle
Gather into components those classes that change for the same reasons and at the same times. Separate into different components those classes that change at different times and for different reasons.
This is the Single Responsibility Principle restated for components. Just as the SRP says that a class should not contain multiples reasons to change, so the Common Closure Principle (CCP) says that a component should not have multiple reasons to change.
The SRP tells us to separate methods into different classes, if they change for different reasons. The CCP tells us to separate classes into different components, if they change for different reasons.
• CRP: The Common Reuse Principle
Don’t force users of a component to depend on things they don’t need.
The ISP advises us not to depend on classes that have methods we don’t use. The CRP advises us not to depend on components that have classes we don’t use.
Don’t depend on things you don’t need.
Allow no cycles in the component dependency graph.
The component structure cannot be designed from the top down. It is not one of the first things about the system that is designed, but rather evolves as the system grows and changes.
Depend in the direction of stability.
One sure way to make a software component difficult to change, is to make lots of other software components depend on it. A component with lots of incoming dependencies is very stable because it requires a great deal of work to reconcile any changes with all the dependent components.
• Fan-in: Incoming dependencies. This metric identifies the number of classes outside this component that depend on classes within the component.
• Fan-out: Outgoing depenencies. This metric identifies the number of classes inside this component that depend on classes outside the component.
• I: Instability: I = Fan-out , (Fan-in + Fan-out). This metric has the range [0, 1]. I = 0 indicates a maximally stable component. I = 1 indicates a maximally unstable component.
A component should be as abstract as it is stable.
Thus, if a component is to be stable, it should consist of interfaces and abstract classes so that it can be extended.
Thus dependencies run in the direction of abstraction.
And the longer you wait to make those decisions, the more information you have with which to make them properly.
A good architect maximizes the number of decisions not made.
Good architects design the policy so that decisions about the details can be delayed and deferred for as long as possible.
Conway’s law says:
Any organization that designs a system will produce a design whose structure is a copy of the organization’s communication structure.
Architects often fall into a trap—a trap that hinges on their fear of duplication.
Duplication is generally a bad thing in software. We don’t like duplicated code. When code is truly duplicated, we are honor-bound as professionals to reduce and eliminate it.
But there are different kinds of duplication. There is true duplication, in which every change to one instance necessitates the same change to every duplicate of that instance. Then there is false or accidental duplication. If two apparently duplicated sections of code evolve along different paths—if they change at different rates, and for different reasons—then they are not true duplicates. Return to them in a few years, and you’ll find that they are very different from each other.
Software architecture is the art of drawing lines that I call boundaries. Those boundaries separate software elements from one another, and restrict those on one side from knowing about those on the other.
Communications between components in a monolith are very fast and inexpensive. They are typically just function calls. Consequently, communications across source-level decoupled boundaries can be very chatty.
Software systems are statements of policy. Indeed, at its core, that’s all a computer program actually is. A computer program is a detailed description of the policy by which inputs are transformed into outputs.
A strict definition of “level” is “the distance from the inputs and outputs.” The farther a policy is from both the inputs and the outputs of the system, the higher its level.
Strictly speaking, business rules are rules or procedures that make or save the business money. Very strictly speaking, these rules would make or save the business money, irrespective of whether they were implemented on a computer. They would make or save money even if they were executed manually.
Business rules are the reason a software system exists.
Architectures are not (or should not be) about frameworks. Architectures should not be supplied by frameworks. Frameworks are tools to be used, not architectures to be conformed to. If your architecture is based on frameworks, then it cannot be based on your use cases.
Good architectures are centered on use cases so that architects can safely describe the structures that support those use cases without committing to frameworks, tools, and environments.
A good software architecture allows decisions about frameworks, databases, web servers, and other environmental issues and tools to be deferred and delayed. Frameworks are options to be left open. A good architecture makes it unnecessary to decide on Rails, or Spring, or Hibernate, or Tomcat, or MySQL, until much later in the project.
Frameworks are tools, not ways of life.
Your architecture should tell readers about the system, not about the frameworks you used in your system.
The overriding rule that makes this architecture work is the Dependency Rule:
Source code dependencies must point only inward, toward higher-level policies.
Tests are not outside the system; rather, they are parts of the system that must be well designed if they are to provide the desired benefits of stability and regression. Tests that are not designed as part of the system tend to be fragile and difficult to maintain. Such tests often wind up on the maintenance room floor—discarded because they are too difficult to maintain.
The organizational structure of data, the data model, is architecturally significant.
The data is significant. The database is a detail.
The text was updated successfully, but these errors were encountered:
Clean Architecture
One of the goals of this book is to cut through all that confusion and to define, once and for all, what design and architecture are. For starters, I’ll assert that there is no difference between them. None at all.
The word “architecture” is often used in the context of something at a high level that is divorced from the lower-level details, whereas “design” more often seems to imply structures and decisions at a lower level.
The goal of software architecture is to minimize the human resources required to build and maintain the required system.
The measure of design quality is simply the measure of the effort required to meet the needs of the customer. If that effort is low, and stays low throughout the lifetime of the system, the design is good. If that effort grows with each new release, the design is bad. It’s as simple as that.
Every software system provides two different values to the stakeholders: behavior and structure.
If you give me a program that works perfectly but is impossible to change, then it won’t work when the requirements change, and I won’t be able to make it work. Therefore the program will become useless.
If you give me a program that does not work but is easy to change, then I can make it work, and keep it working as requirements change. Therefore the program will remain continually useful.
Notice the pattern that I’ve quite deliberately set up in introducing these three programming paradigms: Each of the paradigms removes capabilities from the programmer. None of them adds new capabilities. Each imposes some kind of extra discipline that is negative in its intent. The paradigms tell us what not to do, more than they tell us what to do.
Another way to look at this issue is to recognize that each paradigm takes something away from us. The three paradigms together remove goto statements, function pointers, and assignment. Is there anything left to take away?
The problem that Dijkstra recognized, early on, was that programming is hard, and that programmers don’t do it very well. A program of any complexity contains too many details for a human brain to manage without help. Overlooking just one small detail results in programs that may seem to work, but fail in surprising ways.
Dijkstra’s solution was to apply the mathematical discipline of proof. His vision was the construction of a Euclidian hierarchy of postulates, theorems, corollaries, and lemmas. Dijkstra thought that programmers could use that hierarchy the way mathematicians do. In other words, programmers would use proven structures, and tie them together with code that they would then prove correct themselves.
But the proofs never came. The Euclidean hierarchy of theorems was never built. And programmers at large never saw the benefits of working through the laborious process of formally proving each and every little function correct. In the end, Dijkstra’s dream faded and died. Few of today’s programmers believe that formal proofs are an appropriate way to produce high-quality software.
Of course, formal, Euclidian style, mathematical proofs are not the only strategy for proving something correct. Another highly successful strategy is the scientific method.
Science is fundamentally different from mathematics, in that scientific theories and laws cannot be proven correct.
That is the nature of scientific theories and laws: They are falsifiable but not provable.
Science does not work by proving statements true, but rather by proving statements false.
Dijkstra once said, “Testing shows the presence, not the absence, of bugs.” In other words, a program can be proven incorrect by a test, but it cannot be proven correct. All that tests can do, after sufficient testing effort, is allow us to deem a program to be correct enough for our purposes.
The implications of this fact are stunning. Software development is not a mathematical endeavor, even though it seems to manipulate mathematical constructs. Rather, software is like a science. We show correctness by failing to prove incorrectness, despite our best efforts.
Software architects strive to define modules, components, and services that are easily falsifiable (testable). To do so, they employ restrictive disciplines similar to structured programming, albeit at a much higher level.
The fact that OO languages provide safe and convenient polymorphism means that any source code dependency, no matter where it is, can be inverted.
In short, when the source code in a component changes, only that component needs to be redeployed. This is independent deployability.
If the modules in your system can be deployed independently, then they can be developed independently by different teams. That’s independent developability.
What is OO? There are many opinions and many answers to this question. To the software architect, however, the answer is clear: OO is the ability, through the use of polymorphism, to gain absolute control over every source code dependency in the system. It allows the architect to create a plugin architecture, in which modules that contain high-level policies are independent of modules that contain low-level details. The low-level details are relegated to plugin modules that can be deployed and developed independently from the modules that contain high-level policies.
Why would an architect be concerned with the mutability of variables? The answer is absurdly simple: All race conditions, deadlock conditions, and concurrent update problems are due to mutable variables.
Architects would be wise to push as much processing as possible into the immutable components, and to drive as much code as possible out of those components that must allow mutation.
To summarize:
• Structured programming is discipline imposed upon direct transfer of control.
• Object-oriented programming is discipline imposed upon indirect transfer of control.
• Functional programming is discipline imposed upon variable assignment.
What we have learned over the last half-century is what not to do.
Good software systems begin with clean code.
The goal of the principles is the creation of mid-level software structures that:
• Tolerate change,
• Are easy to understand, and
• Are the basis of components that can be used in many software systems.
• SRP: The Single Responsibility Principle
An active corollary to Conway’s law: The best structure for a software system is heavily influenced by the social structure of the organization that uses it so that each software module has one, and only one, reason to change.
• OCP: The Open-Closed Principle
Bertrand Meyer made this principle famous in the 1980s. The gist is that for software systems to be easy to change, they must be designed to allow the behavior of those systems to be changed by adding new code, rather than changing existing code.
• LSP: The Liskov Substitution Principle
Barbara Liskov’s famous definition of subtypes, from 1988. In short, this principle says that to build software systems from interchangeable parts, those parts must adhere to a contract that allows those parts to be substituted one for another.
• ISP: The Interface Segregation Principle
This principle advises software designers to avoid depending on things that they don’t use.
• DIP: The Dependency Inversion Principle
The code that implements high-level policy should not depend on the code that implements low-level details. Rather, details should depend on policies.
Components are the units of deployment. They are the smallest entities that can be deployed as part of a system. In Java, they are jar files. In Ruby, they are gem files. In .Net, they are DLLs. In compiled languages, they are aggregations of binary files. In interpreted languages, they are aggregations of source files. In all languages, they are the granule of deployment.
• REP: The Reuse/Release Equivalence Principle
The granule of reuse is the granule of release.
This is not simply because, without release numbers, there would be no way to ensure that all the reused components are compatible with each other.
• CCP: The Common Closure Principle
Gather into components those classes that change for the same reasons and at the same times. Separate into different components those classes that change at different times and for different reasons.
This is the Single Responsibility Principle restated for components. Just as the SRP says that a class should not contain multiples reasons to change, so the Common Closure Principle (CCP) says that a component should not have multiple reasons to change.
The SRP tells us to separate methods into different classes, if they change for different reasons. The CCP tells us to separate classes into different components, if they change for different reasons.
• CRP: The Common Reuse Principle
Don’t force users of a component to depend on things they don’t need.
The ISP advises us not to depend on classes that have methods we don’t use. The CRP advises us not to depend on components that have classes we don’t use.
Don’t depend on things you don’t need.
Allow no cycles in the component dependency graph.
The component structure cannot be designed from the top down. It is not one of the first things about the system that is designed, but rather evolves as the system grows and changes.
Depend in the direction of stability.
One sure way to make a software component difficult to change, is to make lots of other software components depend on it. A component with lots of incoming dependencies is very stable because it requires a great deal of work to reconcile any changes with all the dependent components.
• Fan-in: Incoming dependencies. This metric identifies the number of classes outside this component that depend on classes within the component.
• Fan-out: Outgoing depenencies. This metric identifies the number of classes inside this component that depend on classes outside the component.
• I: Instability: I = Fan-out , (Fan-in + Fan-out). This metric has the range [0, 1]. I = 0 indicates a maximally stable component. I = 1 indicates a maximally unstable component.
A component should be as abstract as it is stable.
Thus, if a component is to be stable, it should consist of interfaces and abstract classes so that it can be extended.
Thus dependencies run in the direction of abstraction.
And the longer you wait to make those decisions, the more information you have with which to make them properly.
A good architect maximizes the number of decisions not made.
Good architects design the policy so that decisions about the details can be delayed and deferred for as long as possible.
Conway’s law says:
Any organization that designs a system will produce a design whose structure is a copy of the organization’s communication structure.
Architects often fall into a trap—a trap that hinges on their fear of duplication.
Duplication is generally a bad thing in software. We don’t like duplicated code. When code is truly duplicated, we are honor-bound as professionals to reduce and eliminate it.
But there are different kinds of duplication. There is true duplication, in which every change to one instance necessitates the same change to every duplicate of that instance. Then there is false or accidental duplication. If two apparently duplicated sections of code evolve along different paths—if they change at different rates, and for different reasons—then they are not true duplicates. Return to them in a few years, and you’ll find that they are very different from each other.
Software architecture is the art of drawing lines that I call boundaries. Those boundaries separate software elements from one another, and restrict those on one side from knowing about those on the other.
Communications between components in a monolith are very fast and inexpensive. They are typically just function calls. Consequently, communications across source-level decoupled boundaries can be very chatty.
Software systems are statements of policy. Indeed, at its core, that’s all a computer program actually is. A computer program is a detailed description of the policy by which inputs are transformed into outputs.
A strict definition of “level” is “the distance from the inputs and outputs.” The farther a policy is from both the inputs and the outputs of the system, the higher its level.
Strictly speaking, business rules are rules or procedures that make or save the business money. Very strictly speaking, these rules would make or save the business money, irrespective of whether they were implemented on a computer. They would make or save money even if they were executed manually.
Business rules are the reason a software system exists.
Architectures are not (or should not be) about frameworks. Architectures should not be supplied by frameworks. Frameworks are tools to be used, not architectures to be conformed to. If your architecture is based on frameworks, then it cannot be based on your use cases.
Good architectures are centered on use cases so that architects can safely describe the structures that support those use cases without committing to frameworks, tools, and environments.
A good software architecture allows decisions about frameworks, databases, web servers, and other environmental issues and tools to be deferred and delayed. Frameworks are options to be left open. A good architecture makes it unnecessary to decide on Rails, or Spring, or Hibernate, or Tomcat, or MySQL, until much later in the project.
Frameworks are tools, not ways of life.
Your architecture should tell readers about the system, not about the frameworks you used in your system.
The overriding rule that makes this architecture work is the Dependency Rule:
Source code dependencies must point only inward, toward higher-level policies.
Tests are not outside the system; rather, they are parts of the system that must be well designed if they are to provide the desired benefits of stability and regression. Tests that are not designed as part of the system tend to be fragile and difficult to maintain. Such tests often wind up on the maintenance room floor—discarded because they are too difficult to maintain.
The organizational structure of data, the data model, is architecturally significant.
The data is significant. The database is a detail.
The text was updated successfully, but these errors were encountered: