The Developer Day | Staying Curious

CAT | Patterns

Jul/10

5

What is CQRS?

CQRS is a software architecture pattern which stands for Command Query Responsibility Segregation. The author of the pattern name CQRS is Greg Young who first described it in his blog:

I am going to throw out a quick pattern name for this and call it Command and Query Responsibility Segregation or CQRS as we are in fact simply taking what would usually be one object and splitting its responsibilities into two objects.

At the time of writing CQRS does not have an official definition. It’s difficult to define CQRS with a definition that would be both simple and useful. To describe CQRS at an object level I’ve came up with a definition which is just a reworded sentence from Greg Young’s blog post:

Command Query Responsibility Segregation or CQRS is the creation of two objects where there was previously one. The separation occurs based upon whether the methods are a command or a query.

CQRS can also be defined at a higher level. Greg Young was kind to provide a definition:

Command Query Responsibility Segregation or CQRS is the recognition that there are differing architectural properties when looking at the paths for reads and writes of a system. CQRS allows the specialization of the paths to better provide an optimal solution.

CQRS pattern is similar to CQS by Meyer but is also different. CQS separates command methods that change state from query methods that read state. CQRS goes further and separates the command methods that change state and query methods that read into two different objects.

Benefits of CQRS

  • The most simple benefit of CQRS is that it simplifies read and write models by separating them. Write model no longer contains queries and developers can directly focus on domain model behaviours. What otherwise could have been a repository with hundreds of different read methods mixed with different lazy loading, pre-fetch and paging strategies can now be hidden away in a separate read model.
  • Another reason is Divergent Change. Divergent change occurs when one class is commonly changed in different ways for different reasons. You might be modifying queries more often than commands which might not only break your read queries but your commands as well. By having them separated you minimise the risks of both being broken.
  • The single most important benefit of CQRS is that by separating read and write models you can make different choices on different models. For example you may optimize your write model for write performance and your read system for read performance.
  • Another nice feature of CQRS is the available option to easily distribute work across separate teams. For example the read part of a web e-shop application can be outsourced to less expensive developers offshore.
  • Event sourcing is a different pattern which shares a strong symbiotic relation with CQRS. Once your system reaches an architectural level where you may need multiple data models it might and probably will introduce synchronization issues. It is then impossible to say which model is incorrect. In an event centric system where commands are translated into events by the domain model these events can be used as the primary data model. This not only solves data synchronization issues, but also significantly improves testing by allowing to test for “what didn’t happen” and opens easy doors for integration with other systems since other systems can now listen to the events published by the domain model.
  • Eventual Consistency. In very simple terms Eventual Consistency can be defined as simply just caching. In event centric systems it is possible to delay the handling of published domain model events and handle them in a different thread or a process. This will make write and read data models inconsistent but it might significantly improve the performance of your commands.

In Conclusion

CQRS is a very interesting pattern. By some it may even be considered to be the silver bullet. It isn’t. Like all patterns CQRS has tradeoffs. It may be difficult to sell CQRS to management since it’s not a well known classic approach to software architecture. Less known tools, technologies. As an example in the PHP world there are currently no mature service buses such as NServiceBus in the .NET world. It is almost impossible or more often than not worth the Return of Investment to migrate legacy apps to CQRS.

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Jun/10

22

DDD Resources / Papers / Presentations

Jun/10

21

What is DDD or Domain Driven Design?

Domain Driven Design can be described as a philosophy based on domain modelling. More accurately it may be be described as a very large body of patterns and pattern language in its own right. The term Domain Driven Design or DDD was coined by Eric Evans the author of the book Domain-Driven Design: Tackling Complexity in the Heart of Software also known in the DDD community as the “blue book”.

Understanding the DDD philosophy

The Domain Driven Design philosophy states:

  • Most software projects should focus on business domain
  • Complex domain designs should be based on a model

To understand the meaning of these statements one has to understand the meaning of domain and model.

Domain is a sphere of knowledge, influence or activity. The subject area to which the user applies a program is the domain of the software. In other terms if you work for a bank then banking is your domain.

Model is a system of abstractions that describes selected aspects of a domain and can be used to solve problems related to that domain. For example a map is a model designed to solve a specific problem. A treasure map shows how to find a treasure, a political map shows the borders of countries. A model is a simplification. It is an interpretation of reality that focuses on the problem at hand and ignores the extraneous detail.

Models are designed to be useful to solve domain specific problems. For example in the past the universe was viewed in a geocentric way where the universe revolves around Earth. Heliocentric model is another astronomical model in which the Earth and planets revolve around a stationary Sun at the centre of the universe. Even though geocentric model is not realistic it is a valid model in it’s own right designed to solve a problem – the human desire to be in the centre of everything. It’s not a useful model when it’s used to compare planet movements.

Domain Driven Design advocates designing software systems to reflect the domain model in a very literal way, so that the mapping is obvious, also revising the model continuously and modifying it to be implemented more naturally in software. To tie the implementation to a model well requires tools and languages that support a modelling paradigm, such as object-oriented programming.

A well mapped implementation of a model usually expresses an object model that incorporates both behaviour and data. A decomposed domain model consists of common building blocks: entities, aggregates, value objects, services and factories.

Essential Principles of DDD

The greatest value of a domain model is that it provides a ubiquitous language that ties domain experts and technologists together. Ubiquitous language is a language structured around the domain model and used by all team members to connect all activities of the team with the software. It’s a shared, versatile language between team members and domain experts. A well designed model speaks to the developers through the ubiquitous language. It’s important to understand that a change in the model is a change in the language and vice versa.

When multiple models are in play on a large project it’s beneficial to define bounded contexts where these models apply. A bounded context is a linguistic boundary marking the applicability of distinct models. Usually a subsystem or work owned by another team. For example in a typical e-shop web application a sales reporting application could be defined as a separate bounded context.

Every domain consists of subdomains. For example a very common subdomain is billing. Such a subdomain is usually not the driving part of the domain and therefore not as important. It is harsh reality that not all parts of the design are going to be equally refined therefore priorities must be set. DDD suggests distilling the core domain by distinguishing it from other generic subdomains and applying the top talent to work on it.

Conclusion

DDD helps projects to develop a strong internal language, define clear context boundaries, and focus on the core domain. Domain Driven Design brings structure and cohesion into domain modelling which are much appreciated features of any software project in existence. The blue book has been released six years ago and since then it influenced many developers. Yet I feel it hasn’t reached it’s momentum. One can only hope it will reach widespread adoption.

Update: I’ve added a list of available DDD resources such as papers and video presentations.

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Apr/10

15

Skinny Controllers and Fat Models

Most of the modern web application frameworks follow the MVC design pattern. It’s probably one of the most misunderstood design patterns in existence. There are a lot of discussions what kind of responsibilities each letter holds. Common misinterpretation in MVC is regarding the letter M.

The Model should be understood as a domain model. Meaning a collection of domain objects. Usually an application has one model that is the domain model. Models are often mistakenly referenced to as singular domain entities. For example an Order, a User or an Account. This leads unwary developers to common application design problems.

It’s common to see a web application to have a directory named “models” with class files inside it. Upon closer inspection one can often find that those classes are the nouns of the application. For example those nouns could be a User, an Order or a Product. In this scenario the MVC Model stands for singular application entities.

Problems start to surface when an application developer has to create reports, do input validation or to implement an ACL. These kind of problems don’t naturally fit into entities. For example getting a report of top 10 products doesn’t naturally fit into any entity. Validating a complex search filter made out of multiple input fields also doesn’t fit into any of the entities.

It’s common to see developers adding logic that doesn’t fit anywhere naturally to controller classes or somewhat close entities. For example adding a top 10 products report to an Order entity class as a static method call. Or validating complicated search filters inside controller actions.

In time this steadily leads to bloated controller and entity classes that later on become fat spaghetti dishes. Controller classes containing thousands of lines of code with more private methods than public ones, entity classes with few state changing methods and hundreds of lines long SQL report queries with joins to 10 tables.

To prevent this from happening it is crucial to understand what controller and model stands for. A controller’s responsibility is only to receive input and initiate a response for the view by making calls on model objects. This means that controllers should be very light and skinny. Usually doing nothing else just instantiating classes, getting data from the domain objects and passing it to the view. Model is not a singular entity and can consist of an entire web of interconnected domain objects. The definition fat model means having as many domain objects as needed. Be it reports, validators, filters, entities, strategies and so on.

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Mar/10

6

PHP Anti Patterns

Another talk I’ve atended at PHPUK 2010 was AntiPHPatterns by Stefan Priebsch. While design patterns are core implementation independent solutions to problems that occur over and over again which also serve as a great vocabulary, anti patterns are software patterns that are ineffective or counterproductive. In his presentation Stefan describes some of these anti patterns:

1. Constantitis. Excessive use of global constants is considered to be a code smell. Global constants can be defined anywhere in the code base, there is a risk of name clashes if a constant is already defined, global constants make the code more coupled, testing gets more complicated since constants have to be known beforehand and defined explicitly which might be even more troubling if a constant has to change it’s value for another test. Since class constants are not global it’s OK to use them. Cure for constantitis is not to use global constants and instead use dependency injection.

2. Globalomania. Global variables share the same problems as global constants. Because global variables can be changed it makes them more dangerous than global constants since a change in one part of the codebase can affect the other without anyone noticing. Global variables can be cured by using dependency injection.

3. Singletonitis. Singleton is one of the most popular design patterns. It’s wide success is due to the fact that singletons by implementation are available globally in the entire application. The problem that singleton design pattern tries to solve is to prevent having multiple instances of the same class. This is rarely the problem in most applications and most singletons are being used as global variables instead. Singletons share the same problems as global constants and global variables and therefore should be avoided. Singletonitis has the same cure as constantitis and globalomania.

4. God classes. According to object oriented best practices classes should do one thing only and do it well. Classes should be refined and granular. One of the ways to think about this is to ask yourself what are the responsibilities of this class. In an ideal case you will be able to describe it in one sentence without any “and’s”. When classes start having too many  responsibilities they become god classes. Usually the whole application relies on one of the god classes which makes the application tight coupled and therefore more difficult to maintain. To cure god classes minimize class responsibilities so that objects know everything about themselves and little about others.

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I went to see Udi Dahan speaking on Command and Query Responsibility Segregation. The open talk was organised by Skills Matter who support the Open Source community by organizing free events and courses.

Udi Dahan is a world renowned software architect and speaker. Udi is a specialist in SOA and distributed systems. It is a pleasure to listen to Udi speak because of his ability to present ideas clearly and keep others wanting to see what the next slide holds.

Command Query Responsibility Segregation or CQRS is software architecture pattern. The pattern is about separating (Segregating) the read side (Queries) and write side (Commands) of the system.

A simple example would be an application that does event reservations. All read commands would be executed against a reporting database which is stale and can be de-normalized. All calculations needed to display the data have been done before populating the reporting database. All write commands like making an actual reservation or adding a new event is executed against a Domain Model with it’s own database. Domain Model is never used to execute reporting queries. Reporting databases are generated from the write side database after all needed calculations have been performed.

This architectural pattern simplifies the Business Domain Model making it behavior only making it easier to maintain. Another strong advantage of CQRS is system performance. CQRS approach accepts that reporting data can be stale. This enables easier development of highly scalable distributed systems.

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Feb/10

18

Domain Model Logic Patterns

A great thing about patterns is that it provides a vocabulary. It allows developers to communicate efficiently. Complex ideas can be described at a high technical level without going into small details. Patterns are like butterflies and come in many colors, sizes and shapes and there’s.. lots of them. As butterflies patterns are grouped into families. One of such families is Domain Logic Patterns described in Martin Fowler’s book Patterns of Enterprise Application Architecture.

It’s one of the fundamental families because virtually every application written is described by any of the three patterns that belong to it. To say it simply there are three major ways how an application can be designed. What is more interesting that each of those patterns can be sorted by the amount of effort required to change the application depending on it’s complexity. Deciding which pattern to choose can be tricky because these patterns depend on various characteristics of the project being developed. The size, the complexity, the lifetime of a project and most importantly the skills of the developers.

Transaction Script

Transaction script organizes business logic by routines where each routine handles a single request from the presentation layer. It is the most common way how software beginners write applications. Required steps to perform a task are identified and expressed using language semantics. Common routines can be broken into subroutines. Transaction Scripts can be organised as classes or as global functions. A single file can contain many Transaction Scripts at once. A lot of developers may think they are proficient OOP users while in reality they write data centric Transactions Scripts wrapping them into classes which could be just as easily replaced with simple procedures.

However Transaction scripts have benefits. It’s a simple procedural model so most developers can understand it, it works well with a simple data access layer using Row Data Gateway or Table Data Gateway.

An example transaction script may be booking a hotel room where a single procedure is responsible for checking the availability of the room, calculating the price and updating the database.

class Hotel
{  
	public function __construct(Data_Access_Gateway $gateway)  
	{  
		$this->_gateway = $gateway;  
	}  

	public function bookRoom($userId, $fromDate, $toDate)  
	{  
		$roomId = $this->_gateway->_getRoomIdBetweenDates($dateFrom, $dateTo);  

		if (!$roomId) {  
			return false;  
		}  

		$days = $this->_getAmountOfDays($fromDate, $toDate);  

		if ($days < = 7) {
			$price = $days * 100;
		} else { 
			$price = $days * 80;
		}
		
		$data = array(
			'userId' => $userId,  
			'roomId' => $roomId,
			'fromDate' => $fromDate,  
			'toDate' => $toDate,  
			'price' => $price,  
		);  

		$bookingId = $this->_gateway->insert('bookings', $data);  

		return $bookingId;  
	}  
}

Table Module

A Table Module is many ways the middle man between a Transaction Script and a Domain Model. A Table Module tends to organize domain logic with one class per table in the database. While a Transaction Script will have one class Hotel to do reservations a Table Module may have more fine grained classes like Hotel, Booking, Room. Even though these classes may be singular they do not actually represent individual entities and manages whole collections of entities. For example a Booking class would be responsible for mosts actions performed on the bookings database table. Like a Transaction Script a Table Module is a very data centric model approach and usually has a strong connection with the data access layer.

Table Module has a few benefits over a Transaction Script. There’s less duplication, domain logic is more organized and structured around domain entities. It works well with simple data access layers. However, Table Module doesn’t give you full power of objects in organizing complex domain logic. It lacks instance to instance relationships, has weak support for polymorphism.

To show an example of Table Module let’s re-factor the Transaction Script:

class Hotel
{  
	public function __construct(Data_Access_Gateway $gateway, Booking $booking)  
	{  
		$this->_gateway = $gateway;
		$this->_booking = $booking;
	}  

	public function bookRoom($userId, $fromDate, $toDate)  
	{  
		$roomId = $this->_booking->getRoomBetweenDates($fromDate, $toDate);

		if (!$roomId) {  
			return false;  
		}  

		$days = $this->_getAmountOfDays($fromDate, $toDate);  

		if ($days < = 7) {
			$price = $days * 100;
		} else { 
			$price = $days * 80;
		}
		
		$bookingId = $this->_booking->addBooking($userId, $roomId, $fromDate, $toDate, $price);  

		return $bookingId;  
	}  
}

class Booking
{  
	public function __construct(Data_Access_Gateway $gateway)  
	{  
		$this->_gateway = $gateway;  
	}
	
	public function getRoomBetweenDates($dateFrom, $dateTo)
	{
		return $this->_gateway->getRoomBetweenDates($dateFrom, $dateTo);
	}
	
	public function addBooking($userId, $roomId, $fromDate, $toDate, $price)  
	{  
		$data = array(
			'userId' => $userId,  
			'roomId' => $roomId,
			'fromDate' => $fromDate,  
			'toDate' => $toDate,  
			'price' => $price,  
		);  

		$bookingId = $this->_gateway->insert('bookings', $data);  

		return $bookingId;  
	}  
}

Domain Model

Domain Model organizes domain logic into well defined domain entities that contain both behavior and data. Usually each entity class represents a single entity in the data layer. Domain Model creates a hierarchy of inter connected objects that interact with each other by triggering behaviors on each other. Domain Model shines over Transaction Script and Table Module when it comes to handling domain complexity. It enables the full power of object oriented programming and all available design patterns. Even though Domain Model may sound like a silver bullet to all software development problems it is not. Learning to design applications using Domain Model requires a “paradigm shift” to start looking for entities and behaviors instead of looking for routines. The main downside of Domain Model is the way it works with relational databases. To compare I would like to quote Martin Fowler:

In many ways Domain Model treats the relational database like a crazy aunt who’s shut up in an attic and whom nobody wants to talk about.

Domain Model tries to be data layer agnostic. This is what object relational mappers also known as ORM’s as Hibernate try to solve by hiding the data access layer away.

To show an awkwardly simple example of Domain Model let’s re-factor the Table Module approach. To keep the example simple I’ll ignore the data access layer.

class Hotel
{  
	protected $_hotelId;
	protected $_rooms;
	
	public function bookRoom(User $user, $fromDate, $toDate)  
	{  
		$room = $this->_getRoomBetweenDates($fromDate, $toDate);

		if (is_null($room)) {  
			return false;  
		}  

		$booking = $room->bookRoom(User $user, $fromDate, $toDate);

		return $booking;  
	}
}

class Room
{
	protected $_roomId;
	protected $_bookings = array();
	
	public function bookRoom(User $user, $fromDate, $toDate)
	{
		$days = $this->_getAmountOfDays($fromDate, $toDate);
		
		if ($days < = 7) {
			$booking = new Booking($user, new ShortBookingStrategy($user, $days));
		} else { 
			$booking = new Booking($user, new NormalBookingStrategy($user, $days));
		}
		
		return $booking;
	}
}

class NormalBookingPriceStrategy extends BookingPriceStrategy
{
	public function getPrice()
	{
		$price = $this->_days * 80;
		
		if ($this->_user->isLoyal()) {
			$price = $price / 2;
		}
		
		return $price;
	}
}

class ShortBookingPriceStrategy extends BookingPriceStrategy
{  
	public function getPrice()
	{
		return $this->_days * 100;
	}
}

As you can see there is an instant explosion of classes and the data access layer is out of the picture. Responsibility to efficiently load and keep track of loaded entities is left to object mappers like Hibernate.

The End

Everyone of these domain model patterns have their benefits and downsides. It takes experience to be able to decide which approach to take. For those who are new to Domain Model it is advised to have an experienced team member who is able to guide you through it. While Transaction Script or Table Module can seem like a good solution at some point it may not be as effective later on. It is important to recognize when a different domain model approach might work better. It’s been a long ride for me. Through the years I’ve learned a few things about designing software but I still don’t feel very comfortable writing about it. I would love to hear feedback from you how this article and these examples could be improved to enable better understanding between the different modeling approaches.

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