Isolating the Domain Logic

In one design patterns class, I had an interesting discussion about modelling domain logic. Specifically, it was about isolating the domain logic. An application would typically be divided into three parts:

1. Presentation (e.g. desktop GUI, browser, web service)
2. Domain logic
3. Infrastructure (e.g. persistence storage, e-mail)

The class found it interesting that the dependency arrows were pointing towards the domain logic part. They asked, “Is the diagram intentionally made wrong? Shouldn’t the domain logic part be dependent on the persistence storage?” It was a great question. And I wanted to share and post the discussion and explanation here.

Often Misunderstood

Most developers would usually have this misunderstanding in mind.

Misunderstood

vs.

Proper

And this misunderstanding is largely due to the sequence of operations. It usually starts with a trigger (e.g. a user clicking a button or a link) in the presentation layer, which then calls something within the domain logic layer, which then calls something within the infrastructure layer (e.g. update a database table record).

While this is the correct sequence of operations, there’s something subtle in the way in which the domain logic layer can be implemented. This has something to do with dependency inversion.

Dependency Inversion Principle

The domain logic layer may need something from the infrastructure layer, like some form of access to retrieve from persistence storage. The usual patterns for this are: DAO and repository. I won’t explain these two patterns here. Instead, I would point out that the interface definitions are placed within the domain logic layer, and their implementations are placed in another separate layer.

Placing the (DAO and repository) interface definitions inside the domain logic layer means that it is the domain logic layer that defines it. It is the one that dictates which methods are needed, and what return types are expected. This also marks the boundaries of the domain logic.

This separation between interface and implementation may be subtle, but key. Placing just the interface definitions allows the domain logic part to be free from infrastructure details, and allows it to be unit-tested without actual implementations. The interfaces can have mock implementations during unit testing. This subtle difference makes a big difference in rapid verification of (the development team’s understanding of) business rules.

This separation is the classic dependency inversion principle in action. Domain logic (higher-level modules) should not depend on DAO and repository implementations (low-level modules). Both should depend on abstractions. The domain logic defines the abstractions, and infrastructure implementations depend on these abstractions.

Most novice teams I’ve seen, place the DAO and repository interfaces together with their infrastructure-specific implementations. For example, say we have an StudentRepository and its JPA-specific implementation StudentJpaRepository. I would usually find novice teams placing them in the same package. While this is fine, since the application will still compile successfully. But the separation is gone, and domain logic is no longer isolated.

Now that I’ve explained why and how the domain logic part does not depend on the infrastructure part, I’d like to touch on how the presentation part is accidentally entangled with the domain logic.

Separated Presentation

Another thing I often see with novice teams is how they end up entangling their domain logic with their presentation. And this results into this nasty cyclic dependency. This cyclic dependency is more logical than physical. Which makes it all the more difficult to detect and prevent.

I won’t use a rich GUI presentation example here, since Martin Fowler has already written a great piece on it. Instead, I’ll use a web-browser-based presentation as an example.

Most web-based systems would use a web framework for its presentation. These frameworks usually implement some form of MVC (model-view-controller). The model used is usually the model straight from the domain logic part. Unfortunately, most MVC frameworks require something about the model. In the Java world, most MVC frameworks require that the model follow JavaBean conventions. Specifically, it requires the model to have a public zero-arguments constructor, and getters and setters. The zero-arguments constructor and setters are used to automatically bind parameters (from HTTP POST) to the model. The getters are used in rendering the model in a view.

Because of this implied requirement by MVC frameworks used in the presentation, developers would add a public zero-arguments constructor, getter and setters, to all their domain entities. And they would justify this as being required. Unfortunately, this gets the in the way of implementing domain logic. It gets entangled with the presentation. And worse, I’ve seen domain entities being polluted with code that emits HTML-encoded strings (e.g. HTML code with less-than and greater-than signs encoded) and XML, just because of presentation.

If it is all right to have your domain entity implemented as a JavaBean, then it would be fine to have it used directly in your presentation. But if the domain logic gets a bit more complicated, and requires the domain entity to lose its JavaBean-ness (e.g. no more public zero-arguments constructor, no more setters), then it would be advisable for the domain logic part to implement domain logic, and have the presentation part adapt by creating another JavaBean object to satisfy its MVC needs.

An example I use often is a UserAccount that is used to authenticate a user. In most cases, when a user wishes to change the password, the old password is also needed. This helps prevent unauthorized changing of the password. This is clearly shown in the code below.

public class UserAccount {
  ...
  public void changePassword(
      String oldPassword, String newPassword) {…}
}

But this does not follow JavaBean conventions. And if the MVC presentation framework would not work well with the changePassword method, a naive approach would be to remove the erring method and add a setPassword method (shown below). This weakens the isolation of the domain logic, and causes the rest of the team to implement it all over the place.

public class UserAccount {
  ...
  public void setPassword(String password) {…}
}

It’s important for developers to understand that the presentation depends on the domain logic. And not the other way around. If the presentation has needs (e.g. JavaBean convention), then it should not have the domain logic comply with that. Instead, the presentation should create additional classes (e.g. JavaBeans) that have knowledge of the corresponding domain entities. But unfortunately, I still see a lot of teams forcing their domain entities to look like JavaBeans just because of presentation, or worse, having domain entities create JavaBeans (e.g. DTOs) for presentation purposes.

Arrangement Tips

Here’s a tip in arranging your application. Keep your domain entities and repositories in one package. Keep your repository and other infrastructure implementations in a separate package. Keep your presentation-related classes in its own package. Be mindful of which package depends on which package. The package that contains the domain logic is preferrably at the center of it all. Everything else depends on it.

When using Java, the packages would look something like this:

• com.acme.myapp.context1.domain.model
  • Keep your domain entities, value objects, and repositories (interface definitions only) here
• com.acme.myapp.context1.infrastructure.persistence.jpa
  • Place your JPA-based repository and other JPA persistence-related implementations here
• com.acme.myapp.context1.infrastructure.persistence.jdbc
  • Place your JDBC-based repository and other JDBC persistence-related implementations here
• com.acme.myapp.context1.presentation.web
  • Place your web/MVC presentation components here. If the domain entities needed for presentation do not comply with MVC framework requirements, create additional classes here. These additional classes will adapt the domain entities for presentation-purposes, and still keep the domain entities separated from presentation.

Note that I’ve used context1, since there could be several contexts (or sub-systems) in a given application (or system). I’ll discuss about having multiple contexts and having multiple models in a future post.

That’s all for now. I hope this short explanation can shed some light to those who wonder why their code is arranged and split in a certain way.

Thanks to Juno Aliento for helping me with the class during this interesting discussion.

Happy holidays!

Architectural Layers and Modeling Domain Logic

As I was discussing the PoEAA patterns used to model domain logic (i.e. transaction script, table module, domain model), I noticed that people get the impression (albeit wrong impression) that the domain model pattern is best. So, they set out to apply it on everything.

Not Worthy of Domain Model Pattern

Let's get real. The majority of sub-systems are CRUD-based. Only a certain portion of the system requires the domain model implementation pattern. Or, put it in another way, there are parts of the application that just needs forms over data, and some validation logic (e.g. required/mandatory fields, min/max values on numbers, min/max length on text). For these, the domain model is not worth the effort.

For these, perhaps an anemic domain model would fit nicely.

Anemic Domain Model Isn't As Bad As It Sounds

The anemic domain model isn't as bad as it sounds. There, I said it (at least here in my blog post).

But how does it look like?

package com.acme.bc.domain.model;
...
@Entity
class Person {
 @Id ... private Long id;
 private String firstName;
 private String lastName;
 // ...
 // getters and setters
}
...
interface PersonRepository /* extends CrudRepository<Person, Long> */ {
 // CRUD methods (e.g. find, find/pagination, update, delete)
}

package com.acme.bc.infrastructure.persistence;
...
class PersonRepositoryJpa implements PersonRepository {
 ...
}

In the presentation layer, the controllers can have access to the repository. The repository does its job of abstracting persistence details.

package com.acme.bc.interfaces.web;

@Controller
class PersonsController {
 private PersonRepository personRepository;
 public PersonsController(PersonRepository personRepository) {...}
 // ...
}

In this case, having the Person class exposed to the presentation layer is perfectly all right. The presentation layer can use it directly, since it has a public zero-arguments constructor, getters and setters, which are most likely needed by the view.

And there you have it. A simple CRUD-based application.

Do you still need a service layer? No. Do you still need DTO (data transfer objects)? No. In this simple case of CRUD, you don't need additional services or DTOs.

Yes, the Person looks like a domain entity. But it does not contain logic, and is simply used to transfer data. So, it's really just a DTO. But this is all right since it does the job of holding the data stored-to and retrieved-from persistence.

Now, if the business logic starts to get more complicated, some entities in the initially anemic domain model can become richer with behavior. And if so, those entities can merit a domain model pattern.

Alternative to Anemic Domain Model

As an alternative to the anemic domain model (discussed above), the classes can be moved out of the domain logic layer and in to the presentation layer. Instead of naming it PersonRepository, it is now named PersonDao.

package com.acme.bc.interfaces.web;

@Entity
class Person {...}

@Controller
class PersonsController {
 private PersonDao personDao;
 public PersonsController(PersonDao personDao) {...}
 // ...
}

interface PersonDao /* extends CrudRepository<Person, Long> */ {
 // CRUD methods (e.g. find, find/pagination, update, delete)
}

package com.acme.bc.infrastructure.persistence;

class PersonDaoJpa implements PersonDao {
 ...
}

Too Much Layering

I think that it would be an overkill if you have to go through a mandatory application service that does not add value.

package com.acme.bc.interfaces.web;
...
@Controller
class PersonsController {
 private PersonService personService;
 public PersonsController(PersonService personService) {...}
 // ...
}

package com.acme.bc.application;
...
@Service
class PersonService {
 private PersonRepository personRepository;
 public PersonService(PersonRepository personRepository) {...}
 // expose repository CRUD methods and pass to repository
 // no value add
}

Keep the repository in the domain.model package. Place the repository implementations in another package (e.g. infrastructure.persistence). But why?

The domain.model package is where the repository is defined. The elements in the domain model dictate what methods are needed in the repository interface definition. Thus, the repository definition is placed in the domain.model package. The repository implementations need to follow what new methods are added (or remove unused ones). This packaging follows the dependency inversion principle. The infrastructure.persistence package depends on the domain.model package, and not the other way around.

Application Services for Transactions

So, when would application services be appropriate? The application services are responsible for driving workflow and coordinating transaction management (e.g. by use of the declarative transaction management support in Spring).

If you find the simple CRUD application needing to start transactions in the presentation-layer controller, then it might be a good sign to move them into an application service. This usually happens when the controller needs to update more than one entity that does not have a single root. The usual example here is transferring amounts between bank accounts. A transaction is needed to ensure that debit and credit both succeed, or both fail.

package sample.domain.model;
...
@Entity
class Account {...}
...
interface AccountRepository {...}

package sample.interfaces.web;
...
@Controller
class AccountsController {
 private AccountRepository accountRepository;
 ...
 @Transactional
 public ... transfer(...) {...}
}

If you see this, then it might be a good idea to move this (from the presentation layer) to an application-layer service.

package sample.interfaces.web;
...
@Controller
class AccountsController {
 private AccountRepository accountRepository;
 private TransferService transferService;
 ...
 public ... transfer(...) {...}
}

package sample.application;
...
@Service
@Transactional
class TransferService {
 private AccountRepository accountRepository;
 ...
 public ... transfer(...) {...}
}

package sample.domain.model;
...
@Entity
class Account {...}
...
interface AccountRepository {...}

Domain Model Pattern (only) for Complex Logic

I'll use the double-entry accounting as an example. But I'm sure there are more complex logic that's better suited.

Let's say we model journal entries and accounts as domain entities. The account contains a balance (a monetary amount). But this amount is not something that one would simply set. A journal entry needs to be created. When the journal entry is posted, it will affect the specified accounts. The account will then update its balance.

package ….accounting.domain.model;
...
/** Immutable */
@Entity
class JournalEntry {
 // zero-sum items
 @ElementCollection
 private Collection<JournalEntryItem> items;
 ...
}
...
/** A value object */
@Embeddable
class JournalEntryItem {...}
...
interface JournalEntryRepository {...}
...
@Entity
class Account {...}
...
interface AccountRepository {...}
...
@Entity
class AccountTransaction {...}
...
interface AccountTransactionRepository {...}

Now, in this case, a naive implementation would have a presentation-layer controller create a journal entry object, and use a repository to save it. And at some point in time (or if auto-posting is used), the corresponding account transactions are created, with account balances updated. All this needs to be rolled into a transaction (i.e. all-or-nothing).

Again, this transaction is ideally moved to an application service.

package ….accounting.application;

@Service
@Transactional
class PostingService {...}

If there's a need to allow the user to browse through journal entries and account transactions, the presentation-layer controller can directly use the corresponding repositories. If the domain entities are not suitable for the view technology (e.g. it doesn't follow JavaBean naming conventions), then the presentation-layer can define DTOs that are suitable for the view. Be careful! Don't change the domain entity just to suit the needs of the presentation-layer.

package ….interfaces.web;

@Controller
class AccountsController {
 private AccountRepository accountRepository;
 private AccountTransactionRepository accountTransactionRepository;
 private PostingService postingService;
  ...
}

In Closing...

So, there you have it. Hopefully, this post can shed some light on when (and when not) to use domain model pattern.

Now I think I need a cold one.

Domain Logic Patterns (Martin Fowler's Revenue Recognition Problem)

In the training courses I've given, providing concrete examples (not just UML diagrams) help the course participants understand better. Here in this post, I'm sharing a Java-implementation of the revenue recognition problem (as described in Martin Fowler's PoEAA book). The problem is used to explain three domain logic patterns: transaction script, table module, and domain model.

Revenue Recognition Problem

Just in case you haven't read the book, here's an excerpt from the book that explains the revenue recognition problem, taken from Martin Fowler's PoEAA book (page 112).

Revenue recognition is a common problem in business systems. It's all about when you can actually count the money you receive on your books. If I sell you a cup of coffee, it's a simple matter: I give you the coffee, I take your money, and I count the money to the books that nanosecond. For many things it gets complicated, however. Say you pay me a retainer to be available that year. Even if you pay me some ridiculous fee today, I may not be able to put it on my books right away because the service is to be performed over the course of a year. One approach might be to count only one-twelfth (1/12) of that fee for each month in the year, since you might pull out of the contract after a month when you realize that writing has atrophied my programming skills.

The rules for revenue recognition are many, various, and volatile. Some are set by regulation, some by professional standards, and some by company policy. Revenue tracking ends up being quite a complex problem.

I don't fancy delving into the complexity right now, so instead we'll imagine a company that sells three (3) kinds of products: word processors, databases, and spreadsheets. According to the rules, when you sign a contract for a word processor, you can book all the revenue right away. If it's a spreadsheet, you can book one-third (1/3) today, one-third in sixty (60) days, and one-third in ninety (90) days. If it's a database, you can book one-third today, one-third in thirty (30) days, and one-third in sixty (60) days. There's no basis for these rules other than my own fevered imagination. I'm told that the real rules are equally rational.

Emphasis, formatting, and numbers added for clarity

Domain Logic Patterns Example (Java-implementation)

The working code is available at my GitHub account.

I used the same integration test (AbstractRevenueRecognitionServiceFacadeTests) and applied it against three different implementations: transaction script, table module, and domain model.

Here are the three (3) patterns:

• transaction script — organizes business logic by procedures where each procedure handles a single request from the presentation.
• domain model — an object model of the domain that incorporates both behavior and data.
• table module — a single instance that handles the business logic for all rows in a database table or view.

For transaction script and table module implementations, I used table data gateways.

For domain model, I used data mapper (via JPA/Hibernate). I tried to stick as close as possible to the database schema provided. In fact, all three patterns work with the same database schema.

The transaction script implementation can be found under the revenue.recognition.transaction.script package. The table module implementation is under the revenue.recognition.table.module package. The domain model is found under the following packages: revenue.recognition.interfaces.facade.internal, revenue.recognition.application.impl, and revenue.recognition.domain.model.

Please feel free to review the implementation, and let me know of any items I've missed.

Note that having three (3) different patterns in one application is unnecessary (and quite confusing). I've only done this to better compare the different approaches to organizing domain logic.

Effective Design Patterns and Poetry

In one of the training courses I've been giving on object-oriented design techniques, I was discussing two principles from the GoF book, Design Patterns: Elements of Reusable Object-Oriented Software :

• "Program to an 'interface', not an 'implementation'." (Gang of Four 1995:18)
• Composition over inheritance: "Favor 'object composition' over 'class inheritance'." (Gang of Four 1995:20)

And I was thinking how I can make those two phrases stick more. And a thought occurred to me that perhaps if we make those principles rhyme, it'll be easier for developers to remember, and become more effective.

Rhyme-as-Reason Effect

In one study ^[1], researchers compared the perceived truthfulness of rhyming vs. similar non-rhyming statements. For example:

"What sobriety conceals, alcohol reveals" vs "What sobriety conceals, alcohol unmasks"

Which one do you find is true (or more truthful)?

Here's another one.

"Caution and measure will win you treasure" vs "Caution and measure will win you riches"

The study suggested that "rhyme, like repetition, affords statements an enhancement in processing fluency that can be misattributed to heightened conviction about their truthfulness."

In other words, rhyming makes statements easier to understand. Which, in turn, makes them appear more accurate.

Object-Oriented Design Principles with Poetry

Here's my attempt at making those two principles rhyme.

Program to an interface, not to an implementation.

An implementation is good and fine, an interface is a wiser design.

Programming to an implementation is logical, programming to an interface is exceptional.

Programming to an implementation is what is needed, programming to an interface is why we succeeded.

Favor composition, over inheritance.

Inheritance if you must, in composition we trust.

Next time…

So the next time I get a chance to run another training course on object-oriented design techniques, I'll give these rhymes, some good times.

If you have rhymes of your own, please feel free to hit the comments.

^[1] McGlone, M.S., and Tofighbakhsh, J. (2000) Birds of a feather flock conjointly (?): rhyme as reason in aphorisms. Psychological Science, 11:424-28.

Domain Entities and Draft Forms

Recently, I've been reviewing a funds transfer system that uses the S.W.I.F.T. standard message types. After reviewing it, I find that splitting the domain entity from its draft form would make it easier to maintain. Let me explain further.

Background of Funds Transfer System

The system allows users to record funds transfer requests as S.W.I.F.T. messages. Note that the system users are familiar with the said message types, and are not afraid to enter the values into a form that displays all the possible fields. To put things in perspective, a typical message type like MT 103 (Single Customer Credit Transfer) has about 25 fields. Some fields can have several formats. For example, the Ordering Customer (field 50a), can be in one of three formats: option A, option K, and option F. If we include every possible format, MT 103 would easily have over 70 fields.

Modeling Entities

Based on Eric Evan's DDD book, domain entities have a clear identity and a life-cycle with state transitions that we care about.

The funds transfer system modeled each MT as a domain entity. The MT domain entity has a clear identity, and a life-cycle (from draft to approved). It is mapped to its own table with its own corresponding columns to represent its fields. The message fields are implemented as properties. All fields are mutable (via setters methods).

At this point, you might pause and think about anemic domain models. You're probably thinking that there is hardly any behavior on these MT objects. You're probably thinking that these MT objects are just bags of getters and setters. And you're probably correct. But there's a reason for this. Read on.

The system validates the MTs, and displays which fields have errors. But the errors did not stop the system from persisting/storing the message. The system allowed the draft message to be stored for further modification (until it is approved).

This analysis led me to re-think about how the system modeled messages as entities. While it is tempting to think of them as rich domain entities that represent a real-world funds/credit transfer, they're really not. They're actually request forms. Much like the real-world paper forms that one fills-out, and submits for approval, and possibly being re-submitted due to corrections.

Given this, I would model the draft message as another domain entity that I'll call MessageTypeForm (or MT103Form for MT 103). This domain entity will use a map of fields (still in compliance with S.W.I.F.T. messages, where each field is keyed by an alpha-numeric string) (e.g. get("50a")), and not use a separate property for each field (e.g. getOrderingCustomer()). By using a map of fields, the entity would require lesser code, and can still be persisted (or mapped to table rows).

The CreditTransfer (or SingleCustomerCreditTransfer) would be another entity (not the same as the entity with a map of fields — MT103Form). This credit transfer entity shall have a clear identity and a life-cycle (e.g. being amended or cancelled). It can have a reference (by ID, and not by type) to the MT103Form entity from which it was created. It is also easier to establish invariants on this entity, as opposed to having all properties being mutable.

The validation of MT103Form will have to change from using properties (e.g. getOrderingCustomer()) to using a get method with a key parameter (to determine the message field it wants to retrieve) (e.g. get("50a")).

Deeper Insight

Just because a domain entity is largely made up of getters and setters, does not mean it is anemic.

The "draft/form" entity may look like a bag of getters and setters. But it has an identity, and a life-cycle (from draft to approved). So, it is an entity. Many "draft/form" instances will exist in the system simultaneously. The different instances may even have the same field values, but it is important for us to be able to track individual "draft/form" instances.

Discovering the distinction between the "draft/form" and "fund/credit transfer" entities have made things easier to implement. Having two separate domain entities has made the model better (reflect the problem it is trying to solve).

This deeper insight would not have been possible without the help of the domain experts, and my team mates: Tin, Anson, Richie, and Tina. Thanks guys!

Maker-Checker Design Concept

We've seen the maker-checker concept pop-up several times in our software development experiences with banks. In this post, let me share a possible re-usable design approach. Thanks to Tin, Richie, Tina, Val, and their team, for adding their insights.

What is Maker-Checker?

According to Wikipedia:

Maker-checker (or Maker and Checker, or 4-Eyes) is one of the central principles of authorization in the Information Systems of financial organizations. The principle of maker and checker means that for each transaction, there must be at least two individuals necessary for its completion. While one individual may create a transaction, the other individual should be involved in confirmation/authorization of the same. Here the segregation of duties plays an important role. In this way, strict control is kept over system software and data keeping in mind functional division of labor between all classes of employees.

By transaction, we're referring to a business transaction (e.g. sale, purchase, remittance request), and not a database transaction.

Here are some business rules we can derive from the above definition:

1. For any transaction entry, there must be at least two individuals necessary for its completion.
2. The one who makes the transaction entry (i.e. maker) cannot be the same one who checks (i.e. checker) it.
3. A transaction entry is only considered completed if it has been checked.

Upon further clarification with the domain experts, we've learned the following:

1. The checker cannot make modifications to the transaction entry. Modifications can only be done by maker.
2. If the checker rejects the transaction entry, it should be returned back to maker (with possible comments or suggested changes). The maker can then resubmit changes later.
3. There can be cases when the transaction entry needs another level of checking (after the first one). This would result into three individuals necessary for completion.

A typical user story for this would be something like: As a <manager>, I want to apply maker-checker policy for each <transaction> being entered, so that I can prevent fraud (or improve quality).

Possible usage scenario(s) would be something like this:

For maker:

1. Maker submits a transaction to the system.
2. System determines submitted transaction to be under the maker-checker policy.
3. System stores submitted transaction as "for checking".
4. System displays list of "for checking", "accepted", and "rejected" transactions.

For checker:

1. Checker retrieves list of transactions "for checking".
2. System displays list of transactions "for checking".
3. Checker selects a transaction.
4. System shows the transaction.
5. Checker accepts the transaction.
6. System records "accepted" transaction.

The alternative flow is when the checker rejects the transaction.

5. Checker rejects the transaction.
6. System records "rejected" transaction.

Our analysis shows that the transaction entry can have the following states:

1. for checking,
2. verified,
3. and rejected.

The checker can either accept or reject the entry.

In a future post, I'll share one possible design approach for maker-checker.

SOA and Referential Data Integrity

One of the issues that tends to pop up is how we maintain referential integrity between services. In this post, I'd like to share my experiences on how referential data integrity between services can ruin your SOA.

Database Constraints Between Services Break Boundaries

Two of the Tenets of Service-Orientation are: "boundaries are explicit", and "services are autonomous". The first one implies that internal (private) implementation details should not be leaked outside of a service boundary. And the second one implies that services are not subservient to other services (or other pieces of code).

For purposes of discussion, let's say we have the following:

• a "customer" service that provides customer-related business capability and persists data in a database
• an "order" service that provides order-related business capability and persists data in the same database
• foreign key constraints between customer-related entities/tables and order-related entities/tables. More specifically, an "order" table contains the unique ID of a customer that placed the order, and that this ID needs to exists in the "customer" table.

Do the above services (customer and order) follow the "boundaries are explicit" tenet? Are the services autonomous? Let's examine further.

The way the "customer" service persists data in a database is (private) implementation detail that is internal to it. Likewise, the way the "order" service persists data in a database is also internal to it. But how would you consider the foreign key constraints between their database tables? Is this internal implementation detail leaking outside of a service boundary? (i.e. leaking outside the "customer" service boundary and onto the "order" service boundary)

If the "order"-related database undergoes some schema changes, will it not affect the "customer"-related database schema? When deploying the schema changes to the "order" service, will it not require the "customer" service to be temporarily unavailable? (e.g. due to database restart) If "services are autonomous", how come the "customer" and "order" services are inter-dependent, such that a change in one requires a restart (or a redeploy) on the other?

Split Service, Split Database

A better approach would be to split the databases of the two services, and do away with foreign key constraints. That would allow for explicit boundaries, and autonomy. But this might be unacceptable to some people at first.

A monolithic application (left) split into services in a service-oriented architecture (right).

How could one ensure that enough customer information is received before orders are placed by that customer? In other words, how can a developer ensure that orders are placed by known customers (an existing customer ID)? Well, in SOA, services don't have to! It is not the responsibility of services to maintain this referential integrity. The responsibility of ensuring that an order is placed by a known customer lies with the process of placing orders (not in the service). It is the orchestration layer's responsibility to maintain this.

What if some customer information was modified (e.g. billing address), shouldn't the related orders be affected? Again, the process, or orchestration layer, can become responsible for this. Is this really a change in the customer? Or is it just a change in the placed order? One possible way is we define the process to say that it would copy the customer's billing information, and attach a copy (i.e. duplicate) with the placed order. This would mean that the order's bill-to (billing) address is defined when the order was placed. Another possible way is to leave the bill-to address undetermined until the order is shipped, at which the billing address as provided by the customer service shall be used.

Correlation Between Services and Context Boundaries

A useful way of thinking about this is the Domain-Driven Design notion of Bounded Context. DDD splits a complex domain up into multiple bounded contexts and maps out the relationships between them. And this results into multiple databases.

Re-usable Services

Services (in SOA) are meant to be reusable. In the example, if we reconsider what the customer service contains, we can probably design it in such a way that it doesn't have to be for the purposes of order taking. It can be designed to be reusable and allow for any party's information (e.g. persons, organizations) and not just customers. It could be re-used to store employees, since employees can become customers in the future. It could be re-used to store suppliers (since the business may need to track the suppliers of products being produced and ordered).

The (business) goals of SOA any IT initiative are:

• increase agility (e.g. support new/changing business processes/models, reduce time to solution)
• reduce cost (e.g. re-use business processes and/or applications, improve utility of existing/legacy application)

These goals are further translated as (technical goals):

• increase usability (i.e. re-usability and accessibility across different applications)
• improve maintainability
• reduce redundancy

When we make the boundaries of services explicit and make them autonomous, we can better achieve the goals of re-usability, and reduce redundancy.

Some other services, that come to mind, can become re-usable (when properly designed and split), are:

• Authentication and authorization - if this were (re-)written for each application, it would be a huge cost.
• Billing (or invoicing)
• Product Catalog

Microservices

In a previous post on SOA, I did mention that I find the term Microservices to be misleading. Although the information found on the web were good, I found them to still be unclear with their implementation. Nonetheless, I did find that they do add an exciting twist if you consider that, Microservices:

• can contain their own GUI,
• and use their own database (i.e. decentralized data management).

Closing Thoughts

Finally, when communicating with business people, don't let reuse become the primary measure. They probably won't understand it. Tell them that it does help save development and maintenance costs. Tell them that it provides better time to market, reduce days of inventory, reduce employee turnover, etc.

Nice root beer from Virgil's Sodas. Is this available here in the Philippines? I'm not related to the product in any way. I just saw their Ad online. And being thirsty, I thought of having one.

After this rather long post, I think it's time for a nice cold drink. Root beer anyone?

Domain-Driven Design: Cargo Shipping Example (Part 2)

One of my colleagues asked an interesting question about my previous post that I thought would be worth posting here. He asked about the persistence of value objects. He pointed out that the Cargo had a Delivery property that was replaced (not modified). So, what happens to its persistence? Does it result into a delete and an insert? Let's see.

Let's see how a Delivery object is used in a Cargo object. In the highlighted lines, we can see that the property is indeed being assigned (not modified or updated) with a new delivery object.

public class Cargo implements Entity<Cargo> {

  private TrackingId trackingId;
  private Location origin;
  private RouteSpecification routeSpecification;
  private Itinerary itinerary;
  private Delivery delivery;

  public Cargo(final TrackingId trackingId, final RouteSpecification routeSpecification) {
    Validate.notNull(trackingId, "Tracking ID is required");
    Validate.notNull(routeSpecification, "Route specification is required");

    this.trackingId = trackingId;
    // Cargo origin never changes, even if the route specification changes.
    // However, at creation, cargo orgin can be derived from the initial route specification.
    this.origin = routeSpecification.origin();
    this.routeSpecification = routeSpecification;

    this.delivery = Delivery.derivedFrom(
      this.routeSpecification, this.itinerary, HandlingHistory.EMPTY
    );
  }

  public TrackingId trackingId() { return trackingId; }

  public Location origin() { return origin; }

  public Delivery delivery() { return delivery; }

  public Itinerary itinerary() {
    return DomainObjectUtils.nullSafe(
        this.itinerary, Itinerary.EMPTY_ITINERARY);
  }

  public RouteSpecification routeSpecification() { return routeSpecification; }

  public void specifyNewRoute(final RouteSpecification routeSpecification) {
    Validate.notNull(routeSpecification, "Route specification is required");

    this.routeSpecification = routeSpecification;
    // Handling consistency within the Cargo aggregate synchronously
    this.delivery = delivery.updateOnRouting(this.routeSpecification, this.itinerary);
  }

  public void assignToRoute(final Itinerary itinerary) {
    Validate.notNull(itinerary, "Itinerary is required for assignment");

    this.itinerary = itinerary;
    // Handling consistency within the Cargo aggregate synchronously
    this.delivery = delivery.updateOnRouting(this.routeSpecification, this.itinerary);
  }

  public void deriveDeliveryProgress(final HandlingHistory handlingHistory) {
    // TODO filter events on cargo (must be same as this cargo)

    // Delivery is a value object, so we can simply discard the old one
    // and replace it with a new
    this.delivery = Delivery.derivedFrom(routeSpecification(), itinerary(), handlingHistory);
  }
  . . .
}

Here is the Delivery value object (summarized). It doesn't have mutator methods. The class is immutable. Note that updateOnRouting() and derivedFrom() methods return new Delivery objects.

public class Delivery implements ValueObject<Delivery> {

  private TransportStatus transportStatus;
  private Location lastKnownLocation;
  private Voyage currentVoyage;
  private boolean misdirected;
  private Date eta;
  private HandlingActivity nextExpectedActivity;
  private boolean isUnloadedAtDestination;
  private RoutingStatus routingStatus;
  private Date calculatedAt;
  private HandlingEvent lastEvent;

  private static final Date ETA_UNKOWN = null;
  private static final HandlingActivity NO_ACTIVITY = null;

  /**
   * Creates a new delivery snapshot to reflect changes in routing, i.e.
   * when the route specification or the itinerary has changed
   * but no additional handling of the cargo has been performed.
   *
   * @param routeSpecification route specification
   * @param itinerary itinerary
   * @return An up to date delivery
   */
  Delivery updateOnRouting(RouteSpecification routeSpecification, Itinerary itinerary) {
    Validate.notNull(routeSpecification, "Route specification is required");

    return new Delivery(this.lastEvent, itinerary, routeSpecification);
  }

  /**
   * Creates a new delivery snapshot based on the complete handling history of a cargo,
   * as well as its route specification and itinerary.
   *
   * @param routeSpecification route specification
   * @param itinerary itinerary
   * @param handlingHistory delivery history
   * @return An up to date delivery.
   */
  static Delivery derivedFrom(
       RouteSpecification routeSpecification,
       Itinerary itinerary,
       HandlingHistory handlingHistory) {
    Validate.notNull(routeSpecification, "Route specification is required");
    Validate.notNull(handlingHistory, "Delivery history is required");

    final HandlingEvent lastEvent = handlingHistory.mostRecentlyCompletedEvent();

    return new Delivery(lastEvent, itinerary, routeSpecification);
  }

  /**
   * Internal constructor.
   */
  private Delivery(
        HandlingEvent lastEvent, Itinerary itinerary,
        RouteSpecification routeSpecification) {
    this.calculatedAt = new Date();
    this.lastEvent = lastEvent;

    this.misdirected = calculateMisdirectionStatus(itinerary);
    this.routingStatus = calculateRoutingStatus(itinerary, routeSpecification);
    this.transportStatus = calculateTransportStatus();
    this.lastKnownLocation = calculateLastKnownLocation();
    this.currentVoyage = calculateCurrentVoyage();
    this.eta = calculateEta(itinerary);
    this.nextExpectedActivity = calculateNextExpectedActivity(routeSpecification, itinerary);
    this.isUnloadedAtDestination = calculateUnloadedAtDestination(routeSpecification);
  }

  public TransportStatus transportStatus() {...}

  public Location lastKnownLocation() {...}

  public Voyage currentVoyage() {...}

  public boolean isMisdirected() {...}

  public Date estimatedTimeOfArrival() {...}

  public HandlingActivity nextExpectedActivity() {...}

  public boolean isUnloadedAtDestination() {...}

  public RoutingStatus routingStatus() {...}

  public Date calculatedAt() {...}
  . . .
}

The above entities don't use JPA annotations. So, I dug into the example's ORM which happens to be Hibernate (Cargo.hbm.xml). I saw the following:

<hibernate-mapping default-access="field">
  <class name="se.citerus.dddsample.domain.model.cargo.Cargo" table="Cargo">
    <id name="id" column="id">...</id>

    <many-to-one name="origin" column="origin_id" not-null="false" cascade="none" update="false" foreign-key="origin_fk"/>

    <component name="trackingId" unique="true" update="false">...</component>

    <component name="delivery" lazy="true">
      <property name="misdirected" column="is_misdirected" not-null="true"/>
      <property name="eta" column="eta" not-null="false"/>
      <property name="calculatedAt" column="calculated_at" not-null="true"/>
      <property name="isUnloadedAtDestination" column="unloaded_at_dest" not-null="true"/>

      <property name="routingStatus" column="routing_status" not-null="true">...</property>

      <component name="nextExpectedActivity" update="true">
        <many-to-one name="location" column="next_expected_location_id" foreign-key="next_expected_location_fk" cascade="none"/>
        <property name="type" column="next_expected_handling_event_type">...</property>
        <many-to-one name="voyage" column="next_expected_voyage_id" foreign-key="next_expected_voyage_fk" cascade="none"/>
      </component>

      <property name="transportStatus" column="transport_status" not-null="true">...</property>
      <many-to-one name="currentVoyage" column="current_voyage_id" not-null="false" cascade="none" foreign-key="current_voyage_fk"/>
      <many-to-one name="lastKnownLocation" column="last_known_location_id" not-null="false" cascade="none" foreign-key="last_known_location_fk"/>
      <many-to-one name="lastEvent" column="last_event_id" not-null="false" cascade="none" foreign-key="last_event_fk"/>
    </component>
    ...
  </class>
</hibernate-mapping>

The JPA annotation counterpart of <component> is @Embedded.

The delivery value object is declared as a <component>. A component is a contained object that is persisted as a value type and not an entity reference. That means, it does not have its own table. Instead, its properties are mapped as columns in the surrounding <class>'s table. So here, the delivery object's properties are actually columns in the Cargo table.

Also note how Hibernate was configured to access by field and not by property. This tells Hibernate to use direct field injection, and not use setter methods.

So, does it result into a delete and an insert? The answer is neither. It results into an update!

Note that not all value objects will be persisted this way. The Leg value objects are deleted whenever a new Itinerary object is assigned to a Cargo. This results into Leg value objects being inserted (after the old ones are deleted). So, please check your ORM and persistence mechanisms to be sure, and see if there are any performance problems.

But why all the hassle just to use value objects? Good question. Here's what the cargo shipping example has to say:

When possible, we tend to favor Value Objects over Entities or Domain Events, because they require less attention during implementation. Value Objects can be created and thrown away at will, and since they are immutable we can pass them around as we wish.

So, I guess in the cargo shipping example, the likelihood of an itinerary being replaced is quite minimal. Thus, they didn't have performance problems even when the child leg objects were being deleted (and new ones inserted).

Purpose of SOA

After having a few more discussions about SOA (after my previous post on the topic), I happen to watch this video (again) where Anne Thomas Manes (now with Gartner via the acquisition of Burton Group) gave a talk at QCon London 2007 entitled The Business Value of SOA. Yeah, it's an old video. I'm glad to have watched it again. It helps me go back to the basics, especially with all the on-going confusion with SOA, ESB, SOAP, XML, REST, and the more recent hype around microservices (µservices). In this post, I'll be quoting Anne and writing my takeaways from her 2007 talk. Near the end, I refer to a 2009 blog post from Anne, and a more recent 2012 interview.

Why are we doing SOA again? So, what?

Why SOA?

Before I go about dealing with SOA and what it's all about, I'd like to start with why. Why SOA? In Anne's talk, she pointed out that about 80% of IT budget goes into maintenance and operations. That was 2007. In a 2013 Computerworld article entitled, How to Balance Maintenance and IT Innovation, it mentioned that a significant portion of IT budget goes to maintenance and operations:

In a recent Forrester Research survey of IT leaders at more than 3,700 companies, respondents estimated that they spend an average 72% of the money in their budgets on such keep-the-lights-on functions as replacing or expanding capacity and supporting ongoing operations and maintenance, while only 28% of the money goes toward new projects.

It seems that very little has changed. In Anne's 2007 talk, she pointed out that the problem is not the new applications, but the high cost of maintaining and operating existing applications. Why does an enterprise have so many applications? An enterprise would usually have about 20 or so core capabilities. But why would it have over 400 applications?

Anne points out that redundancy is the source of the problem. She says that there are too many applications, databases, too much money spent on boat anchors, and very little money left for innovation. The purpose of SOA is to reduce redundancy.

So, why SOA? SOA tries to reduce the cost of maintenance and operations by refactoring redundancy. Newer applications should cost less to build too, since there would be a set of services to re-use. With new applications, or newly applied changes to existing applications, organizations can cope with changes in business and become more agile.

SOA Concepts

SOA is an architecture for designing systems. … What is architecture? Architecture is a style of design. Anne said. She stressed that it is a design for systems and not applications.

She continues by explaining that in SOA, a service is the core unit of design. So what's a service?

A service is something that implements a discrete piece of functionality. It exposes its functionality through a well-defined interface.… so that applications can consume it. Anne said.

She points out that the service should be consumed by many applications. And in fact, the service should be consumed by many applications. And in fact, if you were building a service that is designed to be consumed by a single application, you're probably wasting your time. Anne said. And I actually want to stress this word consume. Applications consume services. SOA is not about application to application integration. That's how a lot of people use it today. They use SOA technologies to integrate applications. But actually your goal when you're doing SOA is to say what is the capability that is required by multiple applications. Rather than reimplementing that capability many times over in each application that needs it, you refactor the functionality into a service. And then applications consume it.

If you're figuring out which functionality/capability is required by multiple applications, just look around. Look at the existing applications. Look at your application portfolio. Identify which data and/or functionality is being used in multiple applications. Refactor them out as services!

If a functionality is required by many applications, it should be implemented as a service. And it should not be reimplemented in multiple applications.

If the phrase shared, reusable services is too vague for you or your team, then try using refactor redundancy. If the phrases suite of small services running in its own process and communicating with lightweight mechanisms, often an HTTP resource API or services built around business capabilities and independently deployable, are a bit confusing, or a bit intimidating, try using reducing redundancy.

I find the term microservices to be further misleading. I prefer to focus on the goal of reducing redundancy, than using the latest term that is in vogue.

What SOA is Not?

At about 11:20 into the video, Anne talked about what SOA is not, and touched on why business units are not thrilled about SOA. She says it is not an application-centric approach to building systems. And this is actually one of the most challenging things about SOA, because all your projects are funded based on applications. [If] you think about it, whenever there's a new project, the project that is funded by a business unit, the business unit wants you to build an application to solve a particular problem. The business unit has no interest whatsoever in having you spend extra time to go off and create a piece of capability in this application as a service, which is then gonna get consumed by some other set of business applications by some other business units.

And so there's a whole lot of business reasons, incentive reasons, why business units aren't too thrilled with the idea of you spending their money that will wind up benefiting other people.

The business unit has no interest whatsoever in having you spend extra time to go off and create a piece of capability in this application as a service, which is then gonna get consumed by some other set of business applications by some other business units.

Here, Anne touched a bit on culture and how business units behave. I've witnessed this myself. It's difficult if the incentives of a business unit is just to solve their own problems, and not cooperate with other business units to save organization-wide costs, reduce time to market, and increase competitiveness. So, SOA isn't just an IT initiative. It needs to be a business initiative.

Then Anne continues, But, that's the way people build systems today. And what they're building are monolithic applications. And when you build monolithic applications, you tend to reproduce the same functionality and comparable data in many different systems. And this duplication, this redundancy is actually the source of most of the trouble in today's IT systems.

Now, here are some more of my takeaways from Anne's talk:

• SOA is about design, not about technology.
• SOA is something you do, and not something you build or buy.
• SOA is much more about culture than about technology.
• You can't buy [SOA] governance. It's something that you do. (from OTN Archbeat Podcast: SOA Governance)

All of the above was from a talk Anne gave in 2007. Forward a little bit into early 2009, Anne blogged that SOA is dead. Three years after that, in 2012, Anne Thomas Manes says, people are starting to actually get the architecture. Well, I'm glad to see that we're all starting to get it.

Closing Thoughts

This just happened to me a few months ago. I was in a call with a client relationship manager discussing our approach for a potential project and its proposal. Somehow, the conversation touched on SOA. So, one guy asks if the client is open to using service-oriented architecture. And the client relationship manager answers, Oh yes! They're very much customer service oriented!. Duh?!?

Anne Thomas Manes did a great job peeling away all the technology, and allowed SOA's true value to emerge. For me, she was able to answer the why of SOA, which could be easily lost with all the technology and products that vendors are trying to sell.

After applying SOA to your business, you ought to be asking yourself how much redundancy you've refactored out, and how much time and money these shared services have provided the organization. For measuring the business value of SOA, please watch another talk by Anne Thomas Manes.

This post sort of takes me back in time when SOA was confusing (and probably still is), and helps me clear some things up. I hope it helps you clear things up too.

Domain-Driven Design: Cargo Shipping Example

I've always found the cargo shipping example used in Eric Evan's book to be quite useful in learning DDD. Here are some of my notes.

Bounded Contexts

Eric Evans did mention in his talk "What I've Learned About DDD Since the Book" at QCon London 2009, that ...in Chapter 14 of the book, I finally got around to talking about context boundaries and context maps... but putting context mapping in Chapter 14 was a mistake. It is fundamental. [You] just can't make models work without establishing context boundaries.. Evans said that the reality is that there is always more than one model and mapping these models out is crucial to setting the stage for success of a DDD project.

Ideally, it would be preferable to have a single, unified model. While this is a noble goal, in reality it typically fragments into multiple models. It is useful to recognize this fact of life and work with it. (Wikipedia)

I believe a lot of teams are struggling with DDD because they're looking for a single, cohesive, all-inclusive model of an organization's entire business domain — you know, like an enterprise model. However, when using DDD, that is not the goal. DDD places emphasis on developing models within a context — A description of the conditions under which a particular model applies. Thus, the strategic design principle of Bounded Context.

Unfortunately, the cargo shipping example has only one bounded context — cargo shipping. I was hoping that it had two or more bounded contexts, so that it can illustrate how context mapping works.

Could carrier movement tracking be another context? What about a billing context (where a someone is billed when the cargo is delivered)?

The packages could have been named cargoshipping. But the packages were named as follows:

• se.citerus.dddsample.application
• se.citerus.dddsample.domain.model
• se.citerus.dddsample.infrastructure
• se.citerus.dddsample.interfaces

In Vernon Vaughn's book, a similar naming convention was suggested (for the "optimal purchasing" context):

• com.mycompany.optimalpurchasing.application
• com.mycompany.optimalpurchasing.domain.model
• com.mycompany.optimalpurchasing.infrastructure
• com.mycompany.optimalpurchasing.presentation

Applications

There are three applications (not three contexts) in the example:

• Booking
• Tracking
• Handling (or Incident Logging)

Aggregate Roots

There is one package per aggregate, and to each aggregate belongs entities, value objects, domain events, a repository interface and sometimes factories. The aggregate roots are Cargo, HandlingEvent, Location, and Voyage.

Cargo

The cargo package has the Cargo entity as the aggregate root. It is made up of Itinerary-Leg, Delivery, and RouteSpecification.

Notice how the Cargo entity does not have getter/setter methods that you'd normally see in anemic domain entities. Instead, it has getters that are differently named (not following JavaBean naming conventions)...

public class Cargo implements Entity<Cargo> {

  private TrackingId trackingId;
  private Location origin;
  private RouteSpecification routeSpecification;
  private Itinerary itinerary;
  private Delivery delivery;

  public Cargo(
        final TrackingId trackingId,
        final RouteSpecification routeSpecification) {...}

  public TrackingId trackingId() { return trackingId; }

  public Location origin() { return origin; }

  public Delivery delivery() { return delivery; }

  public Itinerary itinerary() {
    return DomainObjectUtils.nullSafe(
        this.itinerary, Itinerary.EMPTY_ITINERARY);
  }

  public RouteSpecification routeSpecification() {
    return routeSpecification;
  }
  . . .
}

...and mutator methods that aren't named as setters.

public class Cargo implements Entity<Cargo> {

  . . .
  public void specifyNewRoute(
        final RouteSpecification routeSpecification) {...}

  public void assignToRoute(final Itinerary itinerary) {...}

  public void deriveDeliveryProgress(
        final HandlingHistory handlingHistory) {...}
  . . .
}

The life cycle of a cargo begins with the booking procedure, when the tracking id is assigned. It has an origin and a destination (via a RouteSpecification). As the cargo is being handled (transported to its destination), its delivery and transport status changes (from NOT_RECEIVED to CLAIMED).

Handling Event

Although not exactly an aggregate or entity, the HandlingEvent is a domain event used to register the handling event when, for instance, a cargo is unloaded from a carrier at some location at a given time. These events are sent from different applications some time after the event occurred and contain information about the cargo (referenced via tracking id), location, timestamp of the completion of the event, and if applicable, a voyage (referenced via voyage number).

Before I illustrate how a cargo's delivery status is updated through handling events, let me take a closer look at the code behind handling events.

public final class HandlingEvent implements DomainEvent<HandlingEvent> {

  private Type type;
  private Voyage voyage;
  private Location location;
  private Date completionTime;
  private Date registrationTime;
  private Cargo cargo;

  public enum Type implements ValueObject<Type> {
    LOAD(true),
    UNLOAD(true),
    RECEIVE(false),
    CLAIM(false),
    CUSTOMS(false); . . .
  }
  . . .
}

I find it interesting that even though HandlingEvent has a public constructor, it also has a factory — HandlingEventFactory. While the HandlingEvent constructor needs other entities like Cargo, Location, and Voyage, the factory accepts TrackingId, UnLocode, and VoyageNumber to create a new HandlingEvent. Notice how the factory uses the unique IDs of the domain entities to retrieve the entities and create the domain event object.

I also find it interesting that there's HandlingHistory to represent a list (or collection) of HandlingEvents. The list is unmodifiable (i.e. read-only). And has mostRecentlyCompletedEvent() and distinctEventsByCompletionTime() methods.

The HandlingEventRepository is also interesting, since it doesn't have all the CRUD operations. It only has a method to store a HandlingEvent, and to retrieve the handling history of a particular cargo via its unique TrackingId.

public interface HandlingEventRepository {
  void store(HandlingEvent event);
  HandlingHistory lookupHandlingHistoryOfCargo(
      TrackingId trackingId);
}

Updating a Cargo's Delivery Status

Now, let's see how a Cargo's delivery status is updated. Some might be tempted to provide a setStatus(...) method. But if we inspect the cargo shipping domain, the delivery status is based on handling events.

Cargo (aggregate root) entity has a Delivery value object that is the actual transportation of the cargo, as opposed to the customer requirement (RouteSpecification) and the plan (Itinerary). Delivery is updated via a list of handling events (HandlingHistory).

public class Cargo implements Entity<Cargo> {
  private TrackingId trackingId;
  private Location origin;
  private RouteSpecification routeSpecification;
  private Itinerary itinerary;
  private Delivery delivery;
  . . .
  public void deriveDeliveryProgress(
        final HandlingHistory handlingHistory) {
    . . .
  }
}

The delivery of a cargo is defined by its status at the last known location (e.g. on-board carrier in Hong Kong). It's also possible that the cargo is misdirected.

public class Delivery implements ValueObject<Delivery> {
  private TransportStatus transportStatus;
  private Location lastKnownLocation;
  private Voyage currentVoyage;
  private boolean misdirected;
  private Date eta;
  private HandlingActivity nextExpectedActivity;
  private boolean isUnloadedAtDestination;
  private RoutingStatus routingStatus;
  private Date calculatedAt;
  private HandlingEvent lastEvent;
  . . .
}

public enum TransportStatus implements ValueObject<TransportStatus> {
  NOT_RECEIVED, IN_PORT, ONBOARD_CARRIER, CLAIMED, UNKNOWN;
  . . .
}

I find that there's overlap between HandlingEvent, HandlingActivity, and TransportStatus. It could have been merged to simplify and clarify the model. If merged, I'd choose the name of HandlingActivity, since I find the word "event" to be a bit too technology-centric (probably influenced by "domain event"), and the word "activity" sounds more apt for the domain.

Why was HandlingEvent not part of the Cargo aggregate? Good question. At first, I was also thinking that it would be easier if the handling events were part of a cargo aggregate. According to the cargo shipping example:

The main reason for not making HandlingEvent part of the cargo aggregate is performance. HandlingEvents are received from external parties and systems, e.g. warehouse management systems, port handling systems, that call our HandlingReportService webservice implementation. The number of events can be very high and it is important that our webservice can dispatch the remote calls quickly. To be able to support this use case we need to handle the remote webservice calls asynchronously, i.e. we do not want to load the big cargo structure synchronously for each received HandlingEvent. Since all relationships in an aggregate must be handled synchronously we put the HandlingEvent in an aggregate of its own and we are able processes the events quickly and at the same time eliminate dead-locking situations in the system.

Anti-corruption Layer

The cargo shipping example may not have shown context mapping. But it did show one strategic DDD pattern — Anti-corruption Layer. Although not much explanation was put into the code, here's what I can gather from it.

A HandlingReportService interface was defined and exposed as a SOAP-based web service.

public interface HandlingReportService {
  public void submitReport(HandlingReport arg0) throws...;
}

It accepts a HandlingReport. Note that this is not the HandlingEvent domain event. HandlingReport is eventually translated to a HandlingEvent. This effectively shields the cargo shipping context's domain (layer) from being corrupted by all the external applications that would be posting handling events to the system.

<complexType name="handlingReport">
  <complexContent>
    <restriction base="anyType">
      <sequence>
        <element name="completionTime" type="xs:dateTime"/>
        <element name="trackingIds" type="xs:string" maxOccurs="unbounded"/>
        <element name="type" type="xs:string"/>
        <element name="unLocode" type="xs:string"/>
        <element name="voyageNumber" type="xs:string" minOccurs="0"/>
      </sequence>
    </restriction>
  </complexContent>
</complexType>

And since the web service needs to return as soon as possible (due to the huge number of events coming in), it does not write directly to the database. It was implemented to create a HandlingEventRegistrationAttempt that gets sent to a message queue. The message is then asynchronously consumed by HandlingEventRegistrationAttemptConsumer that delegates to HandlingEventService (an application service). HandlingEventServiceImpl (implementation) simply used the HandlingEventRepository to store the handling event. Again, notice how HandlingEventService is using unique IDs as input parameters (not domain entities).

public interface HandlingEventService {
  void registerHandlingEvent(
          Date completionTime,
          TrackingId trackingId,
          VoyageNumber voyageNumber,
          UnLocode unLocode,
          HandlingEvent.Type type)
      throws CannotCreateHandlingEventException;
}

How does cargo.deriveDeliveryProgress(HandlingHistory) get called? From HandlingEventService, the event is stored, and published by calling ApplicationEvents.cargoWasHandled(...). It, in turn, is implemented using a message queue (Yep, another message queue). The event (in the message queue) is consumed by CargoHandledConsumer which then calls CargoInspectionService. And it finally uses repositories to retrieve the cargo in question, and its related handling history.

1. HandlingEventService -> ApplicationEvents
2. ApplicationEvents -> CargoHandledConsumer (via JMS)
3. CargoHandledConsumer -> CargoInspectionService
4. CargoInspectionService -> cargo.deriveDeliveryProgress(...)

Kinda long-winded if you ask me. But looking at the implementation, I think I can understand why asynchronous events were preferred.

Ending Thoughts

Boy, was this a long post or what?!

While the cargo shipping example does leave a lot to be desired, it was still a good example. I learned a lot from it. I hope you do too. If you find other things worth noting in the cargo shipping example, please hit the comments and let me know.

Quantifying Domain Model versus Transaction Script

I've been conducting training classes (with Orange and Bronze) that cover topics like TDD, design patterns (GoF), patterns of enterprise application architecture (based on PoEAA book by Martin Fowler), and others. And a question keeps coming up about comparing (and quantifying) the benefits of domain model pattern compared to transaction script. So, I thought I'd post an explanation here.

Note that the Transaction Script pattern is not bad. Fowler himself says that there are virtues to this pattern:

The glory of Transaction Script is its simplicity. Organizing logic this way is natural for applications with only a small amount of logic, and it involves very little overhead either in performance or in understanding.

…

It's hard to quantify the cutover level, especially when you're more familiar with one pattern than the other. You can refactor a Transaction Script design to a Domain Model design, but it's harder than it needs to be.
However much of an object bigot you become, don't rule out Transaction Script. there are a lot of simple problems out there, and a simple solution will get you up and running faster.

(PoEAA p.111-112)

Here, I used a simple banking example to illustrate the difference between Transaction Script and Domain Model patterns in organizing domain logic. Then, I'll use metrics like method lines of code, and cyclomatic complexity.

Banking Example

In the banking example, we shall implement a very simple money transfer, where an amount is transferred from one account to another.

The MoneyTransferService shall be implemented in two ways: one using Transaction Script, and another using Domain Model.

public interface MoneyTransferService {
  BankingTransaction transfer(
      String fromAccountId, String toAccountId, double amount);
}

Transaction Script

Using a Transaction Script design, the domain logic for transferring money between two accounts is all placed inside the MoneyTransferService implementation.

public class MoneyTransferServiceTransactionScriptImpl
      implements MoneyTransferService {
  private AccountDao accountDao;
  private BankingTransactionRepository bankingTransactionRepository;
  . . .
  @Override
  public BankingTransaction transfer(
      String fromAccountId, String toAccountId, double amount) {
    Account fromAccount = accountDao.findById(fromAccountId);
    Account toAccount = accountDao.findById(toAccountId);
    . . .
    double newBalance = fromAccount.getBalance() - amount;
    switch (fromAccount.getOverdraftPolicy()) {
    case NEVER:
      if (newBalance < 0) {
        throw new DebitException("Insufficient funds");
      }
      break;
    case ALLOWED:
      if (newBalance < -limit) {
        throw new DebitException(
            "Overdraft limit (of " + limit + ") exceeded: " + newBalance);
      }
      break;
    }
    fromAccount.setBalance(newBalance);
    toAccount.setBalance(toAccount.getBalance() + amount);
    BankingTransaction moneyTransferTransaction =
        new MoneyTranferTransaction(fromAccountId, toAccountId, amount);
    bankingTransactionRepository.addTransaction(moneyTransferTransaction);
    return moneyTransferTransaction;
  }
}

The Account entity is merely a bag of getters and setters.

// @Entity
public class Account {
  // @Id
  private String id;
  private double balance;
  private OverdraftPolicy overdraftPolicy;
  . . .
  public String getId() { return id; }
  public void setId(String id) { this.id = id; }
  public double getBalance() { return balance; }
  public void setBalance(double balance) { this.balance = balance; }
  public OverdraftPolicy getOverdraftPolicy() { return overdraftPolicy; }
  public void setOverdraftPolicy(OverdraftPolicy overdraftPolicy) {
    this.overdraftPolicy = overdraftPolicy;
  }
}

The OverdraftPolicy is an enumerated type.

public enum OverdraftPolicy {
  NEVER, ALLOWED
}

Domain Model

Using a Domain Model design, the domain logic for transferring money between two accounts is spread across. This keeps it simple and easier to maintain.

public class MoneyTransferServiceDomainModelImpl
      implements MoneyTransferService {
  private AccountRepository accountRepository;
  private BankingTransactionRepository bankingTransactionRepository;
  . . .
  @Override
  public BankingTransaction transfer(
      String fromAccountId, String toAccountId, double amount) {
    Account fromAccount = accountRepository.findById(fromAccountId);
    Account toAccount = accountRepository.findById(toAccountId);
    . . .
    fromAccount.debit(amount);
    toAccount.credit(amount);
    BankingTransaction moneyTransferTransaction =
        new MoneyTranferTransaction(fromAccountId, toAccountId, amount);
    bankingTransactionRepository.addTransaction(moneyTransferTransaction);
    return moneyTransferTransaction;
  }
}

The Account entity contains behavior and domain logic. Notice how it contains #debit(double) and #credit(double) methods, and not just getters and setters.

// @Entity
public class Account {
  // @Id
  private String id;
  private double balance;
  private OverdraftPolicy overdraftPolicy;
  . . .
  public double balance() { return balance; }
  public void debit(double amount) {
    this.overdraftPolicy.preDebit(this, amount);
    this.balance = this.balance - amount;
    this.overdraftPolicy.postDebit(this, amount);
  }
  public void credit(double amount) {
    this.balance = this.balance + amount;
  }
}

The OverdraftPolicy has two implementations that contain logic. Based on business rules, the OverdraftPolicy implementations throw exceptions to prevent the Account balance from being debited.

public interface OverdraftPolicy {
  void preDebit(Account account, double amount);
  void postDebit(Account account, double amount);
}

public class NoOverdraftAllowed implements OverdraftPolicy {
  public void preDebit(Account account, double amount) {
    double newBalance = account.balance() - amount;
    if (newBalance < 0) {
      throw new DebitException("Insufficient funds");
    }
  }
  public void postDebit(Account account, double amount) {
  }
}

public class LimitedOverdraft implements OverdraftPolicy {
  private double limit;
  . . .
  public void preDebit(Account account, double amount) {
    double newBalance = account.balance() - amount;
    if (newBalance < -limit) {
      throw new DebitException(
          "Overdraft limit (of " + limit + ") exceeded: " + newBalance);
    }
  }
  public void postDebit(Account account, double amount) {
  }
}

Metrics

Now here are some of the metrics (via Eclipse Metrics Plugin).

	Transaction Script	Domain Model
Metric	Maximum	Maximum
McCabe Cyclomatic Complexity	5	2
Number of Classes	4	6
Method Lines of Code	25	9
	Total	Total
Total Lines of Code	82	96

Now here are the metrics screenshots for transaction script

and domain model.

Conclusion

The resulting overall lines of code are almost the same. The Domain Model pattern produces more classes, and simpler methods.

There are more things to compare than just lines of code and cyclomatic complexity. For example, the Domain Model pattern needs more OO design skill, and Transaction Script pattern is so easy to implement.

The good thing is, there's no need to make a decision up-front. One can always start with a Transaction Script (i.e. do the simplest thing that could possibly work), and when complexity starts to set in, it can be refactored to have richer domain entities, and work its way to using a Domain Model pattern.

Let me know (via comments) if anyone wants to see the code. I can upload it to GitHub.