Part 5 in Domain Drive Design series following on from Entities and Records.

The Domain Model is not just model objects

I have asserted that for any non-trivial program the modeling of the problem space should not be handled by individual model objects. Instead it should be handled through the coordinated action of many domain objects. In the last post we introduced entities and records to handle some of the concerns of modeling a system. In this post we discuss the repository pattern as a solution to our need to store and recover those entities and records.

I would recommend reading the post on Entities and Records first. Don’t want to? Well, in summary, records are dumb data stores where the state of the system resides. Entities are stateless wrappers for records that hold behaviour that requires some of the records contents. We do not need to know of the existence of the records and should interact with them only through the entities that wrap them.

The Problem of Persistence

The database is a small concern in a system. It is easy to forget this when developing in frameworks where you have to choose the database up front and where the methods on a model are exact reflections of your database schema. The littering of database concerns across the codebase is a distraction from writing the code most suited to your domain. For this reason we treat the database as a detail and want it isolated from the core entities.

We want to get to a stage where we have record objects that we do not interact with directly and entities that have no knowledge of the database. This is excellent — we have achieved separating knowledge of the database and the main behaviour of the application.

Now we need a way to persist the records. This task falls to a repository. The repository will be the only thing that knows the storage mechanism supporting the application.

Using a Repository

Here is a quick one sentence summary of the repository pattern by Martin Fowler:

A Repository mediates between the domain and data mapping layers, acting like an in-memory domain object collection.

Before proceeding we need to introduce the concept of an aggregate. In the last post we introduced two entities: a User and Credentials both of which wrapped a record object the User::Record . This collection of objects acted together to model a single user. Collections like this are described in Domain Driven Design as an aggregate. Martin Fowler provides an excellent summary on aggregates. This description comes with advice on handling these aggregates. Key is that one of the components should be recognisable as the aggregate root and any reference from outside the aggregate should go to the root only.

In our system the User is the root of the aggregate. This implies we should only be able to get load or save our user entity and the credentials it comes with and not directly load/save the credentials object. For this reason we need a repository of users and not one of credentials, this also explains why the record was named the User::Record as it is an implementation detail that will be used as a foundation to our User::Repository .

The key feature of our repository is that it should act as a collection of user entities. It does not need to look like a database and semantics of SQL or any other query language should not be evident in the API it exposes. The only difference between a repository and typical in-memory collection is that it will probably have a more sophisticated set of query methods than a normal collection.

Let’s design how we would like a repository to be used. We will want simple queries such as first and last . Retrieving by an id value is like a key lookup and so a [] method is useful; or even better, a fetch method. Then there will be queries that are specific to the collection; for example, finding all users by a family name.

I recommend designing your API with pagination in mind unless you are sure it is unnecessary. This is because adding it later can be trouble.

# Simple query adam = User :: Repository . first # => #<User fullname="Adam Smith"> # Complex query, perhaps involving paginated results johnes = User :: Repository . family "johnes" , page: 2 # => [#<User fullname="Emma Johnes">, #<User fullname="Kate Johnes">] # Handling missing entries with useful null objects user = User :: Repository . fetch ( 'ID_001' ) { GuestUser . new } # => #<GuestUser> # Building empty entities with correct record structures new_user = User :: Repository . build # => #<User fullname=""> # Editing user new_user . full_name = "Peter Saxton" # => #<User fullname="Peter Saxton"> # Adding user to collection of users. User :: Repository . save new_user # => #<User::Repository>

Building the Repository

Once we know how we would like to use our repository, we can set about implementing it. There are several ways to go about this but I always try to go for the simplest.

There are some very capable Object Relational Mappers available, the best being Sequel. I don’t want to reinvent handling SQL so I build on top of the Sequel library. The following implementation will realise the behaviour above:

class User :: Repository class << self def build User . new User :: Record . new end def save ( entity ) entity . record . save self end def remove ( entity ) entity . record . destroy self rescue Sequel :: NoExistingObject raise RecordAbsent end def first record = User :: Record . first User . new record end def last_login record = User :: Record . sort ( :last_login_at ) . first User . new record end def family ( last_name , page: 1 , page_size: 15 ) records = User :: Record . where ( :last_name => last_name ) . sort ( :first_name ) . paginate ( page , page_size ) records . map User . new end end end

Design Overkill

This example repository does everything that is required of it. Despite that it fails on being elegant code. For example, the line entity.record.save goes against the good advice of The Law of Demeter.It is also implicitly dependent on the User::Record and User classes.

There are ways to fix some of these design complaints. If you would like to see some of these solutions have a look at Lotus Model or Ruby Object Mapper (ROM). However, in all the projects where I have employed a repository I have not gone much further than the code shown in the example above. As this series is all about well designed code and the benefits that can be got from doing the right thing, the question is: why do I stop here?

To make a generic repository that can be backed by a sequel backend or an in-memory backend is complex. It requires adapters and an in-memory implementation of storage. If queries are sophisticated then the in memory-adapter needs to support a similar set of queries.

Creating a generic query interface when I might never use it is overkill and so I choose to stick with the bad code. The saving grace with this dirty code is that it is contained within the repository.

Another reason to create a generic adapter to the database is to allow testing without the database in place. As shown in the previous post I can test entities without a database by replacing the record objects with simple OpenStructs . The remaining tests that can’t make this replacement are slower yet the number of tests are small because the repository is focused on handling only queries on the collection.

In reality I have found this level of sophistication in the repository a good compromise between isolated code and building adapters that might never be used. As ROM or the Lotus model mature I might find myself using these and getting the isolation I have currently traded away.

Have you tried using the repository pattern or perhaps employ the datamapper pattern? Let me know in the comments below.

The next post will be the last in the series and will introduce interactors. Then we can finally mate our domain model with the framework of choice and make our program available to the world.

Further reading:

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