DDD specifies repository per aggregate, but when embracing Spring Data JPA, we can leverage the benefits only when we declare interface per entity. How this impedance mismatch can be resolved?
I'm hoping to try out repository interfaces encapsulated within the aggregate repository, is that a OK solution or anything better available?
To given an example: Customer is the aggregate root and entities are like Demographics, Identification, AssetSummary etc. where each entity can benefit from having their own repository interfaces. What is the best way without violating DDD much?
…, but when embracing Spring Data JPA, we can leverage the benefits only when we declare interface per entity…
That's wrong and I would like to learn where you get this impression from (feel free to comment). Spring Data repositories are expecting the exactly same approach to your domain model design: you identify aggregates in your domain model and only create repository interfaces for exactly those.
I'd argue that all you need to do is applying the DDD concept to your domain model. Simply don't declare repository interfaces for entities that are not an aggregate root. In fact, if you declared those, you basically break the concept of an aggregate, as the actual root cannot control business constraints anymore as the other entities can be manipulated through the repository interface defined for them, i.e. without using the aggregate root.
Find an example of this applied correctly in this Spring Data example. In it, Order is an aggregate root, LineItem is just an ordinary entity. The same applies to Customer (root) and Address (ordinary entity). Repository interfaces only exist for the aggregate roots.
In fact, that particular relationship is the fundamental principle that makes modules like Spring Data REST working in the first place. It only exposes HTTP resources for aggregate roots, embeds ordinary entities within the representations created and creates links to other aggregates.
Related
I'm aware that copying entity classes and properties into DTOs is considered anti-pattern, so by Exposed domain model pattern the same #Entity can be used as both database entity class, and DTO for service and MVC layer. (see here https://codereview.stackexchange.com/questions/93511/data-transfer-objects-vs-entities-in-java-rest-server-application)
But suppose we have microservice architecture where the same set of properties is used as entity in one project with persistence, and as DTO in another project which uses the first one as a service. What's the proposed pattern in such a situation?
Because the second project doesn't need #Entity related functionality, and if we put that class in shared library, it will be tied unnecessary to JPA specific APIs and libraries. And the alternative is to again use separate DTO classes anti-pattern.
When your requirements for a DTO model exactly match your entity model you are either in a very early stage of the project or very lucky that you just have a simple model. If your model is very simple, then DTOs won't give you many immediate benefits.
At some point, the requirements for the DTO model and the entity model will diverge though. Imagine you add some audit aspects, statistics or denormalization to your entity/persistence model. That kind of data is usually never exposed via DTOs directly, so you will need to split the models. It is also often the case that the main driver for DTOs is the fact that you don't need all the data all the time. If you display objects in e.g. a dropdown you only need a label and the object id, so why would you load the whole entity state for such a use case?
The fact that you have annotations on your DTO models shouldn't bother you that much, what is the alternative? An XML-like mapping? Manual object wiring?
If your model is used by third parties directly, you could use a subclassing i.e. keep the main model free of annotations and have annotated subclasses in your project that extend the main model.
Since implementing a DTO approach correctly, I created Blaze-Persistence Entity Views which will not only simplify the way you define DTOs, but it will also improve the performance of your queries.
If you are interested, I even have an example for an external model that uses entity view subclasses to keep the main model clean.
Thank you for the answers, but emphasize in the question is on microservice (MS) architecture and reusing defined entity POJOs from one MS in another as POJOs. From what I've read on microservices it's closely related to another question - should MSs share any common functionality and classes at all, or be completely independent? It seems there is no definite agreement on it, and also no definite answer, or widely accepted pattern, to this.
From my recent experience here is what I adopted, and it works well so far.
Have common functionality across MSs - yes, in form of a commons project added as dependency to all MSs, with its dependencies set as optional. Share entity classes (expose them in commons) - no.
The main reason is that entity classes are closely related to data store for particular MS. And as the established rule is that MSs shouldn't share data stores, then it makes sense not to share entity classes for those data stores. It helps MSs to be more independent, and freedom to manage their data store in their own way. It means some more typing to add additional DTO classes and conversion between them, but it's a trade-off worth taking to retain MS independence. Reasons Christian Beikov and Maksim Gumerov mentioned apply as well.
What we do share (put in commons) are some common functionality and helper classes (for cloud, discovery, error handling, rest and json configuration...), and pure DTOs, where T is transfer between MSs (rest entities or message payloads).
A current project needs us to persist domain objects in a NoSQL database such as mongoDB.
In many examples (incl. Eric Evans, Vaughn Vernon) the domain objects are serialized and persisted to the mongoDB directly.
We would like to avoid mixing the domain layer with persistence related inforamtion by not having any annotations in our domain objects.
Also we are concerned about corrupting the persisted data by changing the domain object in the future.
We came to the conclusion that we need to have some kind of DTOs translating between the domain objects and the persisted data.
Did anyone of you come across a good solution for such a case?
Yes. Your domain models should be ignorant of persistence. So you need a DTO or what I call data models (apart from the domain models and view models). Your data models will be map to the domain models before persisting to the database. This mapping is pretty common in insert and update operations. For read-only operations (reporting, etc) you can bypass the mapping from data models and to domain models. That will prevent loading the whole object graph of your domain models. This is widely applied in CQRS architecture patterns where read and write commands are separated.
Like you, I want business objects to have no dependency on any kind of specific repository. I solved it like this: That have your business object define its state objects and repository functions as interfaces. Your repository implementation can create an actual state object and inject that into your business object using the constructor.
There are a lot of advantages to this approach (such as having business objects for specific purposes), but you easily achieve complete (two-way) independence of your repository this way. Martin Fowler also hinted at this approach elsewhere.
I actually use the same pattern in my Angular / TypeScript projects. My read-api calls return DTO objects that get state objects injected as well and their properties map directly onto state objects.
These DTOs that end up as untyped javascript objects when they come from the api to the client (Angular) project are then in turn injected as state objects into TypeScript objects, injected in the constructor again and mapped by getters and setters. It works very cleanly and is well maintainable. I have an example on my GitHub (niwra) account (Software-Management repositories), but can expand here if anyone is interested.
MongoDB allows for very clean and Unit-Testable repository implementations, that returns strongly typed aggregates. The only thing I haven't solved cleanly yet is telling MongoDb about state objects for child-collections. Currently that is pretty 'static' still, but I'm sure I'll find some nice solution.
You can store your domain objects as-is in document databases. Vaughn Vernon has posted an article The Ideal Domain-Driven Design Aggregate Store? about this, featuring PostgreSQL new (at that time) JSONB document-like storage.
Of course, you get a risk having your aggregates polluted by BsonX attributes, which you probably do not want. You can avoid this by using convention configuration but you will still need to think about serialisation and this can have an effect on the level of encapsulation.
Another pattern here is to use a separate state object, which is then held as a property inside the aggregate root (or regular entity). I would not call it a "DTO", since this is clearly your aggregate state. You are not transferring anything. Methods inside your aggregate can mutate the state or, even better, the state would be an immutable value object and new state is produced when you need to change the state.
In such case persistence would only care about the state object. You still might be unhappy to have MongoDb attributes on the state object properties and this is reasonable. Then, you would need to have an identical structure inside the persistence mechanism, so you can map properties on-to-one.
A current project needs us to persist domain objects in a NoSQL
database such as mongoDB. In many examples (incl. Eric Evans, Vaughn
Vernon) the domain objects are serialized and persisted to the mongoDB
directly.
I can confirm you that MongoDB is a good choice for persisting DDD models. I use MongoDB as an Event store in my current project. You can use MongoDB even if you are not using Event sourcing, for example using an ODM (Object Document Mapper): you have a document for each Aggregate instance (this applies to any document based database, not only MongoDB) and you store nested entities and value objects as nested documents.
We would like to avoid mixing the domain layer with persistence related inforamtion by not having any annotations in our domain objects.
You can use xml mapping.
Also we are concerned about corrupting the persisted data by changing the domain object in the future.
For this you can use custom migration scripts. If you use Event sourcing then there are event versioning strategies.
We came to the conclusion that we need to have some kind of DTOs translating between the domain objects and the persisted data.
This is a bad conclusion.
If you use CQRS you won't need DTOs because the readmodels are enough.
Reading about using Java Generics in DAO layer, I have a doubt applying this in spring data repositories. I mean, with spring data repositories, you have something like this:
public interface OrderRepository extends CrudRepository<Order,OrderPK>{
}
But if I have other 10 entities, I have to create 10 interfaces like the one above to execute CRUD operations and so on and I think this is not very scalable. Java Generics and DAO is about creating one interface and one implementation and reuse this for entities but with Spring Data repositories I have to create one interface for each entity so ...
You didn't really state a question, so I just add
Is this really true? And if so, why?
and answer it:
Yes, this is (almost) correct. Almost, because you should not create one repository per entity, but one repository per Aggregate Root. See http://static.olivergierke.de/lectures/ddd-and-spring/
Spring Data Repositories offer various features for which Spring Data needs to know, what entity it is dealing with. For example query methods need to know the properties of the entity, in order to convert the method name to JPA based query. So you have to pass in the information to Spring Data at some point and you also have to pass in the information, which entities should be considered Aggregate Roots. The way you do that, is by specifying the interface.
Do you really need that? Well if all you want is generic Crud functionality, you can get that straight out of the box with JPA. But if you want query methods, Pagination, simple native queries and much more Spring Data is a nice way to avoid lots of boiler-plate code.
(Please keep in mind that I'm biased)
I'm trying to implement fully valid persistence ignorance with little effort. I have many questions though:
The simplest option
It's really straightforward - is it okay to have Entities annotated with Spring Data annotations just like in SOA (but make them really do the logic)? What are the consequences other than having to use persistance annotation in the Entities, which doesn't really follow PI principle? I mean is it really the case with Spring Data - it provides nice repositories which do what repositories in DDD should do. The problem is with Entities themself then...
The harder option
In order to make an Entity unaware of where the data it operates on came from it is natural to inject that data as an interface through constructor. Another advantage is that we always could perform lazy loading - which we have by default in Neo4j graph database for instance. The drawback is that Aggregates (which compose of Entities) will be totally aware of all data even if they don't use them - possibly it could led to debugging difficulties as data is totally exposed (DAO's would be hierarchical just like Aggregates). This would also force us to use some adapters for the repositories as they doesn't store real Entities anymore... And any translation is ugly... Another thing is that we cannot instantiate an Entity without such DAO - though there could be in-memory implementations in domain... again, more layers. Some say that injecting DAOs does break PI too.
The hardest option
The Entity could be wrapped around a lazy-loader which decides where data should come from. It could be both in-memory and in-database, and it could handle any operations which need transactions and so on. Complex layer though, but might be generic to some extent perhaps...? Have a read about it here
Do you know any other solution? Or maybe I'm missing something in mentioned ones. Please share your thoughts!
I achieve persistence ignorance (almost) for free, as a side effect of proper domain modeling.
In particular:
if you correctly define each context's boundary, you will obtain small entities without any need for lazy loading (that, actually becomes an antipattern/code smell in a DDD project)
if you can't simply use SQL into your repository, map a set of DTO to your db schema, and use them into factories to initialize entity classes.
In DDD projects, persistence ignorance is relevant for the domain model itself, not for repositories, factories and other applicative code. Indeed you are very unlikely to change the ORM and/or the DB in the future.
The only (but very strong) rational behind persistence ignorance of the domain model is separation of concerns: in the domain model you should express business invariants only! Persistence is an infrastructural concern!
For example without persistence ignorance (and with lazy loading) the domain model should handle possible exceptions from the db, it's complexity grows and business rules are buried under technological details.
Personally I find it near impossible to achieve a clean domain model when trying to use the same entities as the ORM.
My solution is to model my domain entities as I see fit and ensure that any ORM entities don't leak outside of the repositories. This means that my repositories accept and return domain entities.
This means you lose "most of your ORM goodness" and end up "using your ORM for simple CRUD operations".
Both of these trade-offs are fine for me, I would rather have a clean domain model that I can use, rather than one polluted with artefacts from my DB or ORM. It also cuts down the amount of time I spend "wrestling with my ORM" to zero.
As a side-note, I find document databases a much better fit for DDD.
Once you will provide persistence mapping in you domain model:
your code depends on framework. If you decided to change this framework, you want to change persistence layer and model layer source code - more work, more changes, more merging of code etc.
your domain model jar file depends on spring/nhibernate jars etc.
your classes become larger and larger how business code and persistence related code grows
I've to admit that I dont understand harder and hardest option.
We used separated interfaces and implementations for domain entities. Provide separated mapping files using Hibernate along with repositories.
Entities are created using factory (or repository later), identifier is generated within persistence layer, entity does not need it until it's being persisted.
Lazy loading is provided by special implementation of List once:
mapping of an entity contains it
entity/aggregate is fetched from persistence layer
The only issue is related to transaction as when you use lazy-loaded collection out of transaction scope, it fails.
I would follow the simplest option unless I ran into a stone wall. There are also pitfalls such as this when you adopt pi principle.
Somtimes some compromises are acceptable.
public class Order {
private String status;//my orm does not support enum
public Status status() {
return Status.of(this.status);
}
public is(Status status) {
return status() == status;//use status() instead of getStatus() in domain model
}
}
I am working on a large-scale system for a telecom company. I am new to DDD and having hard time linking different pieces together. Our current system is build using NHibernate. It currently has well over 600 tables and all data access is done using NHibernate but for the new system we will be using EF. Below are few functional areas and examples of database tables in each functional area.
Customers
-----> CustomerDemographics
-----> CustomerPayments
-----> CustomerTransactions
RoutingEngine
-----> InboundRoutes
-----> OutboundRoutes
ProvisioningEngine
-----> InboundSwithces
-----> OutboundSwitches
-----> RouterConfigs
-----> GatewayConfigs
BillingEngine
-----> InboundTraffic
-----> OutboundTraffic
Since the system has to be unit-testable, I started abstracting actual entities with a repository pattern. One approach is to create one repository object for each each database table. Of course all these repository classes could be derived from a generic repository interface. However this will be adding quite a bit of overhead in terms of code base maintenance. In DDD, I read about this concept of Aggregates but I am not sure how it should be applied specially in context of EF. Should the Aggregate objects are container of these repositories or are these more of a container of related contexts (meaning something along the lines of Bounded DbContexts)?
One approach is to create one repository object for each each database
table.
In DDD, with the notion of persistence ignorance, database tables typically aren't in a one-to-one mapping with repositories. Instead, repositories should be one-to-one with aggregates.
Of course all these repository classes could be derived from a generic
repository interface.
The repository pattern can be a slippery slope. While it is great for encapsulation, it is easy to get carried away with needless abstraction. Take a look here for an alternative perspective.
Should the Aggregate objects are container of these repositories or
are these more of a container of related contexts (meaning something
along the lines of Bounded DbContexts)?
It seems that what you call "functional areas" are called bounded contexts (BC) in DDD. (Not DbContext in EF). Also, aggregates aren't containers of repositories, they contain a group of related entities from your domain model. The stereotypical example is the sales order model where you have an order aggregate which consists of the order aggregate root and various entities and value objects such as order line items. As stated above, aggregates are the domain objects which repositories should provide access to. Therefore, you should structure your repositories around aggregates, but of course first you have to identify your aggregates and BCs. Take a look at Effective Aggregate Design by Vaughn Vernon.
Also, Having 600 tables in a single BC seems like too much and is a potential sign that you have multiple BCs at play. What BCs achieve is functional cohesion, which in turn is most fitting way to group ("aggregate" conflicts with DDD term) your repositories.
Aggregates in DDD are consistency boundaries - islands of transactional consistency. When you have a cluster of objects in a domain model that should be kept transactionally consistent and can be treated as a meaningful conceptual whole then you likely have an aggregate. Across aggregates you can have eventual consistency.
If you are doing new development, I would recommend initially modeling your domain in objects first, then work out what the database schema should be as a later pass.
I would recommend breaking your one huge model into many more specialized domain models, perhaps along the broad lines of functional areas, noting carefully what key business scenarios each model is intended to handle (i.e. what hard business problems are you trying to solve with each model). Each model will need to have a good boundary around it (i.e. it should be within explicitly defined bounded context), where it is clear what belongs to one model and what belongs to another. This is usually accomplished by aligning the model boundary with some kind of subsystem boundary (architecturally speaking).