In the Vaughn Vernon's Domain-Driven Design Distilled book we can read that we should try to avoid creating technical abstractions that are perhaps too abstract and try to be more explicit by sticking to the concepts of the Ubiquitous Language.
Where I work we've built several tracking applications and in almost every of them there is the problem of having multiple specializations of the same thing, most likely with common behaviors, but different data and validation rules.
For instance, imagine an incident logging application where various kind of incidents are reported over the phone (e.g. car accident, fire, robbery). The information gathering process is similar to every incidents, but the captured data may vary widely as well as the validation rules that constrains this data.
So far, we have always solved these kind of problems with very technical abstractions (this is an oversimplified model, but you should get the idea):
As you can see, the DataValidationRules, DataFields and DataEntries abstractions have very little to do with the business of incident logging. Actually, they are part of a very generic solution to the problem of representing multiple entity specializations with different data in any domain.
I'd like to move away from this kind of very abstract model, but at the same time I do not see what would be the correct approach in making the business concepts explicit. I understand that the answer would be different in every domain, but in essence, should I be looking into having a single class per specialization? E.g. CarAccidentIndicent, FireIncident and RobberyIncident?
With a very limited number of specializations it seems like it could be manageable, but what if I have hundreds of them?
What about the UI? That means I'd have to move away from a generic way of generating the UI as well.
After thinking a little more about it I think I may have found a better and simpler way to express my concerns when it comes to DDD, OO and modeling many specializations.
On the one hand I want to come up with a model that is faithful to the Ubiquitous Language (UL) and model domain concepts explicitly. On the other hand I'm trying to respect the "favor composition over inheritance" mantra I'm so used to apply.
It seems that boths are conflicting because in order to enable composition I'll have to introduce abstractions that are most likely not part of the UL (e.g. Entity--Field composition) and when it comes to explicit modeling I do not see any other way than inheritance with one class per specialization.
Am I wrong in trying to avoid inheritance to represent hundreds of specialized entities that mainly differ in terms of data structure, not behaviors?
Then again, assuming they did differ a lot in terms of behaviors as well I'd have the same dilemma.
Just to be more explicit on the design choices:
In one scenario, composition would be achievable dynamically without requiring multiple classes per specialized compositions:
class Incident {
Set<Detail> details;
IncidentType type;
}
interface Detail {
public DetailType type();
}
class SomeDetail implements Detail {
...
}
class SomeOtherDetail implements Detail {
...
}
In the other scenario compositions are static and do require one class per specialized composition:
class CarAccidentIncident extends Incident {
SomeDetail someDetail;
SomeOtherDetail someOtherDetail;
}
class SomeDetail {}
class SomeOtherDetail {}
Obviously, the second approach is more explicit and offers a natural home for specific behaviors and rules. In the first approach we would have to introduce some abstract and technical concepts like Operation and DetailValidation which may not align well with the UL.
With a small number of different specializations I'd probably choose the latter without a second though, but because there are many of them it seems like I'm leaning more towards dynamic composition (even thought being dynamic is not required). Should I?
When to use DDD?
The thing is, DDD is not necessarily the right fit for all systems. It's particularly well suited to large systems with complex business rules.
If the business rules that need expressing to capture the essence of a FireIncident are simple enough to be encoded in a DataValidationRules record and a set of DataFields, then that suggests that perhaps those rules do not require the complexity of a DDD implementation.
The Domain of Data Validation
However, if you acknowledge that, you can shift your perspective towards intending to actually build a pure data validation engine. The domain of data validation should include entities such as data validation rules, and data fields, and would contemplate such questions related to the lifecycles of rules and fields - e.g. 'what happens if a validation rule changes - do all existing records that have previously been validated need revalidation?'
If the lifecycle of a data validation rule itself is complex enough to warrant it, then by all means, use DDD to implement that domain, although you may still choose to use CRUD if you find there are no complex rules or processes in the domain of data validation.
Who are your Domain Experts?
The further consequence of that is that your domain experts are no longer your end users (the people who know about car accidents and fire incidents) they are now the people (most likely specialists) who craft the validation rules and fields. If using DDD, you need to be asking them what types of rules they need and how they need the rules to work, and implementing using the Ubiquitous Language that they use to talk about the art and process of crafting validation rules.
Those people, in turn, would be 'programming' a next level system (you might say they are using a 4GL language tailored to the domain of incident logging) using your data validation engine. The thing is, their domain experts would be the people who know about car accidents. But the specialists wouldn't strictly be using DDD to craft the rules of a car accident, because they would not be expressing their model in software, but in the constrained language of your data capture and validation engine.
Additions following Update
Have been pondering this since your update and had a few more thoughts/questions:
Data Validation vs Entity Lifecycle/Behavior
Most of your concern is around representing data validation rules on create/update. Something that would help to understand is - what behavior/rules are represented by your entities other than data validation? i.e. in an incident management system, you might track an incident through a set of states such as Reported, WaitingForDispatch, ResponseEnRoute, ResponseOnSite, Resolved, Debriefed? In an insurance system you might track Reported, Verified, AwaitingFunding, Closed, etc.
The reason I ask, is that in the absence of such lifecycle behavior - if the main purpose of your system is pure data validation, then I return to my original thought of wondering if DDD is really the right approach for this system, as DDD brings greatest value when there is complex behavior to be modelled.
If you do have such lifecycles or other complex behavior - then one possibility is to consider the approach from the perspective of different bounded contexts - i.e. have one bounded context for data validation - which uses the approach you've described with more technical abstractions - as it is an efficient way to represent the validations - but another context from the perspective of lifecycle management, in which you could focus more on business abstractions - if all incidents follow similar set of lifecycles, then that context would have a much smaller number of specific entities.
Keeping entities sync'd between contexts is a whole 'nother topic, but not too troublesome if you adopt a service bus or event type technology and publish events when things change.
Updates to Validation Rules?
How do your business experts express requests for changes to validation rules? And how do you implement them? I'm guessing from what you've said, they probably express them in domain terms such as 'FireIncident'. But the implementation is interesting - do you have to craft data modification statements in SQL which get applied as part of a deployment?
Inheritance vs Composition
It seems that boths are conflicting because in order to enable composition I'll have to introduce abstractions that are most likely not part of the UL (e.g. Entity--Field composition)
I do not think this is true - composition does not have to require introducing technical abstractions. With either composition or inheritance, the goal is to distil insights into the domain to discover common patterns.
e.g. look for common behaviours or data validation sets and find the business language term that describes this commonality. e.g. You might find RobberyIncident and FireIncident both apply to Buildings.
If using inheritence you might create a BuildingIncident and RobberyIncident and FireIncident would extend BuildingIncident.
If using composition, you might create a valueobject to represent a Building and both RobberyIncident and FireIncident would contain a Building property. However RobberyIncident would also contain a Robbery property and FireIncident would also contain a Fire property. CarAccidentIncident and CarRobberyIncident would both contain a Car property, but CarRobberyIncident would also contain a Robbery property of the same type as the Robbery property on RobberyIncident - assuming they are truly common behaviours.
You may still have hundreds of classes representing specialised incident types, but they are simply composed of a set of value object properties representing the set of common patterns they are composed of - and those value objects can and should be in terms of ubiquitious language concepts.
My take on this is that not all information is pertinent to the domain.
I think that in many instances we try to apply techniques in an "all-or-nothing" approach whereas we may need to be focusing on the "right tool for the job". In the answer provided by Chris he asks the question "When to use DDD?" and mentions "The thing is, DDD is not necessarily the right fit for all systems." I would argue that DDD may not be appropriate for some parts of a system.
Would DDD be useful to create, say, a word processing application? I don't really think so. Although some good old proper OO would go a long way.
DDD is absolutely great for business behaviour focused bits of a system. However, there are going to be bits that can be modeled in a more technical/generic way that feed into more interesting business functionality. I'm sure that those incidents end up in some business process. An example may be a Claim. The business is very interested in tracking a claim and the claim amount, but where that claim came from isn't all too interesting. For all intents and purposes the "initiating documentation" may be filled in using pen and paper and scanned in to be linked to said claim. One could even start a new claim on the system using a plain text input.
I have been involved in a number of systems where a lot of peripheral data was sucked into the system but actually it wasn't really contributing much (law of diminishing returns and such).
I once worked on a loan system. The original 20 year-old system was re-written in C#. The main moving bits:
Client
Loan Amount
Payment schedule
Financial transactions (interest, payments, etc.)
All-in-all it is really a simple system. Well, 800+ tables later and stacks of developers/BAs and the system is somewhat of a monster. One could even capture stock and title deeds as guarantee. Now, my take would be to scan in some of this information and link it to the loan. However, somehow some business folks decide that they absolutely "must have" this information in the system. It isn't core though, I would say.
On the other end, another system I worked on calculated premiums. It was modeled quite business-like and was quite a maintenance nightmare. It was then re-written very generically by simply defining calculations that work on given inputs. There were some lookup tables for values and so on but no business processing as such.
Sometimes we may need to abstract moving bits into something that makes sense as an input or output and then use that in our domain. I think the UL should be used by ourselves and domain experts but it doesn't mean that we are not going to end up using technical concepts that are not part of the UL, and I think that that is okay. I'm sure a domain expert wouldn't care much for a SqlDbConnection even though we are going to using one of those in our code :) --- similarly we could model some structures outside of the domain proper.
In response to your update and question: I would not create a concrete class unless it really does feature in the UL in a big way. On a side note, I still favour composition over inheritance. I typically implement interfaces where necessary and go with abstract classes when inheriting, just to place some default behaviour when it helps.
The UL, as with any design, represents a model with nuances. We can apply DDD without using domain events. When we do use domain events we may even go with event sourcing. Event sourcing has very little to do with the UL in much the same way that the terms "Aggregate", "Entity", or "Value Object" would. The UL is going to be specific to the domain / domain experts and when we, as domain modelers, talk to each other we can describe various models in terms of DDD tactical patterns in order to bring across some of the specific UL concepts.
We have to listen to how a domain expert describes the problem space. As soon as we hear "When", as stated in so many other places, we know that we are probably dealing with an event. In much the same way we can listen to how a domain expert talks about the aggregates. For instance (totally bogus example):
"When a customer is registered we need to inform the supervisor of the CSR that initiated the request"
More loosely related to your example:
"When an incident takes place we need to capture some specific details regarding the incident. Depending on the type we need to capture different bits and validate that we have sufficient data to process our claim
Between these two we can see a distinct difference in how they are referring to interacting with the problem space. When a domain expert thinks of something in very broad terms I think it is prudent that we do the same.
On the other hand, should the conversation be more along the lines of this:
"When a car accident is registered we need to assign an assessor an wait for an assessment report that has to answer..."
Now we have something much more specific. These are, necessarily, mutually exclusive in that if they only ever talk about specifics then we go with "specific". If they first mention in broad terms and then specifics, we can also work in broad terms.
This is where our modeling is tricky to get right. It is the same nuance as we have in the Address as an aggregate vs value object "debate". It all depends on the context.
These things are going to be tricky and dependent on the domain in order to get right. As Eric Evans did mention: it may take a couple of models to get something that fits just right. This is necessarily so based on one's experience with the domain.
Let's say I've made a list of concepts I'll use to draw my Domain Model. Furthermore, I have a couple of Use Cases from which I did a couple of System Sequence Diagrams.
When drawing the Domain Model, I never know where to start from:
Designing the model as I believe the system to be. This is, if I am modelling a the human body, I start by adding the class concepts of Heart, Brain, Bowels, Stomach, Eyes, Head, etc.
Start by designing what the Use Cases need to get done. This is, if I have a Use Case which is about making the human body swallow something, I'd first draw the class concepts for Mouth, Throat, Stomatch, Bowels, etc.
The order in which I do things is irrelevant? I'd say probably it'd be best to try to design from the Use Case concepts, as they are generally what you want to work with, not other kind of concepts that although help describe the whole system well, much of the time might not even be needed for the current project. Is there any other approach that I am not taking in consideration here? How do you usually approach this?
Thanks
Whether DDD, or not, I would recommend with determining the ubiquitous language (UL) by interviewing the product owner(s). Establishing communication in a way that will have you and the product owners speaking the same language not only aides in communication, but being able to discuss the project in common terms tends to help the domain model define itself.
So, my answer is basically to discuss, listen, and learn. Software serves a need. Understanding the model from the viewpoint of the experts will lay the solid groundwork for the application.
I'd start by a drawing a class diagram with all the relationships and implement only the classes that are necessary according to the requirements of your application.
You can use an anemic approach (attributes plus getters and setters) to keep things simple and avoid the step of writing business logic in the same step. With an anemic model, the logic would go into a corresponding Service class. That way you can consider Use Cases later on.
I know some people don't appreciate this way of doing things but it does help with maintenance and avoids some dependency issues.
Answer to devoured elysium's question below:
In terms of analysis, starting with Use cases (What) and then proceeding to the class diagram (How) sounds like a good rule of thumb. Personally, I'd do the Sequence diagram (When and Who?) afterwards, as you'd need to know between which processes/objects messages need to be sent.
Beyond that my take on things is that UML is simply a way to model a system/project and not a methodology by itself (unlike Merise, RAD, RUP, Scrum, etc.). There is nothing stopping someone starting off with any diagram as long as they have the sufficient information to complete it. In fact, they should be done simultaneously since each of the diagrams is a different perspective of the same system/project.
So, all in all it depends on how you go about the analysis. During my studies I was taught the rigid waterfall approach, where you do a complete analysis from start to finish before producing some code. However, things can be different in practice, as the imperative might be to produce a working application in the least time possible.
For example, I was introduced to the Scrum methodology recently for an exercise involving the creation of a web site where people can post their fictions. As there was a time constraint and a clear vision of what should be achieved, we started right away with a bare bones class diagram to represent the domain model. The Use cases were then deduced from a series of mock screens we'd produced.
From memory, the classes were Story, Chapter, User and Category. This last class was phased out in favour of a more flexible Tag class. As you'd imagine, the complete class diagram of the existing project would be much more complex due to applying domain driven design and the specificities of the Java programming language.
This approach could be viewed as sloppy. However, a web site like this could easily be made in a couple of weeks using an iterative process and still be well designed. The advantage an iterative process has over the waterfall approach is that you can continually adjust requirements as you go. Frequent requirements changing is a reality, as people will often change their minds and the possibility of producing a working application after each iteration allows one to stay on course so to speak.
Of course, when you're presenting a project to a client, a complete analysis with UML diagrams and some mock screens would be preferable so they have an idea of what you're offering. This is where the UML comes in. Once you've explained some of the visual conventions, an individual should be able to understand the diagrams.
To finish off, if you're in the situation where you're trying to determine what a client wants, it's probably a good idea to gradually build up a questionnaire you can bring with you. Interviewing a person is the only way you can determine what concepts/features are really needed for an application, and you should expect to go back in order to clarify certain aspects. Another tip would be to do some quick research on the web when you're confronted with a subject matter you're unfamiliar with.
In your example, this would be to go through the basics of anatomy. Among other things, this will help you decide what the model should contain and what granularity it should have (What group of organs should be considered? How precise does it need to be? Do only the organs need to modeled or should they be decomposed into their constituents like tissues, cells, chemical composition, etc. ?).
I think the place to start would be whatever feels logical and comfortable. It's probably best to start with the use cases, as they give you clear direction and goals, and help you avoid YAGNI situations. Given that you should be trying to develop a strong domain model, it shouldn't really matter, as the whole picture of the domain is important.
I would like to share my experience for such type of situations. I usually start with writing tests and code. And try to cover one end to end use case. This gives me fair enough idea about problem and at the end I also have something working with me which I can show case to my client. Most of the time subsequent stories build on top of previous one, but it also happens to me that subsequent stories require changes in the previous model I came up with. But this does not impact me as I already have good test coverage. In this way I came up with the model which fits for the current problem, not the model which maps the real world.
You start with Business Requirements which can be formalized or not. If formalized you would use Use Case Diagrams.
For example here are use case diagrams for an e-commerce app:
http://askuml.com/blog/e-commerce/
http://askuml.com/files/2010/07/e-commerce-use-case.jpg
http://askuml.com/files/2010/07/e-commerce-use-case2b.jpg
From these use cases, you can naturally deduce the business entities: product, category of product, shopping cart, ... that is start to prepare class diagrams.
This is best practice in many methodologies but this is also just common sense and natural.
Short answer
Pick a use case, draw some collaboration diagram (and a class diagram) to realize the domain objects involved. Concentrate only on those objects participated in order to accomplish use case goal. Write TDD test case to set the expectation and gradually model your domain classes to meet the expectations. TDD is very helpful to understand the expected behaviors and it helps to get the cleaner domain model. You will see your domain evolve gradually along with the TDD expectations.
Long answer
My personal experience with DDD was not easy. That was because we didn't have necessary foundations in the first place. Our team had many weak points in different areas; requirements were not captured properly and we only had a customer representative who was not really helpful (not involved). We didn't have a proper release plan and developers had a lack of Object Oriented concepts, best principles and so on. The major problem we had was spending so much time on trying to understand the domain logic. We sketched many class diagrams and we never got the domain model right, so we stopped doing that and found out what went wrong. The problem was that we tried too hard to understand the domain logic and instead of communicating we made assumptions on the requirements. We decided to change our approach, we applied TDD, we started writing the expected behavior and coded the domain model to meet the TDD's expectations. Sometimes we got stuck writing TDD test cases because we didn't understand the domain. We straight away talked to the customer representative and tried to get more input. We changed our release strategy; applied agile methodology and release frequently so that we got real feedback from the end user. However, needed to ensure the end user expectation was set at the right level. We refactored based on the feedback, and in that way the domain model evolved gradually. Subsequently, we applied design patterns to improve reusability and maintainability. My point here is that DDD alone cannot survive, we have to build the ecosystem that embraces the domain, developers must have strong OOP concepts and must appreciate TDD and unit test. I would say DDD sits on top of all the OOP techniques and practices.
I look around and see some great snippets of code for defining rules, validation, business objects (entities) and the like, but I have to admit to having never seen a great and well-written business layer in its entirety.
I'm left knowing what I don't like, but not knowing what a great one is.
Can anyone point out some good OO business layers (or great business objects) or let me know how they judge a business layer and what makes one great?
Thanks
I’ve never encountered a well written business layer.
Here is Alex Papadimoulis's take on this:
[...] If you think about it, virtually every line of code in a software
application is business logic:
The Customers database table, with
its CustomerNumber (CHAR-13),
ApprovedDate (DATETIME), and
SalesRepName (VARCHAR-35) columns:
business logic. If it wasn’t, it’d
just be Table032 with Column01,
Column02, and Column03.
The
subroutine that extends a ten-percent
discount to first time customers:
definitely business logic. And
hopefully, not soft-coded.
And
the code that highlights past-due
invoices in red: that’s business
logic, too. Internet Explorer
certainly doesn’t look for the strings
“unpaid” and “30+ days” and go, hey,
that sure would look good with a #990000 background!
So how then is possible to encapsulate all of this business logic
in a single layer of code? With
terrible architecture and bad code of
course!
[...] By implying that a system’s architecture should include a layer dedicated to business logic, many developers employ all sorts of horribly clever techniques to achieve that goal. And it always ends up in a disaster.
I imagine this is because business logic, as a general rule, is arbitrary and nasty. Garbage in, garbage out.
Also, most of the really good business layers are most probably proprietary. ;-)
Good business layers have been designed after a thorough domain analysis. If you can capture the business' semantics and isolate it from any kind of implementation, whether that be in data storage or any specific application (including presentation), then the logic should be well-factored and reusable in different contexts.
Just as a good database schema design should capture business semantics and isolate itself from any application, a business layer should do the same and even if a database schema and a business layer describe the same entities and concepts, the two should be usable in separate contexts--a database schema shouldn't have to change even when the business logic changes unless the schema doesn't reflect the current business. A business layer should work with any storage schema provided that it's abstracted via an intermdiate layer. For example, the ADO.NET Entity framework lets you design a conceptual schema which maps to the business layer and has a separate mapping to the storage schema which can be changed without recompiling the business object layer or conceptual layer.
If a person from the business side of things can look at code written with the business layer and have a rough idea of what's going on then it might be a good indication that the objects were designed right--you've succesfully conveyed a solution in the problem domain without obfuscating it with artifacts from the solution domain.
I've always been stuck between a rock and a hard place. Ideally, your business logic wouldn't be at all concerned with database or UI-related issues.
Keys Cause Problems
Still, I find things like primary and foreign keys causing problems. Even tools like Entity Framework don't completely eliminate this creep. It can be extremely inefficient to convert IDs passed as POST data into their respective objects, only to pass this to the business layer, which then passes them to the data layer to just be stripped down again.
Even NoSQL databases come with problems. They tend to return full object models, but they usually return more than you need and can lead to problems because you're assuming that object model won't change. And keys are still found in NoSQL databases.
Reuse vs. Overhead
There's also the issue of code reuse. It's pretty common for data layers to return fully populated objects, including every column in that particular table or tables. However, often business logic only cares about a limited subset of this information. It lends itself to specialized data transfer objects that only carry with them the relavent data. Of course, you need to convert between representations, so you create a mapper class. Then, when you save, you need to somehow convert these lesser objects back into the full database representation or do a partial UPDATE (meaning a another SQL command).
So, I see a lot of business layer classes accepting objects mapping directly to database tables (data transfer objects). I also see a lot of business layers accepting raw UI values (presentation objects), as well. It's also not unusual to see business layers calling out to the database mid-computation to retrieve needed data. To try to grab it up-front would probably be inefficient (think about how and if-statement can affect the data that gets retrieved) and lazy loaded values result in a lot of magic or unintended calls out to the database.
Write Your Logic First
Recently, I've been trying to write the "core" code first. This is the code that performs the actual business logic. I don't know about you, but many times when going over someone else's code, I ask the question, "But, where does it do [business rule]?" Often, the business logic is so crowded with concerns about grabbing data, transforming it and whatnot that I can't even see it (needle in a hay stack). So, now I implement the logic first and as I figure out what data I need, I add it as a parameter or add it to a parameter object. Getting the rest of the code to fit this new interface usually falls on a mediator class of some kind.
Like I said, though, you have to keep a lot in mind when writing business layers, including performance. The approach above has been useful lately because I don't have rights to version control or the database schema yet. I am working in a dark room with just my understanding of the requirements so far.
Write with Testing in Mind
Utiltizing dependency injection can be useful for designing a good architecture up-front. Try to think about how you would test your code without hitting a database or other service. This also lends itself to small, reusable classes that can run in multiple contexts.
Conclusion
My conclusion is that there really is no such thing as a perfect business layer. Even in the same application, there can be times when one approach only works 90% of the time. The best we can do is try to write the simplest thing that works. For the longest time I avoided DTOs and wrapped ADO.NET DataRows with objects so updates were immediately recorded in the underlying DataTable. This was a HUGE mistake because I couldn't copy objects and constraints caused exceptions to be thrown at weird times. I only did it to avoid setting parameter values explicitly.
Martin Fowler has blogged extensively about DSLs. I would recommend starting there.
http://martinfowler.com/bliki/dsl.html
It was helpful to me to learn and play with CSLA.Net (if you are a MS guy). I've never implemented a "pure" CSLA application, but have used many of the ideas presented in the architecture.
Your best bet is keep looking for that elusive magic bullet and use the ideas that best fit the problem you are solving. Keep it simple.
One problem I find is that even when you have a nicely designed business layer it is hard to stop business logic leaking out, and development tools tend to encourage this. For example as soon as you add a validator control to an ASP.NET WebForm you have let business logic leak out into the view. The validation should occur in the business layer and only the results of it displayed in the view. And as soon as you add constraints to a database you then have business logic in your database as well. DBA types tend to disagree strongly with this last point though.
Neither have I. We don't create a business layer in our applications. Instead we use MVC-ARS. The business logic is embedded in the (S) state machine and the (A) action.
Possibly because in reality we are never able to fully decouple the business logic from the "process", the inputs, outputs, interface and that ultimately people find it hard to deal with the abstract let alone relating it back to reality.
What do you suggest for Data Access layer? Using ORMs like Entity Framework and Hibernate OR Code Generators like Subsonic, .netTiers, T4, etc.?
For me, this is a no-brainer, you generate the code.
I'm going to go slightly off topic here because there's a bigger underlying fallacy at play. The fallacy is that these ORM frameworks solve the object/relational impedence mismatch. This claim is a barefaced lie.
I find the best way to resolve the object/relational impedance mismatch is to either use OOP exclusively and use an object database or use the idioms of the relational database exclusively and ignore OOP.
The abstraction "everything is a table" is to me, much more powerful than the abstraction "everything is a class." It takes less code, less intellectual effort and leads to faster code when you code to the database rather than to an object model.
To me this seems obvious. If your application is data driven then surely your code should be data driven too? Yet to say this is hugely controversial.
The central problem here is that OOP becomes a really leaky abstraction when used in conjunction with a database. Code that look perfectly sensible when written to the idioms of OOP looks completely insane when you see the traffic that code generates at the database. When that messiness becomes a performance problem, OOP is the first casualty.
There is really no way to resolve this. Databases work with sets of data. OOP focus on instances of classes. Trying to marry the two is always going to end in divorce.
So to answer your question, I believe you should generate your classes and try and make them map the underlying database structure as closely as possible.
Perhaps controversially, I've always felt that using code generators for the ADO.NET plumbing is fundamentally solving the wrong problem.
At some point, hopefully not too long after learning about Connection Strings, SqlCommands, DataAdapters, and all that, we notice that:
Such code is ugly
It is very boring to write
It's very easy to miss something if you're doing it by hand
It has to be repeated every time you want to access the database
So, the problem to solve is "how to do the same thing lots of times fast"?
I say no.
Using code generators to make this process quick still means that you have a ton of code, all the same, all over your business classes (or your data access layer, if you separate that from your business logic).
And then, if you want to do something generically (like track stored procedure usage, for instance), you end up having to customise your code generator if it doesn't already have the feature you want. And even if it does, you still have to regenerate everything all the time.
I like to do things once, not many times, no matter how fast I can do them.
So I rolled my own Data Access class that knows how to add parameters, set up and close connections, manage transactions, and other cool stuff. It only had to be written once, and calling its methods from a Business object that needs some database stuff done consists of one line of code.
When I needed to make the application support multithreaded database accesses, it required a change to the Data Access class only, and all the business classes just do what they already did.
There is no right answer it all depends on your project. As Simon points out if your application is all data driven, then it might make sense depending on the size and complexity of the domain to use non oop paradigm. I had a lot of success building a system using a Transaction Script pattern, which passed XML Messages around the system.
However this system started to break down after five or six years as the application grew in size and complexity (5 or 6 webs, several web services, tons of COM+ components, legacy and .net code, 8+ databases with 800+ tables 4,000+ procedures). No one knew what anything was, and duplication was running rampant.
There are other ways to alleviate the maintance then OOP; however, if you have a very complex domain then hainvg a rich domain model is ideal IMHO, as it allows for the business rules to be expressed in nice encapsulated components.
To answer your question, avoid code generators if you can. Code generators are a recipe for disaster, but if you do go with code generation do not modify the generated code. Also be sure to have a good process in place that is easy for developers to get the new generated code.
I recommend using either the following: ORM or hand roll a lightweight DAL. I am currently transitioning a project over to nHibernate off my hand rolled DAL and am having a lot of success; however, I like having the option of using either option. Further if you properly seperate your concerns (Data Access from Business Layer from Presentation) you can have a single service layer that might talk to a Dao (Data Access Object) that for one object is an ORM but for another is hand rolled). I like this flexibility as it allows to apply the best tool to the job.
I like nHibernate over a hand rolled DAL because while my DAL does abstract away most of the ADO.Net code you still have to write the code that takes a data reader to an object or an object and creates the parameters.
I've always preferred to go the code generator route, especially in C# where you can make use of extended classes to add functionality to the basic data objects.
Hate to say this, but it depends. If you find an ORM tool that fits your needs go for it. We wrote our own system in small steps while developing the application. We are using C++ and there are not that many tools out there anyway. Ours ended up being a XML description of the database, from that the SQL generation script and the basic object layer and metadata were generated.
Do your homework and evaluate theses tools and you will find a good fit for your needs.
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I really need to see some honest, thoughtful debate on the merits of the currently accepted enterprise application design paradigm.
I am not convinced that entity objects should exist.
By entity objects I mean the typical things we tend to build for our applications, like "Person", "Account", "Order", etc.
My current design philosophy is this:
All database access must be accomplished via stored procedures.
Whenever you need data, call a stored procedure and iterate over a SqlDataReader or the rows in a DataTable
(Note: I have also built enterprise applications with Java EE, java folks please substitute the equvalent for my .NET examples)
I am not anti-OO. I write lots of classes for different purposes, just not entities. I will admit that a large portion of the classes I write are static helper classes.
I am not building toys. I'm talking about large, high volume transactional applications deployed across multiple machines. Web applications, windows services, web services, b2b interaction, you name it.
I have used OR Mappers. I have written a few. I have used the Java EE stack, CSLA, and a few other equivalents. I have not only used them but actively developed and maintained these applications in production environments.
I have come to the battle-tested conclusion that entity objects are getting in our way, and our lives would be so much easier without them.
Consider this simple example: you get a support call about a certain page in your application that is not working correctly, maybe one of the fields is not being persisted like it should be. With my model, the developer assigned to find the problem opens exactly 3 files. An ASPX, an ASPX.CS and a SQL file with the stored procedure. The problem, which might be a missing parameter to the stored procedure call, takes minutes to solve. But with any entity model, you will invariably fire up the debugger, start stepping through code, and you may end up with 15-20 files open in Visual Studio. By the time you step down to the bottom of the stack, you forgot where you started. We can only keep so many things in our heads at one time. Software is incredibly complex without adding any unnecessary layers.
Development complexity and troubleshooting are just one side of my gripe.
Now let's talk about scalability.
Do developers realize that each and every time they write or modify any code that interacts with the database, they need to do a throrough analysis of the exact impact on the database? And not just the development copy, I mean a mimic of production, so you can see that the additional column you now require for your object just invalidated the current query plan and a report that was running in 1 second will now take 2 minutes, just because you added a single column to the select list? And it turns out that the index you now require is so big that the DBA is going to have to modify the physical layout of your files?
If you let people get too far away from the physical data store with an abstraction, they will create havoc with an application that needs to scale.
I am not a zealot. I can be convinced if I am wrong, and maybe I am, since there is such a strong push towards Linq to Sql, ADO.NET EF, Hibernate, Java EE, etc. Please think through your responses, if I am missing something I really want to know what it is, and why I should change my thinking.
[Edit]
It looks like this question is suddenly active again, so now that we have the new comment feature I have commented directly on several answers. Thanks for the replies, I think this is a healthy discussion.
I probably should have been more clear that I am talking about enterprise applications. I really can't comment on, say, a game that's running on someone's desktop, or a mobile app.
One thing I have to put up here at the top in response to several similar answers: orthogonality and separation of concerns often get cited as reasons to go entity/ORM. Stored procedures, to me, are the best example of separation of concerns that I can think of. If you disallow all other access to the database, other than via stored procedures, you could in theory redesign your entire data model and not break any code, so long as you maintained the inputs and outputs of the stored procedures. They are a perfect example of programming by contract (just so long as you avoid "select *" and document the result sets).
Ask someone who's been in the industry for a long time and has worked with long-lived applications: how many application and UI layers have come and gone while a database has lived on? How hard is it to tune and refactor a database when there are 4 or 5 different persistence layers generating SQL to get at the data? You can't change anything! ORMs or any code that generates SQL lock your database in stone.
I think it comes down to how complicated the "logic" of the application is, and where you have implemented it. If all your logic is in stored procedures, and all your application does is call those procedures and display the results, then developing entity objects is indeed a waste of time. But for an application where the objects have rich interactions with one another, and the database is just a persistence mechanism, there can be value to having those objects.
So, I'd say there is no one-size-fits-all answer. Developers do need to be aware that, sometimes, trying to be too OO can cause more problems than it solves.
Theory says that highly cohesive, loosely coupled implementations are the way forward.
So I suppose you are questioning that approach, namely separating concerns.
Should my aspx.cs file be interacting with the database, calling a sproc, and understanding IDataReader?
In a team environment, especially where you have less technical people dealing with the aspx portion of the application, I don't need these people being able to "touch" this stuff.
Separating my domain from my database protects me from structural changes in the database, surely a good thing? Sure database efficacy is absolutely important, so let someone who is most excellent at that stuff deal with that stuff, in one place, with as little impact on the rest of the system as possible.
Unless I am misunderstanding your approach, one structural change in the database could have a large impact area with the surface of your application. I see that this separation of concerns enables me and my team to minimise this. Also any new member of the team should understand this approach better.
Also, your approach seems to advocate the business logic of your application to reside in your database? This feels wrong to me, SQL is really good at querying data, and not, imho, expressing business logic.
Interesting thought though, although it feels one step away from SQL in the aspx, which from my bad old unstructured asp days, fills me with dread.
One reason - separating your domain model from your database model.
What I do is use Test Driven Development so I write my UI and Model layers first and the Data layer is mocked, so the UI and model is build around domain specific objects, then later I map these objects to what ever technology I'm using the the Data Layer. Its a bad idea to let the database structure determine the design of your application. Where possible write the app first and let that influence the structure of your database, not the other way around.
For me it boils down to I don't want my application to be concerned with how the data is stored. I'll probably get slapped for saying this...but your application is not your data, data is an artifact of the application. I want my application to be thinking in terms of Customers, Orders and Items, not a technology like DataSets, DataTables and DataRows...cuz who knows how long those will be around.
I agree that there is always a certain amount of coupling, but I prefer that coupling to reach upwards rather than downwards. I can tweak the limbs and leaves of a tree easier than I can alter it's trunk.
I tend to reserve sprocs for reporting as the queries do tend to get a little nastier than the applications general data access.
I also tend to think with proper unit testing early on that scenario's like that one column not being persisted is likely not to be a problem.
Eric,
You are dead on. For any really scalable / easily maintained / robust application the only real answer is to dispense with all the garbage and stick to the basics.
I've followed a similiar trajectory with my career and have come to the same conclusions. Of course, we're considered heretics and looked at funny. But my stuff works and works well.
Every line of code should be looked at with suspicion.
I would like to answer with an example similar to the one you proposed.
On my company I had to build a simple CRUD section for products, I build all my entities and a separate DAL. Later another developer had to change a related table and he even renamed several fields. The only file I had to change to update my form was the DAL for that table.
What (in my opinion) entities brings to a project is:
Ortogonality: Changes in one layer might not affect other layers (off course if you make a huge change on the database it would ripple through all the layers but most small changes won't).
Testability: You can test your logic with out touching your database. This increases performance on your tests (allowing you to run them more frequently).
Separation of concerns: In a big product you can assign the database to a DBA and he can optimize the hell out of it. Assign the Model to a business expert that has the knowledge necessary to design it. Assign individual forms to developers more experienced on webforms etc..
Finally I would like to add that most ORM mappers support stored procedures since that's what you are using.
Cheers.
I think you may be "biting off more than you can chew" on this topic. Ted Neward was not being flippant when he called it the "Vietnam of Computer Science".
One thing I can absolutely guarantee you is that it will change nobody's point of view on the matter, as has been proven so often on innumerable other blogs, forums, podcasts etc.
It's certainly ok to have open disucssion and debate about a controversial topic, it's just this one has been done so many times that both "sides" have agreed to disagree and just got on with writing software.
If you want to do some further reading on both sides, see articles on Ted's blog, Ayende Rahein, Jimmy Nilson, Scott Bellware, Alt.Net, Stephen Forte, Eric Evans etc.
#Dan, sorry, that's not the kind of thing I'm looking for. I know the theory. Your statement "is a very bad idea" is not backed up by a real example. We are trying to develop software in less time, with less people, with less mistakes, and we want the ability to easily make changes. Your multi-layer model, in my experience, is a negative in all of the above categories. Especially with regards to making the data model the last thing you do. The physical data model must be an important consideration from day 1.
I found your question really interesting.
Usually I need entities objects to encapsulate the business logic of an application. It would be really complicated and inadequate to push this logic into the data layer.
What would you do to avoid these entities objects? What solution do you have in mind?
Entity Objects can facilitate cacheing on the application layer. Good luck caching a datareader.
We should also talk about the notion what entities really are.
When I read through this discussion, I get the impression that most people here are looking at entities in the sense of an Anemic Domain Model.
A lot of people are considering the Anemic Domain Model as an antipattern!
There is value in rich domain models. That is what Domain Driven Design is all about.
I personally believe that OO is a way to conquer complexity. This means not only technical complexity (like data-access, ui-binding, security ...) but also complexity in the business domain!
If we can apply OO techniques to analyze, model, design and implement our business problems, this is a tremendous advantage for maintainability and extensibility of non-trivial applications!
There are differences between your entities and your tables. Entities should represent your model, tables just represent the data-aspect of your model!
It is true that data lives longer than apps, but consider this quote from David Laribee: Models are forever ... data is a happy side effect.
Some more links on this topic:
Why Setters and Getters are evil
Return of pure OO
POJO vs. NOJO
Super Models Part 2
TDD, Mocks and Design
Really interesting question. Honestly I can not prove why entities are good. But I can share my opinion why I like them. Code like
void exportOrder(Order order, String fileName){...};
is not concerned where order came from - from DB, from web request, from unit test, etc. It makes this method more explicitly declare what exactly it requires, instead of taking DataRow and documenting which columns it expects to have and which types they should be. Same applies if you implement it somehow as stored procedure - you still need to push record id to it, while it not necessary should be present in DB.
Implementation of this method would be done based on Order abstraction, not based on how exactly it is presented in DB. Most of such operations which I implemented really do not depend on how this data is stored. I do understand that some operations require coupling with DB structure for perfomance and scalability purposes, just in my experience there are not too much of them. In my experience very often it is enough to know that Person has .getFirstName() returning String, and .getAddress() returning Address, and address has .getZipCode(), etc - and do not care which tables are involed to store that data.
If you have to deal with such problems as you described, like when additional column breaks report perfomance, then for your tasks DB is a critical part, and you indeed should be as close as possible to it. While entities can provide some convenient abstractions they can hide some important details as well.
Scalability is interesting point here - most of websites which require enormous scalability (like facebook, livejournal, flickr) tend to use DB-ascetic approach, when DB is used as rare as possible and scalability issues are solved by caching, especially by RAM usage. http://highscalability.com/ has some interesting articles on it.
There are other good reasons for entity objects besides abstraction and loose coupling. One of the things I like most is the strong typing that you can't get with a DataReader or a DataTable. Another reason is that when done well, proper entity classes can make the code more maintanable by using first-class constructs for domain-specific terms that anyone looking at the code is likely to understand rather than a bunch of strings with field names in them used for indexing a DataRow. Stored procedures are really orthogonal to the use of an ORM since a lot of mapping frameworks give you the ability to map to sprocs.
I wouldn't consider sprocs + datareaders a substitute for a good ORM. With stored procedures, you're still constrained by, and tightly-coupled to, the procedure's type signature, which uses a different type system than the calling code. Stored procedures can be subject to modification to acommodate additional options and schema changes. An alternative to stored procedures in the case where the schema is subject to change is to use views--you can map objects to views and then re-map views to the underlying tables when you change them.
I can understand your aversion to ORMs if your experience mainly consists of Java EE and CSLA. You might want to have a look at LINQ to SQL, which is a very lightweight framework and is primarily a one-to-one mapping with the database tables but usually only needs minor extension for them to be full-blown business objects. LINQ to SQL can also map input and output objects to stored procedures' paramaters and results.
The ADO.NET Entity framework has the added advantage that your database tables can be viewed as entity classes inheriting from each other, or as columns from multiple tables aggregated into a single entity. If you need to change the schema, you can change the mapping from the conceptual model to the storage schema without changing the actual application code. And again, stored procedures can be used here.
I think that more IT projects in enterprises fail because of unmaintainability of the code or poor developer productivity (which can happen from, e.g., context switching between sproc-writing and app-writing) than scalability problems of an application.
I would also like to add to Dan's answer that separating both models could enable your application to be run on different database servers or even database models.
What if you need to scale your app by load balancing more than one web server? You could install the full app on all web servers, but a better solution is to have the web servers talk to an application server.
But if there aren't any entity objects, they won't have very much to talk about.
I'm not saying that you shouldn't write monoliths if its a simple, internal, short life application. But as soon as it gets moderately complex, or it should last a significant amount of time, you really need to think about a good design.
This saves time when it comes to maintaining it.
By splitting application logic from presentation logic and data access, and by passing DTOs between them, you decouple them. Allowing them to change independently.
You might find this post on comp.object interesting.
I'm not claiming to agree or disagree but it's interesting and (I think) relevant to this topic.
A question: How do you handle disconnected applications if all your business logic is trapped in the database?
In the type of Enterprise application I'm interested in, we have to deal with multiple sites, some of them must be able to function in a disconnected state.
If your business logic is encapsulated in a Domain layer that is simple to incorporate into various application types -say, as a dll- then I can build applications that are aware of the business rules and are able, when necessary, to apply them locally.
In keeping the Domain layer in stored procedures on the database you have to stick with a single type of application that needs a permanent line-of-sight to the database.
It's ok for a certain class of environments, but it certainly doesn't cover the whole spectrum of Enterprise applications.
#jdecuyper, one maxim I repeat to myself often is "if your business logic is not in your database, it is only a recommendation". I think Paul Nielson said that in one of his books. Application layers and UI come and go, but data usually lives for a very long time.
How do I avoid entity objects? Stored procedures mostly. I also freely admit that business logic tends to reach through all layers in an application whether you intend it to or not. A certain amount of coupling is inherent and unavoidable.
I have been thinking about this same thing a lot lately; I was a heavy user of CSLA for a while, and I love the purity of saying that "all of your business logic (or at least as much as is reasonably possible) is encapsulated in business entities".
I have seen the business entity model provide a lot of value in cases where the design of the database is different than the way you work with the data, which is the case in a lot of business software.
For example, the idea of a "customer" may consist of a main record in a Customer table, combined with all of the orders the customer has placed, as well as all the customer's employees and their contact information, and some of the properties of a customer and its children may be determined from lookup tables. It's really nice from a development standpoint to be able to work with the Customer as a single entity, since from a business perspective, the concept of Customer contains all of these things, and the relationships may or may not be enforced in the database.
While I appreciate the quote that "if your business rule is not in your database, it's only a suggestion", I also believe that you shouldn't design the database to enforce business rules, you should design it to be efficient, fast and normalized.
That said, as others have noted above, there is no "perfect design", the tool has to fit the job. But using business entities can really help with maintenance and productivity, since you know where to go to modify business logic, and objects can model real-world concepts in an intuitive way.
Eric,
No one is stopping you from choosing the framework/approach that you would wish. If you are going to go the "data driven/stored procedure-powered" path, then by all means, go for it! Especially if it really, really helps you deliver your applications on-spec and on-time.
The caveat being (a flipside to your question that is), ALL of your business rules should be on stored procedures, and your application is nothing more than a thin client.
That being said, same rules apply if you do your application in OOP : be consistent. Follow OOP's tenets, and that includes creating entity objects to represent your domain models.
The only real rule here is the word consistency. Nobody is stopping you from going DB-centric. No one is stopping you from doing old-school structured (aka, functional/procedural) programs. Hell, no one is stopping anybody from doing COBOL-style code. BUT an application has to be very, very consistent once going down this path, if it wishes to attain any degree of success.
I'm really not sure what you consider "Enterprise Applications". But I'm getting the impression you are defining it as an Internal Application where the RDBMS would be set in stone and the system wouldn't have to be interoperable with any other systems whether internal or external.
But what if you had a database with 100 tables which equate to 4 Stored Procedures for each table just for basic CRUD operations that's 400 stored procedures which need to be maintained and aren't strongly-typed so are susceptible to typos nor can be Unit Tested. What happens when you get a new CTO who is an Open Source Evangelist and wants to change the RDBMS from SQL Server to MySql?
A lot of software today whether Enterprise Applications or Products are using SOA and have some requirements for exposing Web Services, at least the software I am and have been involved with do.
Using your approach you would end up exposing a Serialized DataTable or DataRows. Now this may be deemed acceptable if the Client is guaranteed to be .NET and on an internal network. But when the Client is not known then you should be striving to Design an API which is intuitive and in most cases you would not want to be exposing the Full Database schema.
I certainly wouldn't want to explain to a Java developer what a DataTable is and how to use it. There's also the consideration of Bandwith and payload size and serialized DataTables, DataSets are very heavy.
There is no silver bullet with software design and it really depends on where the priorities lie, for me it's in Unit Testable code and loosely coupled components that can be easily consumed be any client.
just my 2 cents
I'd like to offer another angle to the problem of distance between OO and RDB: history.
Any software has a model of reality that is to some degree an abstraction of reality. No computer program can capture all the complexities of reality, and programs are written just to solve a set of problems from reality. Therefore any software model is a reduction of reality. Sometimes the software model forces reality to reduce itself. Like when you want the car rental company to reserve any car for you as long as it is blue and has alloys, but the operator can't comply because your request won't fit in the computer.
RDB comes from a very old tradition of putting information into tables, called accounting. Accounting was done on paper, then on punch cards, then in computers. But accounting is already a reduction of reality. Accounting has forced people to follow its system so long that it has become accepted reality. That's why it is relatively easy to make computer software for accounting, accounting has had its information model, long before the computer came along.
Given the importance of good accounting systems, and the acceptance you get from any business managers, these systems have become very advanced. The database foundations are now very solid and noone hesitates about keeping vital data in something so trustworthy.
I guess that OO must have come along when people have found that other aspects of reality are harder to model than accounting (which is already a model). OO has become a very successful idea, but persistance of OO data is relatively underdeveloped. RDB/Accounting has had easy wins, but OO is a much larger field (basically everything that isn't accounting).
So many of us have wanted to use OO but we still want safe storage of our data. What can be safer than to store our data the same way as the esteemed accounting system does? It is an enticing prospects, but we all run into the same pitfalls. Very few have taken the trouble to think of OO persistence compared to the massive efforts by the RDB industry, who has had the benefit of accounting's tradition and position.
Prevayler and db4o are some suggestions, I'm sure there are others I haven't heard of, but none have seemed to get half the press as, say, hibernation.
Storing your objects in good old files doesn't even seem to be taken seriously for multiuser applications, and especially web applications.
In my everyday struggle to close the chasm between OO and RDB I use OO as much as possible but try to keep inheritance to a minimum. I don't often use SPs. I'll use the advanced query stuff only in aspects that look like accounting.
I'll be happily supprised when the chasm is closed for good. I think the solution will come when Oracle launches something like "Oracle Object Instance Base". To really catch on, it will have to have a reassuring name.
Not a lot of time at the moment, but just off the top of my head...
The entity model lets you give a consistent interface to the database (and other possible systems) even beyond what a stored procedure interface can do. By using enterprise-wide business models you can make sure that all applications affect the data consistently which is a VERY important thing. Otherwise you end up with bad data, which is just plain evil.
If you only have one application then you don't really have an "enterprise" system, regardless of how big that application or your data are. In that case you can use an approach similar to what you talk about. Just be aware of the work that will be needed if you decide to grow your systems in the future.
Here are a few things that you should keep in mind (IMO) though:
Generated SQL code is bad
(exceptions to follow). Sorry, I
know that a lot of people think that
it's a huge time saver, but I've
never found a system that could
generate more efficient code than
what I could write and often the
code is just plain horrible. You
also often end up generating a ton
of SQL code that never gets used.
The exception here is very simple
patterns, like maybe lookup tables.
A lot of people get carried away on
it though.
Entities <> Tables (or even logical data model entities necessarily). A data model often has data rules that should be enforced as closely to the database as possible which can include rules around how table rows relate to each other or other similar rules that are too complex for declarative RI. These should be handled in stored procedures. If all of your stored procedures are simple CRUD procs, you can't do that. On top of that, the CRUD model usually creates performance issues because it doesn't minimize round trips across the network to the database. That's often the biggest bottleneck in an enterprise application.
Sometimes, your application and data layer are not that tightly coupled. For example, you may have a telephone billing application. You later create a separate application which monitors phone usage to a) better advertise to you b) optimise your phone plan.
These applications have different concerns and data requirements (even the data is coming out of the same database), they would drive different designs. Your code base can end up an absolute mess (in either application) and a nightmare to maintain if you let the database drive the code.
Applications that have domain logic separated from the data storage logic are adaptable to any kind of data source (database or otherwise) or UI (web or windows(or linux etc.)) application.
Your pretty much stuck in your database, which isn't bad if your with a company who is satisfied with the current database system your using. However, because databases evolve overtime there might be a new database system that is really neat and new that your company wants to use. What if they wanted to switch to a web services method of data access (like Service Orientated architecture sometime does). You might have to port your stored procedures all over the place.
Also the domain logic abstracts away the UI, which can be more important in large complex systems that have ever evolving UIs (especially when they are constantly searching for more customers).
Also, while I agree that there is no definitive answer to the question of stored procedures versus domain logic. I'm in the domain logic camp (and I think they are winning over time), because I believe that elaborate stored procedures are harder to maintain than elaborate domain logic. But that's a whole other debate
I think that you are just used to writing a specific kind of application, and solving a certain kind of problem. You seem to be attacking this from a "database first" perspective. There are lots of developers out there where data is persisted to a DB but performance is not a top priority. In lots of cases putting an abstraction over the persistence layer simplifies code greatly and the performance cost is a non-issue.
Whatever you are doing, it's not OOP. It's not wrong, it's just not OOP, and it doesn't make sense to apply your solutions to every othe problem out there.
Interesting question. A couple thoughts:
How would you unit test if all of your business logic was in your database?
Wouldn't changes to your database structure, specifically ones that affect several pages in your app, be a major hassle to change throughout the app?
Good Question!
One approach I rather like is to create an iterator/generator object that emits instances of objects that are relevant to a specific context. Usually this object wraps some underlying database access stuff, but I don't need to know that when using it.
For example,
An AnswerIterator object generates AnswerIterator.Answer objects. Under the hood it's iterating over a SQL Statement to fetch all the answers, and another SQL statement to fetch all related comments. But when using the iterator I just use the Answer object that has the minimum properties for this context. With a little bit of skeleton code this becomes almost trivial to do.
I've found that this works well when I have a huge dataset to work on, and when done right, it gives me small, transient objects that are relatively easy to test.
It's basically a thin veneer over the Database Access stuff, but it still gives me the flexibility of abstracting it when I need to.
The objects in my apps tend to relate one-to-one to the database, but I'm finding using Linq To Sql rather than sprocs makes it much easier writing complicated queries, especially being able to build them up using the deferred execution. e.g. from r in Images.User.Ratings where etc. This saves me trying to work out several join statements in sql, and having Skip & Take for paging also simplifies the code rather than having to embed the row_number & 'over' code.
Why stop at entity objects? If you don't see the value with entity objects in an enterprise level app, then just do your data access in a purely functional/procedural language and wire it up to a UI. Why not just cut out all the OO "fluff"?