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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.
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Closed 12 years ago.
Don't get me wrong - OOP currently is the best thing to structure large code bases.
But why do people try to stuff anything into an OO view?
For example: each text book about OOP contains an "introducing example" that tries to express a small view of our real world in an OO inheritance and composition and aggregation construct. And - meanwhile - we all know that it never results in the almighty OO construct that the OO model itself promised! The authors just created an illusion.
My personal opinion is, that OO is nice to structure code, but it is not suited to represent real world data and its relations. IMHO the relational model is superior, probably any other model is superior.
In OO design it became practice to recommend composition over inheritance - whenever possible. So that mighty looking model of an all-inheritance-based-world-of-objects thing that first class books suggest is just an illusion. So, OO itself may be an illusion? And current composition-centric OO models are nothing more than plain data structures with some standardized syntactic sugar - that's not much different than in pre-OOP approaches.
Another example: imagine a really f***ing complex model of our real-world. Besides anything else, there are stone blocks and humans. In an OO model, humans are mammals are animals are organic lifeforms and so on (the strictly rigid inheritance hierarchy OO imposes, you know..). The stone blocks are non-organic things, maybe they are rigid bodies or whatever, it doesn't matter.
If you are an artist and you have to find a stone block that makes a good "template" (?) for a statue of a human with given width, height an thickness, then you have to write a bunch of special case OO code to retrieve these attributes from the human model and from the stone block model. Or alternatively, your whole world model was build to support geometric queries - then it would be easy! But that leads to the conclusion that OOP sucks at representing data in a way that allows us to use it in different use cases. OOP just allows us to represent data for exactly those use cases that we have designed beforehand. Not much more. Any use besides those predeterminated cases can only be done with much fiddling. The relational model at least tries to represent data in a re-useable way. (re-useable: OOP once even occupied that word)
Why all that hate?
I work on a project that uses an ORM - and it just sucks. It started when modeling the database (because of ORM limitations), then came the time to learn the ins and outs of the ORM (and its bugs and further limitations), then came the fear of implicitly happening stuff (new thing(); thing->save() creates a new row, but where is "thing" rooted? why do people try to make objects as "independent" as possible but in the back create much more deeply rooted dependencies on freaking per-table-singletons, that communicate with connection-singletons.. oh my god .. I digress).
So many things that could have been done in a few lines of SQL and a sweet tiny query API were done in hundreds or maybe thousands of lines of "business logic" code (of course in the application layer, not in the database where the data is, and where aggregation functions like count() or sum() would be cheap). I think the people just feel better when they can work in OOP. But that is just stupid.
And the creators of ORM just want to keep the users away from the "dirty stuff". But exactly those people should not write ORM - the perfect example: I strongly believe that the ORM-creator type of people do not even know that a database table can contain compound primary keys! ;-)
So, why is OOP so occupying? It is just a half-baked abstraction, but people swear on it for everything, if you ask some, they may even tell you that OOP will create world peace.
Why is OOP so f***ing occupying/monopolizing?
It seems to me that if your business logic implementation is driving the design of your database, then somebody put the cart before the horse. I thought the idea was to develop a rational (no, I didn't say relational) data model and then implement whatever logic queries and updates the data.
My experience has been that although relational databases are great for storing and querying data from the user's perspective, trying to extend the relational model into a structured programming or OOP paradigm is difficult in the extreme. There's always a translation layer. Today everybody thinks that ORM is the solution. And although technically any translation layer that sits between a relational data store and an object oriented data access layer is ORM, when I see people talking about ORM these days they seem to be talking about some automated way of generating that ORM layer.
I'm not convinced that a generalized ORM solution exists. Every one I've seen is fraught with peril. It's a pain in the neck, but the only reliable ORM layers I've ever seen were hand coded. Granted, I haven't worked much with database stuff in the last five years or so, so things may have changed.
I'll agree with you that OOP is largely just a lot of syntactic sugar around a solid structured design. However, it's good syntactic sugar. It formalizes a lot of things that were considered "best practices" in structured programming, and adds some things (inheritance, interfaces, polymorphism, among others) that were very difficult or impossible to express in purely structured languages. We certainly could have added some or all of those features to structured languages without going all the way to OOP, but why? OOP was the obvious next step in the evolution of procedural programming languages.
OOP is just another tool in the toolbox, but keep in mind that OO has been the focus of mainstream programming languages for over 20 years now - starting with C++ and moving on to Java and C#. This probably has more to do with why the model is currently "so occupying" than anything else.
The point of OOP isn't necessarily to represent every single facet of every single object in the world. The point is to represent the stuff you care about. For instance, assume there's a house. Someone doing real estate stuff would care about the location, selling price, etc. A builder might care about the blueprint ID or something, i dunno. The point is, you model the important stuff and ignore the rest. Add to it later if you find you need more info.
Yes, this makes a "House" class tailored to the app being built, and possibly unsuitable for others. OOP's overall goal isn't to reuse classes, though sometimes that happens. The point is to bundle data with the actions that can affect that data, and to thus reduce a problem conceptually from hundreds of variables and functions to a few objects with known and tested interfaces and related behaviors.
The Human and StoneBlock should both inherit from MaterialObject, which has height width and depth attributes and even implements a biggerThan() method, when given another MaterialObject.
OO is "occupying" because it has shown to be an excellent choice for many programming problems. Similarly, the relational model has shown to be an excellent choice for many data storage and retrieval problems. When I say "many" I mean "so many that all other pale in comparison". In fact, both coupled together are an excellent combo, but there is complexity in mapping where these two paradigms meet, thus ORM.
I almost thought your question was insincere but then decided it was lack of experience (not intended as an insult, just guessing from the questions/assertions). You will find that there are problems so complex that OO is the only feasible way to model them. Not everything is a database-backed web site or reporting tool. Many systems are mostly "business logic" where the best solution is an OO solution (example from my experience: controlling and monitoring robotic aircraft and their various payloads). That being said, many of the popular database-backed web frameworks are OO+RDBMS (Rails, Grails, Java+Spring+Hibernate, etc.) because the combination is so powerful.
While there are certainly fads and sticky but outmoded paradigms, I suggest that when there are many choices (OO, functional programming, RDBMS-centric, etc.) people almost always choose what is the most productive. For at least 10 years, that has been OO for a large portion of software problems.
Because at the end of the day it's a good approximation of the way we ourselves model things. The counter that is often raised to this is that computers have no concept of objects, it's all 1s and 0s, but that analysis is as empty as saying all human thought is nothing more than neurons and electrical impulses (which it probably is, but it's just not a useful way of looking at things).
So you don't like inheritance? Nor do I. Inheritance of behaviour is the poor man's code reuse. Inheritance of interface, on the other hand, is great as it gives us polymorphism.
You don't like ORMs? No one is forcing you to use them. There is a conflict between OOP and RDMS that I don't think is easily fixed and most ORMs attempts to resolve this are quite naive. The limitations of ORM are not a flaw in OOP.
From what I understand, OOP is the most commonly used paradigm for large scale projects. I also know that some smaller subsets of big systems use other paradigms (e.g. SQL, which is declarative), and I also realize that at lower levels of computing OOP isn't really feasible. But it seems to me that usually the pieces of higher level solutions are almost always put together in a OOP fashion.
Are there any scenarios where a truly non-OOP paradigm is actually a better choice for a largescale solution? Or is that unheard of these days?
I've wondered this ever since I've started studying CS; it's easy to get the feeling that OOP is some nirvana of programming that will never be surpassed.
In my opinion, the reason OOP is used so widely isn't so much that it's the right tool for the job. I think it's more that a solution can be described to the customer in a way that they understand.
A CAR is a VEHICLE that has an ENGINE. That's programming and real world all in one!
It's hard to comprehend anything that can fit the programming and real world quite so elegantly.
Linux is a large-scale project that's very much not OOP. And it wouldn't have a lot to gain from it either.
I think OOP has a good ring to it, because it has associated itself with good programming practices like encapsulation, data hiding, code reuse, modularity et.c. But these virtues are by no means unique to OOP.
You might have a look at Erlang, written by Joe Armstrong.
Wikipedia:
"Erlang is a general-purpose
concurrent programming language and
runtime system. The sequential subset
of Erlang is a functional language,
with strict evaluation, single
assignment, and dynamic typing."
Joe Armstrong:
“Because the problem with
object-oriented languages is they’ve
got all this implicit environment that
they carry around with them. You
wanted a banana but what you got was a
gorilla holding the banana and the
entire jungle.”
The promise of OOP was code reuse and easier maintenance. I am not sure it delivered. I see things such as dot net as being much the same as the C libraries we used to get fro various vendors. You can call that code reuse if you want. As for maintenance bad code is bad code. OOP did not help.
I'm the biggest fan of OOP, and I practice OOP every day.
It's the most natural way to write code, because it resembles the real life.
Though, I realize that the OOP's virtualization might cause performance issues.
Of course that depends on your design, the language and the platform you chose (systems written in Garbage collection based languages such as Java or C# might perform worse than systems which were written in C++ for example).
I guess in Real-time systems, procedural programming may be more appropriate.
Note that not all projects that claim to be OOP are in fact OOP. Sometimes the majority of the code is procedural, or the data model is anemic, and so on...
Zyx, you wrote, "Most of the systems use relational databases ..."
I'm afraid there's no such thing. The relational model will be 40 years old next year and has still never been implemented. I think you mean, "SQL databases." You should read anything by Fabian Pascal to understand the difference between a relational dbms and an SQL dbms.
" ... the relational model is usually chosen due to its popularity,"
True, it's popular.
" ... availability of tools,"
Alas without the main tool necessary: an implementation of the relational model.
" support,"
Yup, the relational model has fine support, I'm sure, but it's entirely unsupported by a dbms implementation.
" and the fact that the relational model is in fact a mathematical concept,"
Yes, it's a mathematical concept, but, not being implemented, it's largely restricted to the ivory towers. String theory is also a mathematical concept but I wouldn't implement a system with it.
In fact, despite it's being a methematical concept, it is certainly not a science (as in computer science) because it lacks the first requirement of any science: that it is falsifiable: there's no implementation of a relational dbms against which we can check its claims.
It's pure snake oil.
" ... contrary to OOP."
And contrary to OOP, the relational model has never been implemented.
Buy a book on SQL and get productive.
Leave the relational model to unproductive theorists.
See this and this. Apparently you can use C# with five different programming paradigms, C++ with three, etc.
Software construction is not akin to Fundamental Physics. Physics strive to describe reality using paradigms which may be challenged by new experimental data and/or theories. Physics is a science which searches for a "truth", in a way that Software construction doesn't.
Software construction is a business. You need to be productive, i.e. to achieve some goals for which someone will pay money. Paradigms are used because they are useful to produce software effectively. You don't need everyone to agree. If I do OOP and it's working well for me, I don't care if a "new" paradigm would potentially be 20% more useful to me if I had the time and money to learn it and later rethink the whole software structure I'm working in and redesign it from scratch.
Also, you may be using another paradigm and I'll still be happy, in the same way that I can make money running a Japanese food restaurant and you can make money with a Mexican food restaurant next door. I don't need to discuss with you whether Japanese food is better than Mexican food.
I doubt OOP is going away any time soon, it just fits our problems and mental models far too well.
What we're starting to see though is multi-paradigm approaches, with declarative and functional ideas being incorporated into object oriented designs. Most of the newer JVM languages are a good example of this (JavaFX, Scala, Clojure, etc.) as well as LINQ and F# on the .net platform.
It's important to note that I'm not talking about replacing OO here, but about complementing it.
JavaFX has shown that a declarative
solution goes beyond SQL and XSLT,
and can also be used for binding
properties and events between visual
components in a GUI
For fault tolerant and highly
concurrent systems, functional
programming is a very good fit,
as demonstrated by the Ericsson
AXD301 (programmed using Erlang)
So... as concurrency becomes more important and FP becomes more popular, I imagine that languages not supporting this paradigm will suffer. This includes many that are currently popular such as C++, Java and Ruby, though JavaScript should cope very nicely.
Using OOP makes the code easier to manage (as in modify/update/add new features) and understand. This is especially true with bigger projects. Because modules/objects encapsulate their data and operations on that data it is easier to comprehend the functionality and the big picture.
The benefit of OOP is that it is easier to discuss (with other developers/management/customer) a LogManager or OrderManager, each of which encompass specific functionality, then describing 'a group of methods that dump the data in file' and 'the methods that keep track of order details'.
So I guess OOP is helpful especially with big projects but there are always new concepts turning up so keep on lookout for new stuff in the future, evaluate and keep what is useful.
People like to think of various things as "objects" and classify them, so no doubt that OOP is so popular. However, there are some areas where OOP has not gained a bigger popularity. Most of the systems use relational databases rather than objective. Even if the second ones hold some notable records and are better for some types of tasks, the relational model is unsually chosen due to its popularity, availability of tools, support and the fact that the relational model is in fact a mathematical concept, contrary to OOP.
Another area where I have never seen OOP is the software building process. All the configuration and make scripts are procedural, partially because of the lack of the support for OOP in shell languages, partially because OOP is too complex for such tasks.
Slightly controversial opinion from me but I don't find OOP, at least of a kind that is popularly applied now, to be that helpful in producing the largest scale software in my particular domain (VFX, which is somewhat similar in scene organization and application state as games). I find it very useful on a medium to smaller scale. I have to be a bit careful here since I've invited some mobs in the past, but I should qualify that this is in my narrow experience in my particular type of domain.
The difficulty I've often found is that if you have all these small concrete objects encapsulating data, they now want to all talk to each other. The interactions between them can get extremely complex, like so (except much, much more complex in a real application spanning thousands of objects):
And this is not a dependency graph directly related to coupling so much as an "interaction graph". There could be abstractions to decouple these concrete objects from each other. Foo might not talk to Bar directly. It might instead talk to it through IBar or something of this sort. This graph would still connect Foo to Bar since, albeit being decoupled, they still talk to each other.
And all this communication between small and medium-sized objects which make up their own little ecosystem, if applied to the entire scale of a large codebase in my domain, can become extremely difficult to maintain. And it becomes so difficult to maintain because it's hard to reason about what happens with all these interactions between objects with respect to things like side effects.
Instead what I've found useful is to organize the overall codebase into completely independent, hefty subsystems that access a central "database". Each subsystem then inputs and outputs data. Some other subsystems might access the same data, but without any one system directly talking to each other.
... or this:
... and each individual system no longer attempts to encapsulate state. It doesn't try to become its own ecosystem. It instead reads and writes data in the central database.
Of course in the implementation of each subsystem, they might use a number of objects to help implement them. And that's where I find OOP very useful is in the implementation of these subsystems. But each of these subsystems constitutes a relatively medium to small-scale project, not too large, and it's at that medium to smaller scale that I find OOP very useful.
"Assembly-Line Programming" With Minimum Knowledge
This allows each subsystem to just focus on doing its thing with almost no knowledge of what's going on in the outside world. A developer focusing on physics can just sit down with the physics subsystem and know little about how the software works except that there's a central database from which he can retrieve things like motion components (just data) and transform them by applying physics to that data. And that makes his job very simple and makes it so he can do what he does best with the minimum knowledge of how everything else works. Input central data and output central data: that's all each subsystem has to do correctly for everything else to work. It's the closest thing I've found in my field to "assembly line programming" where each developer can do his thing with minimum knowledge about how the overall system works.
Testing is still also quite simple because of the narrow focus of each subsystem. We're no longer mocking concrete objects with dependency injection so much as generating a minimum amount of data relevant to a particular system and testing whether the particular system provides the correct output for a given input. With so few systems to test (just dozens can make up a complex software), it also reduces the number of tests required substantially.
Breaking Encapsulation
The system then turns into a rather flat pipeline transforming central application state through independent subsystems that are practically oblivious to each other's existence. One might sometimes push a central event to the database which another system processes, but that other system is still oblivious about where that event came from. I've found this is the key to tackling complexity at least in my domain, and it is effectively through an entity-component system.
Yet it resembles something closer to procedural or functional programming at the broad scale to decouple all these subsystems and let them work with minimal knowledge of the outside world since we're breaking encapsulation in order to achieve this and avoid requiring the systems to talk to each other. When you zoom in, then you might find your share of objects being used to implement any one of these subsystems, but at the broadest scale, the systems resembles something other than OOP.
Global Data
I have to admit that I was very hesitant about applying ECS at first to an architectural design in my domain since, first, it hadn't been done before to my knowledge in popular commercial competitors (3DS Max, SoftImage, etc), and second, it looks like a whole bunch of globally-accessible data.
I've found, however, that this is not a big problem. We can still very effectively maintain invariants, perhaps even better than before. The reason is due to the way the ECS organizes everything into systems and components. You can rest assured that an audio system won't try to mutate a motion component, e.g., not even under the hackiest of situations. Even with a poorly-coordinated team, it's very improbable that the ECS will degrade into something where you can no longer reason about which systems access which component, since it's rather obvious on paper and there are virtually no reasons whatsoever for a certain system to access an inappropriate component.
To the contrary it often removed many of the former temptations for hacky things with the data wide open since a lot of the hacky things done in our former codebase under loose coordination and crunch time was done in hasty attempts to x-ray abstractions and try to access the internals of the ecosystems of objects. The abstractions started to become leaky as a result of people, in a hurry, trying to just get and do things with the data they wanted to access. They were basically jumping through hoops trying to just access data which lead to interface designs degrading quickly.
There is something vaguely resembling encapsulation still just due to the way the system is organized since there's often only one system modifying a particular type of components (two in some exceptional cases). But they don't own that data, they don't provide functions to retrieve that data. The systems don't talk to each other. They all operate through the central ECS database (which is the only dependency that has to be injected into all these systems).
Flexibility and Extensibility
This is already widely-discussed in external resources about entity-component systems but they are extremely flexible at adapting to radically new design ideas
in hindsight, even concept-breaking ones like a suggestion for a creature which is a mammal, insect, and plant that sprouts leaves under sunlight all at once.
One of the reasons is because there are no central abstractions to break. You introduce some new components if you need more data for this or just create an entity which strings together the components required for a plant, mammal, and insect. The systems designed to process insect, mammal, and plant components then automatically pick it up and you might get the behavior you want without changing anything besides adding a line of code to instantiate an entity with a new combo of components. When you need whole new functionality, you just add a new system or modify an existing one.
What I haven't found discussed so much elsewhere is how much this eases maintenance even in scenarios when there are no concept-breaking design changes that we failed to anticipate. Even ignoring the flexibility of the ECS, it can really simplify things when your codebase reaches a certain scale.
Turning Objects Into Data
In a previous OOP-heavy codebase where I saw the difficulty of maintaining a codebase closer to the first graph above, the amount of code required exploded because the analogical Car in this diagram:
... had to be built as a completely separate subtype (class) implementing multiple interfaces. So we had an explosive number of objects in the system: a separate object for point lights from directional lights, a separate object for a fish eye camera from another, etc. We had thousands of objects implementing a few dozen abstract interfaces in endless combinations.
When I compared it to ECS, that required only hundreds and we were able to do the exact same things before using a small fraction of the code, because that turned the analogical Car entity into something that no longer requires its class. It turns into a simple collection of component data as a generalized instance of just one Entity type.
OOP Alternatives
So there are cases like this where OOP applied in excess at the broadest level of the design can start to really degrade maintainability. At the broadest birds-eye view of your system, it can help to flatten it and not try to model it so "deep" with objects interacting with objects interacting with objects, however abstractly.
Comparing the two systems I worked on in the past and now, the new one has more features but takes hundreds of thousands of LOC. The former required over 20 million LOC. Of course it's not the fairest comparison since the former one had a huge legacy, but if you take a slice of the two systems which are functionally quite equal without the legacy baggage (at least about as close to equal as we might get), the ECS takes a small fraction of the code to do the same thing, and partly because it dramatically reduces the number of classes there are in the system by turning them into collections (entities) of raw data (components) with hefty systems to process them instead of a boatload of small/medium objects.
Are there any scenarios where a truly non-OOP paradigm is actually a
better choice for a largescale solution? Or is that unheard of these
days?
It's far from unheard of. The system I'm describing above, for example, is widely used in games. It's quite rare in my field (most of the architectures in my field are COM-like with pure interfaces, and that's the type of architecture I worked on in the past), but I've found that peering over at what gamers are doing when designing an architecture made a world of difference in being able to create something that still remains very comprehensible at it grows and grows.
That said, some people consider ECS to be a type of object-oriented programming on its own. If so, it doesn't resemble OOP of a kind most of us would think of, since data (components and entities to compose them) and functionality (systems) are separated. It requires abandoning encapsulation at the broad system level which is often considered one of the most fundamental aspects of OOP.
High-Level Coding
But it seems to me that usually the pieces of higher level solutions
are almost always put together in a OOP fashion.
If you can piece together an application with very high-level code, then it tends to be rather small or medium in scale as far as the code your team has to maintain and can probably be assembled very effectively using OOP.
In my field in VFX, we often have to do things that are relatively low-level like raytracing, image processing, mesh processing, fluid dynamics, etc, and can't just piece these together from third party products since we're actually competing more in terms of what we can do at the low-level (users get more excited about cutting-edge, competitive production rendering improvements than, say, a nicer GUI). So there can be lots and lots of code ranging from very low-level shuffling of bits and bytes to very high-level code that scripters write through embedded scripting languages.
Interweb of Communication
But there comes a point with a large enough scale with any type of application, high-level or low-level or a combo, that revolves around a very complex central application state where I've found it no longer useful to try to encapsulate everything into objects. Doing so tends to multiply complexity and the difficulty to reason about what goes on due to the multiplied amount of interaction that goes on between everything. It no longer becomes so easy to reason about thousands of ecosystems talking to each other if there isn't a breaking point at a large enough scale where we stop modeling each thing as encapsulated ecosystems that have to talk to each other. Even if each one is individually simple, everything taken in as a whole can start to more than overwhelm the mind, and we often have to take a whole lot of that in to make changes and add new features and debug things and so forth if you try to revolve the design of an entire large-scale system solely around OOP principles. It can help to break free of encapsulation at some scale for at least some domains.
At that point it's not necessarily so useful anymore to, say, have a physics system encapsulate its own data (otherwise many things could want to talk to it and retrieve that data as well as initialize it with the appropriate input data), and that's where I found this alternative through ECS so helpful, since it turns the analogical physics system, and all such hefty systems, into a "central database transformer" or a "central database reader which outputs something new" which can now be oblivious about each other. Each system then starts to resemble more like a process in a flat pipeline than an object which forms a node in a very complex graph of communication.
I'm just wondering about large projects - say an airlines reservation system, how many classes/objects it is likely to have.
Objects: customer, airplane, airport, route, ticket, order. That's about all I can think of. The project will probably be hundreds of thousands of lines of code, so are there likely to be more classes (that do un-object related things)?
If so, how many classes are there likely to (roughly) be? How many objects (as per my list so not 10,000 customer objects, how many different names) are there likely to be?
There's really no magic formula for calculating optimal number of classes. The architecture you described above may create a very, very simple airline reservation system. As you continue to refactor, add more features, and accommodate special cases, you could end up with many more classes, e.g., MealPreference, CouponCode, Terminal, Gate, Airline, Baggage, BaggageTransfer, RainCheck, FlightUpgrade etc.
As you should (if you want to be agile), only code exactly what you need at the time, planning ahead for ease of extension. However, any project is going to grow in unanticipated ways over time.
For a real world airline reservation system? Thousands. Easily.
I'd guess around half of them are "infrastructure" classes - mostly related to persistence, logging, integration, etc. Maybe a few hundred domain classes (Airline, Airplane, Flight, Passenger, FrequentFlyer, MaintenanceSchedule, WeatherDelay, etc). And then another half of them would be UI related - controllers, views, view models, etc. to support both customer and internal apps.
The number of classes will be only statistical and it knowing it won't help you to establish any best practice, but I would say it can go to thousands of classes.
What it's important is to keep in mind the best practices and naming conventions, it is important to have a good package structure and name your classes according to its purpose, also keep in mind a high level of cohesion for your classes.
So other than satisfying your curiosity the number doesn't matter.
The number of classes is really relative to the design patterns and architecture you build.
For instance, with a simple console application that adds two numbers together, you can have anywhere from 1 to 10 (rough guess) based on the implementation and abstraction used.
You really can't judge how many "objects" an application is going to contain, without knowing the architecture and patterns.
I agree that it is very dependent on a lot of things; code language, application architecture, number of 3rd party extensions (which typically contain a lot of classes that don't actually ever get called by the app, but are included with .dll or .jar files), and a lot on the habit of the developer and if they tend to use giant monolithic classes, or break down everything into an interface, abstract, simple/stub, and real implementation.
However if your just looking for statistics, I was recently working on a large project for General Motors that had I'd say roughly 3000 classes, plus 200 JSP pages, and a few dozen CSS and JS files supporting the UI. Plugged into that you have DB drivers, Spring framework, a couple apache commons libraries, Hibernate ORM, Mule, JSTL, probably some other core components... the web server itself...
This is entirely dependent on the abstraction you use. In my experience, the closer you follow the problem domain (the closer you try to make your code read like what a business person would say), the more classes you'll have.
All the other answers you've received are wise: basically, "It depends."
But in another sense, there is an optimal number of classes you should have for a given number of methods; that is, given a number of methods, there is a specific number of classes (within a non-hierarchical encapsulation context, and to a close approximation) which minimises the potential structural complexity of those methods. (The actual number is given by the second law of encapsulation, and is equal to the square root of the number of methods divided by the number of public methods per class).
So then the question becomes: how many methods will you need? By analogy to the above, in which methods are encapsulated within classes, so blocks of code are encapsulation within methods, and so the number of methods is fixed by the same second law above.
So then the quesiton is: how many code blocks will I have?
That's answered by the other responses you've received: it depends.
For more, see Encapsulation Theory here:
www.EdmundKirwan.com
Regards,
Ed.
There's been a lot of interest in Service-Oriented Architecture (SOA) at my company recently. Whenever I try to see how we might use it, I always run up against a mental block. Crudely:
Object-orientation says: "keep data and methods that manipulate data (business processes) together";
Service-orientation says: "keep the business process in the service, and pass data to it".
Previous attempts to develop SOA have ended up converting object-oriented code into data structures and separate procedures (services) that manipulate them, which seems like a step backwards.
My question is: what patterns, architectures, strategies etc. allow SOA and OO to work together?
Edit: The answers saying "OO for internals, SOA for system boundaries" are great and useful, but this isn't quite what I was getting at.
Let's say you have an Account object which has a business operation called Merge that combines it with another account. A typical OO approach would look like this:
Account mainAccount = database.loadAccount(mainId);
Account lesserAccount = database.loadAccount(lesserId);
mainAccount.mergeWith(lesserAccount);
mainAccount.save();
lesserAccount.delete();
Whereas the SOA equivalent I've seen looks like this:
Account mainAccount = accountService.loadAccount(mainId);
Account lesserAccount = accountService.loadAccount(lesserId);
accountService.merge(mainAccount, lesserAccount);
// save and delete handled by the service
In the OO case the business logic (and entity awareness thanks to an ActiveRecord pattern) are baked into the Account class. In the SOA case the Account object is really just a structure, since all of the business rules are buried in the service.
Can I have rich, functional classes and reusable services at the same time?
My opinion is that SOA can be useful, at a macro level, but each service probably still will be large enough to need several internal components. The internal components may benefit from OO architecture.
The SOA API should be defined more carefully than the internal APIs, since it is an external API. The data types passed at this level should be as simple as possible, with no internal logic. If there is some logic that belongs with the data type (e.g. validation), there should preferably be one service in charge of running the logic on the data type.
SOA is a good architecture for communicating between systems or applications.
Each application defines a "service" interface which consists of the requests it will handle and the responses expected.
The key points here are well defined services, with a well defined interface. How your services are actually implemented is irrelevant as far as SOA is concerned.
So you are free to implement your services using all the latest and greatest OO techniques, or any other methodology that works for you. ( I have seen extreme cases where the "service" is implemented by actual humans entering data on a screen -- yet everything was still text book SOA!).
I really think SOA is only useful for external interfaces (generally speaking, to those outside your company), and even then, only in cases when performance doesn't really matter, you don't need ordered delivery of messages.
In light of that, I think they can coexist. Keep your applications working and communicating using the OO philosophy, and only when external interfaces (to third parties) are needed, expose them via SOA (this is not essential, but it is one way).
I really feel SOA is overused, or at least architectures with SOA are getting proposed too often. I don't really know of any big systems that use SOA internally, and I doubt they could. It seems like more of a thing you might just use to do mashups or simple weather forecast type requests, not build serious systems on top of.
I think that this is a misunderstanding of object orientation. Even in Java, the methods are generally not part of the objects but of their class (and even this "membership" is not necessary for object orientation, but that is a different subject). A class is just a description of a type, so this is really a part of the program, not the data.
SOA and OO do not contradict each other. A service can accept data, organize them into objects internally, work on them, and finally give them back in whatever format is desired.
I've heard James Gosling say that one could implement SOA in COBOL.
If you read Alan Kay's own description of the origins of OOP, it describes a bunch of little computers interacting to perform something useful.
Consider this description:
Your X should be made up of Ys. Each Y should be responsible for a single concept, and should be describable completely in terms of its interface. One Y can ask another Y to do something via an exchange of messages (per their specified interfaces).
In some cases, an X may be implemented by a Z, which it manages according to its interface. No X is allowed direct access to another X's Z.
I think that the following substitutions are possible:
Term Programing Architecture
---- --------------- ------------
X Program System
Y Objects Services
Z Data structure Database
---- --------------- ------------
result OOP SOA
If you think primarily in terms of encapsulation, information hiding, loose coupling, and black-box interfaces, there is quite a bit of similarity. If you get bogged down in polymorphism, inheritance, etc. you're thinking programming / implementation instead of architecture, IMHO.
If you allow your services to remember state, then they can just be considered to be big objects with a possibly slow invocation time.
If they are not allowed to retain state then they are just as you've said - operators on data.
It sounds like you may be dividing your system up into too many services. Do you have written, mutually agreed criteria for how to divide?
Adopting SOA does not mean throw out all your objects but is about dividing your system into large reusable chunks.