I confused these two architecture's differences.
Generally, Monolithic Architecture refers to different components of an application being combined into a single business logic.
And, Service Oriented Architecture refers to an application composed of discrete and loosely coupled software agents that perform a required function.
But I learned, OOP and design-pattern aim "One function should have One responsibility" and "Decrease dependencies to increase reuse of function".
So my question is :
1. Do monolithic architecture follow OOP?
2. Differences between these two architecture.
1. Do monolithic architecture follow OOP?
Good ones do. Yes. It would be even more of a spaghetti code mess if it was procedural or much harder to write if it was functional. In all the pro companies I've worked for they all had OOP and frameworks like MVP they also incorporate. In my experience supporting companies with problem products, they tended to be a mess. Really boated, little reuse, if one 1MB thing needed to scale the only way to do it was scale the entire 100MB solution.
2. Differences between these two architecture.
Service Orientated Architecture IMO is taking the view each function can be an API, its service orientated. Object Orientated Programming has a few tenants, Polymorphic, Reusable, Encapsulated (and Abstract), it's a style of programming, almost opposite to procedural programming etc.
When you design a service, let's say it's a Weather app that tells temp and also does predictions. You will want to have an abstract generic Weather class, make it support Celsius and Fahrenheit implementations and you can get some polymorphism, or perhaps add an Interface to support different locations or altitude's, and use really generic function names that sound abstract like GetTemp (with function overloads by City, State, Altitude) to encapsulate it in the Weather class.
So here you design it with the Service Orientation in mind and a solid OOP assembly.
"One function should have One responsibility" and "Decrease dependencies to increase reuse of function".
For the second service to predict temp's that will make some calculations and take time to compute so we will want to isolate it, so we can scale it independently. You can do this by using a different project and instead of referencing each other you put in Messaging technology (SQS, RabbitMQ, etc) so they are loosely coupled.
Note: We can simply cache the static GetTemp results in the other project to easily scale that part of the system.
I know that frameworks provide useful interfaces and classes that save a lot of time in implementation phase, so my question is:
Should the framework interfaces and classes be included in my project's class
diagram design or not?
and if it is,
Does this affect the reusability of the design if I decided to change
the framework in the future?
UML diagrams are intended to be read by different interest groups. Business likes to see requirements, use cases and activities. Architects/testers need that as a basis to develop/test the system. And the results produced by the architects (static and behavioral class diagrams) are meant to be read by programmers. Each reader group has a focus on certain parts but will eventually peek more or less into border areas (from their perspective).
So to answer your question: yes, frameworks shall be part of the model. Architects should pay attention as to how to cut the system. Frameworks should be designed with a different (broader) scope. So eventually you have frameworks that will be used only partially in a system. Or a system has a potential for a framework and it will be designed to be easily decoupled. Of course, this is a tricky task and architects need lots of experience to fulfill all the needs that come from business and eventually other stakeholders.
No, theoretically it shouldn't, but you're also free to do so.
As stated by the authors of UML: Rumbaugh, Jacobson, Booch
on The Unified Modeling Language Reference Manual at page 25
Across implementation languages and platforms. The UML is intended to be usable for systems implemented in various implementation languages and platforms, including programming languages, databases, 4GLs, organization documents, firmware, and so on. The front-end work should be identical or similar in all cases, while the back-end work will differ somewhat for each medium.
I'm from a non-programming background and have often come across the terms like Programming Paradigm, Design Pattern and Application Architecture. Although I think I have a vague understanding of what these terms mean, I'd appreciate if someone could clarify what each is, how it is different from the other and how these concepts apply to Objective C.
Programming Paradigm: Something like "Functional Programming", "Procedural Programming", and "Object Oriented Programming". The programming paradigm and the languages that use them inform how the code gets written. For example, in Object Oriented programming the code is divided up into classes (sometimes a language feature, sometimes not (e.g. javascript)), and typically supports inheritance and some type of polymorphism. The programmer creates the classes, and then instances of the classes (i.e. the objects) to carry out the operation of the program. In functional languages, the state changes on the computer are very heavily controlled by the language itself. Functions are first class objects, although not all languages where functions are first class objects are functional programming language (this topic is one of good debate). Code written with a functional languages involves lots of nested functions, almost every step of the program is new function invocation. For procedural programming, C programs and bash scripting are good examples, you just say do step 1, do step 2, etc, without creating classes and whatnot.
Design Pattern: A design pattern is a useful abstraction that can be implemented in any language. It is a "pattern" for doing things. Like if you have a bunch of steps you want to implement, you might use the 'composite' and 'command' patterns so make your implementation more generic. Think of a pattern as an established template for solving a common coding task in a generic way.
Application Architecture: Takes into consideration how you build a system to do stuff. So, for a web application, the architecture might involve x number of gateways behind a load balancer, that asynchronously feed queues. Messages are picked up by y processes running on z machines, with 1 primary db and a backup slave. Application architecture involves choosing the platform, languages, frameworks used. This is different than software architecture, which speaks more to how to actually implement the program given the software stack.
Some quick definitions,
Application Architecture describes the overall architecture of the software. For instance a web-based programs typically use a layered architecture where functionality is divided to several layers, such as user interface (html generation, handling commands from users), business logic (rules how the functions of the software are executed) and database (for persistent data). In contrast, a data processing application could use a so-called pipes and filters architecture, where a piece of data passes through a pipeline where different modules act on the data.
Design Patterns are a much lower level tool, providing proven models on how to organize code to gain specific functionality while not compromising the overall structure. Easy examples might include a Singleton (how to guarantee the existence of a single instance of a code) or a Facade (how to provide a simple external view to a more complex system).
On the other hand paradigms are the other extreme, guiding the principles on how code is actually laid out, and they each require quite different mindsets to apply. For instance, procedural programming is mainly concerned about dividing the program logic into functions and bundling those functions into modules. Object-oriented programming aims to encapsulate the data and the operations that manipulate the data into objects. Functional programming emphasizes the use of functions instead of separate statements following one another, avoiding side-effects and state changes.
Objective-C is mostly an object-oriented extension to C, design patterns and architecture are not language-specific constructs.
A programming paradigm is a fundamental style of computer programming.
Software Design Pattern - are best practice solutions to common software design problem. There are many design patterns for common problems. To learn more about design patterns you can read some books from this list 5 Best Books for Learning Design Patterns
Application Architecture - Applications Architecture is the science and art of ensuring the suite of applications being used by an organization to create the composite application is scalable, reliable, available and manageable.
I guess any of these terms would apply to all programming languages. Design patterns exists in all programming languages.
These are logical terms defined to create higher level of abstraction.
Hope this helps
Think of the vernacular interpretation of those terms (i.e., outside of the field computer science).
Paradigms are all-encompassing views of computation that affect not only what kinds of things you can do, but even what kinds of thoughts you can have; functional programming is an example of a programming paradigm.
Patterns are simply well-established programming tricks, codified in some semi-formal manner.
Application architecture is a broad term describing how complex applications are organised.
Objective-C primarily adds elements of the OO paradigm to the imperative language, C. Patterns and architecture are largely orthogonal to the language.
Simple English words
A paradigm is a way of thinking when programming, where first class concepts are used to organize the software. Ex oop use classes as first class citizens, functional or lambda calculus use functions and their compositions, aspect uses aspects of a system .... And so on. When thinking a solution the first thing that comes to your mind are the first class citizens. The objective is to organize the solution into software components.
A design pattern is a common successful use of software components.
An application architecture is a set of design patterns put together in order to realize use case scdnarios.
Paradigm: a style or approach to programming. For example, In OOP, we use the concept of objects, classes to overall program. These objects contain data & behaviours & we connect them logically to complete the task.
Design Patterns: tried or tested solution, moreover reusable solutions, to the problem we encounter while everyday programming. For example, if we approach OOP paradigm, there are no. of patterns to help us solve specific problem.
I want to develope a big project, but I really don't know what is the best way to model my project. Do I even need to model my project?
What are the most practical OOP software modeling methods in real world projects? What are the best and most useful ones?
Many times its needed to capture the complex structure of classes you have in you OO system, so class diagrams from UML are used for modeling. You can also want to describe interactions of classes, for that sequence diagrams are useful. There are also other UML diagrams and each has its purpose.
If you are looking for an approach to modeling, try looking at Unified Process, which is adevelopment method, which is created by authors of UML and uses UML quite heavily and also describes how UML can be used.
Agile methodology is currently what is recommended. If you add a slice of UML then it would be better :-)
Modeling (design) is the most important part of every project.
In fact as times goes by, we sacrifice performance to gain higher level of design.
Why .NET framework is popular (compare to old tools) ? In most cases its libraries are wrappers over traditional win32 APIs, a waste of performance, instead it provides better design, which makes it easy to learn and use.
So if your project have a good design it would be easy to understand, develop, debug, maintain and extend.
Another example is OOP itself which has classes, interfaces... and bunch of constructor/destructor calls. OOP concepts are borrowed from psychiatry and the way human being see the world.
Here are two different concepts:
1) Design methodology
2) Project management methodology
There are many and I don't name good or bad. Each of them fits a scenario.
About design methodology I prefer DDD (Domain Driven Design) as it maps the industry domain terminology and concepts. So if you have a decision problem about what to do if A->B->C happened, simply you can ask a domain professional and he will say what they do in real world. DDD is good for old enough industries that have cumulative wisdom. I'm not gonna write more about design since we don't know about the project.
Project management methodologies (like agile) are the way you build the building from the map (design). The goal of project management is to use resources optimal (time, money, human resources...). This is done through work breakdown structure and make work as parallel as possible. The most known project management methodology is the traditional one in which we do everything in sequence, as civil engineers do (foundation, structure, walls...). This was good for many centuries until last decades (software industry), since in traditional project management you know where you are, where you want to go, and how to reach there. This way you can buy your furniture for a home that's a land yet !
Software industry has very rapid changes in tools and methods because is was new and no best practices were founded on thousands of failed projects. Many times when a project started it has changes because of changes in developing tools and frameworks. Other source of change is the scope of the project (where to go). Software is an intangible product so you fall in the trap of time estimations easily. For software development best practice are iterative methodologies.
Iterative methodologies suggest, a working incomplete solution which you make more complete in next iterate, rather than a non working partially complete one. This has a time overhead, instead, you sure the solution works and if any problem, you find in early stages. That's why we have nightly builds !
The best is Visual Studio 2010 Ultimate others are too cumbersome. Otherwise use light tools like yuml see http://askuml.com for samples.
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.