Related
Just a quote from hack documentation :
Legacy Vector, Map, and Set
These container types should be avoided in new code; use dict,
keyset, and vec instead.
Early in Hack's life, the library provided mutable and immutable
generic class types called: Vector, ImmVector, Map, ImmMap, Set, and
ImmSet. However, these have been replaced by vec, dict, and keyset,
whose use is recommended in all new code. Each generic type had a
corresponding literal form. For example, a variable of type
Vector might be initialized using Vector {22, 33, $v}, where $v
is a variable of type int.
I wonder why this change was made.
I mean, one of PHP weaknesses is that it has bad oop standard library.
Ex : str_replace and array_values methods are outside of the string/array type itself. The PHP standard library is not consistent, sometimes we must pass the array as the first parameter, other times as the second...
I was glad to see that Hack introduced true OOP encapsulation for collections.
Do you know why they stepped back and wrote utility classes such as C\, Dict\, Keyset\ and Vec\ ?
Will there be in the future an addition to add methods to built-in types (ex : Str\starts_with => "toto"->startsWith("t")) ?
Based on Dwayne Reeves' blog post introducing HSL, it seems that the main advantage is the fact that arrays are native values, not objects. This has two important consequences:
For users, the semantics are different when the values cross through arguments. Objects are passed as references, and mutations affect the original object. On the other hand, values are copied on write after passing through arguments, so without references (which are finally to be completely banned in Hack) the callee can't mutate the value of the caller, with the exception of the much stricter inout parameters.
The article cites the invariance of the mutable containers (Vector, Set, etc.) and generally how shared mutable state couples functions closer together. The soundness issues as discussed in the article are somewhat moot because there were also immutable object containers (ImmVector, ImmSet, etc.), although since these interfaces were written in userland, variance boxed the function type signature into tight constraints. There are tangible differences from this: ImmMap<Tk, +Tv> is invariant in Tk solely because of the (function(Tk): Tv) getter. Meanwhile, dict<+Tk, +Tv> is covariant in both type parameters thanks to the inherent mutation protection from copy-on-write.
For the compiler, static values can be allocated quickly and persist over the lifetime of the server. Objects on the other hand have arbitrarily complicated construction routines in general, and the collection objects weren't going to be special-cased it seems.
I will also mention that for most use cases, there is minimal difference even in code style: e.g. the -> reference chains can be directly replaced with the |> pipe operator. There is also no longer a boundary between the privileged "standard functions" and custom user functions on collection types. Finally, the collection types were final of course, so their objective nature didn't offer any actual hierarchical or polymorphic advantages to the end user anyways.
I've started playing around with Kotlin, but I sense my own limitation in the way I program. My problem is that I still think Java therefore the style is still imperative, my question is to all functional programming zealots , which I believe would be very useful to all people who at the very beginning stage and also need to 'brake' their brain to start building it again; to leave comfort zone and start thinking pseudo and not in "whatever is your first language". I believe it is possible for highly experienced polyglot developers to chew the concepts down to plain advices of what makes your program being written in entirely functional way and what violates the paradigm. I don't know all the quirks but please don't hesitate to include universally accepted terms which might be unknown to me(I can always lookup). At this point I need this set of rules to make myself suffer at first and not break them but then I know I will feel it, analyze guidelines and understand how they are worse/better which of course is my own homework.
So example of these guidelines, would be something like:
Never change state, this can be avoided by using x, y, z
Operate using higher order functions only (I maybe wrong, just example)
I hope the answer will give me long term reference to put myself in extreme conditions where I stop escaping to OOP whenever I feel uncomfortable. And now when I look at Kotlin I understand how I've should've been thinking about problems, it is about intention not about the structure imposed by one language or another. Intention can always be converted to a language of your choice and backed up by design patterns applicable to the language, but to find that middle ground I need to jail myself first from the comfort zone.
Avoid mutable state like the plague.
One of the main points of using functional programming, possibly the main one, is to avoid all the little pitfalls, bugs, issues one needs to deal with when using mutable state. You should do everything you can in order to avoid mutating state. For instance, instead of using C-style for-loops where you need to keep a counter variable updated, use map and other higher-order functions in order to abstract away your iteration patterns. This also means that you should never change the value of a variable if you can avoid that. Instead, you should be defining almost all of your variables, preferrably all of them, as constants, and using functions to compute new values from them instead of mutating them.
Avoid side-effects like the plague.
Mutable state's ugly cousin, side-effects. Side effects mean anything other than taking a value and returning a value in a function. If that function prints data, mutates global variables, sends messages to threads, or anything, anything other than simply taking its parameters, computing a value from them, and returning a value, that function has side-effects. Side-effects are important (see next bullet point), but if you use them a lot, they get impossible to track. Just think of how everyone tells you to avoid global variables in imperative programming. Functional programming goes a step further and tries to avoid all side-effects. The bulk of your program should be made of pure functions. (See ahead)
When you need to use side-effects, keep them contained.
Yes, I just told you to run away from side-effects. However, no program is useful without side-effects of some kind. Graphical User Interface? Side-effect. Audio output? Side-effect. Printing to a shell? Side-effect. So you can't really get rid of side-effects if you want to build useful stuff.
What you should do instead is write your code so that all your side-effecting code lives in a thin layer which mostly calls pure functions and then does the required side-effects using the result of these pure function calls.
Use pure functions for everything you can.
This is sort of the flipside of the previous point. A pure function is a function which has no side-effects and does not mutate anything. It can only take in parameters and return a value. You should use these a lot. For instance, instead of doing your logging within functions which are computing stuff, you should be constructing your log strings using pure functions, and then letting your side-effects layer call these pure functions, call more pure functions in order to format the log strings into a full log, and then output the log itself from your side-effects layer.
Use higher-order functions to structure your code.
Higher-order functions are, in a way, the glue that makes functional programming work. A higher-order function is a function which takes one or more functions as parameters and/or returns a function. The power of higher-order functions is that they can encapsulate many of the patterns which you would use in an imperative-style program in a declarative manner. For instance, let's take a look at the three most common higher-order functions:
map is a function which takes a function and a list of values, applies its function argument to each of those values, and returns a new list with the results. map encapsulates the whole pattern of iterating over a list doing an operation on each value in a declarative manner.
filter is a function which takes a function which returns a boolean and a list of values, applies its function argument to each of those values and returns a list containing only those values for which its function argument returns true. It encapsulates the whole pattern of selecting results from a list in a declarative manner.
reduce, also known as fold, takes an initial value, a binary function and a list of values. It uses its function argument to combine the initial value with the first value of the list, then combines the result with the next value of the list and keeps on doing this until it has reduced the list to just one single value. It encapsulates the entire pattern of obtaining an aggregate value from a list of values.
This is in no way an exhaustive list of higher-order functions, but these three are the most common ones. I hope this has been enough to show how you can structure code which would require a lot of tracking variables using only functions in a declarative manner. If you use these higher-order functions well, it's likely you won't ever need a for or while loop again.
This is definitely not an exhaustive list of functional programming practices, but I think most functional programmers would agree these five guidelines form the core of what functional programming is about. If you want to really learn how to apply these, my advice would be to learn a pure functional programming language such as Haskell, so you are forced to abandon the imperative paradigm and to learn how to structure things functionally instead. I would recommend the fantastic Haskell Programming from First Principles as a starting resource if you choose to go this way. In case you don't want to/can't put down the cash, Brent Yorgey's Haskell course at UPenn is also a great free resource.
I have a very CPU intensive F# program that depends on persistent data-structures - about 40% of the total CPU time is spent in the Map module. So I thought I'd try out the PersistentHashMap in FSharpX collections. (BTW, this is already a big improvement over the previous version of F# in VS2013 where the same program spent 70% of its time in Map. I also notice that running programs with the debugger attached doesn't have the huge penalty it did before - good work guys...) There is also a hot-spot where I'm re-sorting all the time, where instead I should be adding to a Heap, so I thought I'd give that a go as well.
Two issue became immediately apparent:
(1) Swapping out one for the other from an interface perspective proved harder than it seems it should - I.e., making a shim that let me switch from a Map to a PersistentMap, preserving both the needed module-based let-bound functions and Types necessary to use the each map. I know that having full HM type-inference (and no type-classes) is orthogonal to LSP-style referential transparency for the most part - but maybe I was missing some way to do this better with a minimal amount of code.
(2) The biggest problem (which I'd like to focus on here) is the reliance of the F# functional data-structs on oo-style dispatched equality and comparison via the IComparison (when 't : comparison), etc., family of interfaces.
Even for OO programs ISTM that the idea of dispatching equality and comparison is a bad idea -- an object "knows" how to perform its own domain-specific tasks, but it doesn't "know" for the most part what notion of equality is going to be necessary at various points in the program for various purposes -- so equality/comparison should not be part of the object's interface, but when these concepts are needed, they should always be mentioned explicitly. For example, there should never be a .Sort(), only a .SortWith(...). One could argue that even something as basic as structural equality in F# could be explicit a.StructEq(b) or a ~= b - otherwise you always get object.Equals -- but even stipulating that doing things this way is the best for a multi-paradigm language that's a first-class .Net citizen, it seems like there should at least be the option of using passed-in comparison and equality functions, but this is not the case.
This means that: (a) type constraints are enforced even if you don't want them, causing ripples of broken inferred typing (and hundreds of wavy red lines with it being unclear where the actual "problem" is) and (b), that by implementing a notion of equality or comparison that makes one container type happy in one part of your program (and in my case I want to use the same container and item type with two different notions of ordering in two different places), it is likely to silently break (or cause inefficiency, if one subsumes the other) in other parts of the code that depended on the default/previous implementation.
The only way around this that I could think of is wrapping each item a adapter object using new...with object expression - but I really don't want to create so much garbage just to get the code to work.
So, ISTM that we could have a "pure" version of each persistent data struct that could be loaded if desired (even basics like List, etc.) that do not depend on dispatched equality/comparison/hashing and do not impose type constraints - all such needs should be via a passed in fn's at the time of the call. (Dispatched eq/cmp would be only for used for interop with BCL collections that don't accept delegates.) Then we could have a [EqCmpHashThrowNotImplemented] attribute, and I could be sure that there were no default operations happening at all, and I would feel better about the efficiency and predictability of my code. (And this also let's one change from a Record to a Class or visa-versa w/o worrying about any changes in behavior due to default implementations.) Again, this would be optional, but done by with a simple import. (Which does mean that each base core collection type would have to be broken out into its own module, which isn't really a bad idea anyway.)
If I've overlooked a better way to do things or there are some patterns people are using here, I'd be interested.
What is open recursion? Is it specific to OOP?
(I came across this term in this tweet by Daniel Spiewak.)
just copying http://www.comlab.ox.ac.uk/people/ralf.hinze/talks/Open.pdf:
"Open recursion Another handy feature offered by most languages with objects and classes is the ability for one method body to invoke another method of the same object via a special variable called self or, in some langauges, this. The special behavior of self is that it is late-bound, allowing a method defined in one class to invoke another method that is defined later, in some subclass of the first. "
This paper analyzes the possibility of adding OO to ML, with regards to expressivity and complexity. It has the following excerpt on objects, which seems to make this term relatively clear –
3.3. Objects
The simplest form of object is just a record of functions that share a common closure environment that
carries the object state (we can call these simple objects). The function members of the record may or may not
be defined as mutually recursive. However, if one wants to support inheritance with overriding, the structure
of objects becomes more complicated. To enable open recursion, the call-graph of the method functions
cannot be hard-wired, but needs to be implemented indirectly, via object self-reference. Object self-reference
can be achieved either by construction, making each object a recursive, self-referential value (the fixed-point
model), or dynamically, by passing the object as an extra argument on each method call (the self-application
or self-passing model).5 In either case, we will call these self-referential objects.
The name "open recursion" is a bit misleading at first, because it has nothing to do with the recursion that normally is used (a function calling itself); and to that extent, there is no closed recursion.
It basically means, that a thing is referring to itself. I can only guess, but I do think that the term "open" comes from open as in "open for extension".
In that sense an object is open to extension, but still referring to itself.
Perhaps a small example can shed some light on the concept.
Imaging you write a Python class like this one:
class SuperClass:
def method1(self):
self.method2()
def method2(self):
print(self.__class__.__name__)
If you ran this by
s = SuperClass()
s.method1()
It will print "SuperClass".
Now we create a subclass from SuperClass and override method2:
class SubClass(SuperClass):
def method2(self):
print(self.__class__.__name__)
and run it:
sub = SubClass()
sub.method1()
Now "SubClass" will be printed.
Still, we only call method1() as before. Inside method1() the method2() is called, but both are bound to the same reference (self in Python, this in Java). During sub-classing SuperClass method2() is changed, which means that an object of SubClass refers to a different version of this method.
That is open recursion.
In most cases, you override methods and call the overridden methods directly.
This scheme here is using an indirection over self-reference.
P.S.: I don't think this has been invented but discovered and then explained.
Open recursion allows to call another methods of object from within, through special variable like this or self.
In short, open recursion is about something actually not related to OOP, but more general.
The relation with OOP comes from the fact that many typical "OOP" PLs have such properties, but it is essentially not tied to any distinguishing features about OOP.
So there are different meanings, even in same "OOP" language. I will illustrate it later.
Etymology
As mentioned here, the terminology is likely coined in the famous TAPL by BCP, which illustrates the meaning by concrete OOP languages.
TAPL does not define "open recursion" formally. Instead, it points out the "special behavior of self (or this) is that it is late-bound, allowing a method defined in one class to invoke another method that is defined later, in some subclass of the first".
Nevertheless, neither of "open" and "recursion" comes from the OOP basis of a language. (Actually, it is also nothing to do with static types.) So the interpretation (or the informal definition, if any) in that source is overspecified in nature.
Ambiguity
The mentioning in TAPL clearly shows "recursion" is about "method invocation". However, it is not that simple in real languages, which usually do not have primitive semantic rules on the recursive invocation itself. Real languages (including the ones considered as OOP languages) usually specify the semantics of such invocation for the notation of the method calls. As syntactic devices, such calls are subject to the evaluation of some kind of expressions relying on the evaluations of its subexpressions. These evaluations imply the resolution of method name, under some independent rules. Specifically, such rules are about name resolution, i.e. to determine the denotation of a name (typically, a symbol, an identifier, or some "qualified" name expressions) in the subexpression. Name resolution often respects to scoping rules.
OTOH, the "late-bound" property emphasizes how to find the target implementation of the named method. This is a shortcut of evaluation of specific call expressions, but it is not general enough, because entities other than methods can also have such "special" behavior, even make such behavior not special at all.
A notable ambiguity comes from such insufficient treatment. That is, what does a "binding" mean. Traditionally, a binding can be modeled as a pair of a (scoped) name and its bound value, i.e. a variable binding. In the special treatment of "late-bound" ones, the set of allowed entities are smaller: methods instead of all named entities. Besides the considerably undermining the abstraction power of the language rules at meta level (in the language specification), it does not cease the necessity of traditional meaning of a binding (because there are other non-method entities), hence confusing. The use of a "late-bound" is at least an instance of bad naming. Instead of "binding", a more proper name would be "dispatching".
Worse, the use in TAPL directly mix the two meanings when dealing with "recusion". The "recursion" behavior is all about finding the entity denoted by some name, not just specific to method invocation (even in those OOP language).
The title of the chapter (Case Study: Imperative Objects) also suggests some inconsistency. Obviously, the so-called late binding of method invocation has nothing to do with imperative states, because the resolution of the dispatching does not require mutable metadata of invocation. (In some popular sense of implementation, the virtual method table need not to be modifiable.)
Openness
The use of "open" here looks like mimic to open (lambda) terms. An open term has some names not bound yet, so the reduction of such a term must do some name resolution (to compute the value of the expression), or the term is not normalized (never terminate in evaluation). There is no difference between "late" or "early" for the original calculi because they are pure, and they have the Church-Rosser property, so whether "late" or not does not alter the result (if it is normalized).
This is not the same in the language with potentially different paths of dispatching. Even that the implicit evaluation implied by the dispatching itself is pure, it is sensitive to the order among other evaluations with side effects which may have dependency on the concrete invocation target (for example, one overrider may mutate some global state while another can not). Of course in a strictly pure language there can be no observable differences even for any radically different invocation targets, a language rules all of them out is just useless.
Then there is another problem: why it is OOP-specific (as in TAPL)? Given that the openness is qualifying "binding" instead of "dispatching of method invocation", there are certainly other means to get the openness.
One notable instance is the evaluation of a procedure body in traditional Lisp dialects. There can be unbound symbols in the body and they are only resolved when the procedure being called (rather than being defined). Since Lisps are significant in PL history and the are close to lambda calculi, attributing "open" specifically to OOP languages (instead of Lisps) is more strange from the PL tradition. (This is also a case of "making them not special at all" mentioned above: every names in function bodies are just "open" by default.)
It is also arguable that the OOP style of self/this parameter is equivalent to the result of some closure conversion from the (implicit) environment in the procedure. It is questionable to treat such features primitive in the language semantics.
(It may be also worth noting, the special treatment of function calls from symbol resolution in other expressions is pioneered by Lisp-2 dialects, not any of typical OOP languages.)
More cases
As mentioned above, different meanings of "open recursion" may coexist in a same "OOP" language.
C++ is the first instance here, because there are sufficient reasons to make them coexist.
In C++, name resolution are all static, normatively name lookup. The rules of name lookup vary upon different scopes. Most of them are consistent with identifier lookup rules in C (except for the allowance of implicit declarations in C but not in C++): you must first declare the name, then the name can be lookup in the source code (lexically) later, otherwise the program is ill-formed (and it is required to issue an error in the implementation of the language). The strict requirement of such dependency of names are considerable "closed", because there are no later chance to recover from the error, so you cannot directly have names mutually referenced across different declarations.
To work around the limitation, there can be some additional declarations whose sole duty is to break the cyclic dependency. Such declarations are called "forward" declarations. Using of forward declarations still does not require "open" recursion, because every well-formed use must statically see the previous declaration of that name, so each name lookup does not require additional "late" binding.
However, C++ classes have special name lookup rules: some entities in the class scope can be referred in the context prior to their declaration. This makes mutual recursive use of name across different declarations possible without any additional "forward" declarations to break the cycle. This is exactly the "open recursion" in TAPL sense except that it is not about method invocation.
Moreover, C++ does have "open recursion" as per the descriptions in TAPL: this pointer and virtual functions. Rules to determine the target (overrider) of virtual functions are independent to the name lookup rules. A non-static member defined in a derived class generally just hide the entities with same name in the base classes. The dispatching rules kick in only on virtual function calls, after the name lookup (the order is guaranteed since evaulations of C++ function calls are strict, or applicative). It is also easy to introduce a base class name by using-declaration without worry about the type of the entity.
Such design can be seen as an instance of separate of concerns. The name lookup rules allows some generic static analysis in the language implementation without special treatment of function calls.
OTOH, Java have some more complex rules to mix up name lookup and other rules, including how to identify the overriders. Name shadowing in Java subclasses is specific to the kind of entities. It is more complicate to distinguish overriding with overloading/shadowing/hiding/obscuring for different kinds. There also cannot be techniques of C++'s using-declarations in the definition of subclasses. Such complexity does not make Java more or less "OOP" than C++, anyway.
Other consequences
Collapsing the bindings about name resolution and dispatching of method invocation leads to not only ambiguity, complexity and confusion, but also more difficulties on the meta level. Here meta means the fact that name binding can exposing properties not only available in the source language semantics, but also subject to the meta languages: either the formal semantic of the language or its implementation (say, the code to implement an interpreter or a compiler).
For example, as in traditional Lisps, binding-time can be distinguished from evaluation-time, because program properties revealed in binding-time (value binding in the immediate contexts) is more close to meta properties compared to evaluation-time properties (like the concrete value of arbitrary objects). An optimizing compiler can deploy the code generation immediately depending on the binding-time analysis either statically at the compile-time (when the body is to be evaluate more than once) or derferred at runtime (when the compilation is too expensive). There is no such option for languages blindly assume all resolutions in closed recursion faster than open ones (and even making them syntactically different at the very first). In such sense, OOP-specific open recursion is not just not handy as advertised in TAPL, but a premature optimization: giving up metacompilation too early, not in the language implementation, but in the language design.
A little intro:
Class contains fields and methods (let me skip properties this time).
Fields represent a state of the class.
Methods describe behavior of the class.
In a well-designed class, a method won't change the class's state if it throws an exception, right? (In other words, whatever happens, class's state shouldn't be corrupted)
Question:
Is there a framework, a design pattern, best practice or a programming language to call a sequence of methods in a transactional style, so that either class's state don't get changed (in case of exception), or everything succeeds?
E.g.:
// the class foo is now in the state S1
foo.MoveToState2();
// it is now (supposed to be) in the state S2
foo.MoveToFinalState();
// it is now (supposed to be) in the state, namely, S3
Surely, an exception might occur both in MoveToState2() and MoveToFinalState(). But from this block of code I want the class foo to be either in the state S1 or S3.
This is a simple scenario with a single class involved, no if's, no while's, no side effects, but I hope the idea is clear.
Take a look at the Memento pattern
The memento pattern is a software design pattern that provides the ability to restore an object to its previous state (undo via rollback).
Not the most efficient method, but you could have an object that represents your transactional data. When you start a transaction, make a copy of the data and perform all operations on that. When the transaction ends successfully, move the copy to your real data - this can be done using pointers, so need not be too inefficient.
Functional programming is a paradigm that seems to fit well to transactional computations. Since no side-effects are allowed without explicit declaration, you have full control of all data flow.
Therefore software transactional memory can be expressed easily in functional terms - See STM for F#
The key idea is the concept of monads. A monad can be used to model an arbitrary computation through two primitives: Return to return a value and Bind to sequence two computations. Using these two, you can model a transactional monad that controls and saves all state in form of continuations.
One could try to model these in an object-oriented way through a State+Memento pattern, but generally, transactions in imperative languages (like the common OO-ones) are much more difficult to implement since you can perform arbitrary side-effects. But of course you can think of an object defining a transaction scope, that saves, validates and restores data as needed, given they expose a suitable interface for this (the patterns I mentioned above).
The simplest and most reliable "pattern" to use here is an immutable data structure.
Instead of writing:
foo.MoveToState2();
foo.MoveToFinalState();
You write:
MyFoo foo2 = foo.MoveToState2();
MyFoo finalFoo = foo2.MoveToFinalState();
And implement the methods accordingly - that is, MoveToState2 does not actually change anything about MyFoo, it creates a new MyFoo that is in state 2. Similarly with the final state.
This is how the string classes in most OO languages work. Many OO languages are also starting to implement (or have already implemented) immutable collections. Once you have the building blocks, it's fairly straightforward to create an entire immutable "entity".
This would be pretty ugly to implement everywhere, but just saving the state locally, then restoring it in the case of an exception would work in simple scenarios. You'd have to catch and rethrow the exception, which may lose some context in some languages. It might be better to wrap it if possible to retain the context.
try {
save state in local variables
move to new state
} catch (innerException) {
restore state from local variables
throw new exception( innerException )
}
When using object copy approach, you have to watch out that the statements to be rolled-back are only affecting the object's or data itself (and aggregates).
But things are getting really difficult if the side-effects of the statements are "more external". For example I/O operations, network calls. You always have to analyze the overall state-changes of your statements.
It gets also really tricky if you touch static data (or evil mutable singletons). Reverting this data isolated is difficult, because other threads could have modified them in between (you could face lost updates).
Reverting/rollback to the past is often not so trivial ;)
I would also consider the saga pattern, you could pass a copy of the objects current state into MoveToState2 and if it throws an exception you could catch that internally and use the copy of the original state to rollback. You would have to do the same with MoveToState3 too. If however the server crashed during a rollback you might still get corrupted state, that's why databases are so good.
Transactional memory fits here the best.
An option could be a transactional storage. Sample implementation you can find here:
http://www.codeproject.com/KB/dotnet/Transactional_Repository.aspx
Memento pattern
Also let me describe a possible pattern on how to implement such behavior:
Define a base class TransactionalEntity. This class contains dictionary of properties.
All your transactional classes inherit from the TransactionalEntity and should operate over some sort of Dependency Properties/Fields, i.e. properties(getters/setters) which store it's values not in local class fields, but in dictionary, which is stored in the base class.
Then you define TransactionContext class. TransactionContext class internally contains a set of dictionaries (one dictionary for each entity that participates in the transaction) and when a transactional entity participates in transaction, it writes all data to the dictionary in the transaction context. Then all you need is basically four methods:
TransactionContext.StartTransaction();
TransactionalEntity.JoinTransaction(TransactionContext context); //if your language/framework supports Thread Static fields, then you do not need this method
TransactionContext.CommitTransaction();
TransactionContext.RollbackTransaction();
To sum up, you need to store state in base class TransactionalEntity and during transaction TransactionalEntity will cooperate with TransactionContext.
I hope, I've explained it well enough.
I think a Command Pattern could be well suited to this problem.
Linky.
I was astonished that no one suggested explicitly the simplest pattern to use .. the State Pattern
In this way you can also eliminate that 'finalState' method and just use 'handle()'.
How do you know which final state is?
The memento pattern is best used with the Command pattern, and usually applies to GUI operations to implement the undo/redo feature.
Fields represent a state of the class
Fields represents the state of the instanced object. You use many times wrong definitions of the OOP terms. Review and correct.