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Method references to static methods or static methods which return lambdas

While developing I always have to rewrite the same lambda expression over and over again which is quite redundant and most of the cases the code formatting policy imposed by my company does not help. So I moved these common lambdas to a utility class as static methods and use them as method references. The best example I have is the Throwing merger used in conjunction with java.util.stream.Collectors.toMap(Function, Function, BinaryOperator, Supplier).
Always having to write (a,b) -> {throw new IllegalArgumentException("Some message");}; just because I want to use a custom map implementation is a lot of hassle.
//First Form
public static <E> E throwingMerger(E k1, E k2) {
throw new IllegalArgumentException("Duplicate key " + k1 + " not allowed!");
}
//Given a list of Car objects with proper getters
Map<String,Car> numberPlateToCar=cars.stream()//
.collect(toMap(Car::getNumberPlate,identity(),StreamUtils::throwingMerger,LinkedHasMap::new))
//Second Form
public static <E> BinaryOperator<E> throwingMerger() {
return (k1, k2) -> {
throw new IllegalArgumentException("Duplicate key " + k1 + " not allowed!");
};
}
Map<String,Car> numberPlateToCar=cars.stream()//
.collect(toMap(Car::getNumberPlate,identity(),StreamUtils.throwingMerger(),LinkedHasMap::new))
My questions are the following:
Which of the above is the correct approach and why?
Does either one of them offer a performance advantage or compromises performance?

Neither variant is more correct than the other.
Further, there is no significant performance difference, as the relevant bytecode is even identical. In either case, there will be a method holding a throw statement in your class and an instance of a runtime generated class which will invoke that method.
Note that you can find both patterns within the JDK itself.
Function.identity() and Map.Entry.comparingByKey() are examples of factory methods containing a lambda expression
Double::sum, Objects::isNull, or Objects::nonNull are examples of method references to target methods solely existing for the purpose of being referenced that way
Generally, if there are also use cases for invoking the methods directly, it’s preferable to provide them as API methods, which may also be referenced by method references, e.g. Integer::compare, Objects::requireNonNull, or Math::max.
On the other hand, providing a factory method makes the method reference an implementation detail that you can change when there is a reason to do so. E.g., did you know that Comparator.naturalOrder() is not implemented as T::compareTo? Most of the time, you don’t need to know.
Of course, factory methods taking additional parameters can’t be replaced by method references at all; sometimes, you want the parameterless methods of a class to be symmetric to those taking parameters.
There is only a tiny difference in memory consumption. Given the current implementation, every occurrence of, e.g. Objects::isNull, will cause the creation of a runtime class and an instance, which will then be reused for the particular code location. In contrast, the implementation within Function.identity() makes only one code location, hence, one runtime class and instance. See also this answer.
But it must be emphasized that this is specific to a particular implementation, as the strategy is implemented by the JRE, further, we’re talking about a finite, rather small number of code locations and hence, objects.
By the way, these approaches are not contradicting. You could even have both:
// for calling directly
public static <E> E alwaysThrow(E k1, E k2) {
// by the way, k1 is not the key, see https://stackoverflow.com/a/45210944/2711488
throw new IllegalArgumentException("Duplicate key " + k1 + " not allowed!");
}
// when needing a shared BinaryOperator
public static <E> BinaryOperator<E> throwingMerger() {
return ContainingClass::alwaysThrow;
}
Note that there’s another point to consider; the factory method always returns a materialized instance of a particular interface, i.e. BinaryOperator. For methods that need to be bound to different interfaces, depending on the context, you need method references at these places anyway. That’s why you can write
DoubleBinaryOperator sum1 = Double::sum;
BinaryOperator<Double> sum2 = Double::sum;
BiFunction<Integer,Integer,Double> sum3 = Double::sum;
which would not be possible if there was only a factory method returning a DoubleBinaryOperator.

EDIT: Ignore my remarks about avoiding unnecessary allocations, see Holgers answer as to why.
There won't be a noticable performance difference between the two - the first variant is avoiding unnecessary allocations though. I would prefer the method reference as the function does not capture any value and thus does not need a lambda in this context. Compared to creating the IllegalArgumentException, which has to fill its stacktrace before being thrown(which is quite expensive), the performance difference is totally negligible.
Remember: this is more about readability and communicating what your code does than about performance. If you ever hit a performance wall because of this kind of code lambdas and streams just aren't the way to go as they are a pretty elaborate abstraction with many indirections.

Should I use "then" or "flatMap" for control flow?

So, I'm trying to work with Webflux and I've got a scenario "check if an object exists; if so, do stuff, else - indicate error".
That can be written in reactor as:
public Mono<Void> handleObjectWithSomeId(Mono<IdType> id){
return id.
flatMap(repository::exists). //repository.exists returns Mono<Boolean>
flatMap(e -> e ? e : Mono.error(new DoesntExistException())).
then(
//can be replaced with just(someBusinessLogic())
Mono.fromCallable(this::someBusinessLogic)
);
}
or as:
public Mono<Void> handleObjectWithSomeId(Mono<IdType> id){
return id.
flatMap(repository::exists). //repository.exists returns Mono<Boolean>
flatMap(e -> e ? e : Mono.error(new DoesntExistException())).
map(e -> this.someBusinessLogic()));
}
Let's assume that return type of someBusinessLogic cannot be changed and it has to be simple void, not Mono<Void>.
In both cases if the object won't exist, appropriate Mono.error(...) will be produced.
While I understand that then and flatMap have different semantics, effectively I get the same result. Even though in second case I'm using flatMap against its meaning, I get to skip flatMap and fromCallable in favor of simple map with ignored argument (that seems more readable). My point is, both apporaches have advantages and disadvantages when it comes to readability and code quality.
So, here's a summary:
Using then
pros
is semantically correct
cons
in many cases (like above) requires wrapping in ad-hoc Mono/Flux
Using flatMap
pros
simplifies continued "happy scenario" code
cons
is semantically incorrect
What are other pros/cons of both approaches? What should I take under consideration when choosing an operator?
I've found this reactor issue that states that there is not real difference in speed.

TL, DR: If you care about the result of the previous computation, you can use map(), flatMap() or other map variant. Otherwise, if you just want the previous stream finished, use then().
You can see a detailed log of execution for yourself, by placing an .log() call in both methods:
public Mono<Void> handleObjectWithSomeId(Mono<IdType> id) {
return id.log()
.flatMap(...)
...;
}
Like all other operations in Project Reactor, the semantics for then() and flatMap() are already defined. The context mostly defines how these operators should work together to solve your problem.
Let's consider the context you provided in the question. What flatMap() does is, whenever it gets an event, it executes the mapping function asynchronously.
Since we have a Mono<> after the last flatMap() in the question, it will provide the result of previous single computation, which we ignore. Note that if we had a Flux<> instead, the computation would be done for every element.
On the other hand, then() doesn't care about the preceding sequence of events. It just cares about the completion event:
That's why, in your example it doesn't matter very much which one you use. However, in other contexts you might choose accordingly.
You might also find the Which operator do I need? section Project Reactor Reference helpful.

Benefits of Functional decomposition and currying

I have a function, calculate(String A,int B) in legacy code
Double calculate(String A,int B) {
if(A.equals("something")){ return B*1.02; }
if(B.equals("some")) return B*1.0;
else return B;
}
The calculation applied on B depends on the value of A.
In functional style I can break this into:
Function<String, Function<Integer,Double>> strategyA = (a)-> {
if(A.equals("something")) return b -> b*1.02;
if(B.equals("some")) return b -> return b -> b*1.0;
else return b -> b;
}
Then instead of calling calculate(a,b) I would call
strategyA.apply(a).apply(b)
Is the second style better than first one. As per my understanding this involves Strategy pattern and Functional Decomposition and Currying.
If the second approach is indeed better, how would you convince someone?

In Java, the preferred way of delivering a named piece of code is and stays the method. There is no reason to express a method as function, just to have “more functional style”. The reason, why function support was added to Java, is, that you sometimes want to pass a reference to the code to another method or function. In this case, the receiving method defines the required function signature, not the code you’re going to encapsulate.
So your calculate method may be referred to as an ObjIntConsumer<String>, if the receiving method only wants to pass pairs of String and int to it without being interested in the result. Otherwise, it may use a custom functional interface expressing the (String,int) → Double signature. Or when you accept boxing, BiFunction<String,Integer,Double> will do.
Currying allows you to reuse existing interfaces like Function to express functions with multiple arguments, when no builtin interface exists, but given the resulting generic signature, which will appear at least at one place in Java code using such a curried function, the readability suffers a lot, so in most cases, defining a new functional interface will be preferred over currying in most cases…
For other programming languages, having a different syntax and type inference for functional types (or having real function types in the first place, rather than settling on functional interfaces), this will be quite different.

I agree with Holger that in most cases, it does not make sense to write code using functions just for the sake of using functional programming. Functions are just an additional tool that lets you write code such as collection processing in a nicer way.
There is one interesting thing about your example though, which is that you take the String parameter a, then perform some computation and then return another function. This can be sometimes useful if the first operation takes a long time:
Function<String, Function<Integer,Double>> f = (a) -> {
if (some-long-computation(a)) return b -> b*1.02;
if (some-other-long-computation(a)) return b -> return b -> b*1.0;
else return b -> b;
}
When you then invoke f with the String argument, the function will run some-long-computation and some-other-long-computation and return the desired function:
Function<Integer,Double> fast = f.apply("Some input"); // Slow
Double d1 = fast.apply(123); // Fast!
Double d1 = fast.apply(456); // Fast!
If f was an ordinary method, then calling it twice as f("Some input", 123) and
f("Some input", 456) would be slower, because you'd run the expensive computations twice. Of course, this is something you can handle without functional programming too, but it is one place where returning a function actually fits quite nicely.

Null-safe method chaining with Optional

Guava's Optional pattern is great, as it helps remove the ambiguity with null. The transform method is very helpful for creating null-safe method chains when the first part of the chain may be absent, but isn't useful when other parts of the chain are absent.
This question is related to Guava Optional type, when transformation returns another Optional, which asks essentially the same question but for a different use case which I think may not be the intended use of Optional (handling errors).
Consider a method Optional<Book> findBook(String id). findBook(id).transform(Book.getName) works as expected. If there is no book found we get an Absent<String>, if there is a book found we get Present<String>.
In the common case where intermediate methods may return null/absent(), there does not seem to be an elegant way to chain the calls. For example, assume that Book has a method Optional<Publisher> getPublisher(), and we would like to get all the books published by the publisher of a book. The natural syntax would seem to be findBook(id).transform(Book.getPublisher).transform(Publisher.getPublishedBooks), however this will fail because the transform(Publisher.getPublishedBooks) call will actually return an Optional<Optional<Publisher>>.
It seems fairly reasonable to have a transform()-like method on Optional that would accept a function which returns an Optional. It would act exactly like the current implementation except that it simply would not wrap the result of the function in an Optional. The implementation (for Present) might read:
public abstract <V> Optional<V> optionalTransform(Function<? super T, Optional<V>> function) {
return function.apply(reference);
}
The implementation for Absent is unchanged from transform:
public abstract <V> Optional<V> optionalTransform(Function<? super T, Optional<V>> function) {
checkNotNull(function);
return Optional.absent();
}
It would also be nice if there were a way to handle methods that return null as opposed to Optional for working with legacy objects. Such a method would be like transform but simply call Optional.fromNullable on the result of the function.
I'm curious if anyone else has run into this annoyance and found nice workarounds (which don't involve writing your own Optional class). I'd also love to hear from the Guava team or be pointed to discussions related to the issue (I didn't find any in my searching).

You are looking for some Monad, but Guava's Optional (as opposite to for example Scala's Option) is just a Functor.
What the hell is a Functor?!
Functor and Monad are a kind of box, a context that wraps some value.
Functor containing some value of type A knows how to apply function A => B and put the result back into Functor. For example: get something out of Optional, transform, and wrap back into Optional.
In functional programming languages such method is often named 'map'.
Mona.. what?
Monad is almost the same thing as Functor, except that it consumes function returning value wrapped in Monad (A => Monad, for example Int => Optional).
This magic Monad's method is often called 'flatMap'.
Here you can find really awesome explanations for fundamental FP terms: http://adit.io/posts/2013-04-17-functors,_applicatives,_and_monads_in_pictures.html
Functors & Monads are coming!
Optional from Java 8 can be classified as both Functor (http://docs.oracle.com/javase/8/docs/api/java/util/Optional.html#map-java.util.function.Function-) and Monad (http://docs.oracle.com/javase/8/docs/api/java/util/Optional.html#flatMap-java.util.function.Function-).
Nice mon(ad)olog, Marcin, but how can I solve my particular problem?
I'm currently working on a project that uses Java 6 and yesterday I write some helper class, called 'Optionals', which saved me a lot of time.
It provides some helper method, that allows me to turn Optional into Monads (flatMap).
Here is the code: https://gist.github.com/mkubala/046ae20946411f80ac52
Because my project's codebase still uses nulls as a return value, I introduced Optionals.lift(Function), which can be used to wrapping results into the Optional.
Why lifting result into Optional?
To avoid situation when function passed into transform might return null and whole expression would return "present of null" (which by the way is not possible with Guava's Optional, because of this postcondition -> see line #71 of https://code.google.com/p/guava-libraries/source/browse/guava/src/com/google/common/base/Present.java?r=0823847e96b1d082e94f06327cf218e418fe2228#71).
Couple of examples
Let's assume that findEntity() returns an Optional and Entity.getDecimalField(..) may return BigDecimal or null:
Optional<BigDecimal> maybeDecimalValue = Optionals.flatMap(
findEntity(),
new Function<Entity, Optional<BigDecimal>> () {
#Override
public Optional<BigDecimal> apply(Entity input) {
return Optional.fromNullable(input.getDecimalField(..));
}
}
);
Yet another example, assuming that I already have some Function, which extracts decimal values from Entities, and may return nulls:
Function<Entity, Decimal> extractDecimal = .. // extracts decimal value or null
Optional<BigDecimal> maybeDecimalValue = Optionals.flatMap(
findEntity(),
Optionals.lift(extractDecimal)
);
And last, but not least - your use case as an example:
Optional<Publisher> maybePublisher = Optionals.flatMap(findBook(id), Optionals.lift(Book.getPublisher));
// Assuming that getPublishedBooks may return null..
Optional<List<Book>> maybePublishedBooks = Optionals.flatMap(maybePublisher, Optionals.lift(Publisher.getPublishedBooks));
// ..or simpler, in case when getPublishedBooks never returns null
Optional<List<Book>> maybePublishedBooks2 = maybePublisher.transform(Publisher.getPublishedBooks);
// as a one-liner:
Optionals.flatMap(maybePublisher, Optionals.lift(Publisher.getPublishedBooks)).transform(Publisher.getPublishedBooks);

You probably figured that out, but you could add .or(Optional.absent) after every transformation that returns Optional (in your case after .transform(Book.getPublisher) reducing Optional<Optional<T>> to Optional<T>:
Optional<List<Book>> publishedBooks = findBook(id).transform(Book.getPublisher).
or(Optional.absent()).transform(Publisher.getPublishedBooks);
Unfortunately, the type of Optional.absent cannot be inferred here, so the code actually becomes:
Optional<List<Book>> publishedBooks = book.transform(Book.getPublisher).
or(Optional.<Publisher> absent()).transform(Publisher.getPublishedBoooks);
Not too convenient but there doesn't seem to be any other way.

Monads with Java 8

In the interests of helping to understand what a monad is, can someone provide an example using java ? Are they possible ?
Lambda expressions are possible using java if you download the pre-release lambda compatible JDK8 from here http://jdk8.java.net/lambda/
An example of a lambda using this JDK is shown below, can someone provide a comparably simple monad ?
public interface TransformService {
int[] transform(List<Integer> inputs);
}
public static void main(String ars[]) {
TransformService transformService = (inputs) -> {
int[] ints = new int[inputs.size()];
int i = 0;
for (Integer element : inputs) {
ints[i] = element;
}
return ints;
};
List<Integer> inputs = new ArrayList<Integer>(5) {{
add(10);
add(10);
}};
int[] results = transformService.transform(inputs);
}

Just FYI:
The proposed JDK8 Optional class does satisfy the three Monad laws. Here's a gist demonstrating that.
All it takes be a Monad is to provide two functions which conform to three laws.
The two functions:
Place a value into monadic context
Haskell's Maybe: return / Just
Scala's Option: Some
Functional Java's Option: Option.some
JDK8's Optional: Optional.of
Apply a function in monadic context
Haskell's Maybe: >>= (aka bind)
Scala's Option: flatMap
Functional Java's Option: flatMap
JDK8's Optional: flatMap
Please see the above gist for a java demonstration of the three laws.
NOTE: One of the key things to understand is the signature of the function to apply in monadic context: it takes the raw value type, and returns the monadic type.
In other words, if you have an instance of Optional<Integer>, the functions you can pass to its flatMap method will have the signature (Integer) -> Optional<U>, where U is a value type which does not have to be Integer, for example String:
Optional<Integer> maybeInteger = Optional.of(1);
// Function that takes Integer and returns Optional<Integer>
Optional<Integer> maybePlusOne = maybeInteger.flatMap(n -> Optional.of(n + 1));
// Function that takes Integer and returns Optional<String>
Optional<String> maybeString = maybePlusOne.flatMap(n -> Optional.of(n.toString));
You don't need any sort of Monad Interface to code this way, or to think this way. In Scala, you don't code to a Monad Interface (unless you are using Scalaz library...). It appears that JDK8 will empower Java folks to use this style of chained monadic computations as well.
Hope this is helpful!
Update: Blogged about this here.

Java 8 will have lambdas; monads are a whole different story. They are hard enough to explain in functional programming (as evidenced by the large number of tutorials on the subject in Haskell and Scala).
Monads are a typical feature of statically typed functional languages. To describe them in OO-speak, you could imagine a Monad interface. Classes that implement Monad would then be called 'monadic', provided that in implementing Monad the implementation obeys what are known as the 'monad laws'. The language then provides some syntactic sugar that makes working with instances of the Monad class interesting.
Now Iterable in Java has nothing to do with monads, but as a example of a type that the Java compiler treats specially (the foreach syntax that came with Java 5), consider this:
Iterable<Something> things = getThings(..);
for (Something s: things) { /* do something with s */ }
So while we could have used Iterable's Iterator methods (hasNext and company) in an old-style for loop, Java grants us this syntactic sugar as a special case.
So just as classes that implement Iterable and Iterator must obey the Iterator laws (Example: hasNext must return false if there is no next element) to be useful in foreach syntax - there would exist several monadic classes that would be useful with a corresponding do notation (as it is called in Haskell) or Scala's for notation.
So -
What are good examples of monadic classes?
What would syntactic sugar for dealing with them look like?
In Java 8, I don't know - I am aware of the lambda notation but I am not aware of other special syntactic sugar, so I'll have to give you an example in another language.
Monads often serve as container classes (Lists are an example). Java already has java.util.List which is obviously not monadic, but here is Scala's:
val nums = List(1, 2, 3, 4)
val strs = List("hello", "hola")
val result = for { // Iterate both lists, return a resulting list that contains
// pairs of (Int, String) s.t the string size is same as the num.
n <- nums
s <- strs if n == s.length
} yield (n, s)
// result will be List((4, "hola"))
// A list of exactly one element, the pair (4, "hola")
Which is (roughly) syntactic sugar for:
val nums = List(1, 2, 3, 4)
val strs = List("hello", "hola")
val results =
nums.flatMap( n =>
strs.filter(s => s.size == n). // same as the 'if'
map(s => (n, s)) // Same as the 'yield'
)
// flatMap takes a lambda as an argument, as do filter and map
//
This shows a feature of Scala where monads are exploited to provide list comprehensions.
So a List in Scala is a monad, because it obeys Scala's monad laws, which stipulate that all monad implementations must have conforming flatMap, map and filter methods (if you are interested in the laws, the "Monads are Elephants" blog entry has the best description I've found so far). And, as you can see, lambdas (and HoF) are absolutely necessary but not sufficient to make this kind of thing useful in a practical way.
There's a bunch of useful monads besides the container-ish ones as well. They have all kinds of applications. My favorite must be the Option monad in Scala (the Maybe monad in Haskell), which is a wrapper type which brings about null safety: the Scala API page for the Option monad has a very simple example usage: http://www.scala-lang.org/api/current/scala/Option.html
In Haskell, monads are useful in representing IO, as a way of working around the fact that non-monadic Haskell code has indeterminate order of execution.
Having lambdas is a first small step into the functional programming world; monads
require both the monad convention and a large enough set of usable monadic types, as well as syntactic sugar to make working with them fun and useful.
Since Scala is arguably the language closest to Java that also allows (monadic) Functional Programming, do look at this Monad tutorial for Scala if you are (still) interested:
http://james-iry.blogspot.jp/2007/09/monads-are-elephants-part-1.html
A cursory googling shows that there is at least one attempt to do this in Java: https://github.com/RichardWarburton/Monads-in-Java -
Sadly, explaining monads in Java (even with lambdas) is as hard as explaining full-blown Object oriented programming in ANSI C (instead of C++ or Java).

Even though monads can be implemented in Java, any computation involving them is doomed to become a messy mix of generics and curly braces.
I'd say that Java is definitely not the language to use in order to illustrate their working or to study their meaning and essence. For this purpose it is far better to use JavaScript or to pay some extra price and learn Haskell.
Anyway, I am signaling you that I just implemented a state monad using the new Java 8 lambdas. It's definitely a pet project, but it works on a non-trivial test case.
You may find it presented at my blog, but I'll give you some details here.
A state monad is basically a function from a state to a pair (state,content). You usually give the state a generic type S and the content a generic type A.
Because Java does not have pairs we have to model them using a specific class, let's call it Scp (state-content pair), which in this case will have generic type Scp<S,A> and a constructor new Scp<S,A>(S state,A content). After doing that we can say that the monadic function will have type
java.util.function.Function<S,Scp<S,A>>
which is a #FunctionalInterface. That's to say that its one and only implementation method can be invoked without naming it, passing a lambda expression with the right type.
The class StateMonad<S,A> is mainly a wrapper around the function. Its constructor may be invoked e.g. with
new StateMonad<Integer, String>(n -> new Scp<Integer, String>(n + 1, "value"));
The state monad stores the function as an instance variable. It is then necessary to provide a public method to access it and feed it the state. I decided to call it s2scp ("state to state-content pair").
To complete the definition of the monad you have to provide a unit (aka return) and a bind (aka flatMap) method. Personally I prefer to specify unit as static, whereas bind is an instance member.
In the case of the state monad, unit gotta be the following:
public static <S, A> StateMonad<S, A> unit(A a) {
return new StateMonad<S, A>((S s) -> new Scp<S, A>(s, a));
}
while bind (as instance member) is:
public <B> StateMonad<S, B> bind(final Function<A, StateMonad<S, B>> famb) {
return new StateMonad<S, B>((S s) -> {
Scp<S, A> currentPair = this.s2scp(s);
return famb(currentPair.content).s2scp(currentPair.state);
});
}
You notice that bind must introduce a generic type B, because it is the mechanism that allows the chaining of heterogeneous state monads and gives this and any other monad the remarkable capability to move the computation from type to type.
I'd stop here with the Java code. The complex stuff is in the GitHub project. Compared to previous Java versions, lambdas remove a lot of curly braces, but the syntax is still pretty convoluted.
Just as an aside, I'm showing how similar state monad code may be written in other mainstream languages. In the case of Scala, bind (which in that case must be called flatMap) reads like
def flatMap[A, B](famb: A => State[S, B]) = new State[S, B]((s: S) => {
val (ss: S, aa: A) = this.s2scp(s)
famb(aa).s2scp(ss)
})
whereas the bind in JavaScript is my favorite; 100% functional, lean and mean but -of course- typeless:
var bind = function(famb){
return state(function(s) {
var a = this(s);
return famb(a.value)(a.state);
});
};
<shameless>
I am cutting a few corners here, but if you are interested in the details you will find them on my WP blog.</shameless>

Here's the thing about monads which is hard to grasp: monads are a
pattern, not a specific type. Monads are a shape, they are an abstract
interface (not in the Java sense) more than they are a concrete data
structure. As a result, any example-driven tutorial is doomed to
incompleteness and failure.
[...]
The only way to understand monads is to see them for what they are: a mathematical construct.
Monads are not metaphors by Daniel Spiewak
Monads in Java SE 8
List monad
interface Person {
List<Person> parents();
default List<Person> greatGrandParents1() {
List<Person> list = new ArrayList<>();
for (Person p : parents()) {
for (Person gp : p.parents()) {
for (Person ggp : p.parents()) {
list.add(ggp);
}
}
}
return list;
}
// <R> Stream<R> flatMap(Function<? super T, ? extends Stream<? extends R>> mapper)
default List<Person> greatGrandParents2() {
return Stream.of(parents())
.flatMap(p -> Stream.of(p.parents()))
.flatMap(gp -> Stream.of(gp.parents()))
.collect(toList());
}
}
Maybe monad
interface Person {
String firstName();
String middleName();
String lastName();
default String fullName1() {
String fName = firstName();
if (fName != null) {
String mName = middleName();
if (mName != null) {
String lName = lastName();
if (lName != null) {
return fName + " " + mName + " " + lName;
}
}
}
return null;
}
// <U> Optional<U> flatMap(Function<? super T, Optional<U>> mapper)
default Optional<String> fullName2() {
return Optional.ofNullable(firstName())
.flatMap(fName -> Optional.ofNullable(middleName())
.flatMap(mName -> Optional.ofNullable(lastName())
.flatMap(lName -> Optional.of(fName + " " + mName + " " + lName))));
}
}
Monad is a generic pattern for nested control flow encapsulation.
I.e. a way to create reusable components from nested imperative idioms.
Important to understand that a monad is not just a generic wrapper class with a flat map operation.
For example, ArrayList with a flatMap method won't be a monad.
Because monad laws prohibit side effects.
Monad is a formalism. It describes the structure, regardless of content or meaning.
People struggle with relating to meaningless (abstract) things.
So they come up with metaphors which are not monads.
See also:
conversation between Erik Meijer and Gilad Bracha.

the only way to understand monads is by writing a bunch of combinator libraries, noticing the resulting duplication, and then discovering for yourself that monads let you factor out this duplication. In discovering this, everyone builds some intuition for what a monad is… but this intuition isn’t the sort of thing that you can communicate to someone else directly – it seems everyone has to go through the same experience of generalizing to monads from some concrete examples of combinator libraries. however
here i found some materials to learn Mondas.
hope to be useful for you too.
codecommit
james-iry.blogspot
debasishg.blogspot

This blog post gives a step-by-step example of how you might implement a Monad type (interface) in Java and then use it to define the Maybe monad, as a practical application.
This post explains that there is one monad built into the Java language, emphasising the point that monads are more common than many programmers may think and that coders often inadvertently reinvent them.

Despite all controversy about Optional satisfying, or not, the Monad laws, I usually like to look at Stream, Optional and CompletableFuture in the same way. In truth, all them provide a flatMap() and that is all I care and let me embrace the "the tasteful composition of side effects" (cited by Erik Meijer). So we may have corresponding Stream, Optional and CompletableFuture in the following way:
Regarding Monads, I usually simplify it only thinking on flatMap()(from "Principles of Reactive Programming" course by Erik Meijer):

A diagram for the "Optional" Monad in Java.
Your task: Perform operations on the "Actuals" (left side) transforming elements of type T union null to type U union null using the function in the light blue box (the light blue box function). Just one box is shown here, but there may be a chain of the light blue boxes (thus proceeding from type U union null to type V _union null to type W union null etc.)
Practically, this will cause you to worry about null values appearing in the function application chain. Ugly!
Solution: Wrap your T into an Optional<T> using the light green box functions, moving to the "Optionals" (right side). Here, transform elements of type Optional<T> to type Optional<U> using the red box function. Mirroring the application of functions to the "Actuals", there may be several red box functions to be be chained (thus proceeding from type Optional<U> to Optional<V> then to Optional<W> etc.). In the end, move back from the "Optionals" to the "Actuals" through one of the dark green box functions.
No worrying about null values anymore. Implementationwise, there will always be an Optional<U>, which may or may not be empty. You can chain the calls to to the red box functions without null checks.
The key point: The red box functions are not implemented individually and directly. Instead, they are obtained from the blue box functions (whichever have been implemented and are available, generally the light blue ones) by using either the map or the flatMap higher-order functions.
The grey boxes provide additional support functionality.
Simples.

I like to think of monads in slighlty more mathematical (but still informal) fashion. After that I will explain the relationship to one of Java 8's monads CompletableFuture.
First of all, a monad M is a functor. That is, it transforms a type into another type: If X is a type (e.g. String) then we have another type M<X> (e.g. List<String>). Moreover, if we have a transformation/function X -> Y of types, we should get a function M<X> -> M<Y>.
But there is more data to such a monad. We have a so-called unit which is a function X -> M<X> for each type X. In other words, each object of X can be wrapped in a natural way into the monad.
The most characteristic data of a monad, however, is it's product: a function M<M<X>> -> M<X> for each type X.
All of these data should satisfy some axioms like functoriality, associativity, unit laws, but I won't go into detail here and it also doesn't matter for practical usage.
We can now deduce another operation for monads, which is often used as an equivalent definition for monads, the binding operation: A value/object in M<X> can be bound with a function X -> M<Y> to yield another value in M<Y>. How do we achieve this? Well, first we apply functoriality to the function to obtain a function M<X> -> M<M<Y>>. Next we apply the monadic product to the target to obtain a function M<X> -> M<Y>. Now we can plug in the value of M<X> to obtain a value in M<Y> as desired. This binding operation is used to chain several monadic operations together.
Now lets come to the CompletableFuture example, i.e. CompletableFuture = M. Think of an object of CompletableFuture<MyData> as some computation that's performed asynchronously and which yields an object of MyData as a result some time in the future. What are the monadic operations here?
functoriality is realized with the method thenApply: first the computation is performed and as soon as the result is available, the function which is given to thenApply is applied to transform the result into another type
the monadic unit is realized with the method completedFuture: as the documentation tells, the resulting computation is already finished and yields the given value at once
the monadic product is not realized by a function, but the binding operation below is equivalent to it (together with functoriality) and its semantic meaning is simply the following: given a computation of type CompletableFuture<CompletableFuture<MyData>> that computation asynchronously yields another computation in CompletableFuture<MyData> which in turn yields some value in MyData later on, so performing both computations on after the other yields one computation in total
the resulting binding operation is realized by the method thenCompose
As you see, computations can now be wrapped up in a special context, namely asynchronicity. The general monadic structures enable us to chain such computations in the given context. CompletableFuture is for example used in the Lagom framework to easily construct highly asynchronous request handlers which are transparently backed up by efficient thread pools (instead of handling each request by a dedicated thread).

Haskell monads is an interface which specify rules to convert “datatype that is wrapped in another datatype” to another “datatype that is wrapped in another or same datatype”; the conversion steps is specified by a function you define with a format.
The function format takes a datatype and return “datatype that is wrapped in another datatype”. You can specify operations/ calculations during conversion e.g. multiply or lookup something.
It is so difficult to understand because of the nested abstraction. It is so abstracted so that you can reuse the rules to convert datatype in a datatype without custom programming to unwrap the first “ datatype that is wrapped in another datatype” before putting the data to your specified function; Optional with some datatype is an example of “datatype in a datatype”.
The specified function is any lambda confirming the format.
You don’t need to fully understand it; you will write your own reusable interface to solve similar problem. Monad is just exist because some mathematicians already hit and resolve that problem, and create monad for you to reuse. But due to its abstraction, it is difficult to learn and reuse in the first place.
In other words, e.g. Optional is a wrapper class, but some data is wrapped , some not, some function take wrapped data type but some don’t, return type can be of type wrapped or not. To chain calling mixture of function which may wrap or not in parameter/return types, you either do your own custom wrap/unwrap or reuse pattern of functor / applicative / monad to deal with all those wrapped/unwrapped combinations of chained function call. Every time u try to put optional to a method that only accept plain value and return optional, the steps are what monad does.