Round-robin combining observables - scala

I'm new to RxJava, and I've been trying to combine multiple observables in a round-robin way.
So, imagine you have three observables:
o1: --0---1------2--
o2: -4--56----------
o3: -------8---9----
Combining those in a round-robin way would give you something like:
r : --04---815-9-26-
What would be the best way to approach this?
Since it looks like RxJava, RxScala etc. pretty much share API, answer in any language should be fine. :)
Thanks,
Matija

RxJava doesn't have such operator by default. The closest thing is using merge with well paced sources because it uses round-robin to collect values, but this property can't be relied upon. Why do you need this round-robin behavior?
The best bet is to implement this behavior manually. Here is an example without backpressure support.

There is an approach that is very simple to implement and does almost exactly what you want - just zip the three source observables, and than emit the three values from the zipped observable each time a new triplet arrives.
Translated to RxScala
val o1 = Observable.just(1, 2, 3)
val o2 = Observable.just(10, 20, 30)
val o3 = Observable.just(100, 200, 300)
val roundRobinSource = Observable
.zip(Observable.just(o1, o2, o3))
.flatMap(Observable.from[Int])
roundRobinSource.subscribe(println, println)
gives you
1
10
100
2
20
200
3
30
300
Which is precisely what you want.
The problem with this approach is that it will block until a value from each of the three sources arrives, but if your cool with that, I think this is by far the simplest solution. I'm curious, what is your use case?
Update, Take #2
This is actually a fun question. Here is another take, that will trade one drawback for another.
import rx.lang.scala.{Subject, Observable}
val s1 = Subject[Int]()
val s2 = Subject[Int]()
val s3 = Subject[Int]()
val roundRobinSource3 = s1.publish(po1 ⇒ s2.publish(po2 ⇒ s3.publish(po3 ⇒ {
def oneRound: Observable[Int] = po1.take(1) ++ po2.take(1) ++ po3.take(1)
def all: Observable[Int] = oneRound ++ Observable.defer(all)
all
})))
roundRobinSource3.subscribe(println, println, () ⇒ println("Completed"))
println("s1.onNext(1)")
s1.onNext(1)
println("s2.onNext(10)")
s2.onNext(10)
println("s3.onNext(100)")
s3.onNext(100)
println("s2.onNext(20)")
s2.onNext(20)
println("s1.onNext(2)")
s1.onNext(2)
println("s3.onNext(200)")
s3.onNext(200)
println("s1.onCompleted()")
s1.onCompleted()
println("s2.onCompleted()")
s2.onCompleted()
println("s3.onCompleted()")
s3.onCompleted()
println("Done...")
Gives you
s1.onNext(1)
1
s2.onNext(10)
10
s3.onNext(100)
100
s2.onNext(20)
s1.onNext(2)
2
20
s3.onNext(200)
200
s1.onCompleted()
s2.onCompleted()
s3.onCompleted()
Done...
It doesn't block, it round robins, but... it also doesn't complete :( You could make it complete in a stateful manner using a takeUntil, Subject and doOnComplete if you need it, though..
As for the mechanism, it uses the to me somehow mysterious behavior of publish, that keeps track of things already emitted. I have been originally pointed to it by #lopar when he answered my own questiong Implementing a turnstile-like operator with RxJava.
The behavior of publish is actually such a mystery to me, that I have posted a question about it here: https://github.com/ReactiveX/RxJava/issues/2775. If you are curious, you can follow it.

Related

How to implement a recursive Fibonacci sequence in Scala using FS2?

While trying to become familiar with FS2, I came across a nifty recursive implementation using the Scala collections' Stream, and thought I'd have a go at trying it in FS2:
import fs2.{Pure, Stream}
val fibs: Stream[Pure, Int] = Stream[Pure, Int](0) ++ fibs.fold[Int](1)(_ + _)
println(fibs take 10 toList) // This will hang
What is the reason this hangs in FS2, and what is the best way to get a similar, working solution?
Your issue is that Stream.fold consumes all elements of the stream, producing a single final value from the fold. Note that it only emits one element.
The recursive stream only terminates when 10 elements have been emitted (this is specified by take 10). Since this stream is not productive enough, fold continues to add values without stopping.
The simplest way to fix this is to use a combinator that emits the partial results from the fold; this is scan.
Also, FS2 can infer most of the types in this code, so you don't necessarily need as many type annotations.
The following implementation should work fine:
import fs2.{Pure, Stream}
val fibs: Stream[Pure, Int] = Stream(0) ++ fibs.scan(1)(_ + _)
println(fibs take 10 toList)

Scala, Using Responder to abstract a possible Asynchronous computation

I have been looking into scala and AKKA for managing an obviously parallelisable algorithm.
I have some knowledge of functional programming and mostly do Java, so my FP might not be the best yet.
The algorithm I am working with is pretty simple, there is a top computation:
def computeFull(...): FullObject
This computation calls sub computations and then sum it up (to simplify):
def computePartial(...): Int
and computeFull does something like this (again simplifying):
val partials = for(x <- 1 to 10
y <- 1 to 10) yield computePartial(x, y)
partials.foldLeft(0)(_ + _)
So, it's very close to the AKKA example, doing the PI computation. I have many computeFull to call and many computePartial within each of them. So I can wrap all of this in AKKA actors, or to simplify in Futures, calling each computeFull and each computePartial in separate threads. I then can use the fold, zip and map functions of http://doc.akka.io/docs/akka/snapshot/scala/futures.html to combile the futures.
However, this implies that computeFull and computePartial will have to return Futures wrapping the actual results. They thus become dependent on AKKA and assuming that things are run in parallel. In fact, I also have to implicitly pass down the execution contexts within my functions.
I think that this is weird and that the algorithm "shouldn't" know the details of how it is parallelised, or if it is.
After reading about Futures in scala (and not the AKKA one) and looking into Code Continuation. It seems like the Responder monad that is provided by scala (http://www.scala-lang.org/api/current/scala/Responder.html) seems like the right way to abstract how the function calls are run.
I have this vague intuition that computeFull and computePartial could return Responders instead of futures and that when the monad in executed, it decides how the code embedded within the Responder gets executed (if it spawns a new actor or if it is executed on the same thread).
However, I am not really sure how to get to this result. Any suggestions? Do you think I am on the right way?
If you don’t want to be dependent on Akka (but note that Akka-style futures will be moved and included with Scala 2.10) and your computation is a simple fold on a collection you can simply use Scala’s parallel collections:
val partials = for { x <- (1 to 10).par
y <- 1 to 10
} yield computePartial(x, y)
// blocks until everything is computed
partials.foldLeft(0)(_ + _)
Of course, this will block until partials is ready, so it may not be a appropriate situation when you really need futures.
With Scala 2.10 style futures you can make that completely asynchronous without your algorithms ever noticing it:
def computePartial(x: Int, y: Int) = {
Thread.sleep((1000 * math.random).toInt)
println (x + ", " + y)
x * y
}
import scala.concurrent.future
import scala.concurrent.Future
val partials: IndexedSeq[Future[Int]] = for {
x <- 1 to 10
y <- 1 to 10
} yield future(computePartial(x, y))
val futureResult: Future[Int] = Future.sequence(partials).map(_.fold(0)(_ + _))
def useResult(result: Int) = println(s"The sum is $result")
// now I want to use the result of my computation
futureResult map { result => // called when ready
useResult(result)
}
// still no blocking
println("This is probably printed before the sum is calculated.")
So, computePartial does not need to know anything about how it is being executed. (It should not have any side-effects though, even though for the purpose of this example, a println side-effect was included.)
A possible computeFull method should manage the algorithm and as such know about Futures or parallelism. After all this is part of the algorithm.
(As for the Responder: Scala’s old futures use it so I don’t know where this is going. – And isn’t an execution context exactly the means of configuration you are looking for?)
The single actor in akka knows not if he runs in parrallel or not. That is how akka is designed. But if you don't want to rely on akka you can use parrallel collections like:
for (i <- (0 until numberOfPartialComputations).par) yield (
computePartial(i)
).sum
The sum is called on a parrallel collection and is performed in parrallel.

Add streams in Scala

I see at least two different implementations:
def add_streams(s1:Stream[Int], s2:Stream[Int]): Stream[Int] =
Stream.cons(s1.head + s2.head, add_stream(s1.tail, s2.tail))
def add_streams(s1:Stream[Int], s2:Stream[Int]) =
(s1 zip s2) map {case (x,y) => x + y}
I guess the last one is more efficient since it is not recursive.
Is it correct? How would you code such a function ?
The first version is broken as it doesn‘t check for the end of a Stream. (The streams needn’t be of different length for this to happen.) Given that, the zip version is the one to prefer.
First of all: your implementations have different behavior when either of the streams is finite. The first will crash with a NoSuchElementException, while the second will just truncate the longer stream.
I find the latter much more expressive and elegant, anyway, although I doubt the performance difference would be noticeable in most cases.

Use-cases for Streams in Scala

In Scala there is a Stream class that is very much like an iterator. The topic Difference between Iterator and Stream in Scala? offers some insights into the similarities and differences between the two.
Seeing how to use a stream is pretty simple but I don't have very many common use-cases where I would use a stream instead of other artifacts.
The ideas I have right now:
If you need to make use of an infinite series. But this does not seem like a common use-case to me so it doesn't fit my criteria. (Please correct me if it is common and I just have a blind spot)
If you have a series of data where each element needs to be computed but that you may want to reuse several times. This is weak because I could just load it into a list which is conceptually easier to follow for a large subset of the developer population.
Perhaps there is a large set of data or a computationally expensive series and there is a high probability that the items you need will not require visiting all of the elements. But in this case an Iterator would be a good match unless you need to do several searches, in that case you could use a list as well even if it would be slightly less efficient.
There is a complex series of data that needs to be reused. Again a list could be used here. Although in this case both cases would be equally difficult to use and a Stream would be a better fit since not all elements need to be loaded. But again not that common... or is it?
So have I missed any big uses? Or is it a developer preference for the most part?
Thanks
The main difference between a Stream and an Iterator is that the latter is mutable and "one-shot", so to speak, while the former is not. Iterator has a better memory footprint than Stream, but the fact that it is mutable can be inconvenient.
Take this classic prime number generator, for instance:
def primeStream(s: Stream[Int]): Stream[Int] =
Stream.cons(s.head, primeStream(s.tail filter { _ % s.head != 0 }))
val primes = primeStream(Stream.from(2))
It can be easily be written with an Iterator as well, but an Iterator won't keep the primes computed so far.
So, one important aspect of a Stream is that you can pass it to other functions without having it duplicated first, or having to generate it again and again.
As for expensive computations/infinite lists, these things can be done with Iterator as well. Infinite lists are actually quite useful -- you just don't know it because you didn't have it, so you have seen algorithms that are more complex than strictly necessary just to deal with enforced finite sizes.
In addition to Daniel's answer, keep in mind that Stream is useful for short-circuiting evaluations. For example, suppose I have a huge set of functions that take String and return Option[String], and I want to keep executing them until one of them works:
val stringOps = List(
(s:String) => if (s.length>10) Some(s.length.toString) else None ,
(s:String) => if (s.length==0) Some("empty") else None ,
(s:String) => if (s.indexOf(" ")>=0) Some(s.trim) else None
);
Well, I certainly don't want to execute the entire list, and there isn't any handy method on List that says, "treat these as functions and execute them until one of them returns something other than None". What to do? Perhaps this:
def transform(input: String, ops: List[String=>Option[String]]) = {
ops.toStream.map( _(input) ).find(_ isDefined).getOrElse(None)
}
This takes a list and treats it as a Stream (which doesn't actually evaluate anything), then defines a new Stream that is a result of applying the functions (but that doesn't evaluate anything either yet), then searches for the first one which is defined--and here, magically, it looks back and realizes it has to apply the map, and get the right data from the original list--and then unwraps it from Option[Option[String]] to Option[String] using getOrElse.
Here's an example:
scala> transform("This is a really long string",stringOps)
res0: Option[String] = Some(28)
scala> transform("",stringOps)
res1: Option[String] = Some(empty)
scala> transform(" hi ",stringOps)
res2: Option[String] = Some(hi)
scala> transform("no-match",stringOps)
res3: Option[String] = None
But does it work? If we put a println into our functions so we can tell if they're called, we get
val stringOps = List(
(s:String) => {println("1"); if (s.length>10) Some(s.length.toString) else None },
(s:String) => {println("2"); if (s.length==0) Some("empty") else None },
(s:String) => {println("3"); if (s.indexOf(" ")>=0) Some(s.trim) else None }
);
// (transform is the same)
scala> transform("This is a really long string",stringOps)
1
res0: Option[String] = Some(28)
scala> transform("no-match",stringOps)
1
2
3
res1: Option[String] = None
(This is with Scala 2.8; 2.7's implementation will sometimes overshoot by one, unfortunately. And note that you do accumulate a long list of None as your failures accrue, but presumably this is inexpensive compared to your true computation here.)
I could imagine, that if you poll some device in real time, a Stream is more convenient.
Think of an GPS tracker, which returns the actual position if you ask it. You can't precompute the location where you will be in 5 minutes. You might use it for a few minutes only to actualize a path in OpenStreetMap or you might use it for an expedition over six months in a desert or the rain forest.
Or a digital thermometer or other kinds of sensors which repeatedly return new data, as long as the hardware is alive and turned on - a log file filter could be another example.
Stream is to Iterator as immutable.List is to mutable.List. Favouring immutability prevents a class of bugs, occasionally at the cost of performance.
scalac itself isn't immune to these problems: http://article.gmane.org/gmane.comp.lang.scala.internals/2831
As Daniel points out, favouring laziness over strictness can simplify algorithms and make it easier to compose them.

Why should I avoid using local modifiable variables in Scala?

I'm pretty new to Scala and most of the time before I've used Java. Right now I have warnings all over my code saying that i should "Avoid mutable local variables" and I have a simple question - why?
Suppose I have small problem - determine max int out of four. My first approach was:
def max4(a: Int, b: Int,c: Int, d: Int): Int = {
var subMax1 = a
if (b > a) subMax1 = b
var subMax2 = c
if (d > c) subMax2 = d
if (subMax1 > subMax2) subMax1
else subMax2
}
After taking into account this warning message I found another solution:
def max4(a: Int, b: Int,c: Int, d: Int): Int = {
max(max(a, b), max(c, d))
}
def max(a: Int, b: Int): Int = {
if (a > b) a
else b
}
It looks more pretty, but what is ideology behind this?
Whenever I approach a problem I'm thinking about it like: "Ok, we start from this and then we incrementally change things and get the answer". I understand that the problem is that I try to change some initial state to get an answer and do not understand why changing things at least locally is bad? How to iterate over collection then in functional languages like Scala?
Like an example: Suppose we have a list of ints, how to write a function that returns sublist of ints which are divisible by 6? Can't think of solution without local mutable variable.
In your particular case there is another solution:
def max4(a: Int, b: Int,c: Int, d: Int): Int = {
val submax1 = if (a > b) a else b
val submax2 = if (c > d) c else d
if (submax1 > submax2) submax1 else submax2
}
Isn't it easier to follow? Of course I am a bit biased but I tend to think it is, BUT don't follow that rule blindly. If you see that some code might be written more readably and concisely in mutable style, do it this way -- the great strength of scala is that you don't need to commit to neither immutable nor mutable approaches, you can swing between them (btw same applies to return keyword usage).
Like an example: Suppose we have a list of ints, how to write a
function that returns the sublist of ints which are divisible by 6?
Can't think of solution without local mutable variable.
It is certainly possible to write such function using recursion, but, again, if mutable solution looks and works good, why not?
It's not so related with Scala as with the functional programming methodology in general. The idea is the following: if you have constant variables (final in Java), you can use them without any fear that they are going to change. In the same way, you can parallelize your code without worrying about race conditions or thread-unsafe code.
In your example is not so important, however imagine the following example:
val variable = ...
new Future { function1(variable) }
new Future { function2(variable) }
Using final variables you can be sure that there will not be any problem. Otherwise, you would have to check the main thread and both function1 and function2.
Of course, it's possible to obtain the same result with mutable variables if you do not ever change them. But using inmutable ones you can be sure that this will be the case.
Edit to answer your edit:
Local mutables are not bad, that's the reason you can use them. However, if you try to think approaches without them, you can arrive to solutions as the one you posted, which is cleaner and can be parallelized very easily.
How to iterate over collection then in functional languages like Scala?
You can always iterate over a inmutable collection, while you do not change anything. For example:
val list = Seq(1,2,3)
for (n <- list)
println n
With respect to the second thing that you said: you have to stop thinking in a traditional way. In functional programming the usage of Map, Filter, Reduce, etc. is normal; as well as pattern matching and other concepts that are not typical in OOP. For the example you give:
Like an example: Suppose we have a list of ints, how to write a function that returns sublist of ints which are divisible by 6?
val list = Seq(1,6,10,12,18,20)
val result = list.filter(_ % 6 == 0)
Firstly you could rewrite your example like this:
def max(first: Int, others: Int*): Int = {
val curMax = Math.max(first, others(0))
if (others.size == 1) curMax else max(curMax, others.tail : _*)
}
This uses varargs and tail recursion to find the largest number. Of course there are many other ways of doing the same thing.
To answer your queston - It's a good question and one that I thought about myself when I first started to use scala. Personally I think the whole immutable/functional programming approach is somewhat over hyped. But for what it's worth here are the main arguments in favour of it:
Immutable code is easier to read (subjective)
Immutable code is more robust - it's certainly true that changing mutable state can lead to bugs. Take this for example:
for (int i=0; i<100; i++) {
for (int j=0; j<100; i++) {
System.out.println("i is " + i = " and j is " + j);
}
}
This is an over simplified example but it's still easy to miss the bug and the compiler won't help you
Mutable code is generally not thread safe. Even trivial and seemingly atomic operations are not safe. Take for example i++ this looks like an atomic operation but it's actually equivalent to:
int i = 0;
int tempI = i + 0;
i = tempI;
Immutable data structures won't allow you to do something like this so you would need to explicitly think about how to handle it. Of course as you point out local variables are generally threadsafe, but there is no guarantee. It's possible to pass a ListBuffer instance variable as a parameter to a method for example
However there are downsides to immutable and functional programming styles:
Performance. It is generally slower in both compilation and runtime. The compiler must enforce the immutability and the JVM must allocate more objects than would be required with mutable data structures. This is especially true of collections.
Most scala examples show something like val numbers = List(1,2,3) but in the real world hard coded values are rare. We generally build collections dynamically (from a database query etc). Whilst scala can reassign the values in a colection it must still create a new collection object every time you modify it. If you want to add 1000 elements to a scala List (immutable) the JVM will need to allocate (and then GC) 1000 objects
Hard to maintain. Functional code can be very hard to read, it's not uncommon to see code like this:
val data = numbers.foreach(_.map(a => doStuff(a).flatMap(somethingElse)).foldleft("", (a : Int,b: Int) => a + b))
I don't know about you but I find this sort of code really hard to follow!
Hard to debug. Functional code can also be hard to debug. Try putting a breakpoint halfway into my (terrible) example above
My advice would be to use a functional/immutable style where it genuinely makes sense and you and your colleagues feel comfortable doing it. Don't use immutable structures because they're cool or it's "clever". Complex and challenging solutions will get you bonus points at Uni but in the commercial world we want simple solutions to complex problems! :)
Your two main questions:
Why warn against local state changes?
How can you iterate over collections without mutable state?
I'll answer both.
Warnings
The compiler warns against the use of mutable local variables because they are often a cause of error. That doesn't mean this is always the case. However, your sample code is pretty much a classic example of where mutable local state is used entirely unnecessarily, in a way that not only makes it more error prone and less clear but also less efficient.
Your first code example is more inefficient than your second, functional solution. Why potentially make two assignments to submax1 when you only ever need to assign one? You ask which of the two inputs is larger anyway, so why not ask that first and then make one assignment? Why was your first approach to temporarily store partial state only halfway through the process of asking such a simple question?
Your first code example is also inefficient because of unnecessary code duplication. You're repeatedly asking "which is the biggest of two values?" Why write out the code for that 3 times independently? Needlessly repeating code is a known bad habit in OOP every bit as much as FP and for precisely the same reasons. Each time you needlessly repeat code, you open a potential source of error. Adding mutable local state (especially when so unnecessary) only adds to the fragility and to the potential for hard to spot errors, even in short code. You just have to type submax1 instead of submax2 in one place and you may not notice the error for a while.
Your second, FP solution removes the code duplication, dramatically reducing the chance of error, and shows that there was simply no need for mutable local state. It's also, as you yourself say, cleaner and clearer - and better than the alternative solution in om-nom-nom's answer.
(By the way, the idiomatic Scala way to write such a simple function is
def max(a: Int, b: Int) = if (a > b) a else b
which terser style emphasises its simplicity and makes the code less verbose)
Your first solution was inefficient and fragile, but it was your first instinct. The warning caused you to find a better solution. The warning proved its value. Scala was designed to be accessible to Java developers and is taken up by many with a long experience of imperative style and little or no knowledge of FP. Their first instinct is almost always the same as yours. You have demonstrated how that warning can help improve code.
There are cases where using mutable local state can be faster but the advice of Scala experts in general (not just the pure FP true believers) is to prefer immutability and to reach for mutability only where there is a clear case for its use. This is so against the instincts of many developers that the warning is useful even if annoying to experienced Scala devs.
It's funny how often some kind of max function comes up in "new to FP/Scala" questions. The questioner is very often tripping up on errors caused by their use of local state... which link both demonstrates the often obtuse addiction to mutable state among some devs while also leading me on to your other question.
Functional Iteration over Collections
There are three functional ways to iterate over collections in Scala
For Comprehensions
Explicit Recursion
Folds and other Higher Order Functions
For Comprehensions
Your question:
Suppose we have a list of ints, how to write a function that returns sublist of ints which are divisible by 6? Can't think of solution without local mutable variable
Answer: assuming xs is a list (or some other sequence) of integers, then
for (x <- xs; if x % 6 == 0) yield x
will give you a sequence (of the same type as xs) containing only those items which are divisible by 6, if any. No mutable state required. Scala just iterates over the sequence for you and returns anything matching your criteria.
If you haven't yet learned the power of for comprehensions (also known as sequence comprehensions) you really should. Its a very expressive and powerful part of Scala syntax. You can even use them with side effects and mutable state if you want (look at the final example on the tutorial I just linked to). That said, there can be unexpected performance penalties and they are overused by some developers.
Explicit Recursion
In the question I linked to at the end of the first section, I give in my answer a very simple, explicitly recursive solution to returning the largest Int from a list.
def max(xs: List[Int]): Option[Int] = xs match {
case Nil => None
case List(x: Int) => Some(x)
case x :: y :: rest => max( (if (x > y) x else y) :: rest )
}
I'm not going to explain how the pattern matching and explicit recursion work (read my other answer or this one). I'm just showing you the technique. Most Scala collections can be iterated over recursively, without any need for mutable state. If you need to keep track of what you've been up to along the way, you pass along an accumulator. (In my example code, I stick the accumulator at the front of the list to keep the code smaller but look at the other answers to those questions for more conventional use of accumulators).
But here is a (naive) explicitly recursive way of finding those integers divisible by 6
def divisibleByN(n: Int, xs: List[Int]): List[Int] = xs match {
case Nil => Nil
case x :: rest if x % n == 0 => x :: divisibleByN(n, rest)
case _ :: rest => divisibleByN(n, rest)
}
I call it naive because it isn't tail recursive and so could blow your stack. A safer version can be written using an accumulator list and an inner helper function but I leave that exercise to you. The result will be less pretty code than the naive version, no matter how you try, but the effort is educational.
Recursion is a very important technique to learn. That said, once you have learned to do it, the next important thing to learn is that you can usually avoid using it explicitly yourself...
Folds and other Higher Order Functions
Did you notice how similar my two explicit recursion examples are? That's because most recursions over a list have the same basic structure. If you write a lot of such functions, you'll repeat that structure many times. Which makes it boilerplate; a waste of your time and a potential source of error.
Now, there are any number of sophisticated ways to explain folds but one simple concept is that they take the boilerplate out of recursion. They take care of the recursion and the management of accumulator values for you. All they ask is that you provide a seed value for the accumulator and the function to apply at each iteration.
For example, here is one way to use fold to extract the highest Int from the list xs
xs.tail.foldRight(xs.head) {(a, b) => if (a > b) a else b}
I know you aren't familiar with folds, so this may seem gibberish to you but surely you recognise the lambda (anonymous function) I'm passing in on the right. What I'm doing there is taking the first item in the list (xs.head) and using it as the seed value for the accumulator. Then I'm telling the rest of the list (xs.tail) to iterate over itself, comparing each item in turn to the accumulator value.
This kind of thing is a common case, so the Collections api designers have provided a shorthand version:
xs.reduce {(a, b) => if (a > b) a else b}
(If you look at the source code, you'll see they have implemented it using a fold).
Anything you might want to do iteratively to a Scala collection can be done using a fold. Often, the api designers will have provided a simpler higher-order function which is implemented, under the hood, using a fold. Want to find those divisible-by-six Ints again?
xs.foldRight(Nil: List[Int]) {(x, acc) => if (x % 6 == 0) x :: acc else acc}
That starts with an empty list as the accumulator, iterates over every item, only adding those divisible by 6 to the accumulator. Again, a simpler fold-based HoF has been provided for you:
xs filter { _ % 6 == 0 }
Folds and related higher-order functions are harder to understand than for comprehensions or explicit recursion, but very powerful and expressive (to anybody else who understands them). They eliminate boilerplate, removing a potential source of error. Because they are implemented by the core language developers, they can be more efficient (and that implementation can change, as the language progresses, without breaking your code). Experienced Scala developers use them in preference to for comprehensions or explicit recursion.
tl;dr
Learn For comprehensions
Learn explicit recursion
Don't use them if a higher-order function will do the job.
It is always nicer to use immutable variables since they make your code easier to read. Writing a recursive code can help solve your problem.
def max(x: List[Int]): Int = {
if (x.isEmpty == true) {
0
}
else {
Math.max(x.head, max(x.tail))
}
}
val a_list = List(a,b,c,d)
max_value = max(a_list)