Array programming languages (also known as vector or multidimensional languages) generalize operations on scalars to apply transparently to vectors, matrices, and higher dimensional arrays.
Is it possible to achieve this kind of code reuse in Scala?
It's entirely possible (I've done so myself) with appropriate implicits, though the result is not always as utterly seamless as a language that has been designed from the ground up that way.
For example, suppose you want to treat arrays of integers as vectors and want to be able to add them to each other and to scalars. You have to define the operation yourself--Scala can't guess what + should mean on an array. (Good thing, too, because * doesn't usually have the obvious element-by-element meaning on matrices, and it means something else again when it comes to convolution!) You can
class ArraysAdd(a: Array[Int]) {
def +(i: Int) = a.map(_ + i)
def +(b: Array[Int]) = {
if (b.length==a.length) (a,b).zipped.map(_ + _).toArray
else throw new IllegalArgumentException
}
}
class ScalarAddsToArray(i: Int) {
def +(a: Array[Int]) = a.map(_ + i)
}
implicit def array2addable(a: Array[Int]) = new ArraysAdd(a)
implicit def scalar2arrayaddable(i: Int) = new ScalarAddsToArray(i)
and then you can do math on arrays of integers:
scala> Array(1,2,3) + 5
res2: Array[Int] = Array(6, 7, 8)
scala> Array(1,7) + Array(3,2)
res3: Array[Int] = Array(4, 9)
scala> 4 + Array(-2,-3,-1)
res4: Array[Int] = Array(2, 1, 3)
If you want to cover all data types it gets trickier (you need to use generics and Numeric at least--or write a code generator to cover the cases you need), and for efficiency you might want to create a custom "matrix" class instead of sticking with raw arrays that you keep wrapping with extra functionality.
This covers basic operations. For mathematical functions like math.sqrt, if you don't mind typing a map sqrt instead of sqrt(a) then you don't need to do anything. Otherwise, you can overload them all yourself; tedious, but it then lets you use them transparently.
Or you could use a library that has already done much of this for you. (Scalala is the best candidate I know of for matrices.)
There isn't any real way to auto-lift Scala operations, as would you would need to to support array programming languages like APL and J. The closest thing to array auto-lifting in Scala is implicit conversions, and that's not quite right. Scala implicit conversions go the opposite way, converting the targets of the operations, rather than the operations themselves. Even then, Scala implicit conversions don't chain, so even if you could lift a scalar operation to a vector (which you can't), that wouldn't automatically lift it to a 2-D or higher array.
In short, doing APL/J-ish calculation in Scala is going to involve seeing the "map" and "flatMap" methods a lot, explicitly called (as well as fold, reduce, scan, and all the other usual suspects). One benefit is that you can code those operations as for-comprehensions in Scala, which can give some admirable clarity for these sort of batch operations over collections.
Is something like scalala what you're looking for?
Related
Coming from a Java background I am learning Scala and the following has me very confused. Why is the type returned different in these two (very similar but different) constructs, which vary only in how the source collection was build -
val seq1: IndexedSeq[Int] = for (i <- 1 to 10) yield i
vs.
val seq2: Array[Int] = for (i <- Array(1, 2, 3)) yield i
Please do point to me the right literature so that I can understand the core fundamentals at play here.
There are, in general, two different styles of collection operation libraries:
type-preserving: that is what you are confused about in your question
generic (not in the "parametric polymorphism sense" but the standard English sense of the word) or maybe "homogeneous"
Type-preserving collection operations try to preserve the type exactly for operations like filter, take, drop, etc. that only take existing elements unmodified. For operations like map, it tries to find the closest super type that can still hold the result. E.g. mapping over an IntSet with a function from Int to String can obviously not result in an IntSet, but only in a Set. Mapping an IntSet to Boolean could be represented in a BitSet, but I know of no collections framework that is clever enough to actually do that.
Generic / homogeneous collection operations always return the same type. Usually, this type is chosen to be very general, to accommodate the widest range of use cases. For example, In .NET, collection operations return IEnumerable, in Java, they return Streams, in C++, they return iterators, in Ruby, they return arrays.
Until recently, it was only possible to implement type-preserving collection operations by duplicating all operations for all types. For example, the Smalltalk collections framework is type-preserving, and it does this by having every single collections class re-implement every single collections operation. This results in a lot of duplicated code and is a maintenance nightmare. (It is no coincidence that many new object-oriented abstractions that get invented have their first paper written about how it can be applied to the Smalltalk collections framework. See Traits: Composable Units of Behaviour for an example.)
To my knowledge, the Scala 2.8 re-design of the collections framework (see also this answer on SO) was the first time someone managed to create type-preserving collections operations while minimizing (though not eliminating) duplication. However, the Scala 2.8 collections framework was widely criticized as being overly complex, and it has required constant work over the last decade. In fact, it actually lead to a complete re-design of the Scala documentation system as well, just to be able to hide the very complex type signatures that the type-preserving operations require. But, this still wasn't enough, so the collections framework was completely thrown out and re-designed yet again in Scala 2.13. (And this re-design took several years.)
So, the simple answer to your question is this: Scala tries as much as possible to preserve the type of the collection.
In your second case, the type of the collection is Array, and when you map over an Array, you get back an Array.
In your first case, the type of the collection is Range. Now, a Range doesn't actually have elements, though. It only has a beginning and an end and a step, and it produces the elements on demand while you are iterating over it. So, it is not that easy to produce a new Range with the new elements. The map function would basically need to be able to "reverse engineer" your mapping function to figure out what the new beginning and end and step should be. (Which is equivalent to solving the Halting Problem, or in other words impossible.) And what if you do something like this:
val seq1: IndexedSeq[Int] = for (i <- 1 to 10) yield scala.util.Random.nextInt(i)
Here, there isn't even a well-defined step, so it is actually impossible to build a Range that does this.
So, clearly, mapping over a Range cannot return a Range. So, it does the next best thing: It returns the most precise super type of Range that can contain the mapped values. In this case, that happens to be IndexedSeq.
There is a wrinkle, in that type-preserving collections operations challenge what we consider to be part of the contract of certain operations. For example, most people would argue that the cardinality of a collection should be invariant under map, in other words, map should map each element to exactly one new element and thus map should never change the size of the collection. But, what about this code:
Set(1, 2, 3).map { _ % 2 == 0 }
//=> Set(true, false)
Here, you get back a collection with fewer elements from a map, which is only supposed to transform elements, not remove them. But, since we decided we want type-preserving collections, and a Set cannot have duplicate values, the two false values are actually the same value, so there is only one of them in the set.
[It could be argued that this actually only demonstrates that Sets aren't collections and shouldn't be treated as collections. Sets are predicates ("Is this element a member?") rather than collections ("Give me all your elements!")]
this happens because construction:
for (x <-someSeq) yield x
is the same as:
someSeq.map(x => x)
for () yield is just syntactic sugar for flatMap/map function.
As we know map function doesn't change type of object-container, it changes elements inside container.
So, in your example 1 to 10 has a type Range.Inclusive which extends Range, and Range extends IndexedSeq. Mapped IndexedSeq has the same type IndexedSeq.
for (i <- 1 to 10) yield i the same as (1 to 10).map(x => x)
In second case: for (i <- Array(1, 2, 3)) yield i you have an Array, and mapped Array type also Array.
for (i <- Array(1, 2, 3)) yield i the same as Array(1, 2, 3).map(x => x)
I think the right literature would be Scala for the Impatient by Cay Horstmann. The first edition is a little outdated but it's held up pretty well. The book is a fairly easy read almost to the end (I admit I don't quite understand lexical parsers or actors, but that's probably on me not Horstmann).
One of the things that Horstmann's book explains early on is that although you can use for like in Java, it can actually do much more sophisticated things. As a toy example, consider this Java procedure:
public static void main(String[] args) {
HashSet<Integer> squaresMod10 = new HashSet<>();
for (int i = 1; i < 11; i++) {
squaresMod10.add(i * i % 10);
}
for (Integer j : squaresMod10) {
System.out.print(j + " ");
}
}
If you're using Java 8 or later, your IDE's probably advising you that you "can use functional operators." NetBeans rewrote it thus for me:
squaresMod10.forEach((j) -> {
System.out.print(j + " ");
});
In Scala, you can use "functional operations" for both the i and j loops in this example. Rather than fire up IntelliJ just for this, I'll use the local Scala REPL on my system.
scala> (1 to 10).map(i => i * i % 10)
res2: IndexedSeq[Int] = Vector(1, 4, 9, 6, 5, 6, 9, 4, 1, 0)
scala> (1 to 10).map(i => i * i % 10).toSet
res3: scala.collection.immutable.Set[Int] = HashSet(0, 5, 1, 6, 9, 4)
scala> for (j <- res3) System.out.print(j + " ")
^
warning: method + in class Int is deprecated (since 2.13.0):
Adding a number and a String is deprecated. Use the string interpolation `s"$num$str"`
0 5 1 6 9 4
In Java, what would you be going for with seq1 and seq2? With a standard for loop in Java, you're essentially guiding the computer through the process of looking at each element of an ad hoc collection one by one and performing a certain operation on it.
Lambdas like the one NetBeans wrote for me still become If and Goto constructs at the JVM level, just like regular for loops in Java, as do a lot of what Horstmann calls "for comprehensions" in Scala. Either way, you're delegating the nuts and bolts of how exactly that happens to the compiler. In other words, you're not micro-managing.
However, as your seq2 example shows, it's still possible to wrap collections unnecessarily.
scala> 1 to 10
res5: scala.collection.immutable.Range.Inclusive = Range 1 to 10
scala> res5 == seq1
res6: Boolean = true
scala> Array(1, 2, 3)
res7: Array[Int] = Array(1, 2, 3)
scala> res7 == seq2
res8: Boolean = false
Okay, that didn't quite go the way I thought it would, but my point stands: in this vacuum, it's unnecessary to wrap 1 to 10 into another IndexedSeq[Int] and it's unnecessary to wrap an Array[Int] into another Array[Int]. Your for "statements" are merely wrapping unnamed collections into named collections.
I am new to scala. Currently I am trying to develop a calculator as scala project.
But while trying to find the sum I am getting problem. The following code doesn't compile.
def add [T](values :List[T]) : Option[T]={
if (values.isInstanceOf[String]) {
None
}
else if(values.isInstanceOf[Int] || values.isInstanceOf[Long] || values.isInstanceOf[Double]) {
Some(values.sum)
}
}
When you're new to a language, it is important to start slow and discover the abstractions rather than impose them right away without the experience to make it work. I also think it is a bad idea to just rely on standard library functions (unless you are in a hurry) like sum or to look at third-party libraries until you fully understand what the standard library offers.
So in other words, first let's look at ways to solve the problem just for Int:
def add(values: List[Int]) = values.reduce((a, b) => a + b)
The reduce function works because the type of the result matches the type of what is in the list. The contents are Ints, and the sum is an Int. You will also find this can be simplified with some syntactic sugar:
def add(values: List[Int]) = values.reduce(_ + _)
Wait until you see all the ways underscores get used in Scala.
As you keep studying, you will find that in order to accumulate a value that is a different type than what's in the list, you can use one of the fold* methods. But they can work here too, so for example you could also do this:
def add(values: List[Int]) = values.foldLeft(0)(_ + _)
Now try to define the same function for Long, Double, or even String. You will find the implementations will be almost identical.
It's only when you see implementations that look really similar if not identical that you should even think about abstractions. One option to solve this problem is with type classes. They aren't easy for beginners though. Daniel Westheide wrote a great post on the topic, and frankly you would do well to read the whole series as you learn Scala.
I've really simplified things and glossed over a lot of topics (like why foldLeft has two parameter lists with the first one being 0 or what those crazy implicit things are in Daniel's type classes post), but my advice is to be patient and move slowly. Scala isn't easy, but you'll get it.
(But if you are in a hurry and you are a beginner, I think you are better off just using sum and then writing separate functions for each type. This would be an example of what agile author Kenny Rubin would call "necessary" or "unavoidable" technical debt, and that's to be expected from beginners. Just refactor later.)
Or more generic (example with amm):
# import $ivy.`org.typelevel:cats_2.12:0.8.1`
import $ivy.$
# import cats._, cats.implicits._
import cats._, cats.implicits._
# List(1, 2, 3).combineAll
res2: Int = 6
# List("1", "2", "3").combineAll
res3: String = "123"
# List(List(1, 2), List(), List(3)).combineAll
res5: List[Int] = List(1, 2, 3)
Or via fiddle. It works by using the Monoid and Traversable typeclass.
Writing Scala code, I regularly encounter cases where I have "processor" functions that operate iteratively on a collection of elements and also need to know the length of the collection.
On the other hand I have "provider" functions that generate collections and so already know the length. The generated collections may be List[T], Array[T] or Set[T], etc., but even in the case of List[T], my generator knows the size (even if the List type does not store it).
So I would naturally declare the "processor" functions as taking the most generic type that seems to fit all collection types, Iterable[T], as a parameter. However, they then internally need to find out the size via iterative collection traversal at a cost of O(N), which is undesirable.
So my naive solution would be to create a new type like IterableWithSize[T] and have the provider and processor functions create and take this type. Neither Seq[T] nor IndexedSeq[T] seem to fit the bill. But this seems like a relatively common use case, so I'm suspecting that there is a more idiomatic way to do this. What would that be?
In Scala collections, performance sensitive methods like size are not inherited from traits but overridden in the bottom type. For example see the implementation of immutable.HashSet:
https://lampsvn.epfl.ch/trac/scala/browser/scala/tags/R_2_9_0_1/src//library/scala/collection/immutable/HashSet.scala
So you don't need to care about it. Just define an high-level common trait like Traversable or Iterable and you're done.
Actually, there's no idiomatic way around that. Scala collections were really meant to be traversed or used in other prescribed manners (such as Set.contains or Map.get). Checking for size is not part of them, and some of them are not even finite.
Now, IndexedSeq is a relatively safe bet -- it guarantees O(logn) indexed access, which is only possible if you have O(logn) size. Also, Set and Map are reasonably safe as well, for similar reasons. But if you are looking for a trait that gives you a guarantee on size speed, there isn't one.
I don't think there is an idiomatic way to do this. But here are two alternatives:
(1) Extend Scala's List/Set/Array collections and override the size method. This is not as difficult as it seems at first glance.
(2) Wrap your List/Set/Array collections together with the size and define an implicit unwrapper like:
class IterableWithSizeWrapper[E](private val c: Iterable[E], val size: Int)
object IterableWithSizeWrapper {
implicit def unwrap[E](iws: IterableWithSizeWrapper[E]): Iterable[E] = iws.c
}
object ListWithSizeTest {
def process[E](iws: IterableWithSizeWrapper[E]) {
// iws.size uses your cached size value
// iws.take(i) forces the unwrap to the original collect
// so iws.take(i).size takes the calculated size
for (i <- 0 to iws.size) assert(iws.take(i).size == i)
}
def main(args: Array[String]) {
process(new IterableWithSizeWrapper(List(1,2,3), 3))
process(new IterableWithSizeWrapper(Set(1,2,3), 3))
process(new IterableWithSizeWrapper(Array(1,2,3), 3))
}
}
How about Traversable? All your collections you mention inherit from it (Array indirectly via WrappedArray) and it provides size and toIterable (or toIterator) for traversal.
Your processor functions should accept Seq[T]. A Seq is precisely an Iterable that "has a length". Your only remaining problem is making length efficient. AFAIK it is already efficient in all cases except for List. To make List.length efficient just do as the others describe: Create an implementation of Seq that wraps a List and stores its length.
So in reading this question it was pointed out that instead of the procedural code:
def expand(exp: String, replacements: Traversable[(String, String)]): String = {
var result = exp
for ((oldS, newS) <- replacements)
result = result.replace(oldS, newS)
result
}
You could write the following functional code:
def expand(exp: String, replacements: Traversable[(String, String)]): String = {
replacements.foldLeft(exp){
case (result, (oldS, newS)) => result.replace(oldS, newS)
}
}
I would almost certainly write the first version because coders familiar with either procedural or functional styles can easily read and understand it, while only coders familiar with functional style can easily read and understand the second version.
But setting readability aside for the moment, is there something that makes foldLeft a better choice than the procedural version? I might have thought it would be more efficient, but it turns out that the implementation of foldLeft is actually just the procedural code above. So is it just a style choice, or is there a good reason to use one version or the other?
Edit: Just to be clear, I'm not asking about other functions, just foldLeft. I'm perfectly happy with the use of foreach, map, filter, etc. which all map nicely onto for-comprehensions.
Answer: There are really two good answers here (provided by delnan and Dave Griffith) even though I could only accept one:
Use foldLeft because there are additional optimizations, e.g. using a while loop which will be faster than a for loop.
Use fold if it ever gets added to regular collections, because that will make the transition to parallel collections trivial.
It's shorter and clearer - yes, you need to know what a fold is to understand it, but when you're programming in a language that's 50% functional, you should know these basic building blocks anyway. A fold is exactly what the procedural code does (repeatedly applying an operation), but it's given a name and generalized. And while it's only a small wheel you're reinventing, but it's still a wheel reinvention.
And in case the implementation of foldLeft should ever get some special perk - say, extra optimizations - you get that for free, without updating countless methods.
Other than a distaste for mutable variable (even mutable locals), the basic reason to use fold in this case is clarity, with occasional brevity. Most of the wordiness of the fold version is because you have to use an explicit function definition with a destructuring bind. If each element in the list is used precisely once in the fold operation (a common case), this can be simplified to use the short form. Thus the classic definition of the sum of a collection of numbers
collection.foldLeft(0)(_+_)
is much simpler and shorter than any equivalent imperative construct.
One additional meta-reason to use functional collection operations, although not directly applicable in this case, is to enable a move to using parallel collection operations if needed for performance. Fold can't be parallelized, but often fold operations can be turned into commutative-associative reduce operations, and those can be parallelized. With Scala 2.9, changing something from non-parallel functional to parallel functional utilizing multiple processing cores can sometimes be as easy as dropping a .par onto the collection you want to execute parallel operations on.
One word I haven't seen mentioned here yet is declarative:
Declarative programming is often defined as any style of programming that is not imperative. A number of other common definitions exist that attempt to give the term a definition other than simply contrasting it with imperative programming. For example:
A program that describes what computation should be performed and not how to compute it
Any programming language that lacks side effects (or more specifically, is referentially transparent)
A language with a clear correspondence to mathematical logic.
These definitions overlap substantially.
Higher-order functions (HOFs) are a key enabler of declarativity, since we only specify the what (e.g. "using this collection of values, multiply each value by 2, sum the result") and not the how (e.g. initialize an accumulator, iterate with a for loop, extract values from the collection, add to the accumulator...).
Compare the following:
// Sugar-free Scala (Still better than Java<5)
def sumDoubled1(xs: List[Int]) = {
var sum = 0 // Initialized correctly?
for (i <- 0 until xs.size) { // Fenceposts?
sum = sum + (xs(i) * 2) // Correct value being extracted?
// Value extraction and +/* smashed together
}
sum // Correct value returned?
}
// Iteration sugar (similar to Java 5)
def sumDoubled2(xs: List[Int]) = {
var sum = 0
for (x <- xs) // We don't need to worry about fenceposts or
sum = sum + (x * 2) // value extraction anymore; that's progress
sum
}
// Verbose Scala
def sumDoubled3(xs: List[Int]) = xs.map((x: Int) => x*2). // the doubling
reduceLeft((x: Int, y: Int) => x+y) // the addition
// Idiomatic Scala
def sumDoubled4(xs: List[Int]) = xs.map(_*2).reduceLeft(_+_)
// ^ the doubling ^
// \ the addition
Note that our first example, sumDoubled1, is already more declarative than (most would say superior to) C/C++/Java<5 for loops, because we haven't had to micromanage the iteration state and termination logic, but we're still vulnerable to off-by-one errors.
Next, in sumDoubled2, we're basically at the level of Java>=5. There are still a couple things that can go wrong, but we're getting pretty good at reading this code-shape, so errors are quite unlikely. However, don't forget that a pattern that's trivial in a toy example isn't always so readable when scaled up to production code!
With sumDoubled3, desugared for didactic purposes, and sumDoubled4, the idiomatic Scala version, the iteration, initialization, value extraction and choice of return value are all gone.
Sure, it takes time to learn to read the functional versions, but we've drastically foreclosed our options for making mistakes. The "business logic" is clearly marked, and the plumbing is chosen from the same menu that everyone else is reading from.
It is worth pointing out that there is another way of calling foldLeft which takes advantages of:
The ability to use (almost) any Unicode symbol in an identifier
The feature that if a method name ends with a colon :, and is called infix, then the target and parameter are switched
For me this version is much clearer, because I can see that I am folding the expr value into the replacements collection
def expand(expr: String, replacements: Traversable[(String, String)]): String = {
(expr /: replacements) { case (r, (o, n)) => r.replace(o, n) }
}
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)