So I've been working with parallel collections in Scala for a graph project I'm working on, I've got the basics of the graph class defined, it is currently using a scala.collection.mutable.HashMap where the key is Int and the value is ListBuffer[Int] (adjacency list). (EDIT: This has since been change to ArrayBuffer[Int]
I had done a similar thing a few months ago in C++, with a std::vector<int, std::vector<int> >.
What I'm trying to do now is run a metric between all pairs of vertices in the graph, so in C++ I did something like this:
// myVec = std::vector<int> of vertices
for (std::vector<int>::iterator iter = myVec.begin(); iter != myVec.end(); ++iter) {
for (std::vector<int>::iterator iter2 = myVec.begin();
iter2 != myVec.end(); ++iter2) {
/* Run algorithm between *iter and *iter2 */
}
}
I did the same thing in Scala, parallelized, (or tried to) by doing this:
// vertexList is a List[Int] (NOW CHANGED TO Array[Int] - see below)
vertexList.par.foreach(u =>
vertexList.foreach(v =>
/* Run algorithm between u and v */
)
)
The C++ version is clearly single-threaded, the Scala version has .par so it's using parallel collections and is multi-threaded on 8 cores (same machine). However, the C++ version processed 305,570 pairs in a span of roughly 3 days, whereas the Scala version so far has only processed 23,573 in 17 hours.
Assuming I did my math correctly, the single-threaded C++ version is roughly 3x faster than the Scala version. Is Scala really that much slower than C++, or am I completely mis-using Scala (I only recently started - I'm about 300 pages into Programming in Scala)?
Thanks!
-kstruct
EDIT To use a while loop, do I do something like..
// Where vertexList is an Array[Int]
vertexList.par.foreach(u =>
while (i <- 0 until vertexList.length) {
/* Run algorithm between u and vertexList(i) */
}
}
If you guys mean use a while loop for the entire thing, is there an equivalent of .par.foreach for whiles?
EDIT2 Wait a second, that code isn't even right - my bad. How would I parallelize this using while loops? If I have some var i that keeps track of the iteration, then wouldn't all threads be sharing that i?
From your comments, I see that your updating a shared mutable HashMap at the end of each algorithm run. And if you're randomizing your walks, a shared Random is also a contention point.
I recommend two changes:
Use .map and .flatMap to return an immutable collection instead of modifying a shared collection.
Use a ThreadLocalRandom (from either Akka or Java 7) to reduce contention on the random number generator
Check the rest of your algorithm for further possible contention points.
You may try running the inner loop in parallel, too. But without knowing your algorithm, it's hard to know if that will help or hurt. Fortunately, running all combinations of parallel and sequential collections is very simple; just switch out pVertexList and vertexList in the code below.
Something like this:
val pVertexList = vertexList.par
val allResult = for {
u <- pVertexList
v <- pVertexList
} yield {
/* Run algorithm between u and v */
((u -> v) -> result)
}
The value allResult will be a ParVector[((Int, Int), Int)]. You may call .toMap on it to convert that into a Map.
Why mutable? I don't think there's a good parallel mutable map on Scala 2.9.x -- particularly because just such a data structure was added to the upcoming Scala 2.10.
On the other hand... you have a List[Int]? Don't use that, use a Vector[Int]. Also, are you sure you aren't wasting time elsewhere, doing the conversions from your mutable maps and buffers into immutable lists? Scala data structures are different than C++'s so you might well be incurring in complexity problems elsewhere in the code.
Finally, I think dave might be onto something when he asks about contention. If you have contention, parallelism might well make things slower. How faster/slower does it run if you do not make it parallel? If making it not parallel makes it faster, then you most likely do have contention issues.
I'm not completely sure about it, but I think foreach loops in foreach loops are rather slow, because lots of objects get created. See: http://scala-programming-language.1934581.n4.nabble.com/for-loop-vs-while-loop-performance-td1935856.html
Try rewriting it using a while loop.
Also Lists are only efficient for head access, Arrays are probably faster.
Related
I was reading the following presentation:
http://www.idt.mdh.se/kurser/DVA201/slides/parallel-4up.pdf
and the author claims that the map function is built very well for parallelism (specifically he supports his claim on page 3 or slides 9 and 10).
If one were given the problem of increasing each value of a list by +1, I can see how looping through the list imperatively would require a index value to change and hence cause potential race condition problems. But I'm curious how the map function better allows a programmer to successfully code in parallel.
Is it due to the way map is recursively defined? So each function call can be thrown to a different thread?
I hoping someone can provide some specifics, thanks!
The map function applies the same pure function to n elements in a collection and aggregates the results. It doesn't matter the order in which you apply the function to the members of the collection because by definition the return value of the function is purely dependent upon the input.
The others already explained that the standard map implementation isn't parallel.
But in Scala, since you tagged it, you can get the parallel version as simply as
val list = ... // some list
list.par.map(x => ...) // instead of list.map(x => ...)
See also Parallel Collections Overview and documentation for ParIterable and other types in the scala.collection.parallel package.
You can find the implementation of the parallel map in https://github.com/scala/scala/blob/v2.12.1/src/library/scala/collection/parallel/ParIterableLike.scala, if you want (look for def map and class Map). It requires very non-trivial infrastructure and certainly isn't just taking the recursive definition of sequential map and parallelizing it.
If one had defined map via a loop how would that break down?
The slides give F# parallel arrays as the example at the end and at https://github.com/fsharp/fsharp/blob/master/src/fsharp/FSharp.Core/array.fs#L266 you can see the non-parallel implementation there is a loop:
let inline map (mapping: 'T -> 'U) (array:'T[]) =
checkNonNull "array" array
let res : 'U[] = Microsoft.FSharp.Primitives.Basics.Array.zeroCreateUnchecked array.Length
for i = 0 to res.Length-1 do
res.[i] <- mapping array.[i]
res
I am learning functional programming using scala. In general I notice that for loops are not much used in functional programs instead they use map.
Questions
What are the advantages of using map over for loop in terms of performance, readablity etc ?
What is the intention of bringing in a map function when it can be achieved using loop ?
Program 1: Using For loop
val num = 1 to 1000
val another = 1000 to 2000
for ( i <- num )
{
for ( j <- another)
{
println(i,j)
}
}
Program 2 : Using map
val num = 1 to 1000
val another = 1000 to 2000
val mapper = num.map(x => another.map(y => (x,y))).flatten
mapper.map(x=>println(x))
Both program 1 and program 2 does the same thing.
The answer is quite simple actually.
Whenever you use a loop over a collection it has a semantic purpose. Either you want to iterate the items of the collection and print them. Or you want to transform the type of the elements to another type (map). Or you want to change the cardinality, such as computing the sum of the elements of a collection (fold).
Of course, all that can also be done using for - loops but to the reader of the code, it is more work to figure out which semantic purpose the loop has, compared to a well known named operation such as map, iter, fold, filter, ...
Another aspect is, that for loops lead to the dark side of using mutable state. How would you sum the elements of a collection in a for loop without mutable state? You would not. Instead you would need to write a recursive function. So, for good measure, it is best to drop the habit of thinking in for loops early and enjoy the brave new functional way of doing things.
I'll start by quoting Programming in Scala.
"Every for expression can be expressed in terms of the three higher-order functions map, flatMap and filter. This section describes the translation scheme, which is also used by the Scala compiler."
http://www.artima.com/pins1ed/for-expressions-revisited.html#23.4
So the reason that you noticed for-loops are not used as much is because they technically aren't needed, and any for expressions you do see are just syntactic sugar which the compiler will translate into some equivalent. The rules for translating a for expression into a map/flatMap/filter expression are listed in the link above.
Generally speaking, in functional programming there is no index variable to mutate. This means one typically makes heavy use of function calls (often in the form of recursion) such as list folds in place of a while or for loop.
For a good example of using list folds in place of while/for loops, I recommend "Explain List Folds to Yourself" by Tony Morris.
https://vimeo.com/64673035
If a function is tail-recursive (denoted with #tailrec) then it can be optimized so as to not incur the high use of the stack which is common in recursive functions. In this case the compiler can translate the tail-recursive function to the "while loop equivalent".
To answer the second part of Question 1, there are some cases where one could make an argument that a for expression is clearer (although certainly there are cases where the opposite is true too.) One such example is given in the Coursera.org course "Functional Programming with Scala" by Dr. Martin Odersky:
for {
i <- 1 until n
j <- 1 until i
if isPrime(i + j)
} yield (i, j)
is arguably more clear than
(1 until n).flatMap(i =>
(1 until i).withFilter(j => isPrime(i + j))
.map(j => (i, j)))
For more information check out Dr. Martin Odersky's "Functional Programming with Scala" course on Coursera.org. Lecture 6.5 "Translation of For" in particular discusses this in more detail.
Also, as a quick side note, in your example you use
mapper.map(x => println(x))
It is generally more accepted to use foreach in this case because you have the intent of side-effecting. Also, there is short hand
mapper.foreach(println)
As for Question 2, it is better to use the map function in place of loops (especially when there is mutation in the loop) because map is a function and it can be composed. Also, once one is acquainted and used to using map, it is very easy to reason about.
The two programs that you have provided are not the same, even if the output might suggest that they are. It is true that for comprehensions are de-sugared by the compiler, but the first program you have is actually equivalent to:
val num = 1 to 1000
val another = 1000 to 2000
num.foreach(i => another.foreach(j => println(i,j)))
It should be noted that the resultant type for the above (and your example program) is Unit
In the case of your second program, the resultant type of the program is, as determined by the compiler, Seq[Unit] - which is now a Seq that has the length of the product of the loop members. As a result, you should always use foreach to indicate an effect that results in a Unit result.
Think about what is happening at the machine-language level. Loops are still fundamental. Functional programming abstracts the loop that is implemented in conventional programming.
Essentially, instead of writing a loop as you would in conventional or imparitive programming, the use of chaining or pipelining in functional programming allows the compiler to optimize the code for the user, and map is simply mapping the function to each element as a list or collection is iterated through. Functional programming, is more convenient, and abstracts the mundane implementation of "for" loops etc. There are limitations to this convenience, particularly if you intend to use functional programming to implement parallel processing.
It is arguable depending on the Software Engineer or developer, that the compiler will be more efficient and know ahead of time the situation it is implemented in. IMHO, mid-level Software Engineers who are familiar with functional programming, well versed in conventional programming, and knowledgeable in parallel processing, will implement both conventional and functional.
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) }
}
Passing messages around with actors is great. But I would like to have even easier code.
Examples (Pseudo-code)
val splicedList:List[List[Int]]=biglist.partition(100)
val sum:Int=ActorPool.numberOfActors(5).getAllResults(splicedList,foldLeft(_+_))
where spliceIntoParts turns one big list into 100 small lists
the numberofactors part, creates a pool which uses 5 actors and receives new jobs after a job is finished
and getallresults uses a method on a list. all this done with messages passing in the background. where maybe getFirstResult, calculates the first result, and stops all other threads (like cracking a password)
With Scala Parallel collections that will be included in 2.8.1 you will be able to do things like this:
val spliced = myList.par // obtain a parallel version of your collection (all operations are parallel)
spliced.map(process _) // maps each entry into a corresponding entry using `process`
spliced.find(check _) // searches the collection until it finds an element for which
// `check` returns true, at which point the search stops, and the element is returned
and the code will automatically be done in parallel. Other methods found in the regular collections library are being parallelized as well.
Currently, 2.8.RC2 is very close (this or next week), and 2.8 final will come in a few weeks after, I guess. You will be able to try parallel collections if you use 2.8.1 nightlies.
You can use Scalaz's concurrency features to achieve what you want.
import scalaz._
import Scalaz._
import concurrent.strategy.Executor
import java.util.concurrent.Executors
implicit val s = Executor.strategy[Unit](Executors.newFixedThreadPool(5))
val splicedList = biglist.grouped(100).toList
val sum = splicedList.parMap(_.sum).map(_.sum).get
It would be pretty easy to make this prettier (i.e. write a function mapReduce that does the splitting and folding all in one). Also, parMap over a List is unnecessarily strict. You will want to start folding before the whole list is ready. More like:
val splicedList = biglist.grouped(100).toList
val sum = splicedList.map(promise(_.sum)).toStream.traverse(_.sum).get
You can do this with less overhead than creating actors by using futures:
import scala.actors.Futures._
val nums = (1 to 1000).grouped(100).toList
val parts = nums.map(n => future { n.reduceLeft(_ + _) })
val whole = (0 /: parts)(_ + _())
You have to handle decomposing the problem and writing the "future" block and recomposing it in to a final answer, but it does make executing a bunch of small code blocks in parallel easy to do.
(Note that the _() in the fold left is the apply function of the future, which means, "Give me the answer you were computing in parallel!", and it blocks until the answer is available.)
A parallel collections library would automatically decompose the problem and recompose the answer for you (as with pmap in Clojure); that's not part of the main API yet.
I'm not waiting for Scala 2.8.1 or 2.9, it would rather be better to write my own library or use another, so I did more googling and found this: akka
http://doc.akkasource.org/actors
which has an object futures with methods
awaitAll(futures: List[Future]): Unit
awaitOne(futures: List[Future]): Future
but http://scalablesolutions.se/akka/api/akka-core-0.8.1/
has no documentation at all. That's bad.
But the good part is that akka's actors are leaner than scala's native ones
With all of these libraries (including scalaz) around, it would be really great if scala itself could eventually merge them officially
At Scala Days 2010, there was a very interesting talk by Aleksandar Prokopec (who is working on Scala at EPFL) about Parallel Collections. This will probably be in 2.8.1, but you may have to wait a little longer. I'll lsee if I can get the presentation itself. to link here.
The idea is to have a collections framework which parallelizes the processing of the collections by doing exactly as you suggest, but transparently to the user. All you theoretically have to do is change the import from scala.collections to scala.parallel.collections. You obviously still have to do the work to see if what you're doing can actually be parallelized.
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)