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What is the difference between a var and val definition in Scala and why does the language need both? Why would you choose a val over a var and vice versa?
As so many others have said, the object assigned to a val cannot be replaced, and the object assigned to a var can. However, said object can have its internal state modified. For example:
class A(n: Int) {
var value = n
}
class B(n: Int) {
val value = new A(n)
}
object Test {
def main(args: Array[String]) {
val x = new B(5)
x = new B(6) // Doesn't work, because I can't replace the object created on the line above with this new one.
x.value = new A(6) // Doesn't work, because I can't replace the object assigned to B.value for a new one.
x.value.value = 6 // Works, because A.value can receive a new object.
}
}
So, even though we can't change the object assigned to x, we could change the state of that object. At the root of it, however, there was a var.
Now, immutability is a good thing for many reasons. First, if an object doesn't change internal state, you don't have to worry if some other part of your code is changing it. For example:
x = new B(0)
f(x)
if (x.value.value == 0)
println("f didn't do anything to x")
else
println("f did something to x")
This becomes particularly important with multithreaded systems. In a multithreaded system, the following can happen:
x = new B(1)
f(x)
if (x.value.value == 1) {
print(x.value.value) // Can be different than 1!
}
If you use val exclusively, and only use immutable data structures (that is, avoid arrays, everything in scala.collection.mutable, etc.), you can rest assured this won't happen. That is, unless there's some code, perhaps even a framework, doing reflection tricks -- reflection can change "immutable" values, unfortunately.
That's one reason, but there is another reason for it. When you use var, you can be tempted into reusing the same var for multiple purposes. This has some problems:
It will be more difficult for people reading the code to know what is the value of a variable in a certain part of the code.
You may forget to re-initialize the variable in some code path, and end up passing wrong values downstream in the code.
Simply put, using val is safer and leads to more readable code.
We can, then, go the other direction. If val is that better, why have var at all? Well, some languages did take that route, but there are situations in which mutability improves performance, a lot.
For example, take an immutable Queue. When you either enqueue or dequeue things in it, you get a new Queue object. How then, would you go about processing all items in it?
I'll go through that with an example. Let's say you have a queue of digits, and you want to compose a number out of them. For example, if I have a queue with 2, 1, 3, in that order, I want to get back the number 213. Let's first solve it with a mutable.Queue:
def toNum(q: scala.collection.mutable.Queue[Int]) = {
var num = 0
while (!q.isEmpty) {
num *= 10
num += q.dequeue
}
num
}
This code is fast and easy to understand. Its main drawback is that the queue that is passed is modified by toNum, so you have to make a copy of it beforehand. That's the kind of object management that immutability makes you free from.
Now, let's covert it to an immutable.Queue:
def toNum(q: scala.collection.immutable.Queue[Int]) = {
def recurse(qr: scala.collection.immutable.Queue[Int], num: Int): Int = {
if (qr.isEmpty)
num
else {
val (digit, newQ) = qr.dequeue
recurse(newQ, num * 10 + digit)
}
}
recurse(q, 0)
}
Because I can't reuse some variable to keep track of my num, like in the previous example, I need to resort to recursion. In this case, it is a tail-recursion, which has pretty good performance. But that is not always the case: sometimes there is just no good (readable, simple) tail recursion solution.
Note, however, that I can rewrite that code to use an immutable.Queue and a var at the same time! For example:
def toNum(q: scala.collection.immutable.Queue[Int]) = {
var qr = q
var num = 0
while (!qr.isEmpty) {
val (digit, newQ) = qr.dequeue
num *= 10
num += digit
qr = newQ
}
num
}
This code is still efficient, does not require recursion, and you don't need to worry whether you have to make a copy of your queue or not before calling toNum. Naturally, I avoided reusing variables for other purposes, and no code outside this function sees them, so I don't need to worry about their values changing from one line to the next -- except when I explicitly do so.
Scala opted to let the programmer do that, if the programmer deemed it to be the best solution. Other languages have chosen to make such code difficult. The price Scala (and any language with widespread mutability) pays is that the compiler doesn't have as much leeway in optimizing the code as it could otherwise. Java's answer to that is optimizing the code based on the run-time profile. We could go on and on about pros and cons to each side.
Personally, I think Scala strikes the right balance, for now. It is not perfect, by far. I think both Clojure and Haskell have very interesting notions not adopted by Scala, but Scala has its own strengths as well. We'll see what comes up on the future.
val is final, that is, cannot be set. Think final in java.
In simple terms:
var = variable
val = variable + final
val means immutable and var means mutable.
Full discussion.
The difference is that a var can be re-assigned to whereas a val cannot. The mutability, or otherwise of whatever is actually assigned, is a side issue:
import collection.immutable
import collection.mutable
var m = immutable.Set("London", "Paris")
m = immutable.Set("New York") //Reassignment - I have change the "value" at m.
Whereas:
val n = immutable.Set("London", "Paris")
n = immutable.Set("New York") //Will not compile as n is a val.
And hence:
val n = mutable.Set("London", "Paris")
n = mutable.Set("New York") //Will not compile, even though the type of n is mutable.
If you are building a data structure and all of its fields are vals, then that data structure is therefore immutable, as its state cannot change.
Thinking in terms of C++,
val x: T
is analogous to constant pointer to non-constant data
T* const x;
while
var x: T
is analogous to non-constant pointer to non-constant data
T* x;
Favoring val over var increases immutability of the codebase which can facilitate its correctness, concurrency and understandability.
To understand the meaning of having a constant pointer to non-constant data consider the following Scala snippet:
val m = scala.collection.mutable.Map(1 -> "picard")
m // res0: scala.collection.mutable.Map[Int,String] = HashMap(1 -> picard)
Here the "pointer" val m is constant so we cannot re-assign it to point to something else like so
m = n // error: reassignment to val
however we can indeed change the non-constant data itself that m points to like so
m.put(2, "worf")
m // res1: scala.collection.mutable.Map[Int,String] = HashMap(1 -> picard, 2 -> worf)
"val means immutable and var means mutable."
To paraphrase, "val means value and var means variable".
A distinction that happens to be extremely important in computing (because those two concepts define the very essence of what programming is all about), and that OO has managed to blur almost completely, because in OO, the only axiom is that "everything is an object". And that as a consequence, lots of programmers these days tend not to understand/appreciate/recognize, because they have been brainwashed into "thinking the OO way" exclusively. Often leading to variable/mutable objects being used like everywhere, when value/immutable objects might/would often have been better.
val means immutable and var means mutable
you can think val as java programming language final key world or c++ language const key world。
Val means its final, cannot be reassigned
Whereas, Var can be reassigned later.
It's as simple as it name.
var means it can vary
val means invariable
Val - values are typed storage constants. Once created its value cant be re-assigned. a new value can be defined with keyword val.
eg. val x: Int = 5
Here type is optional as scala can infer it from the assigned value.
Var - variables are typed storage units which can be assigned values again as long as memory space is reserved.
eg. var x: Int = 5
Data stored in both the storage units are automatically de-allocated by JVM once these are no longer needed.
In scala values are preferred over variables due to stability these brings to the code particularly in concurrent and multithreaded code.
Though many have already answered the difference between Val and var.
But one point to notice is that val is not exactly like final keyword.
We can change the value of val using recursion but we can never change value of final. Final is more constant than Val.
def factorial(num: Int): Int = {
if(num == 0) 1
else factorial(num - 1) * num
}
Method parameters are by default val and at every call value is being changed.
In terms of javascript , it same as
val -> const
var -> var
In Scala 2.10, MurmurHash for some reason is deprecated, saying I should use MurmurHash3 now. But the API is different, and there is no useful scaladocs for MurmurHash3 -> fail.
For instance, current code:
trait Foo {
type Bar
def id: Int
def path: Bar
override def hashCode = {
import util.MurmurHash._
var h = startHash(2)
val c = startMagicA
val k = startMagicB
h = extendHash(h, id, c, k)
h = extendHash(h, path.##, nextMagicA(c), nextMagicB(k))
finalizeHash(h)
}
}
How would I do this using MurmurHash3 instead? This needs to be a fast operation, preferably without allocations, so I do not want to construct a Product, Seq, Array[Byte] or whathever MurmurHash3 seems to be offering me.
The MurmurHash3 algorithm was changed, confusingly, from an algorithm that mixed in its own salt, essentially (c and k), to one that just does more bit-mixing. The basic operation is now mix, which you should fold over all your values, after which you should finalizeHash (the Int argument for length is for convenience also, to help with distinguishing collections of different length). If you want to replace your last mix by mixLast, it's a little faster and removes redundancy with finalizeHash. If it takes you too long to detect what the last mix is, just mix.
Typically for a collection you'll want to mix in an extra value to indicate what type of collection it is.
So minimally you'd have
override def hashCode = finalizeHash(mixLast(id, path.##), 0)
and "typically" you'd
// Pick any string or number that suits you, put in companion object
val fooSeed = MurmurHash3.stringHash("classOf[Foo]")
// I guess "id" plus "path" is two things?
override def hashCode = finalizeHash(mixLast( mix(fooSeed,id), path.## ), 2)
Note that the length field is NOT there to give a high-quality hash that mixes in that number. All mixing of important hash values should be done with mix.
Looking at the source code of MurmurHash3 suggests something like this:
override def hashCode = {
import util.hashing.MurmurHash3._
val h = symmetricSeed // I'm not sure which seed to use here
val h1 = mix(h, id)
val h2 = mixLast(h1, path ##)
finalizeHash(h2, 2)
}
or, in (almost) one line:
import util.hashing.MurmurHash3._
override def hashCode = finalizeHash(mix(mix(symmetricSeed, id), path ##), 2)
I'd like to have some basic knowledge of how deeply my function call is nested. Consider the following:
scala> def decorate(f: => Unit) : Unit = { println("I am decorated") ; f }
decorate: (f: => Unit)Unit
scala> decorate { println("foo") }
I am decorated
foo
scala> decorate { decorate { println("foo") } }
I am decorated
I am decorated
foo
For the last call, I'd like to be able to get the following:
I am decorated 2x
I am decorated 1x
foo
The idea is that the decorate function knows how deeply its nested. Ideas?
Update: As Nikita had thought, my example doesn't represent what I'm really after. The goal is not to produce the strings so much as to be able to pass some state through a series of calls to the same nested function. I think Régis Jean-Gilles is pointing me in the right direction.
You can use the dynamic scope pattern. More prosaically this means using a thread local variable (scala's DynamicVariable is done just for that) to store the current nesting level. See my answer to this other question for a partical example of this pattern: How to define a function that takes a function literal (with an implicit parameter) as an argument?
This is suitable only if you want to know the nesting level for a very specific method though. If you want a generic mecanism that works for any method then this won't work (as you'd need a distinct variable for each method). In this case the only alternative I can think of is to inspect the stack, but not only is it not very reliable, it is also extremely slow.
UPDATE: actually, there is a way to apply the dynamic scope pattern in a generic way (for any possible method). The important part is to be able to implicitly get a unique id for each method. from there, it is just a matter of using this id as a key to associate a DynamicVariable to the method:
import scala.util.DynamicVariable
object FunctionNestingHelper {
private type FunctionId = Class[_]
private def getFunctionId( f: Function1[_,_] ): FunctionId = {
f.getClass // That's it! Beware, implementation dependant.
}
private val currentNestings = new DynamicVariable( Map.empty[FunctionId, Int] )
def withFunctionNesting[T]( body: Int => T ): T = {
val id = getFunctionId( body )
val oldNestings = currentNestings.value
val oldNesting = oldNestings.getOrElse( id, 0 )
val newNesting = oldNesting + 1
currentNestings.withValue( oldNestings + ( id -> newNesting) ) {
body( newNesting )
}
}
}
Usage:
import FunctionNestingHelper._
def decorate(f: => Unit) = withFunctionNesting { nesting: Int =>
println("I am decorated " + nesting + "x") ; f
}
To get a unique id for the method, I actually get an id for a the closure passed to withFunctionNesting (which you must call in the method where you need to retrieve the current nesting). And that's where I err on the implementation dependant side: the id is just the class of the function instance. This does work as expected as of now (because every unary function literal is implemented as exactly one class implementing Function1 so the class acts as a unique id), but the reality is that it might well break (although unlikely) in a future version of scala. So use it at your own risk.
Finally, I suggest that you first evaluate seriously if Nikita Volkov's suggestion of going more functional would not be a better solution overall.
You could return a number from the function and count how many levels you are in on the way back up the stack. But there is no easy way to count on the way down like you have given example output for.
Since your question is tagged with "functional programming" following are functional solutions. Sure the program logic changes completely, but then your example code was imperative.
The basic principle of functional programming is that there is no state. What you're used to have as a shared state in imperative programming with all the headache involved (multithreading issues and etc.) - it is all achieved by passing immutable data as arguments in functional programming.
So, assuming the "state" data you wanted to pass was the current cycle number, here's how you'd implement a function using recursion:
def decorated ( a : String, cycle : Int ) : String
= if( cycle <= 0 ) a
else "I am decorated " + cycle + "x\n" + decorated(a, cycle - 1)
println(decorated("foo", 3))
Alternatively you could make your worker function non-recursive and "fold" it:
def decorated ( a : String, times : Int )
= "I am decorated " + times + "x\n" + a
println( (1 to 3).foldLeft("foo")(decorated) )
Both codes above will produce the following output:
I am decorated 3x
I am decorated 2x
I am decorated 1x
foo
Perhaps this is just my background in more imperative programming, but I like having return statements in my code.
I understand that in Scala, returns are not necessary in many methods, because whatever the last computed value is is returned by default. I understand that this makes perfect sense for a "one-liner", e.g.
def square(x) = x * x
I also understand the definitive case for using explicit returns (when you have several branches your code could take, and you want to break out of the method for different branches, e.g. if an error occurs). But what about multiline functions? Wouldn't it be more readable and make more sense if there was an explicit return, e.g.
def average(x: List[Int]) : Float = {
var sum = 0
x.foreach(sum += _)
return sum / x.length.toFloat
}
def average(x: List[Int]) : Float =
x.foldLeft(0)(_ + _) / x.length.toFloat
UPDATE: While I was aiming to show how an iterative code can be made into a functional expression, #soc rightly commented that an even shorter version is x.sum / x.length.toFloat
I usually find that in Scala I have less need to "return in the middle". Furthermore, a large function is broken into smaller expressions that are clearer to reason. So instead of
var x = ...
if (some condition) x = ...
else x = ...
I would write
if (some condition) ...
else ....
Similar thing happens using match expressions. And you can always have helper nested classes.
Once you are comfortable with many forms of expressions that evaluate to a result (e.g., 'if') without a return statement, then having one in your methods looks out of place.
At one place of work we had a rule of not having 'return' in the middle of a method since it is easy for a reader of your code to miss it. If 'return' is only at the last line, what's the point of having it at all?
The return doesn't tell you anything extra, so I find it actually clutters my understanding of what goes on. Of course it returns; there are no other statements!
And it's also a good idea to get away from the habit of using return because it's really unclear where you are returning to when you're using heavily functional code:
def manyFutures = xs.map(x => Futures.future { if (x<5) return Nil else x :: Nil })
Where should the return leave execution? Outside of manyFutures? Just the inner block?
So although you can use explicit returns (at least if you annotate the return type explicitly), I suggest that you try to get used to the last-statement-is-the-return-value custom instead.
Your sense that the version with the return is more readable is probably a sign that you are still thinking imperatively rather than declaratively. Try to think of your function from the perspective of what it is (i.e. how it is defined), rather than what it does (i.e. how it is computed).
Let's look at your average example, and pretend that the standard library doesn't have the features that make an elegant one-liner possible. Now in English, we would say that the average of a list of numbers is the sum of the numbers in the list divided by its length.
def average(xs: List[Int]): Float = {
var sum = 0
xs.foreach(sum += _)
sum / xs.length.toFloat
}
This description is fairly close to the English version, ignoring the first two lines of the function. We can factor out the sum function to get a more declarative description:
def average(xs: List[Int]): Float = {
def sum(xs: List[Int]): Int = // ...
sum(xs) / xs.length.toFloat
}
In the declarative definition, a return statement reads quite unnaturally because it explicitly puts the focus on doing something.
I don't miss the return-statement at the end of a method because it is unnecessary. Furthermore, in Scala each method does return a value - even methods which should not return something. Instead of void there is the type Unit which is returned:
def x { println("hello") }
can be written as:
def x { println("hello"); return Unit }
But println returns already the type Unit - thus when you explicitly want to use a return-statement you have to use it in methods which return Unit, too. Otherwise you do not have continuous identical build-on code.
But in Scala there is the possibility to break your code in a lot of small methods:
def x(...): ReturnType = {
def func1 = ... // use funcX
def func2 = ... // use funcX
def func3 = ... // use funcX
funcX
}
An explicitly used return-statement does not help to understand the code better.
Also Scala has a powerful core library which allows you to solve many problems with less code:
def average(x: List[Int]): Float = x.sum.toFloat / x.length
I believe you should not use return, I think it somehow goes against the elegant concept that in Scala everything is an expression that evaluates to something.
Like your average method: it's just an expression that evaluates to Float, no need to return anything to make that work.
What is the difference between a var and val definition in Scala and why does the language need both? Why would you choose a val over a var and vice versa?
As so many others have said, the object assigned to a val cannot be replaced, and the object assigned to a var can. However, said object can have its internal state modified. For example:
class A(n: Int) {
var value = n
}
class B(n: Int) {
val value = new A(n)
}
object Test {
def main(args: Array[String]) {
val x = new B(5)
x = new B(6) // Doesn't work, because I can't replace the object created on the line above with this new one.
x.value = new A(6) // Doesn't work, because I can't replace the object assigned to B.value for a new one.
x.value.value = 6 // Works, because A.value can receive a new object.
}
}
So, even though we can't change the object assigned to x, we could change the state of that object. At the root of it, however, there was a var.
Now, immutability is a good thing for many reasons. First, if an object doesn't change internal state, you don't have to worry if some other part of your code is changing it. For example:
x = new B(0)
f(x)
if (x.value.value == 0)
println("f didn't do anything to x")
else
println("f did something to x")
This becomes particularly important with multithreaded systems. In a multithreaded system, the following can happen:
x = new B(1)
f(x)
if (x.value.value == 1) {
print(x.value.value) // Can be different than 1!
}
If you use val exclusively, and only use immutable data structures (that is, avoid arrays, everything in scala.collection.mutable, etc.), you can rest assured this won't happen. That is, unless there's some code, perhaps even a framework, doing reflection tricks -- reflection can change "immutable" values, unfortunately.
That's one reason, but there is another reason for it. When you use var, you can be tempted into reusing the same var for multiple purposes. This has some problems:
It will be more difficult for people reading the code to know what is the value of a variable in a certain part of the code.
You may forget to re-initialize the variable in some code path, and end up passing wrong values downstream in the code.
Simply put, using val is safer and leads to more readable code.
We can, then, go the other direction. If val is that better, why have var at all? Well, some languages did take that route, but there are situations in which mutability improves performance, a lot.
For example, take an immutable Queue. When you either enqueue or dequeue things in it, you get a new Queue object. How then, would you go about processing all items in it?
I'll go through that with an example. Let's say you have a queue of digits, and you want to compose a number out of them. For example, if I have a queue with 2, 1, 3, in that order, I want to get back the number 213. Let's first solve it with a mutable.Queue:
def toNum(q: scala.collection.mutable.Queue[Int]) = {
var num = 0
while (!q.isEmpty) {
num *= 10
num += q.dequeue
}
num
}
This code is fast and easy to understand. Its main drawback is that the queue that is passed is modified by toNum, so you have to make a copy of it beforehand. That's the kind of object management that immutability makes you free from.
Now, let's covert it to an immutable.Queue:
def toNum(q: scala.collection.immutable.Queue[Int]) = {
def recurse(qr: scala.collection.immutable.Queue[Int], num: Int): Int = {
if (qr.isEmpty)
num
else {
val (digit, newQ) = qr.dequeue
recurse(newQ, num * 10 + digit)
}
}
recurse(q, 0)
}
Because I can't reuse some variable to keep track of my num, like in the previous example, I need to resort to recursion. In this case, it is a tail-recursion, which has pretty good performance. But that is not always the case: sometimes there is just no good (readable, simple) tail recursion solution.
Note, however, that I can rewrite that code to use an immutable.Queue and a var at the same time! For example:
def toNum(q: scala.collection.immutable.Queue[Int]) = {
var qr = q
var num = 0
while (!qr.isEmpty) {
val (digit, newQ) = qr.dequeue
num *= 10
num += digit
qr = newQ
}
num
}
This code is still efficient, does not require recursion, and you don't need to worry whether you have to make a copy of your queue or not before calling toNum. Naturally, I avoided reusing variables for other purposes, and no code outside this function sees them, so I don't need to worry about their values changing from one line to the next -- except when I explicitly do so.
Scala opted to let the programmer do that, if the programmer deemed it to be the best solution. Other languages have chosen to make such code difficult. The price Scala (and any language with widespread mutability) pays is that the compiler doesn't have as much leeway in optimizing the code as it could otherwise. Java's answer to that is optimizing the code based on the run-time profile. We could go on and on about pros and cons to each side.
Personally, I think Scala strikes the right balance, for now. It is not perfect, by far. I think both Clojure and Haskell have very interesting notions not adopted by Scala, but Scala has its own strengths as well. We'll see what comes up on the future.
val is final, that is, cannot be set. Think final in java.
In simple terms:
var = variable
val = variable + final
val means immutable and var means mutable.
Full discussion.
The difference is that a var can be re-assigned to whereas a val cannot. The mutability, or otherwise of whatever is actually assigned, is a side issue:
import collection.immutable
import collection.mutable
var m = immutable.Set("London", "Paris")
m = immutable.Set("New York") //Reassignment - I have change the "value" at m.
Whereas:
val n = immutable.Set("London", "Paris")
n = immutable.Set("New York") //Will not compile as n is a val.
And hence:
val n = mutable.Set("London", "Paris")
n = mutable.Set("New York") //Will not compile, even though the type of n is mutable.
If you are building a data structure and all of its fields are vals, then that data structure is therefore immutable, as its state cannot change.
Thinking in terms of C++,
val x: T
is analogous to constant pointer to non-constant data
T* const x;
while
var x: T
is analogous to non-constant pointer to non-constant data
T* x;
Favoring val over var increases immutability of the codebase which can facilitate its correctness, concurrency and understandability.
To understand the meaning of having a constant pointer to non-constant data consider the following Scala snippet:
val m = scala.collection.mutable.Map(1 -> "picard")
m // res0: scala.collection.mutable.Map[Int,String] = HashMap(1 -> picard)
Here the "pointer" val m is constant so we cannot re-assign it to point to something else like so
m = n // error: reassignment to val
however we can indeed change the non-constant data itself that m points to like so
m.put(2, "worf")
m // res1: scala.collection.mutable.Map[Int,String] = HashMap(1 -> picard, 2 -> worf)
"val means immutable and var means mutable."
To paraphrase, "val means value and var means variable".
A distinction that happens to be extremely important in computing (because those two concepts define the very essence of what programming is all about), and that OO has managed to blur almost completely, because in OO, the only axiom is that "everything is an object". And that as a consequence, lots of programmers these days tend not to understand/appreciate/recognize, because they have been brainwashed into "thinking the OO way" exclusively. Often leading to variable/mutable objects being used like everywhere, when value/immutable objects might/would often have been better.
val means immutable and var means mutable
you can think val as java programming language final key world or c++ language const key world。
Val means its final, cannot be reassigned
Whereas, Var can be reassigned later.
It's as simple as it name.
var means it can vary
val means invariable
Val - values are typed storage constants. Once created its value cant be re-assigned. a new value can be defined with keyword val.
eg. val x: Int = 5
Here type is optional as scala can infer it from the assigned value.
Var - variables are typed storage units which can be assigned values again as long as memory space is reserved.
eg. var x: Int = 5
Data stored in both the storage units are automatically de-allocated by JVM once these are no longer needed.
In scala values are preferred over variables due to stability these brings to the code particularly in concurrent and multithreaded code.
Though many have already answered the difference between Val and var.
But one point to notice is that val is not exactly like final keyword.
We can change the value of val using recursion but we can never change value of final. Final is more constant than Val.
def factorial(num: Int): Int = {
if(num == 0) 1
else factorial(num - 1) * num
}
Method parameters are by default val and at every call value is being changed.
In terms of javascript , it same as
val -> const
var -> var