For Scala 3 macros, does anyone know of a way to find all functions with a given annotation?
For instance:
#fruit
def apple(): Int = ???
#fruit
def banana(): Int = ???
#fruit
def coconut(): Int = ???
#fruit
def durian(): Int = ???
def elephant(): Int = ???
#fruit
def fig(): Int = ???
I would want to find a list of apple, banana, coconut, durian, fig. They could be defined anywhere, but in my case they will all be in a single package.
This solution will extract all the definitions with some annotation from a given package. I will leverage also the compile-time reflection.
This solution will extract all the definitions with some annotations from a given package. I will also leverage the compile-time reflection.
So, To solve your problem, we need to divide it in:
methods gathering from a package;
filter only methods with a given annotation;
transform symbols in function application.
I suppose that you can pass the package and the annotation (and also the return type) as a type argument. So the macro signature is something like that:
inline def findAllFunction[P, A <: ConstantAnnotation, R]: List[() => R] =
${Implementation.myMacroImpl[P, A, R]()}
The first point is straightforward. we could extract all the methods defined as:
def methodsFromPackage(packageSymbol: Symbol): List[Symbol] =
packageSymbol.declaredTypes
.filter(_.isClassDef)
.flatMap(_.declaredMethods)
The second point is also quite easy. Symbol class has the method hasAnnotation that could be used in this case:
def methodsAnnotatatedWith(
methods: List[Symbol],
annotation: Symbol
): List[Symbol] =
methods.filter(_.hasAnnotation(annotation))
The last point is a little bit challenging. Here we should construct the method call. So we need to create the AST that correspond to the method call. Inspired by this example, we can call definition using Apply. Select and This serve to select the correct method that will be called:
def transformToFunctionApplication(methods: List[Symbol]): Expr[List[() => R]] =
val appliedDef = methods
.map(definition => Select(This(definition.owner), definition))
.map(select => Apply(select, List.empty))
.map(apply => '{ () => ${ apply.asExprOf[R] } })
Expr.ofList(appliedDef)
Here I used lamba call, if you want to return directly the value you should change the last two instructions:
def transformToFunctionApplication(methods: List[Symbol]): Expr[List[R]] =
val appliedDef = methods
.map(definition => Select(This(definition.owner), definition))
.map(select => Apply(select, List.empty))
.map(apply => apply.asExprOf[R])
Expr.ofList(appliedDef)
To sum up, the all methods could be defined as:
def myMacroImpl[P: Type, A: Type, R: Type]()(using
Quotes
): Expr[List[() => R]] = {
import quotes.reflect.*
val annotation = TypeRepr.of[A].typeSymbol
val moduleTarget = TypeRepr.of[P].typeSymbol
def methodsFromPackage(packageSymbol: Symbol): List[Symbol] =
packageSymbol.declaredTypes
.filter(_.isClassDef)
.flatMap(_.declaredMethods)
def methodsAnnotatatedWith(
methods: List[Symbol],
annotation: Symbol
): List[Symbol] =
methods.filter(_.hasAnnotation(annotation))
def transformToFunctionApplication(
methods: List[Symbol]
): Expr[List[() => R]] =
val appliedDef = methods
.map(definition => Select(This(definition.owner), definition))
.map(select => Apply(select, List.empty))
.map(apply => '{ () => ${ apply.asExprOf[R] } })
Expr.ofList(appliedDef)
val methods = methodsFromPackage(moduleTarget)
val annotatedMethod = methodsAnnotatatedWith(methods, annotation)
transformToFunctionApplication(annotatedMethod)
}
Finally, you can use the macro as:
package org.tests
import org.tests.Macros.fruit
package foo {
#fruit
def check(): Int = 10
#fruit
def other(): Int = 11
}
#main def hello: Unit =
println("Hello world!")
println(Macros.findAllFunction[org.tests.foo, fruit, Int].map(_.apply())) /// List(10, 11)
Scastie
I have an iteration module which can apply an arbitrary function (Build generic reusable iteration module from higher order function) and would love to wrap it into a progressbar.
val things = Range(1,10)
def iterationModule[A](
iterationItems: Seq[A],
functionToApply: A => Any
): Unit = {
iterationItems.foreach(functionToApply)
}
def foo(s:Int) = println(s)
iterationModule[Int](things, foo)
A basic progressbar could look like:
import me.tongfei.progressbar.ProgressBar
val pb = new ProgressBar("Test", things.size)
things.foreach(t=> {
println(t)
pb.step
})
But how can the function which is passed to the iterator module be intercepted and surrounded with a progressbar, i.e. call the pb.step?
An annoying possibility would be to pass the mutable pb object into each function (have it implement an interface).
But is it also possible to intercept and surround the function being passed by this stepping logic?
However, when looping with Seq().par.foreach, this might be problematic.
I need the code to work in Scala 2.11.
edit
A more complex example:
val things = Range(1,100).map(_.toString)
def iterationModule[A](
iterationItems: Seq[A],
functionToApply: A => Any,
parallel: Boolean = false
): Unit = {
val pb = new ProgressBar(functionToApply.toString(), iterationItems.size)
if (parallel) {
iterationItems.par.foreach(functionToApply)
} else {
iterationItems.foreach(functionToApply)
}
}
def doStuff(inputDay: String, inputConfigSomething: String): Unit = println(inputDay + "__"+ inputConfigSomething)
iterationModule[String](things, doStuff(_, "foo"))
The function should be able to take the iteration item and additional parameters.
edit 2
import me.tongfei.progressbar.ProgressBar
val things = Range(1,100).map(_.toString)
def doStuff(inputDay: String, inputConfigSomething: String): Unit = println(inputDay + "__"+ inputConfigSomething)
def iterationModulePb[A](items: Seq[A], f: A => Any, parallel: Boolean = false): Unit = {
val pb = new ProgressBar(f.toString, items.size)
val it = if (parallel) {
items.par.iterator
} else {
items.iterator
}
it.foreach { x =>
f(x)
pb.step()
}
}
iterationModulePb[String](things, doStuff(_, "foo"))
After a little discussion I figured out how to use a Seq with standard iterators.
For Scala 2.13 this would be the most general form.
import me.tongfei.progressbar.ProgressBar
def iterationModule[A](items: IterableOnce[A], f: A => Any): Unit = {
val (it, pb) =
if (items.knowSize != -1)
items.iterator -> new ProgressBar("Test", items.knowSize)
else {
val (iter1, iter2) = items.iterator.split
iter1 -> new ProgressBar("Test", iter2.size)
}
it.foreach { x =>
f(x)
pb.step()
}
}
Note: most of the changes are just to make the code more generic, but the general idea is just to create a function that wraps both the original function and the call to the ProgressBar.
Edit
A simplified solution for 2.11
def iterationModule[A](items: Seq[A], parallel: Boolean = false)
(f: A => Any): Unit = {
val pb = new ProgressBar("test", items.size)
val it = if (parallel) {
items.iterator.par
} else {
items.iterator
}
it.foreach { a =>
f(a)
pb.step()
}
}
I have an idea (vague), to pass (or chain) some implicit value in this manner, not introducing parameters to block f:
def block(x: Int)(f: => Unit)(implicit v: Int) = {
implicit val nv = v + x
f
}
def fun(implicit v: Int) = println(v)
such that if I used something alike:
implicit val ii: Int = 0
block(1) {
block(2) {
fun
}
}
It would print 3.
If I could say def block(x: Int)(f: implicit Int => Unit).
In other words I'm seeking for some design pattern which will allow me to implement this DSL: access some cumulative value inside nested blocks but without explicitly passing it as parameter. Is it possible? (implicits are not necessary, just a hint to emphasize that I don't want to pass that accumulator explicitly). Of course upper code will print 0.
EDIT: One of possible usages: composing http routes, in a following manner
prefix("path") {
prefix("subpath") {
post("action1") { (req, res) => do action }
get("action2") { (req, res) => do action }
}
}
Here post and get will access (how?) accumulated prefix, say List("path", "subpath") or "/path/subpath/".
Consider using DynamicVariable for this. It's really simple to use, and thread-safe:
val acc: DynamicVariable[Int] = new DynamicVariable(0)
def block(x: Int)(f: => Unit) = {
acc.withValue(acc.value + x)(f)
}
def fun = println(acc.value)
Passing state via implicit is dirty and will lead to unexpected and hard to track down bugs. What you're asking to do is build a function that can compose in such a way that nested calls accumulate over some operation and anything else uses that value to execute the function?
case class StateAccum[S](init: S){
val op: S => S
def flatMap[A <: S](f: S => StateAccum[A]) ={
val StateAccum(out) = f(s)
StateAccum(op(init, out))
}
def apply(f: S => A) = f(init)
}
which could allow you do exactly what you're after with a slight change in how you're calling it.
Now, if you really want the nested control structures, your apply would have to use an implicit value to distinguish the types of the return such that it applied the function to one and a flatMap to StateAccum returns. It gets crazy but looks like the following:
def apply[A](f: S => A)(implicit mapper: Mapper[S, A]): mapper.Out = mapper(this, f)
trait Mapper[S, A]{
type Out
def apply(s: StateAccum[S], f: S => A): Out
}
object Mapper extends LowPriorityMapper{
implicit def acuum[S, A <: S] = new Mapper[S, StateAccum[A]]{
type Out = StateAccum[A]
def apply(s: StateAccum[S], f: S => StateAccum[A]) = s.flatMap(f)
}
}
trait LowPriorityMapper{
implicit def acuum[S, A] = new Mapper[S, A]{
type Out = A
def apply(s: StateAccum[S], f: S => A) = f(s.init)
}
}
The general question is how to return additional information from methods, beside the actual result of the computation. But I want, that this information can silently be ignored.
Take for example the method dropWhile on Iterator. The returned result is the mutated iterator. But maybe sometimes I might be interested in the number of elements dropped.
In the case of dropWhile, this information could be generated externally by adding an index to the iterator and calculating the number of dropped steps afterwards. But in general this is not possible.
I simple solution is to return a tuple with the actual result and optional information. But then I need to handle the tuple whenever I call the method - even if I'm not interested in the optional information.
So the question is, whether there is some clever way of gathering such optional information?
Maybe through Option[X => Unit] parameters with call-back functions that default to None? Is there something more clever?
Just my two cents here…
You could declare this:
case class RichResult[+A, +B](val result: A, val info: B)
with an implicit conversion to A:
implicit def unwrapRichResult[A, B](richResult: RichResult[A, B]): A = richResult.result
Then:
def someMethod: RichResult[Int, String] = /* ... */
val richRes = someMethod
val res: Int = someMethod
It's definitely not more clever, but you could just create a method that drops the additional information.
def removeCharWithCount(str: String, x: Char): (String, Int) =
(str.replace(x.toString, ""), str.count(x ==))
// alias that drops the additional return information
def removeChar(str: String, x: Char): String =
removeCharWithCount(str, x)._1
Here is my take (with some edits with a more realistic example):
package info {
trait Info[T] { var data: Option[T] }
object Info {
implicit def makeInfo[T]: Info[T] = new Info[T] {
var data: Option[T] = None
}
}
}
Then suppose your original method (and use case) is implemented like this:
object Test extends App {
def dropCounterIterator[A](iter: Iterator[A]) = new Iterator[A] {
def hasNext = iter.hasNext
def next() = iter.next()
override def dropWhile(p: (A) => Boolean): Iterator[A] = {
var count = 0
var current: Option[A] = None
while (hasNext && p({current = Some(next()); current.get})) { count += 1 }
current match {
case Some(a) => Iterator.single(a) ++ this
case None => Iterator.empty
}
}
}
val i = dropCounterIterator(Iterator.from(1))
val ii = i.dropWhile(_ < 10)
println(ii.next())
}
To provide and get access to the info, the code would be modified only slightly:
import info.Info // line added
object Test extends App {
def dropCounterIterator[A](iter: Iterator[A]) = new Iterator[A] {
def hasNext = iter.hasNext
def next() = iter.next()
// note overloaded variant because of extra parameter list, not overriden
def dropWhile(p: (A) => Boolean)(implicit info: Info[Int]): Iterator[A] = {
var count = 0
var current: Option[A] = None
while (hasNext && p({current = Some(next()); current.get})) { count += 1 }
info.data = Some(count) // line added here
current match {
case Some(a) => Iterator.single(a) ++ this
case None => Iterator.empty
}
}
}
val i = dropCounterIterator(Iterator.from(1))
val info = implicitly[Info[Int]] // line added here
val ii = i.dropWhile((x: Int) => x < 10)(info) // line modified
println(ii.next())
println(info.data.get) // line added here
}
Note that for some reason the type inference is affected and I had to annotate the type of the function passed to dropWhile.
You want dropWhileM with the State monad threading a counter through the computation.
Locked. This question and its answers are locked because the question is off-topic but has historical significance. It is not currently accepting new answers or interactions.
What are the hidden features of Scala that every Scala developer should be aware of?
One hidden feature per answer, please.
Okay, I had to add one more. Every Regex object in Scala has an extractor (see answer from oxbox_lakes above) that gives you access to the match groups. So you can do something like:
// Regex to split a date in the format Y/M/D.
val regex = "(\\d+)/(\\d+)/(\\d+)".r
val regex(year, month, day) = "2010/1/13"
The second line looks confusing if you're not used to using pattern matching and extractors. Whenever you define a val or var, what comes after the keyword is not simply an identifier but rather a pattern. That's why this works:
val (a, b, c) = (1, 3.14159, "Hello, world")
The right hand expression creates a Tuple3[Int, Double, String] which can match the pattern (a, b, c).
Most of the time your patterns use extractors that are members of singleton objects. For example, if you write a pattern like
Some(value)
then you're implicitly calling the extractor Some.unapply.
But you can also use class instances in patterns, and that is what's happening here. The val regex is an instance of Regex, and when you use it in a pattern, you're implicitly calling regex.unapplySeq (unapply versus unapplySeq is beyond the scope of this answer), which extracts the match groups into a Seq[String], the elements of which are assigned in order to the variables year, month, and day.
Structural type definitions - i.e. a type described by what methods it supports. For example:
object Closer {
def using(closeable: { def close(): Unit }, f: => Unit) {
try {
f
} finally { closeable.close }
}
}
Notice that the type of the parameter closeable is not defined other than it has a close method
Type-Constructor Polymorphism (a.k.a. higher-kinded types)
Without this feature you can, for example, express the idea of mapping a function over a list to return another list, or mapping a function over a tree to return another tree. But you can't express this idea generally without higher kinds.
With higher kinds, you can capture the idea of any type that's parameterised with another type. A type constructor that takes one parameter is said to be of kind (*->*). For example, List. A type constructor that returns another type constructor is said to be of kind (*->*->*). For example, Function1. But in Scala, we have higher kinds, so we can have type constructors that are parameterised with other type constructors. So they're of kinds like ((*->*)->*).
For example:
trait Functor[F[_]] {
def fmap[A, B](f: A => B, fa: F[A]): F[B]
}
Now, if you have a Functor[List], you can map over lists. If you have a Functor[Tree], you can map over trees. But more importantly, if you have Functor[A] for any A of kind (*->*), you can map a function over A.
Extractors which allow you to replace messy if-elseif-else style code with patterns. I know that these are not exactly hidden but I've been using Scala for a few months without really understanding the power of them. For (a long) example I can replace:
val code: String = ...
val ps: ProductService = ...
var p: Product = null
if (code.endsWith("=")) {
p = ps.findCash(code.substring(0, 3)) //e.g. USD=, GBP= etc
}
else if (code.endsWith(".FWD")) {
//e.g. GBP20090625.FWD
p = ps.findForward(code.substring(0,3), code.substring(3, 9))
}
else {
p = ps.lookupProductByRic(code)
}
With this, which is much clearer in my opinion
implicit val ps: ProductService = ...
val p = code match {
case SyntheticCodes.Cash(c) => c
case SyntheticCodes.Forward(f) => f
case _ => ps.lookupProductByRic(code)
}
I have to do a bit of legwork in the background...
object SyntheticCodes {
// Synthetic Code for a CashProduct
object Cash extends (CashProduct => String) {
def apply(p: CashProduct) = p.currency.name + "="
//EXTRACTOR
def unapply(s: String)(implicit ps: ProductService): Option[CashProduct] = {
if (s.endsWith("=")
Some(ps.findCash(s.substring(0,3)))
else None
}
}
//Synthetic Code for a ForwardProduct
object Forward extends (ForwardProduct => String) {
def apply(p: ForwardProduct) = p.currency.name + p.date.toString + ".FWD"
//EXTRACTOR
def unapply(s: String)(implicit ps: ProductService): Option[ForwardProduct] = {
if (s.endsWith(".FWD")
Some(ps.findForward(s.substring(0,3), s.substring(3, 9))
else None
}
}
But the legwork is worth it for the fact that it separates a piece of business logic into a sensible place. I can implement my Product.getCode methods as follows..
class CashProduct {
def getCode = SyntheticCodes.Cash(this)
}
class ForwardProduct {
def getCode = SyntheticCodes.Forward(this)
}
Manifests which are a sort of way at getting the type information at runtime, as if Scala had reified types.
In scala 2.8 you can have tail-recursive methods by using the package scala.util.control.TailCalls (in fact it's trampolining).
An example:
def u(n:Int):TailRec[Int] = {
if (n==0) done(1)
else tailcall(v(n/2))
}
def v(n:Int):TailRec[Int] = {
if (n==0) done(5)
else tailcall(u(n-1))
}
val l=for(n<-0 to 5) yield (n,u(n).result,v(n).result)
println(l)
Case classes automatically mixin the Product trait, providing untyped, indexed access to the fields without any reflection:
case class Person(name: String, age: Int)
val p = Person("Aaron", 28)
val name = p.productElement(0) // name = "Aaron": Any
val age = p.productElement(1) // age = 28: Any
val fields = p.productIterator.toList // fields = List[Any]("Aaron", 28)
This feature also provides a simplified way to alter the output of the toString method:
case class Person(name: String, age: Int) {
override def productPrefix = "person: "
}
// prints "person: (Aaron,28)" instead of "Person(Aaron, 28)"
println(Person("Aaron", 28))
It's not exactly hidden, but certainly a under advertised feature: scalac -Xprint.
As a illustration of the use consider the following source:
class A { "xx".r }
Compiling this with scalac -Xprint:typer outputs:
package <empty> {
class A extends java.lang.Object with ScalaObject {
def this(): A = {
A.super.this();
()
};
scala.this.Predef.augmentString("xx").r
}
}
Notice scala.this.Predef.augmentString("xx").r, which is a the application of the implicit def augmentString present in Predef.scala.
scalac -Xprint:<phase> will print the syntax tree after some compiler phase. To see the available phases use scalac -Xshow-phases.
This is a great way to learn what is going on behind the scenes.
Try with
case class X(a:Int,b:String)
using the typer phase to really feel how useful it is.
You can define your own control structures. It's really just functions and objects and some syntactic sugar, but they look and behave like the real thing.
For example, the following code defines dont {...} unless (cond) and dont {...} until (cond):
def dont(code: => Unit) = new DontCommand(code)
class DontCommand(code: => Unit) {
def unless(condition: => Boolean) =
if (condition) code
def until(condition: => Boolean) = {
while (!condition) {}
code
}
}
Now you can do the following:
/* This will only get executed if the condition is true */
dont {
println("Yep, 2 really is greater than 1.")
} unless (2 > 1)
/* Just a helper function */
var number = 0;
def nextNumber() = {
number += 1
println(number)
number
}
/* This will not be printed until the condition is met. */
dont {
println("Done counting to 5!")
} until (nextNumber() == 5)
#switch annotation in Scala 2.8:
An annotation to be applied to a match
expression. If present, the compiler
will verify that the match has been
compiled to a tableswitch or
lookupswitch, and issue an error if it
instead compiles into a series of
conditional expressions.
Example:
scala> val n = 3
n: Int = 3
scala> import annotation.switch
import annotation.switch
scala> val s = (n: #switch) match {
| case 3 => "Three"
| case _ => "NoThree"
| }
<console>:6: error: could not emit switch for #switch annotated match
val s = (n: #switch) match {
Dunno if this is really hidden, but I find it quite nice.
Typeconstructors that take 2 type parameters can be written in infix notation
object Main {
class FooBar[A, B]
def main(args: Array[String]): Unit = {
var x: FooBar[Int, BigInt] = null
var y: Int FooBar BigInt = null
}
}
Scala 2.8 introduced default and named arguments, which made possible the addition of a new "copy" method that Scala adds to case classes. If you define this:
case class Foo(a: Int, b: Int, c: Int, ... z:Int)
and you want to create a new Foo that's like an existing Foo, only with a different "n" value, then you can just say:
foo.copy(n = 3)
in scala 2.8 you can add #specialized to your generic classes/methods. This will create special versions of the class for primitive types (extending AnyVal) and save the cost of un-necessary boxing/unboxing :
class Foo[#specialized T]...
You can select a subset of AnyVals :
class Foo[#specialized(Int,Boolean) T]...
Extending the language. I always wanted to do something like this in Java (couldn't). But in Scala I can have:
def timed[T](thunk: => T) = {
val t1 = System.nanoTime
val ret = thunk
val time = System.nanoTime - t1
println("Executed in: " + time/1000000.0 + " millisec")
ret
}
and then write:
val numbers = List(12, 42, 3, 11, 6, 3, 77, 44)
val sorted = timed { // "timed" is a new "keyword"!
numbers.sortWith(_<_)
}
println(sorted)
and get
Executed in: 6.410311 millisec
List(3, 3, 6, 11, 12, 42, 44, 77)
You can designate a call-by-name parameter (EDITED: this is different then a lazy parameter!) to a function and it will not be evaluated until used by the function (EDIT: in fact, it will be reevaluated every time it is used). See this faq for details
class Bar(i:Int) {
println("constructing bar " + i)
override def toString():String = {
"bar with value: " + i
}
}
// NOTE the => in the method declaration. It indicates a lazy paramter
def foo(x: => Bar) = {
println("foo called")
println("bar: " + x)
}
foo(new Bar(22))
/*
prints the following:
foo called
constructing bar 22
bar with value: 22
*/
You can use locally to introduce a local block without causing semicolon inference issues.
Usage:
scala> case class Dog(name: String) {
| def bark() {
| println("Bow Vow")
| }
| }
defined class Dog
scala> val d = Dog("Barnie")
d: Dog = Dog(Barnie)
scala> locally {
| import d._
| bark()
| bark()
| }
Bow Vow
Bow Vow
locally is defined in "Predef.scala" as:
#inline def locally[T](x: T): T = x
Being inline, it does not impose any additional overhead.
Early Initialization:
trait AbstractT2 {
println("In AbstractT2:")
val value: Int
val inverse = 1.0/value
println("AbstractT2: value = "+value+", inverse = "+inverse)
}
val c2c = new {
// Only initializations are allowed in pre-init. blocks.
// println("In c2c:")
val value = 10
} with AbstractT2
println("c2c.value = "+c2c.value+", inverse = "+c2c.inverse)
Output:
In AbstractT2:
AbstractT2: value = 10, inverse = 0.1
c2c.value = 10, inverse = 0.1
We instantiate an anonymous inner
class, initializing the value field
in the block, before the with
AbstractT2 clause. This guarantees
that value is initialized before the
body of AbstractT2 is executed, as
shown when you run the script.
You can compose structural types with the 'with' keyword
object Main {
type A = {def foo: Unit}
type B = {def bar: Unit}
type C = A with B
class myA {
def foo: Unit = println("myA.foo")
}
class myB {
def bar: Unit = println("myB.bar")
}
class myC extends myB {
def foo: Unit = println("myC.foo")
}
def main(args: Array[String]): Unit = {
val a: A = new myA
a.foo
val b: C = new myC
b.bar
b.foo
}
}
placeholder syntax for anonymous functions
From The Scala Language Specification:
SimpleExpr1 ::= '_'
An expression (of syntactic category Expr) may contain embedded underscore symbols _ at places where identifiers are legal. Such an expression represents an anonymous function where subsequent occurrences of underscores denote successive parameters.
From Scala Language Changes:
_ + 1 x => x + 1
_ * _ (x1, x2) => x1 * x2
(_: Int) * 2 (x: Int) => x * 2
if (_) x else y z => if (z) x else y
_.map(f) x => x.map(f)
_.map(_ + 1) x => x.map(y => y + 1)
Using this you could do something like:
def filesEnding(query: String) =
filesMatching(_.endsWith(query))
Implicit definitions, particularly conversions.
For example, assume a function which will format an input string to fit to a size, by replacing the middle of it with "...":
def sizeBoundedString(s: String, n: Int): String = {
if (n < 5 && n < s.length) throw new IllegalArgumentException
if (s.length > n) {
val trailLength = ((n - 3) / 2) min 3
val headLength = n - 3 - trailLength
s.substring(0, headLength)+"..."+s.substring(s.length - trailLength, s.length)
} else s
}
You can use that with any String, and, of course, use the toString method to convert anything. But you could also write it like this:
def sizeBoundedString[T](s: T, n: Int)(implicit toStr: T => String): String = {
if (n < 5 && n < s.length) throw new IllegalArgumentException
if (s.length > n) {
val trailLength = ((n - 3) / 2) min 3
val headLength = n - 3 - trailLength
s.substring(0, headLength)+"..."+s.substring(s.length - trailLength, s.length)
} else s
}
And then, you could pass classes of other types by doing this:
implicit def double2String(d: Double) = d.toString
Now you can call that function passing a double:
sizeBoundedString(12345.12345D, 8)
The last argument is implicit, and is being passed automatically because of the implicit de declaration. Furthermore, "s" is being treated like a String inside sizeBoundedString because there is an implicit conversion from it to String.
Implicits of this type are better defined for uncommon types to avoid unexpected conversions. You can also explictly pass a conversion, and it will still be implicitly used inside sizeBoundedString:
sizeBoundedString(1234567890L, 8)((l : Long) => l.toString)
You can also have multiple implicit arguments, but then you must either pass all of them, or not pass any of them. There is also a shortcut syntax for implicit conversions:
def sizeBoundedString[T <% String](s: T, n: Int): String = {
if (n < 5 && n < s.length) throw new IllegalArgumentException
if (s.length > n) {
val trailLength = ((n - 3) / 2) min 3
val headLength = n - 3 - trailLength
s.substring(0, headLength)+"..."+s.substring(s.length - trailLength, s.length)
} else s
}
This is used exactly the same way.
Implicits can have any value. They can be used, for instance, to hide library information. Take the following example, for instance:
case class Daemon(name: String) {
def log(msg: String) = println(name+": "+msg)
}
object DefaultDaemon extends Daemon("Default")
trait Logger {
private var logd: Option[Daemon] = None
implicit def daemon: Daemon = logd getOrElse DefaultDaemon
def logTo(daemon: Daemon) =
if (logd == None) logd = Some(daemon)
else throw new IllegalArgumentException
def log(msg: String)(implicit daemon: Daemon) = daemon.log(msg)
}
class X extends Logger {
logTo(Daemon("X Daemon"))
def f = {
log("f called")
println("Stuff")
}
def g = {
log("g called")(DefaultDaemon)
}
}
class Y extends Logger {
def f = {
log("f called")
println("Stuff")
}
}
In this example, calling "f" in an Y object will send the log to the default daemon, and on an instance of X to the Daemon X daemon. But calling g on an instance of X will send the log to the explicitly given DefaultDaemon.
While this simple example can be re-written with overload and private state, implicits do not require private state, and can be brought into context with imports.
Maybe not too hidden, but I think this is useful:
#scala.reflect.BeanProperty
var firstName:String = _
This will automatically generate a getter and setter for the field that matches bean convention.
Further description at developerworks
Implicit arguments in closures.
A function argument can be marked as implicit just as with methods. Within the scope of the body of the function the implicit parameter is visible and eligible for implicit resolution:
trait Foo { def bar }
trait Base {
def callBar(implicit foo: Foo) = foo.bar
}
object Test extends Base {
val f: Foo => Unit = { implicit foo =>
callBar
}
def test = f(new Foo {
def bar = println("Hello")
})
}
Build infinite data structures with Scala's Streams :
http://www.codecommit.com/blog/scala/infinite-lists-for-the-finitely-patient
Result types are dependent on implicit resolution. This can give you a form of multiple dispatch:
scala> trait PerformFunc[A,B] { def perform(a : A) : B }
defined trait PerformFunc
scala> implicit val stringToInt = new PerformFunc[String,Int] {
def perform(a : String) = 5
}
stringToInt: java.lang.Object with PerformFunc[String,Int] = $anon$1#13ccf137
scala> implicit val intToDouble = new PerformFunc[Int,Double] {
def perform(a : Int) = 1.0
}
intToDouble: java.lang.Object with PerformFunc[Int,Double] = $anon$1#74e551a4
scala> def foo[A, B](x : A)(implicit z : PerformFunc[A,B]) : B = z.perform(x)
foo: [A,B](x: A)(implicit z: PerformFunc[A,B])B
scala> foo("HAI")
res16: Int = 5
scala> foo(1)
res17: Double = 1.0
Scala's equivalent of Java double brace initializer.
Scala allows you to create an anonymous subclass with the body of the class (the constructor) containing statements to initialize the instance of that class.
This pattern is very useful when building component-based user interfaces (for example Swing , Vaadin) as it allows to create UI components and declare their properties more concisely.
See http://spot.colorado.edu/~reids/papers/how-scala-experience-improved-our-java-development-reid-2011.pdf for more information.
Here is an example of creating a Vaadin button:
val button = new Button("Click me"){
setWidth("20px")
setDescription("Click on this")
setIcon(new ThemeResource("icons/ok.png"))
}
Excluding members from import statements
Suppose you want to use a Logger that contains a println and a printerr method, but you only want to use the one for error messages, and keep the good old Predef.println for standard output. You could do this:
val logger = new Logger(...)
import logger.printerr
but if logger also contains another twelve methods that you would like to import and use, it becomes inconvenient to list them. You could instead try:
import logger.{println => donotuseprintlnt, _}
but this still "pollutes" the list of imported members. Enter the über-powerful wildcard:
import logger.{println => _, _}
and that will do just the right thing™.
require method (defined in Predef) that allow you to define additional function constraints that would be checked during run-time. Imagine that you developing yet another twitter client and you need to limit tweet length up to 140 symbols. Moreover you can't post empty tweet.
def post(tweet: String) = {
require(tweet.length < 140 && tweet.length > 0)
println(tweet)
}
Now calling post with inappropriate length argument will cause an exception:
scala> post("that's ok")
that's ok
scala> post("")
java.lang.IllegalArgumentException: requirement failed
at scala.Predef$.require(Predef.scala:145)
at .post(<console>:8)
scala> post("way to looooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooong tweet")
java.lang.IllegalArgumentException: requirement failed
at scala.Predef$.require(Predef.scala:145)
at .post(<console>:8)
You can write multiple requirements or even add description to each:
def post(tweet: String) = {
require(tweet.length > 0, "too short message")
require(tweet.length < 140, "too long message")
println(tweet)
}
Now exceptions are verbose:
scala> post("")
java.lang.IllegalArgumentException: requirement failed: too short message
at scala.Predef$.require(Predef.scala:157)
at .post(<console>:8)
One more example is here.
Bonus
You can perform an action every time requirement fails:
scala> var errorcount = 0
errorcount: Int = 0
def post(tweet: String) = {
require(tweet.length > 0, {errorcount+=1})
println(tweet)
}
scala> errorcount
res14: Int = 0
scala> post("")
java.lang.IllegalArgumentException: requirement failed: ()
at scala.Predef$.require(Predef.scala:157)
at .post(<console>:9)
...
scala> errorcount
res16: Int = 1
Traits with abstract override methods are a feature in Scala that is as not widely advertised as many others. The intend of methods with the abstract override modifier is to do some operations and delegating the call to super. Then these traits have to be mixed-in with concrete implementations of their abstract override methods.
trait A {
def a(s : String) : String
}
trait TimingA extends A {
abstract override def a(s : String) = {
val start = System.currentTimeMillis
val result = super.a(s)
val dur = System.currentTimeMillis-start
println("Executed a in %s ms".format(dur))
result
}
}
trait ParameterPrintingA extends A {
abstract override def a(s : String) = {
println("Called a with s=%s".format(s))
super.a(s)
}
}
trait ImplementingA extends A {
def a(s: String) = s.reverse
}
scala> val a = new ImplementingA with TimingA with ParameterPrintingA
scala> a.a("a lotta as")
Called a with s=a lotta as
Executed a in 0 ms
res4: String = sa attol a
While my example is really not much more than a poor mans AOP, I used these Stackable Traits much to my liking to build Scala interpreter instances with predefined imports, custom bindings and classpathes. The Stackable Traits made it possible to create my factory along the lines of new InterpreterFactory with JsonLibs with LuceneLibs and then have useful imports and scope varibles for the users scripts.