I have a task to transform the following code-block:
val instance = instanceFactory.create
val result = instance.ackForResult
to for-comprehension expression.
As for-comprehension leans on enumeration of elements, I tried to get around it with wrapper class:
case class InstanceFactoryWrapper(value:InstanceFactory) {
def map(f: InstanceFactory => Instance): Instance
= value.create()
}
where map-method must handle only one element and return a single result: Instance
I tested this approach with this expression:
for {
mediationApi <- InstanceFactoryWrapper(instanceFactoryWrapper)
}
But it does't work: IDEA recommends me to use foreach in this part. But "foreach" doesn't return anything, as opposed to map.
What am I doing wrong?
Simply put when working with List\Option\Either or other lang types comprehensions are useful to transform nested map\flatMap\withFilter into sequences.
Use custom classes in for-comprehension
But what about your own classes or other 3rd party ones?
You need to implement monadic operations in order to use them in for-comprehensions.
The bare minimum: map and flatMap.
Take the following example with a custom Config class:
case class Config[T](content: T) {
def flatMap[S](f: T => Config[S]): Config[S] =
f(content)
def map[S](f: T => S): Config[S] =
this.copy(content = f(content))
}
for {
first <- Config("..")
_ = println("Going through a test")
second <- Config(first + "..")
third <- Config(second + "..")
} yield third
This is how you enable for-comprehension.
Related
I'm trying to implement a functional Breadth First Search in Scala to compute the distances between a given node and all the other nodes in an unweighted graph. I've used a State Monad for this with the signature as :-
case class State[S,A](run:S => (A,S))
Other functions such as map, flatMap, sequence, modify etc etc are similar to what you'd find inside a standard State Monad.
Here's the code :-
case class Node(label: Int)
case class BfsState(q: Queue[Node], nodesList: List[Node], discovered: Set[Node], distanceFromSrc: Map[Node, Int]) {
val isTerminated = q.isEmpty
}
case class Graph(adjList: Map[Node, List[Node]]) {
def bfs(src: Node): (List[Node], Map[Node, Int]) = {
val initialBfsState = BfsState(Queue(src), List(src), Set(src), Map(src -> 0))
val output = bfsComp(initialBfsState)
(output.nodesList,output.distanceFromSrc)
}
#tailrec
private def bfsComp(currState:BfsState): BfsState = {
if (currState.isTerminated) currState
else bfsComp(searchNode.run(currState)._2)
}
private def searchNode: State[BfsState, Unit] = for {
node <- State[BfsState, Node](s => {
val (n, newQ) = s.q.dequeue
(n, s.copy(q = newQ))
})
s <- get
_ <- sequence(adjList(node).filter(!s.discovered(_)).map(n => {
modify[BfsState](s => {
s.copy(s.q.enqueue(n), n :: s.nodesList, s.discovered + n, s.distanceFromSrc + (n -> (s.distanceFromSrc(node) + 1)))
})
}))
} yield ()
}
Please can you advice on :-
Should the State Transition on dequeue in the searchNode function be a member of BfsState itself?
How do I make this code more performant/concise/readable?
First off, I suggest moving all the private defs related to bfs into bfs itself. This is the convention for methods that are solely used to implement another.
Second, I suggest simply not using State for this matter. State (like most monads) is about composition. It is useful when you have many things that all need access to the same global state. In this case, BfsState is specialized to bfs, will likely never be used anywhere else (it might be a good idea to move the class into bfs too), and the State itself is always run, so the outer world never sees it. (In many cases, this is fine, but here the scope is too small for State to be useful.) It'd be much cleaner to pull the logic of searchNode into bfsComp itself.
Third, I don't understand why you need both nodesList and discovered, when you can just call _.toList on discovered once you've done your computation. I've left it in in my reimplementation, though, in case there's more to this code that you haven't displayed.
def bfsComp(old: BfsState): BfsState = {
if(old.q.isEmpty) old // You don't need isTerminated, I think
else {
val (currNode, newQ) = old.q.dequeue
val newState = old.copy(q = newQ)
adjList(curNode)
.filterNot(s.discovered) // Set[T] <: T => Boolean and filterNot means you don't need to write !s.discovered(_)
.foldLeft(newState) { case (BfsState(q, nodes, discovered, distance), adjNode) =>
BfsState(
q.enqueue(adjNode),
adjNode :: nodes,
discovered + adjNode,
distance + (adjNode -> (distance(currNode) + 1)
)
}
}
}
def bfs(src: Node): (List[Node], Map[Node, Int]) = {
// I suggest moving BfsState and bfsComp into this method
val output = bfsComp(BfsState(Queue(src), List(src), Set(src), Map(src -> 0)))
(output.nodesList, output.distanceFromSrc)
// Could get rid of nodesList and say output.discovered.toList
}
In the event that you think you do have a good reason for using State here, here are my thoughts.
You use def searchNode. The point of a State is that it is pure and immutable, so it should be a val, or else you reconstruct the same State every use.
You write:
node <- State[BfsState, Node](s => {
val (n, newQ) = s.q.dequeue
(n, s.copy(q = newQ))
})
First off, Scala's syntax was designed so that you don't need to have both a () and {} surrounding an anonymous function:
node <- State[BfsState, Node] { s =>
// ...
}
Second, this doesn't look quite right to me. One benefit of using for-syntax is that the anonymous functions are hidden from you and there is minimal indentation. I'd just write it out
oldState <- get
(node, newQ) = oldState.q.dequeue
newState = oldState.copy(q = newQ)
Footnote: would it make sense to make Node an inner class of Graph? Just a suggestion.
How can I make a class support for keywords in scala?
e.g:
class A(data: String) {
...
}
val a = A("I'm A")
for {
data <- a
} yield {
data
}
Thanks
The compiler rewrites all for comprehensions into the necessary constituent parts: map(), flatMap(), withFilter(), foreach(). That's why many Scala syntax rules are suspended inside the for comprehension, e.g. can't create variables in the standard fashion, val x = 2, and can't throw in println() statements.
In your example, this will work.
class A(data: String) {
def map[B](f: (String) => B) = f(data)
}
val a = new A("I'm A")
for {
data <- a
} yield {
data
} // res0: String = I'm A
But note that if you have multiple generators (the <- is a generator) then only the final one is turned into a map() call. The previous generators are all flatMap() calls.
If your for comprehension includes an if condition then you'll need a withFilter() as well.
I recommend avoiding for comprehensions until you have a good feel for how they work.
Trying to emit a for yield block from a blackbox macro, but I'm failing to understand how you can create the block with valid syntax.
So below source is a hardcoded param name as this block is later inserted inside a method that will have the matching param name. params is just params: Seq[c.universe.ValDef], enclosing the case class fields.
def extract(source: Source): Option[CaseClass] = { ... }
val extractors = accessors(c)(params) map {
case (nm, tpe) => {
val newTerm = TermName(nm.toString + "Opt")
q"""$newTerm <- DoStuff[$tpe].apply("$nm", source)"""
}
}
val extractorNames = accessors(c)(params) map {
case (nm, tpe) => TermName(nm.toString + "Opt")
}
This is basically taking a case class, and outputting a for yield black to basically recreate the case class from a comprehension.
Every field in the case class of the form name: Type is transformed to a set of extractors that yield the same case class instance back if the for comprehension is successful.
case class Test(id: Int, text: String)
Will be macro transformed to the following, where Extract is just a type class and Extract.apply[T : Extract] is just materialising the context bound with implicitly[Extract[T]]:
for {
idOpt <- Extract[Int].apply("id", source): Option[Int]
textOpt <- Extract[String].apply("text", source): Option[String]
} yield Test(idOpt, textOpt)
The problem comes in having to quote the inner for yield expressions with and output a <- b blocks.
def extract(source: Source): Option[$typeName] = {
for {(..$extractors)} yield $companion.apply(..$extractorNames)
}
The error is ';' expected but '<-' found, which is pretty obvious as a <- b is invalid Scala by itself. What is the correct way to generate and quasiquote the expression block such that the above would work?
Here is a list of all the different kinds of quasiquotes.
There you can see that to express the a <- b syntax you need the fq interpolator.
So that code will probably become:
val extractors = accessors(c)(params) map {
case (nm, tpe) => {
val newTerm = TermName(nm.toString + "Opt")
fq"""$newTerm <- DoStuff[$tpe].apply("$nm", source)"""
}
}
And then with the normal interpolator:
q"for (..$extractors) yield $companion.apply(..$extractorNames)"
I have a sequence of parameters. For each parameter I have to perform DB query, which may or may not return a result. Simply speaking, I need to stop after the first result is non-empty. Of course, I would like to avoid doing unnecessary calls. The caveat is - I need to have this operation(s) contained as a another Future - or any "most reactive" approach.
Speaking of code:
//that what I have
def dbQuery(p:Param): Future[Option[Result]] = {}
//my list of params
val input = Seq(p1,p2,p3)
//that what I need to implements
def getFirstNonEmpty(params:Seq[Param]): Future[Option[Result]]
I know I can possibly just wrap entire function in yet another Future and execute code sequentially (Await? Brrr...), but that not the cleanest solution.
Can I somehow create lazy initialized collection of futures, like
params.map ( p => FutureWhichWontStartUnlessAskedWhichWrapsOtherFuture { dbQuery(p) }).findFirst(!_.isEmpty())
I believe it's possible!
What do you think about something like this?
def getFirstNonEmpty(params: Seq[Param]): Future[Option[Result]] = {
params.foldLeft(Future.successful(Option.empty[Result])) { (accuFtrOpt, param) =>
accuFtrOpt.flatMap {
case None => dbQuery(param)
case result => Future.successful(result)
}
}
}
This might be overkill, but if you are open to using scalaz we can do this using OptionT and foldMap.
With OptionT we sort of combine Future and Option into one structure. We can get the first of two Futures with a non-empty result using OptionT.orElse.
import scalaz._, Scalaz._
import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global
val someF: Future[Option[Int]] = Future.successful(Some(1))
val noneF: Future[Option[Int]] = Future.successful(None)
val first = OptionT(noneF) orElse OptionT(someF)
first.run // Future[Option[Int]] = Success(Some(1))
We could now get the first non-empty Future from a List with reduce from the standard library (this will however run all the Futures) :
List(noneF, noneF, someF).map(OptionT.apply).reduce(_ orElse _).run
But with a List (or other collection) we can't be sure that there is at least one element, so we need to use fold and pass a start value. Scalaz can do this work for us by using a Monoid. The Monoid[OptionT[Future, Int]] we will use will supply the start value and combine the Futures with the orElse used above.
type Param = Int
type Result = Int
type FutureO[x] = OptionT[Future, x]
def query(p: Param): Future[Option[Result]] =
Future.successful{ println(p); if (p > 2) Some(p) else None }
def getFirstNonEmpty(params: List[Param]): Future[Option[Result]] = {
implicit val monoid = PlusEmpty[FutureO].monoid[Result]
params.foldMap(p => OptionT(query(p))).run
}
val result = getFirstNonEmpty(List(1,2,3,4))
// prints 1, 2, 3
result.foreach(println) // Some(3)
This is an old question, but if someone comes looking for an answer, here is my take. I solved it for a use case that required me to loop through a limited number of futures sequentially and stop when the first of them returned a result.
I did not need a library for my use-case, a light-weight combination of recursion and pattern matching was sufficient. Although the question here does not have the same problem as a sequence of futures, looping through a sequence of parameters would be similar.
Here would be the pseudo-code based on recursion.
I have not compiled this, fix the types being matched/returned.
def getFirstNonEmpty(params: Seq[Param]): Future[Option[Result]] = {
if (params.isEmpty) {
Future.successful(None)
} else {
val head = params.head
dbQuery(head) match {
case Some(v) => Future.successful(Some(v))
case None => getFirstNonEmpty(params.tail)
}
}
}
Basically, I would like to be able to build a custom extractor without having to store it in a variable prior to using it.
This isn't a real example of how I would use it, it would more likely be used in the case of a regular expression or some other string pattern like construct, but hopefully it explains what I'm looking for:
def someExtractorBuilder(arg:Boolean) = new {
def unapply(s:String):Option[String] = if(arg) Some(s) else None
}
//I would like to be able to use something like this
val {someExtractorBuilder(true)}(result) = "test"
"test" match {case {someExtractorBuilder(true)}(result) => result }
//instead I would have to do this:
val customExtractor = someExtractorBuilder(true)
val customExtractor(result) = "test"
"test" match {case customExtractor(result) => result}
When just doing a single custom extractor it doesn't make much difference, but if you were building a large list of extractors for a case statement, it could make things more difficult to read by separating all of the extractors from their usage.
I expect that the answer is no you can't do this, but I thought I'd ask around first :D
Parameterising extractors would be cool, but we don't have the resources to implement them right now.
Nope.
8.1.7 Extractor Patterns
An extractor pattern x (p 1 , . . . ,
p n ) where n ≥ 0 is of the same
syntactic form as a constructor
pattern. However, instead of a case
class, the stable identifier x denotes
an object which has a member method
named unapply or unapplySeq that
matches the pattern.
One can customize extractors to certain extent using implicit parameters, like this:
object SomeExtractorBuilder {
def unapply(s: String)(implicit arg: Boolean): Option[String] = if (arg) Some(s) else None
}
implicit val arg: Boolean = true
"x" match {
case SomeExtractorBuilder(result) =>
result
}
Unfortunately this cannot be used when you want to use different variants in one match, as all case statements are in the same scope. Still, it can be useful sometimes.
Late but there is a scalac plugin in one of my lib providing syntax ~(extractorWith(param), bindings):
x match {
case ~(parametrizedExtractor(param)) =>
"no binding"
case ~(parametrizedExtractor(param), (a, b)) =>
s"extracted bindings: $a, $b"
}
https://github.com/cchantep/acolyte/blob/master/scalac-plugin/readme.md
Though what you are asking isn't directly possible,
it is possible to create an extractor returning a contaner that gets evaluated value in the if-part of the case evaluation. In the if part it is possible to provide parameters.
object DateExtractor {
def unapply(in: String): Option[DateExtractor] = Some(new DateExtractor(in));
}
class DateExtractor(input:String){
var value:LocalDate=null;
def apply():LocalDate = value;
def apply(format: String):Boolean={
val formater=DateTimeFormatter.ofPattern(format);
try{
val parsed=formater.parse(input, TemporalQueries.localDate());
value=parsed
true;
} catch {
case e:Throwable=>{
false
}
}
}
}
Usage:
object DateExtractorUsage{
def main(args: Array[String]): Unit = {
"2009-12-31" match {
case DateExtractor(ext) if(ext("dd-MM-yyyy"))=>{
println("Found dd-MM-yyyy date:"+ext())
}
case DateExtractor(ext) if(ext("yyyy-MM-dd"))=>{
println("Found yyyy-MM-dd date:"+ext())
}
case _=>{
println("Unable to parse date")
}
}
}
}
This pattern preserves the PartialFunction nature of the piece of code. I find this useful since I am quite a fan of the collect/collectFirst methods, which take a partial function as a parameter and typically does not leave room for precreating a set of extractors.