I want to update a sequence in Scala, I have this code :
def update(userId: Long): Either[String, Int] = {
Logins.findByUserId(userId) map {
logins: Login => update(login.id,
Seq(NamedParameter("random_date", "prefix-" + logins.randomDate)))
} match {
case sequence : Seq(Nil, Int) => sequence.foldLeft(Right(_) + Right(_))
case _ => Left("error.logins.update")
}
}
Where findByUserId returns a Seq[Logins] and update returns Either[String, Int] where Int is the number of updated rows,
and String would be the description of the error.
What I want to achieve is to return an String if while updating the list an error happenes or an Int with the total number of updated rows.
The code is not working, I think I should do something different in the match, I don't know how I can check if every element in the Seq of Eithers is a Right value.
If you are open to using Scalaz or Cats you can use traverse. An example using Scalaz :
import scalaz.std.either._
import scalaz.std.list._
import scalaz.syntax.traverse._
val logins = Seq(1, 2, 3)
val updateRight: Int => Either[String, Int] = Right(_)
val updateLeft: Int => Either[String, Int] = _ => Left("kaboom")
logins.toList.traverseU(updateLeft).map(_.sum) // Left(kaboom)
logins.toList.traverseU(updateRight).map(_.sum) // Right(6)
Traversing over the logins gives us a Either[String, List[Int]], if we get the sum of the List we get the wanted Either[String, Int].
We use toList because there is no Traverse instance for Seq.
traverse is a combination of map and sequence.
We use traverseU instead of traverse because it infers some of the types for us (otherwise we should have introduced a type alias or a type lambda).
Because we imported scalaz.std.either._ we can use map directly without using a right projection (.right.map).
You shouldn't really use a fold if you want to exit early. A better solution would be to recursively iterate over the list, updating and counting successes, then return the error when you encounter one.
Here's a little example function that shows the technique. You would probably want to modify this to do the update on each login instead of just counting.
val noErrors = List[Either[String,Int]](Right(10), Right(12))
val hasError = List[Either[String,Int]](Right(10), Left("oops"), Right(12))
def checkList(l: List[Either[String,Int]], goodCount: Int): Either[String, Int] = {
l match {
case Left(err) :: xs =>
Left(err)
case Right(_) :: xs =>
checkList(xs, (goodCount + 1))
case Nil =>
Right(goodCount)
}
}
val r1 = checkList(noErrors, 0)
val r2 = checkList(hasError, 0)
// r1: Either[String,Int] = Right(2)
// r2: Either[String,Int] = Left(oops)
You want to stop as soon as an update fails, don't you?
That means that you want to be doing your matching inside the map, not outside. Try is actually a more suitable construct for this purpose, than Either. Something like this, perhaps:
def update(userId: Long): Either[String, Int] = Try {
Logins.findByUserId(userId) map { login =>
update(login.id, whatever) match {
case Right(x) => x
case Left(s) => throw new Exception(s)
}
}.sum
}
.map { n => Right(n) }
.recover { case ex => Left(ex.getMessage) }
BTW, a not-too-widely-known fact about scala is that putting a return statement inside a lambda, actually returns from the enclosing method. So, another, somewhat shorter way to write this would be like this:
def update(userId: Long): Either[String, Int] =
Logins.findByUserId(userId).foldLeft(Right(0)) { (sum,login) =>
update(login.id, whatever) match {
case Right(x) => Right(sum.right + x)
case error#Left(s) => return error
}
}
Also, why in the world does findUserById return a sequence???
Related
I have many functions in my code defined with return type as Either[Throwable, String] and all of them have one argument of type String. Three representative functions of my code are defined as:
val f1 = (input: String) => {
/* Processing from the input using a library in my actual code returns a Either[Throwable, String] */
if (input == "a") Left(new Exception(input))
else Right("Success")
}
val f2 = (input: String) => {
if (input == "b") Left(new Exception(input))
else Right("Success")
}
val f3 = (input: String) => {
if (input == "c") Left(new Exception(input))
else Right("Success")
}
To chain the function outputs, I'm writing code like:
def x(input: String) = f1(input) match {
case Left(value) => Left(value)
case Right(_) => f2(input) match {
case Left(value) => Left(value)
case Right(_) => f3(input)
}
}
Since this is just three functions so this might look like a short code. However there are multiple such matches that are happening in my code, so it's a very long code. I am looking to avoid such a chaining.
I know that Scala has a way to chain functions like f1.andThen(f2).andThen(f3), however the problem is that in each andThen we need to pass the same argument, in this case being input. However I want to chain these functions so that if there is a Left output, it should not go to the next andThen.
I believe this can be simplified using Functional Programming, but I don't know where to start. Is there a way we can achieve this using Scala functional programming?
If you have cats in scope, then all you need to do is this:
import cats.syntax.all._
val functions: List[String => Either[Throwable, Unit]] = List(
// put your functions here.
)
val result: Either[Throwable, Unit] =
functions.traverse_(f => f(input))
Otherwise, you may emulate it using this:
val init: Either[Throwable, Unit] = Right(())
functions.foldLeft(init) {
case (acc, f) =>
acc.flatMap(_ => f(input))
}
There is simple abstract look up service with possibility to retrieve value by key:
trait LookUp[F[_]] {
def read(key: String): F[Option[String]]
}
There is use case of this service, idea is to give implemented storage and accumulator with starting key, then ask for value from db if the result is None then stop and Return None, if the value is found then add it to accumulator list and look up for next value as key from previous call result. Execution stops when retrieved value already is found before or None is retrieved. Then a string of all acc elements is returned as result.
Tried like this:
def readWhileFound[F[_]: Monad](db: LookUp[F], acc: List[String]): F[Option[String]] = {
for{ result <- db.read(acc.head)} yield result match {
case Some(value) if(!acc.contains(value)) => readWhileFound(db, value::acc)
case _ => acc.mkstring("")
}
}
But I'm not able to get types right getting mismatch errors like:
found : F[cats.data.OptionT[[_]F[_],String]]
required: cats.data.OptionT[F,String]
Approach number 2:
def readWhileFound[F[_]: Monad](key: String, db: LookUp[F])(implicit m: Monad[F]): F[Option[String]] = {
m.tailRecM((Option(key), List.empty[String])) { case (currentK, accum) =>
currentK match {
case Some(value) if(!accum.contains(value)) => m.pure(Left((db.read(value), value :: accum)))
case _ => m.pure(Right(Some(accum.mkString(""))))
}
}
}
Getting compiler error:
(Found) F[Option[String]]
required: Option[String]
case Some(value) if(!accum.contains(value)) => m.pure(Left((db.read(value), value :: accum)))
Looks like db.read(value) somehow should be unwrapped out of F
This looks like a great use case for fs2:
You should be able to do something like this:
import fs2.Stream
def readWhileFound[F[_]: Concurrent](db: LookUp[F])(initialKey: String): F[List[String] =
Stream.unfoldEval(initialKey) { currentKey =>
db.read(key = currentKey).map(k => (k, k))
}.compile.toList
You are match-ing on the wrong expression in your first implementation. You should match on result, not on the entire for-comprehension. The implementation below should do what you're after.
def readWhileFound[F[_]: Monad](db: LookUp[F], startKey: String): F[Option[String]] = {
def loop(currKey: String, seenKeys: Set[String]): F[Option[String]] = {
db.read(currKey).flatMap {
case Some(nextKey) if !seenKeys.contains(nextKey) =>
loop(nextKey, seenKeys + nextKey)
case _ if seenKeys.nonEmpty => seenKeys.mkString("").some.pure[F]
case _ => none[String].pure[F]
}
}
loop(startKey, Set.empty)
}
I've replaced List with Set for the accumulated values because its contains method is more efficient, but if you care about the order in the returned result then you'll have to either go back to List (less efficient) or use two accumulators (one Set, the other List).
I am still learning the basics of Scala, therefore I am asking for your understanding. Is it any possible way to use fold method to print only names beginning with "A"
Object Scala {
val names: List[String] = List("Adam", "Mick", "Ann");
def main(args: Array[String]) {
println(names.foldLeft("my list of items starting with A: ")(_+_));
}
}
}
Have a look at the signature of foldLeft
def foldLeft[B](z: B)(op: (B, A) => B): B
where
z is the initial value
op is a function taking two arguments, namely accumulated result so far B, and the next element to be processed A
returns the accumulated result B
Now consider this concrete implementation
val names: List[String] = List("Adam", "Mick", "Ann")
val predicate: String => Boolean = str => str.startsWith("A")
names.foldLeft(List.empty[String]) { (accumulated: List[String], next: String) =>
if (predicate(next)) accumulated.prepended(next) else accumulated
}
here
z = List.empty[String]
op = (accumulated: List[String], next: String) => if (predicate(next)) accumulated.prepended(next) else accumulated
Usually we would write this inlined and rely on type inference so we do not have two write out full types all the time, so it becomes
names.foldLeft(List.empty[String]) { (acc, next) =>
if (next.startsWith("A")) next :: acc else acc
}
// val res1: List[String] = List(Ann, Adam)
On of the key ideas when working with List is to always prepend an element instead of append
names.foldLeft(List.empty[String]) { (accumulated: List[String], next: String) =>
if (predicate(next)) accumulated.appended(next) else accumulated
}
because prepending is much more efficient. However note how this makes the accumulated result in reverse order, so
List(Ann, Adam)
instead of perhaps required
List(Adam, Ann)
so often-times we perform one last traversal by calling reverse like so
names.foldLeft(List.empty[String]) { (acc, next) =>
if (next.startsWith("A")) next :: acc else acc
}.reverse
// val res1: List[String] = List(Adam, Ann)
The answer from #Mario Galic is a good one and should be accepted. (It's the polite thing to do).
Here's a slightly different way to filter for starts-with-A strings.
val names: List[String] = List("Adam", "Mick", "Ann")
println(names.foldLeft("my list of items starting with A: "){
case (acc, s"A$nme") => acc + s"A$nme "
case (acc, _ ) => acc
})
//output: "my list of items starting with A: Adam Ann"
I have a function like this:
def foo(item: Item) : Option[Int] = Try{
// Some code that can blow up
}.toOption
I have a list of items and I want to map through them, and apply the above function. But if the function above blows up and returns a None then the result of the map should be an error:
items.map{
item => foo(item)
}
Is map not the right thing to do here? It doesn't seem like it
This is called traverse. If you can use cats, it is as simple as:
import cats.implicits._
val result = items.traverse(foo) // Option[List[Int]]
If not, you can implement it pretty easily:
def traverse[A, B](data: List[A])(f: A => Option[B]): Option[List[B]] = {
#annotation.tailrec
def loop(remaining: List[A], acc: List[B]): Option[List[B]] =
remaining match {
case a :: as => f(a) match {
case Some(b) => loop(remaining = as, b :: acc)
case None => None
}
case Nil => Some(acc.reverse)
}
loop(remaining = data, acc = List.empty)
}
Which you can use like:
val result = traverse(items)(foo) // Option[List[Int]]
(however, I would suggest you to use cats instead, since it is more general).
For out-of-the-box short-circuiting, consider wrapping the list-mapping with Try like so
def fooUnsafe(item: Item): Int = // might throw
Try(items.map(fooUnsafe))
If you wish to keep def foo(item: Item) : Option[Int] signature then the following will also short-circuit
Try(list.map(v => foo(v).get))
I would like to convert a List[Box[T]] into a Box[List[T]].
I know that I could use foldRight, but I can't find an elegant way into doing so.
EDIT I would like to keep the properties of Box, that is to say, if there is any failure, return a Box with this failure.
If you only want to collect the "Full" values
I'm not sure why you'd want a Box[List[T]], because the empty list should suffice to signal the lack of any values. I'll assume that's good enough for you.
I don't have a copy of Lift handy, but I know that Box is inspired by Option and has a flatMap method, so:
Long form:
for {
box <- list
value <- box
} yield value
Shorter form:
list.flatMap(identity)
Shortest form:
list.flatten
If you want to collect the failures too:
Here's the mapSplit function I use for this kind of problem. You can easily adapt it to use Box instead of Either:
/**
* Splits the input list into a list of B's and a list of C's, depending on which type of value the mapper function returns.
*/
def mapSplit[A,B,C](in: Traversable[A])(mapper: (A) ⇒ Either[B,C]): (Seq[B], Seq[C]) = {
#tailrec
def mapSplit0(in: Traversable[A], bs: Vector[B], cs: Vector[C]): (Seq[B], Seq[C]) = {
in match {
case t if t.nonEmpty ⇒
val a = t.head
val as = t.tail
mapper(a) match {
case Left(b) ⇒ mapSplit0(as, bs :+ b, cs )
case Right(c) ⇒ mapSplit0(as, bs, cs :+ c)
}
case t ⇒
(bs, cs)
}
}
mapSplit0(in, Vector[B](), Vector[C]())
}
And when I just want to split something that's already a Seq[Either[A,B]], I use this:
/**
* Splits a List[Either[A,B]] into a List[A] from the lefts and a List[B] from the rights.
* A degenerate form of {#link #mapSplit}.
*/
def splitEither[A,B](in: Traversable[Either[A,B]]): (Seq[A], Seq[B]) = mapSplit(in)(identity)
It's really easier to do this with a tail-recursive function than with a fold:
final def flip[T](l: List[Option[T]], found: List[T] = Nil): Option[List[T]] = l match {
case Nil => if (found.isEmpty) None else Some(found.reverse)
case None :: rest => None
case Some(x) :: rest => flip(rest, x :: found)
}
This works as expected:
scala> flip(List(Some(3),Some(5),Some(2)))
res3: Option[List[Int]] = Some(List(3, 5, 2))
scala> flip(List(Some(1),None,Some(-1)))
res4: Option[List[Int]] = None
One can also do this with Iterator.iterate, but it's more awkward and slower, so I would avoid that approach in this case.
(See also my answer in the question 4e6 linked to.)