I'd like to implement validation for a sequence of operations that all return Either[Error,Item]
It should be fail-fast (in my initial need), I mean, returning Either[Error,Seq[Item]].
If there is an error, it's obvious i do not want the following operations to be performed.
But in the future i may want to collect all the errors instead of returning only the first one.
I know Scalaz can do the job but for now I quite don't understand all parts of Scalaz and I'm pretty sure there's a simpler way to do it without using Scalaz, but using by-name parameters for exemple.
Is there a way to store by-name parameters in a sequence?
So that i can create a sequence of by-name values that represent my operations?
I mean, some kind of type Seq[=> Either[Error,Item]]
Then I could do something like calling takeWhile or collectFirst or something somilar, without all the operations being performed before the creation of the sequence?
I would expect the operations to be performed only when iterating on the sequence.
Thanks
You can indeed use a Seq[() => Either[Error, Item]] to defer the computation at collection creation time. So for example
val doSomething1: () => Either[Error, Item] = () => { println(1); Right(1) }
val doSomething2: () => Either[Error, Item] = () => { println(2); Right(2) }
val doSomething3: () => Either[Error, Item] = () => { println(3); Left("error") }
val doSomething4: () => Either[Error, Item] = () => { println(4); Right(3) }
val doSomething5: () => Either[Error, Item] = () => { println(5); Left("second error") }
val l = Seq(doSomething1, doSomething2, doSomething3, doSomething4, doSomething5)
(Items are Ints in the example and Errors are Strings)
Then you can process them lazily stopping at first failure using the following recursive function:
def processUntilFailure(l: Seq[() => Either[Error, Item]]): Either[Error, Seq[Item]] = {
l.headOption.map(_.apply() match {
case Left(error) => Left(error)
case Right(item) => processUntilFailure(l.tail).right.map(_ :+ item)
}).getOrElse(Right(Nil))
}
So now when I run processUntilFailure(l)
scala> processUntilFailure(l)
1
2
3
res1: Either[Error,Seq[Item]] = Left(error)
If you wanted to generate a Either[Seq[String], Seq[Int]] (processing all the operations). You could do it with a little change:
def processAll(l: Seq[() => Either[Error, Item]]): Either[Seq[Error], Seq[Item]] = {
l.headOption.map(_.apply() match {
case Left(error) => processAll(l.tail) match {
case Right(_) => Left(Seq(error))
case Left(previousErrors) => Left(previousErrors :+ error)
}
case Right(item) => processAll(l.tail).right.map(_ :+ item)
}).getOrElse(Right(Nil))
}
The only change as you can see is the Left case in the pattern match. Running this one:
scala> processAll(l)
1
2
3
4
5
res0: Either[Seq[Error],Seq[Item]] = Left(List(second error, error))
processAll can be replaced with a generic foldLeft on l
val zero: Either[Seq[Error], Seq[Item]] = Right(Seq[Item]())
l.foldLeft(zero) { (errorsOrItems: Either[Seq[Error], Seq[Item]], computation: () => Either[String, Int]) =>
computation.apply().fold(
{ (error: String) => Left(errorsOrItems.left.toOption.map(_ :+ error).getOrElse(Seq(error))) },
{ (int: Int) => errorsOrItems.right.map(_ :+ int) })
}
processUntilFailure can as well but not easily. Since aborting early from a fold is tricky. Here's a good answer about other possible approaches when you find yourself needing to do that.
You should be able to pull this off with the type Seq[Function0[Either[Error, Item]]]. Function0 is, obviously, a zero-argument function. The rest should be self-explanatory.
Scalaz provides the type IO for exactly this purpose, so you could actually use that as well. You may not want to yet, however, if you're just beginning to work with Scalaz.
Related
I want to implement a function representing a while loop using the State monad from cats.
I did it like this:
def whileLoopState[S](cond: S => Boolean)(block: S => S): State[S, Unit] = State { state =>
if (cond(state)) {
val nextState = block(state)
whileLoopState(cond)(block).run(nextState).value
} else {
(state, ())
}
}
The problem with this implementation is that it's not stack safe
because the recursive call is not in tail position, so the following
results in stack overflow error:
whileLoopState[Int](s => s > 0) { s =>
println(s)
s - 1
}.run(10000).value
Cats has tailRecM method implemented for every instance of Monad trait
that allows to make monadic recursive functions stack safe:
type WhileLoopState[A] = State[Unit, A]
def whileLoopStateTailRec[S](cond: S => Boolean)(block: S => S)(initialState: S): WhileLoopState[S] = Monad[WhileLoopState]
.tailRecM(initialState) { newState =>
State { _ =>
if (cond(newState)) {
val nextState = block(newState)
((), Left(nextState))
} else {
((), Right(newState))
}
}
}
Now this works:
whileLoopStateTailRec[Int](s => s > 0) { s =>
println(s)
s - 1
} (10000).run().value
but the implementation of whileLoopStateTailRec seems too convoluted
for a simple case like this and therefore raises my suspicion that I'm not doing things correctly.
Is there a way to simplify it?
Is it possible to use State[A, Unit] instead of State[Unit, A] so that the state is kept in the proper slot?
Is it possible to make recursive function using State monad stack safe without using tailRecM?
You can either just take advantage that flatMap on State is stack safe like this:
def whileLoopState[S](cond: S => Boolean)(block: S => S): State[S, Unit] =
State.get[S].flatMap { s =>
if (cond(s)) State.set(block(s)) >> whileLoopState(cond)(block)
else State.pure(())
}
Or, even better, just reuse existing syntax:
def whileLoopState[S](cond: S => Boolean)(block: S => S): State[S, Unit] =
State.modify(block).whileM_(State.inspect(cond))
You can see the code running here.
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'm new to cats. I'm creating State instances to handle deserialisation of types from a byte stream. e.g.
val int: State[Seq[Byte], Int] = State[Seq[Byte], Int] {
case bs if bs.length >= 4 =>
bs.drop(4) -> ByteBuffer.wrap(bs.take(4).toArray).getInt
case _ => throw new EOFException()
}
I have implemented a parser of Option[Int] in terms of the above, like so:
val unit: State[Seq[Byte], Unit] = State[Seq[Byte], Unit](_ -> Unit)
val optInt: State[Seq[Byte], Option[Int]] = int.flatMap(i =>
if (i == 1) int.map(Some(_)) else unit.map(_ => None)
)
I feel that I've missed a trick here, as the implementation seems too verbose. Can I write this more succinctly? Can I do away with needing to define unit?
I wouldn't say that's too verbose, but I'd do two tricks with this:
Replace conditional with pattern matching function
Use State.pure instead of manually creating/transforming State values such as your unit.
val optInt: State[Seq[Byte], Option[Int]] = int.flatMap {
case 1 => int.map(Some(_))
case _ => State.pure(None)
}
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???