If I have a Seq, I can map over it.
val ss = Seq("1", "2", "3")
println(ss.map(s => s.toInt)) // List(1, 2, 3)
But sometimes, the function that you pass to map can fail.
val ss = Seq("1", "2", "c")
println(ss.map(s => try { Success(s.toInt) } catch { case e: Throwable => Failure(e) })) // List(Success(1), Success(2), Failure(java.lang.NumberFormatException: For input string: "c"))
This last one will return a Seq[Try[Int]]. What I really want though is a Try[Seq[Int]], where if any one of the mapping is a Failure, it stops the iteration and returns the Failure instead. If there is no error, I want it to just return all the converted elements, unpacked from the Try.
What is the idiomatic Scala way to do this?
You may be overthinking this. The anonymous function in your map is essentially the same as Try.apply. If you want to end up with Try[Seq[Int]] then you can wrap the Seq in Try.apply and map within:
scala> val ss = Try(Seq("1", "2", "c").map(_.toInt))
ss: scala.util.Try[Seq[Int]] = Failure(java.lang.NumberFormatException: For input string: "c")
If any of the toInts fails, it will throw an exception and stop executing, and become a Failure.
Not sure it's idiomatic, but I would do something like this:
import util.{Try, Success, Failure}
import collection.mutable.ListBuffer
def toInt(s: String) =
// Correct usage would be Try(s.toInt)
try {
Success(s.toInt)
}
catch {
case e: Throwable => Failure(e)
}
def convert[A](ss: Seq[String], f: String => Try[A]) = {
ss.foldLeft(Try(ListBuffer[A]())) {
case (a, s) =>
for {
xs <- a
x <- f(s)
}
yield xs :+ x
}.map(_.toSeq)
}
scala> convert(List("1", "2"), toInt)
scala.util.Try[Seq[Int]] = Success(List(1, 2))
scala> convert(List("1", "c"), toInt)
scala.util.Try[Seq[Int]] = Failure(java.lang.NumberFormatException: For input string: "c")
If you really want to exit early instead of skipping elements you can use good old recursion:
def convert[A](ss: Seq[String], f: String => Try[A]) = {
#annotation.tailrec
def loop(ss: Seq[String], acc: ListBuffer[A]): Try[Seq[A]] = {
ss match {
case h::t =>
f(h) match {
case Success(x) => loop(t, acc :+ x)
case Failure(e) => Failure(e)
}
case Nil =>
Success(acc.toSeq)
}
}
loop(ss, ListBuffer[A]())
}
Related
I have an iterable of arrays that I am trying to turn into case classes, and I'm mapping over them to do so. In the event of an array being non-convertable to a case class, I want to log a warning and proceed with the mapping. However, when I implement the warning, the return type changes from Iterable[MyCaseClass] to Iterable[Any] which is not what I want. E.g.:
case class MyCaseClass(s1: String, s2: String)
object MyCaseClass {
def apply(sa: Array[String]) = new MyCaseClass(sa(0), sa(1))
}
val arrayIterable: Iterable[Array[String]] = Iterable(Array("a", "b"), Array("a", "b", "c"))
def badReturnType(): Iterable[Any] = { // Iterable[Any] is undesireable
arrayIterable map {
case sa: Array[String] if sa.length == 2 => MyCaseClass(sa)
case _ => println("something bad happened!") // but warnings are good
}
}
def desiredReturnType(): Iterable[MyCaseClass] = { // Iterable[MyCaseClass] is desireable
arrayIterable map {
case sa: Array[String] if sa.length == 2 => MyCaseClass(sa)
// but no warnings if things go wrong!
}
}
I want to write a function that meets the following criteria:
maps over the Iterable, converting each element to a MyCaseClass
log warnings when I get an array that cant be converted to a MyCaseClass
after logging the warning, the array passed into the match criteria is ignored/discarded
the return type should be Iterable[MyCaseClass].
How can I meet these conditions?
Consider using List instead of Array, and try wrapping in Option in combination with flatMap
l flatMap {
case e if e.length == 2 => Some(MyCaseClass(e))
case e => println(s"$e is wrong length"); None
}
Another approach is partitionMap
val (lefts, rights) = l.partitionMap {
case e if e.size == 2 => Right(MyCaseClass(e))
case e => Left(s"$e is wrong length")
}
lefts.foreach(println)
rights
You can do something like this:
final case class MyCaseClass(s1: String, s2: String)
def parse(input: Array[String]): Either[String, MyCaseClass] = input match {
case Array(s1, s2) => Right(MyCaseClass(s1, s2))
case _ => Left(s"Bad input: ${input.mkString("[", ", ", "]")}")
}
def logErrors(validated: Either[String, _]): Unit = validated match {
case Left(error) => println(error)
case Right(_) => ()
}
def validateData(data: IterableOnce[Array[String]]): List[MyCaseClass] =
data
.iterator
.map(parse)
.tapEach(logErrors)
.collect {
case Right(value) => value
}.toList
Which you can use like this:
val arrayIterable = Iterable(Array("a", "b"), Array("a", "b", "c"))
validateData(arrayIterable)
// Bad input: [a, b, c]
// res14: List[MyCaseClass] = List(MyCaseClass("a", "b"))
USE CASE
I have a list of files that can might have a valid mime type or not.
In my code, I represent this using an Option.
I need to convert a Seq[Option[T]] to Option[Seq[T]] so that I do not process the list if some of the files are invalid.
ERROR
This is the error in the implementation below:
found : (Option[Seq[A]], Option[A]) => Option[Seq[A]]
[error] required: (Option[Any], Option[Any]) => Option[Any]
[error] s.fold(init)(liftOptionItem[A])
IMPLEMENTATION
def liftOptionItem[A](acc: Option[Seq[A]], itemOption: Option[A]): Option[Seq[A]] = {
{
acc match {
case None => None
case Some(items) =>
itemOption match {
case None => None
case Some(item) => Some(items ++ Seq(item))
}
}
}
}
def liftOption[A](s: Seq[Option[A]]): Option[Seq[A]] = {
s.fold(Some(Seq()))(liftOptionItem[A])
}
This implementation returns Option[Any] instead of Option[Seq[A] as the type of the liftOptionItem[A] does not fit in.
If you use TypeLevel Cats:
import cats.implicits._
List(Option(1), Option(2), Option(3)).traverse(identity)
Returns:
Option[List[Int]] = Some(List(1, 2, 3))
You have to use List so use a toList first:
Seq(Option(1), Option(2), Option(3)).toList.traverse(identity).map(_.toSeq)
using scalaz:
import scalaz._
import Sclaza._
val x:List[Option[Int]] = List(Option(1))
x.sequence[Option, Int] //returns Some(List(1))
val y:List[Option[Int]] = List(None, Option(1))
y.sequence[Option, Int] // returns None
If you dont want to use functional libraries like cats or Scalaz you could use a foldLeft
def seqToOpt[A](seq: Seq[Option[A]]): Option[Seq[A]] =
seq.foldLeft(Option(Seq.empty[A])){
(res, opt) =>
for {
seq <- res
v <- opt
} yield seq :+ v
}
Tail-recursive Solution: It returns None if any one of the seq element is None.
def seqToOption[T](s: Seq[Option[T]]): Option[Seq[T]] = {
#tailrec
def seqToOptionHelper(s: Seq[Option[T]], accum: Seq[T] = Seq[T]()): Option[Seq[T]] = {
s match {
case Some(head) :: Nil => Option(head +: accum)
case Some(head) :: tail => seqToOptionHelper(tail, head +: accum)
case _ => None
}
}
seqToOptionHelper(s)
}
Dealing with None in case statements is the reason for returning the Option[Seq[Any]] type in stead of Option[Seq[A]] type. We need to make the function
liftOptionItem[A] to return Option[Seq[Any]] type. And the compilation error can be fixed with the following changes in both the functions.(Because fold does not go in any particular order, there are constraints on the start value and thus return value , the foldLeft is used in stead of fold.)
def liftOptionItem[A](acc: Option[Seq[Any]], itemOption: Option[A]): Option[Seq[Any]] = {
{
acc match {
case None => Some(Nil)
case Some(items)=>
itemOption match {
case None => Some(items ++ Seq("None"))
case Some(item) => Some(items ++ Seq(item))
}
}
}
}
def liftOption[A](s: Seq[Option[A]]): Option[Seq[Any]] = {
s.foldLeft(Option(Seq[Any]()))(liftOptionItem[A])
}
Now, code compiles.
In Scala REPL:
scala> val list1 = Seq(None,Some(21),None,Some(0),Some(43),None)
list1: Seq[Option[Int]] = List(None, Some(21), None, Some(0), Some(43), None)
scala> liftOption(list1)
res2: Option[Seq[Any]] = Some(List(None, 21, None, 0, 43, None))
scala> val list2 = Seq(None,Some("String1"),None,Some("String2"),Some("String3"),None)
list2: Seq[Option[String]] = List(None, Some(String1), None, Some(String2), Some(String3), None)
scala> liftOption(list2)
res3: Option[Seq[Any]] = Some(List(None, String1, None, String2, String3, None))
There is no really "beautiful" way to make this with out scalaz or cats.
But you can try something like this.
def seqToOpt[A](seq: Seq[Option[A]]): Option[Seq[A]] = {
val flatten = seq.flatten
if (flatten.isEmpty) None
else Some(flatten)
}
Say I want to do a simple conversion of strings to ints thus:
List("1", "3", "55", "x", "7") => List(1, 3, 55, 7)
One way to do this would be the following recursive call:
def recurse1(strs: List[String]): List[Int] = strs match {
case h :: t =>
try {
h.toInt :: recurse1(t)
}
catch {
case _ : java.lang.NumberFormatException =>
recurse1(t)
}
case _ =>
List()
}
However this cannot be compiled as tail recursive due to line 4 in the code. So to get around this I can redefine the function as follows:
def recurse2(strs: List[String], accum: List[Int] = List()): List[Int] = strs match {
case h :: t =>
try {
recurse2(t, h.toInt :: accum)
}
catch {
case _ : java.lang.NumberFormatException =>
recurse2(t, accum)
}
case _ =>
accum.reverse
}
So my question is this. Is there an idiom I can use in scala that will allow me to do this tail recursively but without having to pass a variable to accumulate the values?
Maybe your recurse method is just for illustration but for completeness I'll add to #pamu's answer how to use standard functions:
def foo(ss: List[String]): List[Int] =
ss.map(s => Try(s.toInt).toOption)
.filter(_.isDefined)
.map(_.get)
or
def foo(ss: List[String]): List[Int] =
ss.map(s => Try(s.toInt))
.collect { case Success(n) => n }
Usually, I see programmers write a helper function which takes many arguments (internally) which is specific to method/algorithm you are using. They write a minimal interface function around the ugly internal function which is tail recursive and takes only inputs required and hides the internal mechanism.
def reverse(input: List[Sting]): List[Int] = {
def helper(strs: List[String], accum: List[Int] = List()): List[Int] =
strs match {
case h :: t =>
try {
helper(t, h.toInt :: accum)
}
catch {
case _ : java.lang.NumberFormatException =>
helper(t, accum)
}
case _ =>
accum.reverse
}
helper(input, List.empty[Sting])
}
Let's say that I have a list of tuples:
val xs: List[(Seq[String], Option[String])] = List(
(Seq("Scala", "Python", "Javascript"), Some("Java")),
(Seq("Wine", "Beer"), Some("Beer")),
(Seq("Dog", "Cat", "Man"), None)
)
and a function that returns the index of the string if it is in the sequence of strings:
def getIndex(s: Seq[String], e: Option[String]): Option[Int] =
if (e.isEmpty) None
else Some(s.indexOf(e.get))
Now I am trying to map over xs with getIndex and return only those that I found a valid index. One way to do this is as follows:
xs.map{case (s, e) => {
val ii = getIndex(s, e) // returns an Option
ii match { // unpack the option
case Some(idx) => (e, idx)
case None => (e, -1) // give None entries a placeholder with -1
}
}}.filter(_._2 != -1) // filter out invalid entries
This approach is quite verbose and clunky to me. flatMap does not work here because I am returning a tuple instead of just the index. What is the idiomatic way to do this?
A for comprehension is one way to achieve this:
scala> val xs: List[(Seq[String], Option[String])] = List(
(Seq("Scala", "Python", "Javascript"), Some("Java")),
(Seq("Wine", "Beer"), Some("Beer")),
(Seq("Dog", "Cat", "Man"), None)
)
xs: List[(Seq[String], Option[String])] = List((List(Scala, Python, Javascript),Some(Java)), (List(Wine, Beer),Some(Beer)), (List(Dog, Cat, Man),None))
scala> def getIndex(seq: Seq[String], e: Option[String]): Option[Int] =
e.map(seq.indexOf(_)).filter(_ != -1) // notice we're doing the filter here
getIndex: getIndex[](val seq: Seq[String],val e: Option[String]) => Option[Int]
scala> for {
(seq, string) <- xs
index <- getIndex(seq, string)
s <- string
} yield (s, index)
res0: List[(String, Int)] = List((Beer,1))
There are a lot of ways to do this. One of them is this:
val result = xs.flatMap { tuple =>
val (seq, string) = tuple
string.map(s => (s, seq.indexOf(s))).filter(_._2 >= 0)
}
Maybe this looks a bit more idiomatic:
val two = xs.filter {case (s, e) => e.isDefined}
.map {case (s, e) => (e, s.indexOf(e.get)) }
.filter {case (e, i) => i > 0}
We can use the collect method to combine a map and filter:
xs.collect { case (s, e) if e.isDefined => (e, s.indexOf(e.get)) }
.filter { case (e, i) => i > 0 }
map and getOrElse might get things a little clearer:
// use map you will get Some(-1) if the element doesn't exist or None if the element is None
xs.map{case (s, e) => (e, e.map(s.indexOf(_)))}.
// check if the index is positive and use getOrElse to return false if it's None
filter{case (e, idx) => idx.map(_ >= 0).getOrElse(false)}
// res16: List[(Option[String], Option[Int])] = List((Some(Beer),Some(1)))
Or:
xs.map{ case (s, e) => (e, e.map(s.indexOf).getOrElse(-1)) }.filter(_._2 != -1)
// res17: List[(Option[String], Int)] = List((Some(Beer),1)
I am working on a method that has 3 possible outcomes for multiple items: Error, Invalid and Success. For each of these I need to return a json list identifying which items were in error, invalid and successful.
My current attempt follows. I have used Object to represent the class my objects are as fully explaining would take too long. The Object class has a method process which returns a boolean to indicate success or error and throws an exception when the object is invalid:
def process(list: List[Objects]) = {
val successIds = new ListBuffer[Int]();
val errorIds = new ListBuffer[Int]();
val invalidIds = new ListBuffer[Int]();
list.foreach( item => {
try {
if (item.process) {
successIds ++ item.id
} else {
errorIds ++ item.id
}
} catch {
case e: Exception => invalidIds ++ item.id
}
})
JsonResult(
Map("success" -> successIds,
"failed" -> errorIds,
"invalid" -> invalidIds)
)
}
Problem is using Mutable data structures isn't very "Scala-y". I would prefer to build up these lists in some more functional way but I am quite new to scala. Any thoughts or hints as to how this might be done?
My though is using something like the flatMap method that takes a tuple of collections and collates them in the same way the flatMap method does for a single collection:
def process(list: List[Objects]) = {
val (success, error, invalid) = list.flatMap( item => {
try {
if (item.process) {
(List(item.id), List.empty, List.empty)
} else {
(List.empty, List(item.id), List.empty)
}
} catch {
case e: Exception =>
(List.empty, List.empty, List(item.id))
}
})
JsonResult(
Map("success" -> success,
"failed" -> error,
"invalid" -> invalid)
)
}
flatMap isn't what you need here - you need groupBy:
def process(list: List[Objects]) = {
def result(x: Objects) =
try if (x.process) "success" else "failed"
catch {case _ => "invalid"}
JsonResult(list groupBy result mapValues (_ map (_.id)))
}
There's always recursion:
class Ob(val id: Int) { def okay: Boolean = id < 5 }
#annotation.tailrec def process(
xs: List[Ob],
succ: List[Int] = Nil,
fail: List[Int] = Nil,
invalid: List[Int] = Nil
): (List[Int], List[Int], List[Int]) = xs match {
case Nil => (succ.reverse, fail.reverse, invalid.reverse)
case x :: more =>
val maybeOkay = try { Some(x.okay) } catch { case e: Exception => None }
if (!maybeOkay.isDefined) process(more, succ, fail, x.id :: invalid)
else if (maybeOkay.get) process(more, x.id :: succ, fail, invalid)
else process(more, succ, x.id :: fail, invalid)
}
Which works as one would hope (skip the reverses if you don't care about order):
scala> process(List(new Ob(1), new Ob(7), new Ob(2),
new Ob(4) { override def okay = throw new Exception("Broken") }))
res2: (List[Int], List[Int], List[Int]) = (List(1,2),List(7),List(4))
Adapted to make it compile without "Objects"
def procex (item: String): Boolean = ((9 / item.toInt) < 1)
def process (list: List[String]) = {
val li: List[(Option[String], Option[String], Option[String])] = list.map (item => {
try {
if (procex (item)) {
(Some (item), None, None)
} else {
(None, Some (item), None)
}
} catch {
case e: Exception =>
(None, None, Some (item))
}
})
li
}
// below 10 => failure
val in = (5 to 15).map (""+_).toList
// 0 to throw a little exception
val ps = process ("0" :: in)
val succeeders = ps.filter (p=> p._1 != None).map (p=>p._1)
val errors = ps.filter (p=> p._2 != None).map (p=>p._2)
val invalides = ps.filter (p=> p._3 != None).map (p=>p._3)
What doesn't work:
(1 to 3).map (i=> ps.filter (p=> p._i != None).map (p=>p._i))
_i doesn't work.