I've got a problem with returning a list after handling futures in scala. My code looks like this:
def getElements(arrayOfIds: Future[Seq[Int]]): Future[Seq[Element]] = {
var elementArray: Seq[Element] = Seq()
arrayOfIds.map {
ids => ids.map(id => dto.getElementById(id).map {
case Some(element) => elementArray = elementArray :+ element
case None => println("Element not found")
})
}
arrayOfIds.onComplete(_ => elementArray)
}
I'd like to do something like .onComplete, however the return type is
Unit and I'd like to return a Future[Seq[Whatever]]. Is there clean way to handle futures like this? Thanks!
Please provide the type of function dto.getElementById. If it is Int => Future[Option[Element]], then:
def getElements(arrayOfIds: Future[Seq[Int]]): Future[Seq[Element]] = {
val allElements: Future[Seq[Option[Element]]] = arrayOfIds.flatMap( ids =>
Future.sequence(ids.map(dto.getElementById))
)
allElements.map(_.flatMap{
case None => println();None
case some => some
})
}
Without logging, it would be:
arrayOfIds.flatMap( ids => Future.traverse(ids.map(dto.getElementById))(_.flatten))
Instead of assigning the result to a mutable variable, return it from the continuation of the Future. You can use flatMap to extract only the Element results which actually contain a value:
def getElements(arrayOfIds: Future[Seq[Int]]): Future[Seq[Element]] = {
arrayOfIds.flatMap(id => Future.fold(id.map(getElementById))(Seq.empty[Element])(_ ++ _))
}
Related
I want to avoid Runtime undefined behaivors as follows:
val jsonExample = Json.toJson(0)
jsonExample.asOpt[Instant]
yield Some(1970-01-01T00:00:00Z)
How can I verify this using partial function with a lift or some other way, to check thatits indeed Instant, or how you recommend to validate?
ex1:
val jsonExample = Json.toJson(Instant.now())
jsonExample match { ... }
ex2:
val jsonExample = Json.toJson(0)
jsonExample match { ... }
Examples for desired output:
validateInstant(Json.toJson(Instant.now())) -> return Some(...)
validateInstant(Json.toJson(0)) -> return None
I can do somthing as follows, maybe some other ideas?
Just wanted to add a note regarding parsing json, there some runtime undefined problems when we are trying to parse .asOpt[T]
for example:
Json.toJson("0").asOpt[BigDecimal] // yields Some(0)
Json.toJson(0).asOpt[Instant] // yields Some(1970-01-01T00:00:00Z)
We can validate it as follows or some other way:
Json.toJson("0") match {
case JsString(value) => Some(value)
case _ => None
}
Json.toJson(0) match {
case JsNumber(value) => Some(value)
case _ => None
}
Json.toJson(Instant.now()) match {
case o # JsString(_) => o.asOpt[Instant]
case _ => None
}
You can use Option:
def filterNumbers[T](value: T)(implicit tjs: Writes[T]): Option[Instant] = {
Option(Json.toJson(value)).filter(_.asOpt[JsNumber].isEmpty).flatMap(_.asOpt[Instant])
}
Then the following:
println(filterNumbers(Instant.now()))
println(filterNumbers(0))
will output:
Some(2021-02-22T10:35:13.777Z)
None
I have the following implementation:
val dateFormats = Seq("dd/MM/yyyy", "dd.MM.yyyy")
implicit def dateTimeCSVConverter: CsvFieldReader[DateTime] = (s: String) => Try {
val elem = dateFormats.map {
format =>
try {
Some(DateTimeFormat.forPattern(format).parseDateTime(s))
} catch {
case _: IllegalArgumentException =>
None
}
}.collectFirst {
case e if e.isDefined => e.get
}
if (elem.isDefined)
elem.get
else
throw new IllegalArgumentException(s"Unable to parse DateTime $s")
}
So basically what I'm doing is that, I'm running over my Seq and trying to parse the DateTime with different formats. I then collect the first one that succeeds and if not I throw the Exception back.
I'm not completely satisfied with the code. Is there a better way to make it simpler? I need the exception message passed on to the caller.
The one problem with your code is it tries all patterns no matter if date was already parsed. You could use lazy collection, like Stream to solve this problem:
def dateTimeCSVConverter(s: String) = Stream("dd/MM/yyyy", "dd.MM.yyyy")
.map(f => Try(DateTimeFormat.forPattern(format).parseDateTime(s))
.dropWhile(_.isFailure)
.headOption
Even better is the solution proposed by jwvh with find (you don't have to call headOption):
def dateTimeCSVConverter(s: String) = Stream("dd/MM/yyyy", "dd.MM.yyyy")
.map(f => Try(DateTimeFormat.forPattern(format).parseDateTime(s))
.find(_.isSuccess)
It returns None if none of patterns matched. If you want to throw exception on that case, you can uwrap option with getOrElse:
...
.dropWhile(_.isFailure)
.headOption
.getOrElse(throw new IllegalArgumentException(s"Unable to parse DateTime $s"))
The important thing is, that when any validation succeedes, it won't go further but will return parsed date right away.
This is a possible solution that iterates through all the options
val dateFormats = Seq("dd/MM/yyyy", "dd.MM.yyyy")
val dates = Vector("01/01/2019", "01.01.2019", "01-01-2019")
dates.foreach(s => {
val d: Option[Try[DateTime]] = dateFormats
.map(format => Try(DateTimeFormat.forPattern(format).parseDateTime(s)))
.filter(_.isSuccess)
.headOption
d match {
case Some(d) => println(d.toString)
case _ => throw new IllegalArgumentException("foo")
}
})
This is an alternative solution that returns the first successful conversion, if any
val dateFormats = Seq("dd/MM/yyyy", "dd.MM.yyyy")
val dates = Vector("01/01/2019", "01.01.2019", "01-01-2019")
dates.foreach(s => {
dateFormats.find(format => Try(DateTimeFormat.forPattern(format).parseDateTime(s)).isSuccess) match {
case Some(format) => println(DateTimeFormat.forPattern(format).parseDateTime(s))
case _ => throw new IllegalArgumentException("foo")
}
})
I made it sweet like this now! I like this a lot better! Use this if you want to collect all the successes and all the failures. Note that, this might be a bit in-efficient when you need to break out of the loop as soon as you find one success!
implicit def dateTimeCSVConverter: CsvFieldReader[DateTime] = (s: String) => Try {
val (successes, failures) = dateFormats.map {
case format => Try(DateTimeFormat.forPattern(format).parseDateTime(s))
}.partition(_.isSuccess)
if (successes.nonEmpty)
successes.head.get
else
failures.head.get
}
I have a collection of type Future[Option[String]] and I map it to a function that returns Future[Option[Profile]], but this create a return type of Future[Option[Future[Option[Profile]]]] because queryProfile return type is `Future[Option[Profile]]'
val users: Future[Option[User]] = someQuery
val email: Future[Option[String]] = users map(opSL => opSL map(_.email) )
val userProfile = email map {opE => opE map {E => queryProfile(E)}}
I need to use the Profile object contained deep inside val userProfile without unpacking all these levels, what would be the right way to use flatMap or `flatten', or is there a better approach all together ?
You can get a "partial Future" with something like this:
val maybeProfile: Future[Profile] = users
.collect { case Some(u) => u.email }
.flatMap { email => queryProfile(email) }
.collect { case Some(p) => p }
Now maybeProfile contains the (completely "naked"/unwrapped) Profile instance, but only if it was able to find it. You can .map it as usual to do something else with it, that'll work in the usual ways.
If you want to ever block and wait for completion, you will have to handle the missing case at some point. For example:
val optionalProfile: Option[Profile] = Await.result(
maybeProfile
.map { p => Some(p) } // Or just skip the last `collect` above
.recover { case _:NoSuchElementException => None },
1 seconds
)
If you are happy with just having Future[Option[Profile]], and would prefer to have the "unwrapping" magic, and handling the missing case localized in one place, you can put the two fragments from above together like this:
val maybeProfile: Future[Option[Profile]] = users
.collect { case Some(u) => u.email }
.flatMap { email => queryProfile(email) }
.recover { case _:NoSuchElementException => None }
Or use Option.fold like the other answer suggested:
val maybeProfile: Future[Option[Profile]] = users
.map { _.map(_.email) }
.flatMap { _.fold[Future[Option[Profile]]](Future.successful(None))(queryProfile) }
Personally, I find the last option less readable though.
Personally I think a monad transformer such as OptionT provided by scalaz/cats would be the cleanest approach:
val users = OptionT[Future,User](someQuery)
def queryProfile(email:String) : OptionT[Future,Profile] = ...
for {
u <- users
p <- queryProfile(u.email)
} yield p
I'd just create a helper method like this:
private def resolveProfile(optEmail: Option[String]): Future[Option[Profile] =
optEmail.fold(Future.successful(None)) { email =>
queryProfile(email).map(Some(_))
}
which then allows you to just flatMap your original email future like so:
val userProfile = email.flatMap(resolveProfile)
I have method with param type Future[List[MyRes]]. MyRes has two option fields id and name. Now I want to create map of id and name if both present. I am able to create map with default value as follow but I don't want to have default value just skip the entry with null value on either.
def myMethod(myRes: Future[List[MyRes]]): Future[Map[Long, String]] = {
myRes.map (
_.map(
o =>
(o.id match {
case Some(id) => id.toLong
case _ => 0L
}) ->
(o.name match {
case Some(name) => name
case _ => ""
})
).toMap)
Any suggestion?
You are looking for collect :)
myRes.map {
_.iterator
.map { r => r.id -> r.name }
.collect { case(Some(id), Some(name) => id -> name }
.toMap
}
If your MyRes thingy is a case class, then you don't need the first .map:
myRes.map {
_.collect { case MyRes(Some(id), Some(name)) => id -> name }
.toMap
}
collect is like .map, but it takes a PartialFunction, and skips over elements on which it is not defined. It is kinda like your match statement but without the defaults.
Update:
If I am reading your comment correctly, and you want to log a message when either field is a None, collect won't help with that, but you can do flatMap:
myRes.map {
_.flatMap {
case MyRes(Some(id), Some(name)) => Some(id -> name)
case x => loger.warn(s"Missing fields in $x."); None
}
.toMap
}
Try this:
def myMethod(myRes: Future[List[MyRes]]): Future[Map[Long, String]] = {
myRes.map (
_.flatMap(o =>
(for (id <- o.id; name <- o.name) yield (id.toLong -> name)).toList
).toMap
)
}
The trick is flattening List[Option[(Long,String)]] by using flatMap and converting the Option to a List.
Given a list of inputs that could be valid or invalid, is there a nice way to transform the list but to fail given one or more invalid inputs and, if necessary, to return information about those invalid inputs? I have something like this, but it feels very inelegant.
def processInput(inputList: List[Input]): Try[List[Output]] = {
inputList map { input =>
if (isValid(input)) Left(Output(input))
else Right(input)
} partition { result =>
result.isLeft
} match {
case (valids, Nil) =>
val outputList = valids map { case Left(output) => output }
Success(outputList)
case (_, invalids) =>
val errList = invalids map { case Right(invalid) => invalid }
Failure(new Throwable(s"The following inputs were invalid: ${errList.mkString(",")}"))
}
}
Is there a better way to do this?
I think you can simplify your current solution quite a bit with standard scala:
def processInput(inputList: List[Input]): Try[List[Output]] =
inputList.partition(isValid) match {
case (valids, Nil) => Success(valids.map(Output))
case (_, invalids) => Failure(new Throwable(s"The following inputs were invalid: ${invalids.mkString(",")}"))
}
Or, you can have a quite elegant solution with scalactic's Or.
import org.scalactic._
def processInputs(inputList: List[Input]): List[Output] Or List[Input] =
inputList.partition(isValid) match {
case (valid, Nil) => Good(valid.map(Output))
case (_, invalid) => Bad(invalid)
}
The result is of type org.scalactic.Or, which you then have to match to Good or Bad. This approach is more useful if you want the list of invalid inputs, you can match it out of Bad.
scalaz's validation is designed exactly for this. Try reading the tale of three nightclubs for how this would work, but the body of your function would probably end up just consisting of something like:
def processInput(inputList: List[Input]): Validation[List[Output]] = {
inputList foldMap { input =>
if (isValid(input)) Failure(Output(input))
else Success(List(input))
}