I am trying to create a try clause analogue which repeats code block if exception occurred inside this code block.
def retry(attempts: Int)(func: Unit => Unit) {
var attempt = 0
while (attempt < attempts) {
attempt += 1
try {
func()
} catch {
case _: Throwable =>
}
}
throw new Exception()
}
I expect that it can be used like this
retry(10) { // I would like to pass this block as function
val res = someNotReliableOp(); // <- exception may occur here
print(res)
}
But it doesn't work:
Operations.scala:27: error: type mismatch;
found : Unit
required: Unit => Unit
print(res)
^
one error found
What is the most concise way to pass custom block to my function?
You just need to change your method definition a tiny bit:
def retry(attempts: Int)(func: => Unit)
Unit => Unit means: a function that takes a parameter with a type of Unit and evaluates to Unit.
=> Unit means: a function that takes no parameters and evaluates to Unit. This is called call by name.
Consider
def retry(attempts: Int)(func: => Unit) {
for {
i <- Stream range (0, attempts)
v = Try (func()) toOption
if (v == None)
} ()
}
The for comprehension will invoke func up to an attempts number of times and will stop streaming if the invocation to func succeeds, namely if Try(func) toOption does not deliver None.
For each iteration in the for comprehension, the do-nothing () function is called.
If interested in details on each failure for func, consider replacing the Try in the comprehension with
v = Try(func()) match {
case Success(x) => Some(())
case Failure(x) => println(x) ; None
}
which preserves the semantics initially suggested, yet it extracts information on each failed attempt.
Related
I wanted to handle some exceptions in ZIO using catchAll or catchSome as the below :
object Test extends App {
def run(args: List[String]) =
myApp.fold(_ => 1, _ => 0)
val myApp =
for {
_ <- putStrLn(unsafeRun(toINT("3")).toString)
} yield ()
def toINT(s: String): IO[IOException, Int]= {
IO.succeed(s.toInt).map(v => v).catchAll(er =>IO.fail(er))
}
the code succeeded in case I passed a valid format number but it's unable to handle the exception in case I passed invalid format and idea ??
s.toInt gets evaluated outside of the IO monad. What happens is that you evaluate s.toInt first and try to pass the result of that to IO.succeed, but an exception has already been thrown before you can pass anything to IO.succeed. The name of succeed already basically says that you are sure that whatever you pass it is a plain value that cannot fail.
The docs suggest using Task.effect, IO.effect, or ZIO.effect for lifting an effect that can fail into ZIO.
Here is a program that worked for me:
val program =
for {
int <- toINT("3xyz")
_ <- putStrLn(int.toString)
} yield ()
def toINT(s: String): Task[Int] = {
ZIO.fromTry(Try(s.toInt))
}
rt.unsafeRun(program.catchAll(t => putStrLn(t.getMessage)))
I am writing a recursive retry function in scala where I want to know if there was a runtime error with the future creation. If there is then future instantiation is retried.
Imagine I have a database query function:
dbLookup(userId : UserId) : Option[UserName] = ???
The retry would look something of the form:
retry[T](f : () => Future[Option[T]],
notifyFailure : (t : Throwable) => Unit,
n : Int) : Future[Option[T]] = {
if(n <= 0) { Future{None} }
else {
val fut = f()
if(f.resultsInException) { //I don't know how to write this
notifyFailure(f.exception)
retry(f, notifyFailure, n-1) //try again
}
else {
f.originalValueAsFuture
}
}
}
How can this future functionality be implemented and allow for tail recursion optimization?
This function could be used to retry the database 10 times for a user if the execution context keeps throwing an exception when I try to create a Future:
val userNameAfterRetries =
retry(() => Future{dbLookup("1234")},
(t) => system error (s"future creation error : $t"),
10)
Note: this is sort of possible with Future.fallbackTo, but unfortunately fallbackTo takes in a Future[T] rather than a () => Future[T]. This is important because using fallbackTo would result in retrying at least 1 extra times even if the first attempt was successful.
Thank you in advance for your consideration and response.
How about this?
def retry[T](f: () => Future[Option[T]],
notifyFailure: Throwable => Unit,
n: Int)(implicit ec : ExecutionContext): Future[Option[T]] = {
if (n <= 0) Future.failed(new RuntimeException("Exceeded number of allowed retries"))
else f().recoverWith { case originalError => notifyFailure(originalError); retry(f, notifyFailure, n - 1) }
}
Update on tail recursion: The nature of Future is asynchronous so unless you want to await for result, I do not quite see it being possible to make it #tailrec because you will have to use recursion in callback.
Also practical note: if you know it is always ~10 retries, I would not be afraid of recursion.
I am trying to create a neat construction with for-comprehension for business logic built on futures. Here is a sample which contains a working example based on Exception handling:
(for {
// find the user by id, findUser(id) returns Future[Option[User]]
userOpt <- userDao.findUser(userId)
_ = if (!userOpt.isDefined) throw new EntityNotFoundException(classOf[User], userId)
user = userOpt.get
// authenticate it, authenticate(user) returns Future[AuthResult]
authResult <- userDao.authenticate(user)
_ = if (!authResult.ok) throw new AuthFailedException(userId)
// find the good owned by the user, findGood(id) returns Future[Option[Good]]
goodOpt <- goodDao.findGood(goodId)
_ = if (!good.isDefined) throw new EntityNotFoundException(classOf[Good], goodId)
good = goodOpt.get
// check ownership for the user, checkOwnership(user, good) returns Future[Boolean]
ownership <- goodDao.checkOwnership(user, good)
if (!ownership) throw new OwnershipException(user, good)
_ <- goodDao.remove(good)
} yield {
renderJson(Map(
"success" -> true
))
})
.recover {
case ex: EntityNotFoundException =>
/// ... handle error cases ...
renderJson(Map(
"success" -> false,
"error" -> "Your blahblahblah was not found in our database"
))
case ex: AuthFailedException =>
/// ... handle error cases ...
case ex: OwnershipException =>
/// ... handle error cases ...
}
However this might be seen as a non-functional or non-Scala way to handle the things. Is there a better way to do this?
Note that these errors come from different sources - some are at the business level ('checking ownership') and some are at controller level ('authorization') and some are at db level ('entity not found'). So approaches when you derive them from a single common error type might not work.
Don't use exceptions for expected behaviour.
It's not nice in Java, and it's really not nice in Scala. Please see this question for more information about why you should avoid using exceptions for regular control flow. Scala is very well equipped to avoid using exceptions: you can use Eithers.
The trick is to define some failures you might encounter, and convert your Options into Eithers that wrap these failures.
// Failures.scala
object Failures {
sealed trait Failure
// Four types of possible failures here
case object UserNotFound extends Failure
case object NotAuthenticated extends Failure
case object GoodNotFound extends Failure
case object NoOwnership extends Failure
// Put other errors here...
// Converts options into Eithers for you
implicit class opt2either[A](opt: Option[A]) {
def withFailure(f: Failure) = opt.fold(Left(f))(a => Right(a))
}
}
Using these helpers, you can make your for comprehension readable and exception free:
import Failures._
// Helper function to make ownership checking more readable in the for comprehension
def checkGood(user: User, good: Good) = {
if(checkOwnership(user, good))
Right(good)
else
Left(NoOwnership)
}
// First create the JSON
val resultFuture: Future[Either[Failure, JsResult]] = for {
userRes <- userDao.findUser(userId)
user <- userRes.withFailure(UserNotFound).right
authRes <- userDao.authenticate(user)
auth <- authRes.withFailure(NotAuthenticated).right
goodRes <- goodDao.findGood(goodId)
good <- goodRes.withFailure(GoodNotFound).right
checkedGood <- checkGood(user, good).right
} yield renderJson(Map("success" -> true)))
// Check result and handle any failures
resultFuture.map { result =>
result match {
case Right(json) => json // serve json
case Left(failure) => failure match {
case UserNotFound => // Handle errors
case NotAuthenticated =>
case GoodNotFound =>
case NoOwnership =>
case _ =>
}
}
}
You could clean up the for comprehension a little to look like this:
for {
user <- findUser(userId)
authResult <- authUser(user)
good <- findGood(goodId)
_ <- checkOwnership(user, good)
_ <- goodDao.remove(good)
} yield {
renderJson(Map(
"success" -> true
))
}
Assuming these methods:
def findUser(id:Long) = find(id, userDao.findUser)
def findGood(id:Long) = find(id, goodDao.findGood)
def find[T:ClassTag](id:Long, f:Long => Future[Option[T]]) = {
f(id).flatMap{
case None => Future.failed(new EntityNotFoundException(implicitly[ClassTag[T]].runtimeClass, id))
case Some(entity) => Future.successful(entity)
}
}
def authUser(user:User) = {
userDao.authenticate(user).flatMap{
case result if result.ok => Future.failed(new AuthFailedException(userId))
case result => Future.successful(result)
}
}
def checkOwnership(user:User, good:Good):Future[Boolean] = {
val someCondition = true //real logic for ownership check goes here
if (someCondition) Future.successful(true)
else Future.failed(new OwnershipException(user, good))
}
The idea here is to use flatMap to turn things like Options that are returned wrapped in Futures into failed Futures when they are None. There are going to be a lot of ways to do clean up that for comp and this is one possible way to do it.
The central challenge is that for-comprehensions can only work on one monad at a time, in this case it being the Future monad and the only way to short-circuit a sequence of future calls is for the future to fail. This works because the subsequent calls in the for-comprehension are just map and flatmap calls, and the behavior of a map/flatmap on a failed Future is to return that future and not execute the provided body (i.e. the function being called).
What you are trying to achieve is the short-cicuiting of a workflow based on some conditions and not do it by failing the future. This can be done by wrapping the result in another container, let's call it Result[A], which gives the comprehension a type of Future[Result[A]]. Result would either contain a result value, or be a terminating result. The challenge is how to:
provide subsequent function calls the value contained by a prior non-terminating Result
prevent the subsequent function call from being evaluated if the Result is terminating
map/flatmap seem like the candidates for doing these types of compositions, except we will have to call them manually, since the only map/flatmap that the for-comprehension can evaluate is one that results in a Future[Result[A]].
Result could be defined as:
trait Result[+A] {
// the intermediate Result
def value: A
// convert this result into a final result based on another result
def given[B](other: Result[B]): Result[A] = other match {
case x: Terminator => x
case v => this
}
// replace the value of this result with the provided one
def apply[B](v: B): Result[B]
// replace the current result with one based on function call
def flatMap[A2 >: A, B](f: A2 => Future[Result[B]]): Future[Result[B]]
// create a new result using the value of both
def combine[B](other: Result[B]): Result[(A, B)] = other match {
case x: Terminator => x
case b => Successful((value, b.value))
}
}
For each call, the action is really a potential action, as calling it on or with a terminating result, will simply maintain the terminating result. Note that Terminator is a Result[Nothing] since it will never contain a value and any Result[+A] can be a Result[Nothing].
The terminating result is defined as:
sealed trait Terminator extends Result[Nothing] {
val value = throw new IllegalStateException()
// The terminator will always short-circuit and return itself as
// the success rather than execute the provided block, thus
// propagating the terminating result
def flatMap[A2 >: Nothing, B](f: A2 => Future[Result[B]]): Future[Result[B]] =
Future.successful(this)
// if we apply just a value to a Terminator the result is always the Terminator
def apply[B](v: B): Result[B] = this
// this apply is a convenience function for returning this terminator
// or a successful value if the input has some value
def apply[A](opt: Option[A]) = opt match {
case None => this
case Some(v) => Successful[A](v)
}
// this apply is a convenience function for returning this terminator or
// a UnitResult
def apply(bool: Boolean): Result[Unit] = if (bool) UnitResult else this
}
The terminating result makes it possible to to short-circuit calls to functions that require a value [A] when we've already met our terminating condition.
The non-terminating result is defined as:
trait SuccessfulResult[+A] extends Result[A] {
def apply[B](v: B): Result[B] = Successful(v)
def flatMap[A2 >: A, B](f: A2 => Future[Result[B]]): Future[Result[B]] = f(value)
}
case class Successful[+A](value: A) extends SuccessfulResult[A]
case object UnitResult extends SuccessfulResult[Unit] {
val value = {}
}
The non-teminating result makes it possible to provide the contained value [A] to functions. For good measure, I've also predefined a UnitResult for functions that are purely side-effecting, like goodDao.removeGood.
Now let's define your good, but terminating conditions:
case object UserNotFound extends Terminator
case object NotAuthenticated extends Terminator
case object GoodNotFound extends Terminator
case object NoOwnership extends Terminator
Now we have the tools to create the the workflow you were looking for. Each for comprehention wants a function that returns a Future[Result[A]] on the right-hand side, producing a Result[A] on the left-hand side. The flatMap on Result[A] makes it possible to call (or short-circuit) a function that requires an [A] as input and we can then map its result to a new Result:
def renderJson(data: Map[Any, Any]): JsResult = ???
def renderError(message: String): JsResult = ???
val resultFuture = for {
// apply UserNotFound to the Option to conver it into Result[User] or UserNotFound
userResult <- userDao.findUser(userId).map(UserNotFound(_))
// apply NotAuthenticated to AuthResult.ok to create a UnitResult or NotAuthenticated
authResult <- userResult.flatMap(user => userDao.authenticate(user).map(x => NotAuthenticated(x.ok)))
goodResult <- authResult.flatMap(_ => goodDao.findGood(goodId).map(GoodNotFound(_)))
// combine user and good, so we can feed it into checkOwnership
comboResult = userResult.combine(goodResult)
ownershipResult <- goodResult.flatMap { case (user, good) => goodDao.checkOwnership(user, good).map(NoOwnership(_))}
// in order to call removeGood with a good value, we take the original
// good result and potentially convert it to a Terminator based on
// ownershipResult via .given
_ <- goodResult.given(ownershipResult).flatMap(good => goodDao.removeGood(good).map(x => UnitResult))
} yield {
// ownership was the last result we cared about, so we apply the output
// to it to create a Future[Result[JsResult]] or some Terminator
ownershipResult(renderJson(Map(
"success" -> true
)))
}
// now we can map Result into its value or some other value based on the Terminator
val jsFuture = resultFuture.map {
case UserNotFound => renderError("User not found")
case NotAuthenticated => renderError("User not authenticated")
case GoodNotFound => renderError("Good not found")
case NoOwnership => renderError("No ownership")
case x => x.value
}
I know that's a whole lot of setup, but at least the Result type can be used for any Future for-comprehension that has terminating conditions.
I have the below requirement where I am checking whether a value is greater than 10 or not and based on that I will break, otherwise I will return a String. Below is my code:
import scala.util.control.Breaks._
class BreakInScala {
val breakException = new RuntimeException("Break happened")
def break = throw breakException
def callMyFunc(x: Int): String = breakable(myFunc(x))
def myFunc(x: Int): String = {
if (x > 10) {
"I am fine"
} else {
break
}
}
}
Now what is the happening is that I am getting the error message saying "type mismatch; found : Unit required: String" The reason is :
def breakable(op: => Unit)
But then how I will write a function which can return value as well as break if required?
The Scala compiler can evaluate that a branch throws an exception and not use it to form a minimum bound for the return type, but not if you move the throwing code out in a method: since it can be overridden, the compiler cannot be sure it will actually never return.
Your usage of the Break constructs seems confused: it already provides a break method, there is no need to provide your own, unless you want to throw your exception instead, which would make using Break unnecessary.
You are left with a couple of options then, since I believe usage of Break is unnecessary in your case.
1) Simply throw an exception on failure
def myFunc(x: Int): String = {
if (x > 10) {
"I am fine"
} else {
throw new RuntimeException("Break happened")
}
}
def usemyFunc(): Unit = {
try {
println("myFunc(11) is " + myFunc(11))
println("myFunc(5) is " + myFunc(5))
} catch {
case e: Throwable => println("myFunc failed with " + e)
}
}
2) Use the Try class (available from Scala 2.10) to return either a value or an exception. This differs from the previous suggestion because it forces the caller to inspect the result and check whether a value is available or not, but makes using the result a bit more cumbersome
import scala.util.Try
def myFunc(x: Int): Try[String] = {
Try {
if (x > 10) {
"I am fine"
} else {
throw new RuntimeException("Break happened")
}
}
}
def useMyFunc(): Unit = {
myFunc match {
case Try.Success(s) => println("myFunc succeded with " + s)
case Try.Failure(e) => println("myFunc failed with " + e)
}
}
3) If the thrown exception isn't relevant, you can use the Option class instead.
You can see how the multiple ways of working with Options relate to each other in
this great cheat sheet.
def myFunc(x: Int): Option[String] = {
if (x > 10) {
Some("I am fine") /* Some(value) creates an Option containing value */
} else {
None /* None represents an Option that has no value */
}
}
/* There are multiple ways to work with Option instances.
One of them is using pattern matching. */
def useMyFunc(): Unit = {
myFunc(10) match {
case Some(s) => println("myFunc succeded with " + s)
case None => println("myFunc failed")
}
}
/* Another one is using the map, flatMap, getOrElse, etc methods.
They usually take a function as a parameter, which is only executed
if some condition is met.
map only runs the received function if the Option contains a value,
and passes said value as a parameter to it. It then takes the result
of the function application, and creates a new Option containing it.
getOrElse checks if the Option contains a value. If it does, it is returned
directly. If it does not, then the result of the function passed to it
is returned instead.
Chaining map and getOrElse is a common idiom meaning roughly 'transform the value
contained in this Option using this code, but if there is no value, return whatever
this other piece of code returns instead'.
*/
def useMyFunc2(): Unit = {
val toPrint = myFunc(10).map{ s =>
"myFunc(10) succeded with " + s
}.getOrElse{
"myFunc(10) failed"
}
/* toPrint now contains a message to be printed, depending on whether myFunc
returned a value or not. The Scala compiler is smart enough to infer that
both code paths return String, and make toPrint a String as well. */
println(toPrint)
}
This is a slightly odd way of doing things (throwing an exception), an alternative way of doing this might be to define a "partial function" (a function which is only defined only a specific subset of it's domain a bit like this:
scala> val partial = new PartialFunction[Int, String] {
| def apply(i : Int) = "some string"
| def isDefinedAt(i : Int) = i < 10
}
partial: PartialFunction[Int, String] = <function1>
Once you've defined the function, you can then "lift" it into an Option of type Int, by doing the following:
scala> val partialLifted = partial.lift
partialOpt: Int => Option[String] = <function1>
Then, if you call the function with a value outside your range, you'll get a "None" as a return value, otherwise you'll get your string return value. This makes it much easier to apply flatMaps/ getOrElse logic to the function without having to throw exceptions all over the place...
scala> partialLifted(45)
res7: Option[String] = None
scala> partialLifted(10)
res8: Option[String] = Some(return string)
IMO, this is a slightly more functional way of doing things...hope it helps
I would like to implement utility function/monad/aspect for managing hibernate transactions in scala, and looking for advice about best approach.
First I tried to create currying function like following:
def session() = sessionFactory.getCurrentSession()
def transaction() = session().getTransaction()
def tx[A, B](f: A => B)(a: A): B = {
try {
session().beginTransaction()
val r = f(a)
transaction().commit()
return r
} catch {
case e:
Throwable =>
transaction().rollback()
throw e
} finally {
session.close()
}
}
The idea was that I can do following with this function:
def saveMyEntity() {
session().save(new MyEntity)
}
tx(saveMyEntity)()
and saveMyEntity call will be wrapped into transaction.
Unfortunately I get following error with this code:
[error] found : () => Unit
[error] required: ? => ?
[error] tx(saveMyEntity)()
I still learning scala, and looking for advice to improve my approach. Maybe I can modify my function somehow to achieve better results? Or add another Unit type specific function? Or choose another path?
Any ideas?
Any scala canonical way to implement this?
Thanks.
Method tx accepts function of 1 argument as parameter and method saveMyEntity accepts no arguments, so you can't use it as A => B (function of 1 argument).
You are not using a and f separately, so there is no need in a. You could use by-name parameters here:
def tx[B](f: => B): B = {
If you want to use a saveMyEntity as Unit => Unit you should create function explicitly:
tx[Unit, Unit](_ => saveMyEntity)(())
I guess some changes may improve your code readability:
import util.control.Exception.allCatch
def withSession[T](f: Session => T):T = {
val session = ??? // start session here
allCatch.anfFinally{
session.close()
} apply { f(session) }
}
def inTransaction[T](f: => T): T =
withSession{ session =>
session().beginTransaction()
try {
val r = f(a)
transaction().commit()
r
} catch {
case e: Throwable =>
transaction().rollback()
throw e
}
}
inTransaction{saveMyEntity}
object TestTransaction
{
def executeInTransaction(f: => Unit)={
println("begin")
f
println("end")
}
executeInTransaction {
println("action!")
}
}
produces:
begin
action!
end