I'm failing to get a simple scalaz-stream example running, reading from TCP and writing to std out.
val src = tcp.reads(1024)
val addr = new InetSocketAddress(12345)
val p = tcp.server(addr, concurrentRequests = 1) {
src ++ tcp.lift(io.stdOutLines)
}
p.run.run
It just sits there, not printing anything.
I've also tried various arrangements using to, always with the tcp.lift incantation to get a Process[Connection, A], including
tcp.server(addr, concurrentRequests = 1)(src) map (_ to tcp.lift(io.stdOutLines))
which doesn't even compile.
Do I need to wye the source and print streams together? An example I found on the original pull request for tcp replacing nio seemed to indicate this, but wye no longer appears to exist on Process, so confusion reigns unfortunately.
Edit it turns out that in addition to the type problems explained by Paul, you also need to run the inner processes "manually", for example by doing p.map(_.run.run).run.run. I don't suppose that's the idiomatic way to do this, but it does work.
You need to pass src through the sink to actually write anything. I think this should do it:
import scalaz.stream.{io,tcp,text}
import scalaz.stream.tcp.syntax._
val p = tcp.server(addr, concurrentRequests = 1) {
tcp.reads(1024).pipe(text.utf8Decode) through tcp.lift(io.stdOutLines)
}
p.run.run
The expression src ++ tcp.lift(io.stdOutLines) should really be a type error. The type of tcp.reads(1024) is Process[Connection,ByteVector], and the type of tcp.lift(io.stdOutLines) is Process[Connection, String => Task[Unit]]. Appending those two processes does not make sense, and the only reason it typechecks is due to the covariance of Process[+F[_],+O]. Scala is "helpfully" inferring Any when you append two processes with unrelated output types.
A future release of scalaz-stream may add a constraint on ++ and other functions that exploit covariance to make sure the least upper bound that gets computed isn't something useless like Any or Serializable. This would go a long way to preventing mistakes like this. In the meantime, make sure you understand the types of all the functions you are working with, what they do, and how you are sticking them together.
Related
I'm currently working on my functional programming - I am fairly new to it. Am i using Options correctly here? I feel pretty insecure on my skills currently. I want my code to be as safe as possible - Can any one point out what am I doing wrong here or is it not that bad? My code is pretty straight forward here:
def main(args: Array[String]): Unit =
{
val file = "myFile.txt"
val myGame = Game(file) //I have my game that returns an Option here
if(myGame.isDefined) //Check if I indeed past a .txt file
{
val solutions = myGame.get.getAllSolutions() //This returns options as well
if(solutions.isDefined) //Is it possible to solve the puzzle(crossword)
{
for(i <- solutions.get){ //print all solutions to the crossword
i.solvedCrossword foreach println
}
}
}
}
-Thanks!! ^^
When using Option, it is recommended to use match case instead of calling 'isDefined' and 'get'
Instead of the java style for loop, use higher-order function:
myGame match {
case Some(allSolutions) =>
val solutions = allSolutions.getAllSolutions
solutions.foreach(_.solvedCrossword.foreach(println))
case None =>
}
As a rule of thumb, you can think of Option as a replacement for Java's null pointer. That is, in cases where you might want to use null in Java, it often makes sense to use Option in Scala.
Your Game() function uses None to represent errors. So you're not really using it as a replacement for null (at least I'd consider it poor practice for an equivalent Java method to return null there instead of throwing an exception), but as a replacement for exceptions. That's not a good use of Option because it loses error information: you can no longer differentiate between the file not existing, the file being in the wrong format or other types of errors.
Instead you should use Either. Either consists of the cases Left and Right where Right is like Option's Some, but Left differs from None in that it also takes an argument. Here that argument can be used to store information about the error. So you can create a case class containing the possible types of errors and use that as an argument to Left. Or, if you never need to handle the errors differently, but just present them to the user, you can use a string with the error message as the argument to Left instead of case classes.
In getAllSolutions you're just using None as a replacement for the empty list. That's unnecessary because the empty list needs no replacement. It's perfectly fine to just return an empty list when there are no solutions.
When it comes to interacting with the Options, you're using isDefined + get, which is a bit of an anti pattern. get can be used as a shortcut if you know that the option you have is never None, but should generally be avoided. isDefined should generally only be used in situations where you need to know whether an option contains a value, but don't need to know the value.
In cases where you need to know both whether there is a value and what that value is, you should either use pattern matching or one of Option's higher-order functions, such as map, flatMap, getOrElse (which is kind of a higher-order function if you squint a bit and consider by-name arguments as kind-of like functions). For cases where you want to do something with the value if there is one and do nothing otherwise, you can use foreach (or equivalently a for loop), but note that you really shouldn't do nothing in the error case here. You should tell the user about the error instead.
If all you need here is to print it in case all is good, you can use for-comprehension which is considered quite idiomatic Scala way
for {
myGame <- Game("mFile.txt")
solutions <- myGame.getAllSolutions()
solution <- solutions
crossword <- solution.solvedCrossword
} println(crossword)
I' was curious if the hashCode() function returns always the same value for a lambda or something in Scala?
My tests have shown to me some static value that does not change even over builds. Is this intended behavior or may it change in future?
If this was some static behavior it would help me a lot building my library.
EDIT:
Let's take this source code:
object Main {
def main(args: Array[String]): Unit = {
val x = (s: String) => 1
val y = (s: String) => 2
println(x.hashCode())
println(y.hashCode())
}
}
It's output on console is for me always 1792393294 and 226170135.
What I'm currently doing is implementing a parser combinator library which I implemented in several languages. And I need to know when wrapper classes are the same (e.g. the underlying functions are the same) so I can implement something like a call stack, which I need to parse as far as possible on failure but prevent endless recursion on error.
Thanks in advance!
The default implementation for hashCode (at least in the Oracle JVM) is in terms of the (initial) memory address of that particular object. So if your program has constructed exactly the same sized objects in exactly the same order before constructing that object, it will in practice return the same value every time.
But this is not at all reliable; most programs do not do exactly the same things every time they run. As soon as you're doing something like responding to user input, that perfect reproducibility will disappear - e.g. maybe you sometimes add enough entries to a HashMap to trigger an enlargement of the table, and sometimes not. And if you construct the same value later in the program, it will of course have a different address; try doing
val z = (s: String) => 1
and observe that it will have a different hashCode from x. Not to mention that the numbers may well be different across different JVMs, different versions of the same JVM, or even when the same JVM is launched with a different -Xms setting.
Computers are often a lot more deterministic in practice than in theory. But this is not the kind of thing that's specified to happen, and certainly not something to rely on in your programs.
I just start to be used to deal with monadic operations.
For the Option type, this Cheat Sheet of Tony Morris helped:
http://blog.tmorris.net/posts/scalaoption-cheat-sheet/
So in the end it seems easy to understand that:
map transforms the value of inside an option
flatten permits to transform Option[Option[X]] in Option[X]
flatMap is somehow a map operation producing an Option[Option[X]] and then flattened to Option[X]
At least it is what I understand until now.
For Either, it seems a bit more difficult to understand since Either itself is not right biaised, does not have map / flatMap operations... and we use projection.
I can read the Scaladoc but it is not as clear as the Cheat Sheet on Options.
Can someone provide an Either Sheet Cheat to describe the basic monadic operations?
It seems to me that Either.joinRight is a bit like RightProjection.flatMap and seems to be the equivalent of Option.flatten for Either.
It seems to me that if Either was Right biaised, then Either.flatten would be Either.joinRight no?
In this question: Either, Options and for comprehensions I ask about for comprehension with Eiher, and one of the answers says that we can't mix monads because of the way it is desugared into map/flatMap/filter.
When using this kind of code:
def updateUserStats(user: User): Either[Error,User] = for {
stampleCount <- stampleRepository.getStampleCount(user).right
userUpdated <- Right(copyUserWithStats(user,stampleCount)).right
userSaved <- userService.update(userUpdated).right
} yield userSaved
Does this mean that all my 3 method calls must always return Either[Error,Something]?
I mean if I have a method call Either[Throwable,Something] it won't work right?
Edit:
Is Try[Something] exactly the same as a right-biaised Either[Throwable,Something]?
Either was never really meant to be an exception handling based structure. It was meant to represent a situation where a function really could possible return one of two distinct types, but people started the convention where the left type is a supposed to be a failed case and the right is success. If you want to return a biased type for some pass/fail type business checks logic, then Validation from scalaz works well. If you have a function that could return a value or a Throwable, then Try would be a good choice. Either should be used for situations where you really might get one of two possible types, and now that I am using Try and Validation (each for different types of situations), I never use Either any more.
How do I create a properly functional configurable object in Scala? I have watched Tony Morris' video on the Reader monad and I'm still unable to connect the dots.
I have a hard-coded list of Client objects:
class Client(name : String, age : Int){ /* etc */}
object Client{
//Horrible!
val clients = List(Client("Bob", 20), Client("Cindy", 30))
}
I want Client.clients to be determined at runtime, with the flexibility of either reading it from a properties file or from a database. In the Java world I'd define an interface, implement the two types of source, and use DI to assign a class variable:
trait ConfigSource {
def clients : List[Client]
}
object ConfigFileSource extends ConfigSource {
override def clients = buildClientsFromProperties(Properties("clients.properties"))
//...etc, read properties files
}
object DatabaseSource extends ConfigSource { /* etc */ }
object Client {
#Resource("configuration_source")
private var config : ConfigSource = _ //Inject it at runtime
val clients = config.clients
}
This seems like a pretty clean solution to me (not a lot of code, clear intent), but that var does jump out (OTOH, it doesn't seem to me really troublesome, since I know it will be injected once-and-only-once).
What would the Reader monad look like in this situation and, explain it to me like I'm 5, what are its advantages?
Let's start with a simple, superficial difference between your approach and the Reader approach, which is that you no longer need to hang onto config anywhere at all. Let's say you define the following vaguely clever type synonym:
type Configured[A] = ConfigSource => A
Now, if I ever need a ConfigSource for some function, say a function that gets the n'th client in the list, I can declare that function as "configured":
def nthClient(n: Int): Configured[Client] = {
config => config.clients(n)
}
So we're essentially pulling a config out of thin air, any time we need one! Smells like dependency injection, right? Now let's say we want the ages of the first, second and third clients in the list (assuming they exist):
def ages: Configured[(Int, Int, Int)] =
for {
a0 <- nthClient(0)
a1 <- nthClient(1)
a2 <- nthClient(2)
} yield (a0.age, a1.age, a2.age)
For this, of course, you need some appropriate definition of map and flatMap. I won't get into that here, but will simply say that Scalaz (or Rúnar's awesome NEScala talk, or Tony's which you've seen already) gives you all you need.
The important point here is that the ConfigSource dependency and its so-called injection are mostly hidden. The only "hint" that we can see here is that ages is of type Configured[(Int, Int, Int)] rather than simply (Int, Int, Int). We didn't need to explicitly reference config anywhere.
As an aside, this is the way I almost always like to think about monads: they hide their effect so it's not polluting the flow of your code, while explicitly declaring the effect in the type signature. In other words, you needn't repeat yourself too much: you say "hey, this function deals with effect X" in the function's return type, and don't mess with it any further.
In this example, of course the effect is to read from some fixed environment. Another monadic effect you might be familiar with include error-handling: we can say that Option hides error-handling logic while making the possibility of errors explicit in your method's type. Or, sort of the opposite of reading, the Writer monad hides the thing we're writing to while making its presence explicit in the type system.
Now finally, just as we normally need to bootstrap a DI framework (somewhere outside our usual flow of control, such as in an XML file), we also need to bootstrap this curious monad. Surely we'll have some logical entry point to our code, such as:
def run: Configured[Unit] = // ...
It ends up being pretty simple: since Configured[A] is just a type synonym for the function ConfigSource => A, we can just apply the function to its "environment":
run(ConfigFileSource)
// or
run(DatabaseSource)
Ta-da! So, contrasting with the traditional Java-style DI approach, we don't have any "magic" occurring here. The only magic, as it were, is encapsulated in the definition of our Configured type and the way it behaves as a monad. Most importantly, the type system keeps us honest about which "realm" dependency injection is occurring in: anything with type Configured[...] is in the DI world, and anything without it is not. We simply don't get this in old-school DI, where everything is potentially managed by the magic, so you don't really know which portions of your code are safe to reuse outside of a DI framework (for example, within your unit tests, or in some other project entirely).
update: I wrote up a blog post which explains Reader in greater detail.
Why using foreach, map, flatMap etc. are considered better than using get for Scala Options? If I useisEmpty I can call get safely.
Well, it kind of comes back to "tell, don't ask". Consider these two lines:
if (opt.isDefined) println(opt.get)
// versus
opt foreach println
In the first case, you are looking inside opt and then reacting depending on what you see. In the second case you are just telling opt what you want done, and let it deal with it.
The first case knows too much about Option, replicates logic internal to it, is fragile and prone to errors (it can result in run-time errors, instead of compile-time errors, if written incorrectly).
Add to that, it is not composable. If you have three options, a single for comprehension takes care of them:
for {
op1 <- opt1
op2 <- opt2
op3 <- opt3
} println(op1+op2+op3)
With if, things start to get messy fast.
One nice reason to use foreach is parsing something with nested options. If you have something like
val nestedOption = Some(Some(Some(1)))
for {
opt1 <- nestedOption
opt2 <- opt1
opt3 <- opt2
} println(opt3)
The console prints 1. If you extend this to a case where you have a class that optionally stores a reference to something, which in turn stores another reference, for comprehensions allow you to avoid a giant "pyramid" of None/Some checking.
There are already excellent answers to the actual question, but for more Option-foo you should definitely check out Tony Morris' Option Cheat Sheet.
The reason it's more useful to apply things like map, foreach, and flatMap directly to the Option instead of using get and then performing the function is that it works on either Some or None and you don't have to do special checks to make sure the value is there.
val x: Option[Int] = foo()
val y = x.map(_+1) // works fine for None
val z = x.get + 1 // doesn't work if x is None
The result for y here is an Option[Int], which is desirable since if x is optional, then y might be undetermined as well. Since get doesn't work on None, you'd have to do a bunch of extra work to make sure you didn't get any errors; extra work that is done for you by map.
Put simply:
If you need to do something (a procedure when you don't need to capture the return value of each invocation) only if the option is defined (i.e. is a Some): use foreach (if you care about the results of each invocation, use map)
If you need to do something if the option defined and something else if it's not: use isDefined in an if statement
If you need the value if the option is a Some, or a default value if it is a None: use getOrElse
Trying to perform our Operations with get is more imperative style where u need to tel what to do and how to do . In other words , we are dictating things and digging more into the Options internals. Where as map,flatmap are more functional way of doing things where we are say what to do but not how to do.