I am not able to understand how contramap works when writing a case class with a single field as json.
say I have a case class and I want to create its json
case class SomeThingContainer (something:SomeThing)
I would write its write as follows:
implicit val somethingContainerWrites:Writes[SomeThingContainer] = (
(JsPath \ "something").write[Something]
).contramap{(somethingContainer:SomeThingContainer)=>somethingContainer.something}
If I have a model as follows,
val somethingContainerVariable = SomethingContainer(something)
Somewhere in the application, I would call toJson[SomeThingContainer] to convert the model into Json. This would look for implicit Writes[SomeThingContainer] which would be somethingContainerWrites. From here, how is the JSON getting created? I could somewhat understand how unapply _ works if there are multiple fields but I am not able to comprehend how contramap does its magic.
The contramap is used for composing functors.
A Writes is a (contravariant) functor X => JsValue. (it knows how to create a JsValue from a given X)
Apparently, you already have defined a Writes[Something] somewhere (since you're calling JsPath.write[Something] which implicitly requires it).
Inside the parens, you create a new Writes[Something] which just uses the former to write a Something to a different path in a JSON object.
Now, to be able to create a JsValue from a SomeThingContainer all you need to do is to convert the SomeThingContainer to a Something -- since you already have a Writes[Something] at hand -- and use that.
This is what the contramap call does: from the Writes[Something] you have defined it creates a new Writes[SomeThingContainer] which, when given a SomeThingContainer, first calls the given "conversion function" producing a Something. Then, it calls the Writes[Something] with that.
To illustrate, what contramap does:
Writes[X]: Functor[X => JsValue]
C: Y => X
Writes[X].contramap(C) <==> Writes[Y]: Functor[Y => X => JsValue]
Related
I have a nested JSON in my database. I have figured out the case class for the same. I am using circe, slick and Akka HTTP in my Web api application.
My case class is :
case class Sen
(
sentences: Array[File]
)
case class File
(
content: String,
)
I have written GetResult for the same nesting. I have problems with the array in the case class.
implicit lazy val getFile = GetResult(r => Array[File](r.<<))
implicit lazy val SenObj = GetResult(r => Sen(getFile(r)))
Can anyone tell me how to solve this?
Following is the error I get while compiling
Error:diverging implicit expansion for type slick.jdbc.GetResult[T]
starting with method createGetTuple22 in object GetResult
implicit lazy val getFile = GetResult(r => Array[File](r.<<))
Your definition of getFile is manually constructing an Array, and specifically you're asking for an Array[File]. There's no GetResult[File], meaning that r.<< won't be able to convert a column value into a File.
Can anyone tell me how to solve this?
You'll at least need a GetResult[File] defined.
However, it's not clear from the question how the JSON part is intended to work:
Perhaps you have a column containing text which your application treats as JSON. If that's the case, I suggest doing JSON array conversion outside of your Slick code layer. That will keep the mapping to and from the database straightforward.
Or perhaps you have a JSON-type in your database and you're using database-specific operations. In that case, I guess it'll depend on what control you have there, and it probably does make sense to try to do JSON-array operations at the Slick layer. (That's the case for Postgress, for example, via the pg-slick library)
But that's a different question.
As a general note, I suggest always being explicit about the types of GetResult you are defining:
implicit lazy val getFile: GetResult[Array[File]]=
GetResult(r => Array[File](r.<<))
implicit lazy val SenObj: GetResult[Sen] =
GetResult(r => Sen(getFile(r)))
...to be clear about what instances you have available. I find that helps in debugging these situations.
Following on form this excellent set of answers on how to define union types in Scala. I've been using the Miles Sabin definition of Union types, but one questions remains.
How do you work with these if the type isn't know until Runtime? For example:
trait inv[-A] {}
type Or[A,B] = {
type check[X] = (inv[A] with inv[B]) <:< inv[X]
}
case class Foo[A : (Int Or String)#check](a: A)
Foo(1) // Foo[Int] = Foo(1)
Foo("hi") // Foo[String] = Foo(hi)
Foo(2.0) // Error!
This example works since the parameter A is know at compile time, and calling Foo(1) is really calling Foo[Int](1). However, what do you do if parameter A isn't known until runtime? Maybe you're paring a file that contains the data for Foo's, in which case the type parameter of Foo isn't know until you read the data. There's no easy way to set parameter A in this case.
The best solutions I've been able to come up with are:
Pattern Match on the data you've read and then create different Foo's based that type. In my case this isn't feasible because my case-class actually contains dozens of union types, so there'd be hundreds of combinations of types to pattern match.
Cast the type you've just read to be (String or Int), so you have a single type to pass around, that passes the Type Class constraint when you create Foo with it. Then return Foo[_] instead. This puts the onus back on the Foo user to work out the type of each field (since they'll appear to be type Any), but at least it defers having to know the type until the field is actually used, in which case a pattern match seems more tractable.
The second solution looks like this:
def parseLine: Any // Parses data point, but can be either a String or
// Int, so returns Any.
def mkFoo: Foo[_] = {
val a = parseLine.asInstanceOf[Int with String]
Foo(a) // Passes type constraint now
}
In practice I've ended up using the second solution, but I'm wondering if there's something better I can do?
Another way to state the problem is: What does it mean to return a Union Type? Functions can only return a single type, and the trickery we use with Miles Sabin union types is only useful for the types you pass in, not for the types you return.
PS. For context, why this is a problem in my case is that I'm generating a set of case-classes from a Json schema file. Json naturally supports union types, so I would like to make my case classes reflect that too. This works great in one direction: users creating case-classes to be serialized out to Json. But gets sticky in the other direction: user's parsing Json files to have a set of populated case classes returned to them.
The "standard" Scala solution to this problem is to use an ordinary discriminated-union type (ie, to forego true union types altogether):
sealed trait Foo
case class IntFoo(x: Int) extends Foo
case class StringFoo(x: String) extends Foo
This reflects the fact that, as you observe, the particular type of the member is a runtime value; the JVM type-tag of the Foo instance provides this runtime value.
Miles Sabin's implementation of union types is very clever, but I'm not sure if it provides any practical benefit, because it only restricts the type of thing that can go into a Foo, but provides the user of a Foo with no computable version of that restriction, in the way a match provides you with a computable version of the sealed trait. In general, for a restriction to be useful, it needs two sides: a check that only the right things are put in, and an extractor (aka an eliminator) that allows the same right things to come out the other end.
Perhaps if you gave some explanation of why you're looking for a purer union type it would illuminate whether regular discriminated unions are sufficient or if you really need something more.
There's a reason every JSON parser for Scala requires well defined types into which the JSON will be converted, even if some fields have to be dropped: you cannot work with something you don't know the type of.
To given an example, say you have a, and maybe a is a String, maybe it's an Int, but you don't know what it is. Why computation could you possibly make with a, not knowing its type? Why would your code compute the sum of all a's, for instance, if you didn't know in advance it was a number?
Generally, the answer to that is to perform user-provided data manipulation at runtime over data with unknown characteristics, as the user itself sees that it's a number and decides they want to know what the sum of that field is. Fine, but you are going the wrong way about it if so.
There is a well defined way to represent JSON data in Scala (and, for that matter, any data that has the same characteristics as JSON. Which is using a hierarchy of classes. A json value may be a json object, array or one of a number of primitives. A json object contains a list of key/value pairs, whose keys are json strings and values are json values. And so on. This is easy to represent, and there are many library doing so already. In fact, there are so many that there's a project called Json4s which presents a unified API which can be used and is implemented by many of the aforementioned libraries.
Things like the records which Miles Sabin's Shapeless library provide are intended to be used when the input doesn't have a well defined schema, but the program knows what it needs from that input. And, yes, the program might know what to do with a if it is an Int or a String, but not every possible value.
The next Scala 3 (mid 2020) based on Dotty will implement the proposal for Union Type from last Sept. 2018
You see it in "a tour of Scala 3" (June 2019)
Union Types Provide ad-hoc combinations of types
Subsetting = Subtyping
No boxing overhead
case class UserName(name: String)
case class Password(hash: Hash)
def help(id: UserName | Password) = {
val user = id match {
case UserName(name) => lookupName(name)
case Password(hash) => lookupPassword(hash)
}
...
}
Union Types Work also with singleton types
Great for JS interop
type Command = "Click" | "Drag" | "KeyPressed"
def handleEvent(kind: Command) = kind match {
case "Click" => MouseClick()
case "Drag" => MoveTo()
case "KeyPressed" => KeyPressed()
}
I'm admittedly very new to Scala, and I'm having trouble with the syntactical sugar I see in many Scala examples.
It often results in a very concise statement, but honestly so far (for me) a bit unreadable.
So I wish to take a typical use of the Option class, safe-dereferencing, as a good place to start for understanding, for example, the use of the underscore in a particular example I've seen.
I found a really nice article showing examples of the use of Option to avoid the case of null.
https://medium.com/#sinisalouc/demystifying-the-monad-in-scala-cc716bb6f534#.fhrljf7nl
He describes a use as so:
trait User {
val child: Option[User]
}
By the way, you can also write those functions as in-place lambda
functions instead of defining them a priori. Then the code becomes
this:
val result = UserService.loadUser("mike")
.flatMap(user => user.child)
.flatMap(user => user.child)
That looks great! Maybe not as concise as one can do in groovy, but not bad.
So I thought I'd try to apply it to a case I am trying to solve.
I have a type Person where the existence of a Person is optional, but if we have a person, his attributes are guaranteed. For that reason, there are no use of the Option type within the Person type itself.
The Person has an PID which is of type Id. The Id type consists of two String types; the Id-Type and the Id-Value.
I've used the Scala console to test the following:
class Id(val idCode : String, val idVal : String)
class Person(val pid : Id, val name : String)
val anId: Id = new Id("Passport_number", "12345")
val person: Person = new Person(anId, "Sean")
val operson : Option[Person] = Some(person)
OK. That setup my person and it's optional instance.
I learned from the above linked article that I could get the Persons Id-Val by using flatMap; Like this:
val result = operson.flatMap(person => Some(person.pid)).flatMap(pid => Some(pid.idVal)).getOrElse("NoValue")
Great! That works. And if I infact have no person, my result is "NoValue".
I used flatMap (and not Map) because, unless I misunderstand (and my tests with Map were incorrect) if I use Map I have to provide an alternate or default Person instance. That I didn't want to have to do.
OK, so, flatMap is the way to go.
However, that is really not a very concise statement.
If I were writing that in more of a groovy style, I guess i'd be able to do something like this:
val result = person?.pid.idVal
Wow, that's a bit nicer!
Surely Scala has a means to provide something at least nearly as nice as Groovy?
In the above linked example, he was able to make his statement more concise using some of that syntactical sugar I mentioned before. The underscore:
or even more concise:
val result = UserService.loadUser("mike")
.flatMap(_.child)
.flatMap(_.child)
So, it seems in this case the underscore character allows you to skip specifying the type (as the type is inferred) and replace it with underscore.
However, when I try the same thing with my example:
val result = operson.flatMap(Some(_.pid)).flatMap(Some(_.idVal)).getOrElse("NoValue")
Scala complains.
<console>:15: error: missing parameter type for expanded function ((x$2) => x$2.idVal)
val result = operson.flatMap(Some(_.pid)).flatMap(Some(_.idVal)).getOrElse("NoValue")
Can someone help me along here?
How am I misunderstanding this?
Is there a short-hand method of writing my above lengthy statement?
Is flatMap the best way to achieve what I am after? Or is there a better more concise and/or readable way to do it ?
thanks in advance!
Why do you insist on using flatMap? I'd just use map for your example instead:
val result = operson.map(_.pid).map(_.idVal).getOrElse("NoValue")
or even shorter:
val result = operson.map(_.pid.idVal).getOrElse("NoValue")
You should only use flatMap with functions that return Options. Your pid and idVals are not Options, so just map them instead.
You said
I have a type Person where the existence of a Person is optional, but if we have a person, his attributes are guaranteed. For that reason, there are no use of the Option type within the Person type itself.
This is the essential difference between your example and the User example. In the User example, both the existence of a User instance, and its child field are options. This is why, to get a child, you need to flatMap. However, since in your example, only the existence of a Person is not guaranteed, after you've retrieved an Option[Person], you can safely map to any of its fields.
Think of flatMap as a map, followed by a flatten (hence its name). If I mapped on child:
val ouser = Some(new User())
val child: Option[Option[User]] = ouser.map(_.child)
I would end up with an Option[Option[User]]. I need to flatten that to a single Option level, that's why I use flatMap in the first place.
If you looking for the most concise solution, consider this:
val result = operson.fold("NoValue")(_.pid.idVal)
Though one could find it not clear or confusing
I am trying to analyze Scala code written by someone else, and in doing so, I would like to be able to write Unit Tests (that were not written before the code was written, unfortunately).
Being a relative Newbie to Scala, especially in the Futures concept area, I am trying to understand the following line of code.
val niceAnalysis:Option[(niceReport) => Future[niceReport]] = None
Update:
The above line of code should be:
val niceAnalysis:Option[(NiceReport) => Future[NiceReport]] = None
- Where NiceReport is a case class
-----------Update ends here----------------
Since I am trying to mock up an Actor, I created this new Actor where I introduce my niceAnalysis val as a field.
The first problem I see with this "niceAnalysis" thing is that it looks like an anonymous function.
How do I "initialize" this val, or to give it an initial value.
My goal is to create a test in my test class, where I am going to pass in this initialized val value into my test actor's receive method.
My naive approach to accomplish this looked like:
val myActorUnderTestRef = TestActorRef(new MyActorUnderTest("None))
Neither does IntelliJ like it. My SBT compile and test fails.
So, I need to understand the "niceAnalyis" declaration first and then understand how to give it an initial value. Please advise.
You are correct that this is a value that might contain a function from type niceReport to Future[niceReport]. You can pass an anonymous function or just a function pointer. The easiest to understand might be the pointer, so I will provide that first, but the easiest in longer terms would be the anonymous function most likely, which I will show second:
import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global
def strToFuture(x: String) = Future{ x } //merely wrap the string in a future
val foo = Option(strToFuture)
Conversely, the one liner is as follows:
val foo = Option((x:String)=>Future{x})
I've been learning Scala and decided to play with JSON parsing using json4s. I decided to use XPath syntax for deserializing and came across this strange bit of syntax I've never seen before.
val json = JsonMethods.parse("""{"meaningOfLife": 42}""")
val JInt(x) = json\"meaningOfLife"
The part confusing me is this bit right here
val JInt(x) = ...
I can't wrap my mind around what's happening there, I don't even know how to search this syntax or what it's called. Can anyone help me out? Scala is an amazing language with a lot of neat features that I'm not used to in other languages like C++ and Java.
Edit
To clarify, I'm confused because x isn't defined, but it's somehow being passed into a function or constructor then being assigned to the result of json\"meaningOfLife" which returns a JValue.
Edit 2
After some research and playing around, I figured out that this has something to do with case classes. I was able to run the following code.
case class MyCaseClass (x: Int)
val MyCaseClass(x) = new MyCaseClass(5)
println(x, x.getClass) // prints (5,int)
Which, after looking at some of the code, gives me a good understanding at what's happening.
val MyCaseClass(x) = MyCaseClass(5)
Is extracting (for lack of a better term) the Int value 5 from the instantiated MyCaseClass and storing that into x, meaning x will be of type Int.
In the code for json4s a JInt is a JValue which the \ operator returns. So the JInt(x) is taking out a BigInt (stored in the class JInt) and putting that into the value x from what I gather.
But I still have a question. What is this process called? Is there any documentation on it?
It's called "irrefutable pattern matching" and it's essentially equivalent to this bit of code:
val json = JsonMethods.parse("""{"meaningOfLife": 42}""")
val x = json match {
case JInt(xMatched) => xMatched
}
In other words, any case class or any extractor that fits the pattern of the declaration's left-hand-side can be used in this way.
Addendum:
The "irrefutable" means that a MatchError will be thrown if the pattern cannot be satisfied.