At my work we use a typical heavy enterprise stack of Hibernate, Spring, and JSF to handle our application, but after learning Scala I've wanted to try to replicate much of our functionality within a more minimal Scala stack (Squeryl, Scalatra, Scalate) to see if I can decrease code and improve performance (an Achilles heal for us right now).
Often my way of doing things is influenced by our previous stack, so I'm open to advice on a way of doing things that are closer to Scala paradigms. However, I've chosen some of what I do based on previous paradigms we have in the Java code base so that other team members will hopefully be more receptive to the work I'm doing. But here is my question:
We have a domain class like so:
class Person(var firstName: String, var lastName: String)
Within a jade template I make a call like:
.section
- view(fields)
The backing class has a list of fields like so:
class PersonBean(val person: Person) {
val fields: Fields = Fields(person,
List(
Text(person.firstName),
Text(person.lastName)
))
}
Fields has a base object (person) and a list of Field objects. Its template prints all its fields templates. Text extends Field and its Jade template is supposed to print:
<label for="person:firstName">#{label}</label>: <input type="text" id="person:firstName" value="#{value}" />
Now the #{value} is simply a call to person.firstName. However, to find out the label I reference a ResourceBundle and need to produce a string key. I was thinking of using a naming convention like:
person.firstName.field=First Name
So the problem then becomes, how can I within the Text class (or parent Field class) discover what the parameter being passed in is? Is there a way I can pass in person.firstName and find that it is calling firstName on class Person? And finally, am I going about this completely wrong?
If you want to take a walk on the wild side, there's a (hidden) API in Scala that allows you to grab the syntax tree for a thunk of code - at runtime.
This incantation goes something like:
scala.reflect.Code.lift(f).tree
This should contain all the information you need, and then some, but you'll have your work cut out interpreting the output.
You can also read a bit more on the subject here: Can I get AST from live scala code?
Be warned though... It's rightly classified as experimental, do this at your own risk!
You can never do this anywhere from within Java, so I'm not wholly clear as to how you are just following the idiom you are used to. The obvious reason that this is not possible is that Java is pass-by-value. So in:
public void foo(String s) { ... }
There is no sense that the parameter s is anything other than what it is. It is not person.firstName just because you called foo like:
foo(person.firstName);
Because person.firstName and s are completely separate references!
What you could do is replacing the fields (e.g. firstname) with actual objects, which have a name attribute.
I did something similiar in a recent blog post:http://blog.schauderhaft.de/2011/05/01/binding-scala-objects-to-swing-components/
The property doesn't have a name property (yet), but it is a full object but is still just as easy to use as a field.
I would not be very surprised if the following is complete nonsense:
Make the parameter type of type A that gets passed in not A but Context[A]
create an implicit that turns any A into a Context[A] and while doing so captures the value of the parameter in a call-by-name parameter
then use reflection to inspect the call-by-name parameter that gets passed in
For this to work, you'd need very specific knowledge of how stuff gets turned into call-by-name functions; and how to extract the information you want (if it's present at all).
Related
Following is my use case
I am using Cats for validation of my config. My config file is in json.
I deserialize my config file to my case class Config using lift-json and then validate it using Cats. I am using this as a guide.
My motive for using Cats is to collect all errors iff present at time of validation.
My problem is the examples given in the guide, are of the type
case class Person(name: String, age: Int)
def validatePerson(name: String, age: Int): ValidationResult[Person] = {
(validateName(name),validate(age)).mapN(Person)
}
But in my case I already deserialized my config into my case class ( below is a sample ) and then I am passing it for validation
case class Config(source: List[String], dest: List[String], extra: List[String])
def vaildateConfig(config: Config): ValidationResult[Config] = {
(validateSource(config.source), validateDestination(config.dest))
.mapN { case _ => config }
}
The difference here is mapN { case _ => config }. As I already have a config if everything is valid I dont want to create the config anew from its members. This arises as I am passing config to validate function not it's members.
A person at my workplace told me this is not the correct way, as Cats Validated provides a way to construct an object if its members are valid. The object should not exist or should not be constructible if its members are invalid. Which makes complete sense to me.
So should I make any changes ? Is the above I'm doing acceptable ?
PS : The above Config is just an example, my real config can have other case classes as its members which themselves can depend on other case classes.
One of the central goals of the kind of programming promoted by libraries like Cats is to make invalid states unrepresentable. In a perfect world, according to this philosophy, it would be impossible to create an instance of Config with invalid member data (through the use of a library like Refined, where complex constraints can be expressed in and tracked by the type system, or simply by hiding unsafe constructors). In a slightly less perfect world, it might still be possible to construct invalid instances of Config, but discouraged, e.g. through the use of safe constructors (like your validatePerson method for Person).
It sounds like you're in an even less perfect world where you have instances of Config that may or may not contain invalid data, and you want to validate them to get "new" instances of Config that you know are valid. This is totally possible, and in some cases reasonable, and your validateConfig method is a perfectly legitimate way to solve this problem, if you're stuck in that imperfect world.
The downside, though, is that the compiler can't track the difference between the already-validated Config instances and the not-yet-validated ones. You'll have Config instances floating around in your program, and if you want to know whether they've already been validated or not, you'll have to trace through all the places they could have come from. In some contexts this might be just fine, but for large or complex programs it's not ideal.
To sum up: ideally you'd validate Config instances whenever they are created (possibly even making it impossible to create invalid ones), so that you don't have to remember whether any given Config is good or not—the type system can remember for you. If that's not possible, because of e.g. APIs or definitions you don't control, or if it just seems too burdensome for a simple use case, what you're doing with validateConfig is totally reasonable.
As a footnote, since you say above that you're interested in looking in more detail at Refined, what it provides for you in a situation like this is a way to avoid even more functions of the shape A => ValidationResult[A]. Right now your validateName method, for example, probably takes a String and returns a ValidationResult[String]. You can make exactly the same argument against this signature as I have against Config => ValidationResult[Config] above—once you're working with the result (by mapping a function over the Validated or whatever), you just have a string, and the type doesn't tell you that it's already been validated.
What Refined allows you to do is write a method like this:
def validateName(in: String): ValidationResult[Refined[String, SomeProperty]] = ...
…where SomeProperty might specify a minimum length, or the fact that the string matches a particular regular expression, etc. The important point is that you're not validating a String and returning a String that only you know something about—you're validating a String and returning a String that the compiler knows something about (via the Refined[A, Prop] wrapper).
Again, this may be (okay, probably is) overkill for your use case—you just might find it nice to know that you can push this principle (tracking validation in types) even further down through your program.
One typical way of managing state in Lift is to create a singleton object extending SessionVar, like in this example taken from the documentation:
object MySnippetCompanion {
object mySessionVar extends SessionVar[String]("hello")
}
The case for using SessionVars is clear and I've been using them in practice as needed. I also roughly understand how they work inside.
Still, I can't help but wonder why the mechanism for "session variables", which are clearly associated with the current session (usually just one out of many sessions in the system), was designed to be used via a singleton? This goes so against my intuition that at first glance I was tempted to believe that Lift was somehow able to override Scala's language features and to make object mean something different that in regular Scala.
Even though I now understand how it works, I can't grasp the rationale for such a design, which, at least for me, breaks the rule of least astonishment. Can someone point out any advantages or perhaps explain why such a design decision could have been made?
Session variables in Lift use Scala's DynamicVariable. Basically they allow you to statically reference a variable in a code-block and then later on call the code and substitute a value:
import scala.util.DynamicVariable
val x = new DynamicVariable(1)
def printIt() {
println(x.value)
}
printIt()
//> 1
x.withValue(2)(printIt())
//> 2
So each time a request is handled, the scope of these dynamic variables is changed to the current session, completely hiding the state change of the current session to you as a programmer.
The other option would be to pass around a "sessionID" object which you would have to use when you want to access session specific data. Not really handy.
The reason you have to use the object keyword is that object is unique in that it defines both a value and a class. This allows Lift to call getClass to get a name that uniquely identifies this SessionVar vs. any other one, which Lift needs in order to serialize and deserialize every piece of session state in the right place(s). Furthermore if the SessionVar is in a class that has two instances (for instance a snippet rendered in two tabs), they will both refer to the same piece of session state. (The flip side of the coin is that the same SessionVar instance can be referenced by two different sessions and mean the right thing to each.)
Actually at times this is insufficient --- for instance, if you define a SessionVar in a trait, and have two different classes that inherit the trait, but you need them two have two different values. The solution in that case is to override the def for the "name salt", which is combined with getClass to identify the SessionVar.
I found this code example in Programming in Scala, 2nd Ed. (Chapter 25, Listing 25.11):
object PrefixMap extends {
def empty[T] = ...
def apply[T](kvs: (String, T)*): PrefixMap[T] = ...
...
}
Why is the extends clause there without a superclass name? It looks like extending an anonymous class, but for what purpose? The accompanying text doesn't explain or even mention this construct anywhere. The code actually compiles and apparently works perfectly with or without it.
OTOH I found the exact same code on several web pages, including this (which looks like the original version of the chapter in the book). I doubt that a typo could have passed below the radars of so many readers up to now... so am I missing something?
I tried to google it, but struggled even to find proper search terms for it. So could someone explain whether this construct has a name and/or practical use in Scala?
Looks like a print error to me. It will work all the same, though, which probably helped hide it all this time.
Anyway, that object is extending a structural type, though it could also be an early initialization, if you had with XXX at the end. MMmmm. It looks more like an early initialization without any class or trait to be initialized later, actually... structure types do not contain code, I think.
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.
I have a collection of objects and for each object I have its type FullName and either its value (as string) or a subtree of inner objects details (again type FullName and value or a subtree).
For each root object I need to create a piece of XML that will be xml de-serializable.
The problem with XmlSerializer is that i.e. following object
int age = 33;
will be serialized to
<int>33</int>
At first this seems to be perfectly ok, however when working with reflection you will be using System.Int32 as type name and int is an alias and this
<System.Int32>33</System.Int32>
will not deserialize.
Now additional complexity comes from the fact that I need to handle any possible data type.
So solutions that utilize System.Activator.CreateInstance(..) and casting won't work unless I go down the path of code gen & compilation as a way of achieving this (which I would rather avoid)
Notes:
A quick research with .NET Reflector revealed that XmlSerializer uses internal class TypeScope, look at its static constructor to see that it initializes an inner HashTable with mappings.
So the question is what is the best (and I mean elegant and solid) way to workaround this sad fact?
I don't exactly see, where your problem originally stems from. XmlSerializer will use the same syntax/mapping for serializing as for deserializing, so there is no conflict when using it for both serializing and deserializing.
Probably the used type-tags are some xml-standard thing, but not sure about that.
I guess the problem is more your usage of reflection. Do you instantiate your
imported/deserialized objects by calling Activator.CreateInstance ?
I would recommend the following instead, if you have some type Foo to be created from the xml in xmlReader:
Foo DeserializedObject = (Foo)Serializer(typeof(Foo)).Deserialize(xmlReader);
Alternatively, if you don't want to switch to the XmlSerializer completely, you could do some preprocessing of your input. The standard way would then be to create some XSLT, in which you transform all those type-elements to their aliases or vice versa. then before processing the XML, you apply your transformation using System.Xml.Xsl.XslCompiledTransform and use your (or the reflector's) mappings for each type.
Why don't you serialize all the field's type as an attribute?
Instead of
<age>
<int>33</int>
</age>
you could do
<age type="System.Int32">
33
</age>