I have some difficulties to understand the concept of failure and success in the Icon programming language. See the highlighted part below from a tutorial for Icon https://www2.cs.arizona.edu/icon/docs/ipd266.htm. How does success/failure in Icon differ from boolean values in other languages?
If find returned a boolean signifying success/failure, it wouldn't be able to also return "the position at which "or" occurs in line". In languages like C, this can be worked around by taking and modifying an out parameter, but this is awkward and error-prone. In languages with algebraic datatypes, you could have the function return something like Option<Int>. Icon encodes "optionality" and the corresponding control flow on the language level – not only can any function fail, but control structures (like if) work by detecting and handling failures. (This distinguishes it from languages where values are "nullable", but null values simply crash the program or cause an exception to be thrown.)
Related
As I learn new components in Kotlin, I came accross requireNotNull and checkNotNull but the only difference I've found is that requireNotNull can throw an IllegalArgumentException while checkNotNull can throw an IllegalStateException. Is this the only reason why there are two methods, or I'm missing some under-the-hood implementation detail?
The exception types are the only practical difference, as far as the compiler is concerned — but there's a big difference in intent, for anyone reading the code:
• require…() functions are for checking parameters, to confirm that a function's input fulfils its contract. So you'd normally call them first thing in a function. (Of course, Kotlin's non-nullable types mean that you wouldn't need to call requireNotNull() for a single parameter; but you might need to check a more complex condition on a combination of parameters or their sub-objects.) That's why they throw IllegalArgumentException: it's checking that the arguments are legal.
• check…() functions are for checking the relevant properties, to confirm that the object or whatever is in a valid state for this function to be called now. (Again, any properties that were never null would be typed accordingly, so checkNotNull() is more appropriate for cases where a property, combination, and/or sub-property can be null, but this function mustn't be called when they are.) So they throw IllegalStateException: they're checking that the object's current state allows the function to be called.
In both cases, you could of course write a standard if check (as you would in Java). Or you could use the Elvis operator ?: to do the check the first time the possibly-null value is used. But these functions give you an alternative that's in a more declarative form: you'd normally put them at the top of the function, where they spell out what the function's contract is, in a way that's obvious to anyone glancing at the code.
As a linked answer points out, there are also assert…() functions, which again have more of a semantic difference than a practical one. Those are for detecting programming errors away from the boundary of a function call: for confirming invariants and other conditions, and for all the checks in unit tests and other automated tests.
(Assertions have another important difference: they can be enabled and disabled from the command-line. Though in my experience, that's not a very good thing. If a check is important, it should always be run: be mandatory; if not, then it should be removed, or at least moved to automated tests, once the code is debugged.)
It is a semantic difference and hence it throws different exceptions. RequireNotNull is used to check input values, typically at the beginning of a method, while checkNotNull is used anywhere to check the current state.
If you're looking for differences in implementation, the best place to go would be the source code. In this case it seems like there are no differences aside from the different exception thrown, the source for both methods is otherwise identical.
checkNotNull
[...]
if (value == null) {
val message = lazyMessage()
throw IllegalStateException(message.toString())
} else {
return value
}
requireNotNull
[...]
if (value == null) {
val message = lazyMessage()
throw IllegalArgumentException(message.toString())
} else {
return value
}
Therefore the difference is purely semantic. The answer from #gidds details some good scenarios for using them both.
In a language with dependent types you can have Type-in-Type which simplifies the language and gives it a lot of power. This makes the language logically inconsistent but this might not be a problem if you are interested in programming only and not theorem proving.
In the Cayenne paper (a dependently typed language for programming) it is mentioned about Type-in-Type that "the unstratified type system would make it impossible during type checking to determine if an expression corresponds to a type or a real value and it would be impossible to remove the types at runtime" (section 2.4).
I have two questions about this:
In some dependently typed languages (like Agda) you can explicitly say which variables should be erased. In that case does Type-in-Type still cause problems?
We could extend the hierarchy one extra step with Kind where Type : Kind and Kind : Kind. This is still inconsistent but it seems that now you can know if a term is a type or a value. Is this correct?
the unstratified type system would make it impossible during type
checking to determine if an expression corresponds to a type or a real
value and it would be impossible to remove the types at runtime
This is not correct. Type-in-type prevents erasure of proofs, but it does not prevent erasure of types, assuming that we have parametric polymorphism with no typecase operation. Recent GHC Haskell is an example for a system which supports type-in-type, type erasure and type-level computation at the same time, but which does not support proof erasure. In dependently typed settings, we always know if a term is a type or not; we just check whether its type is Type.
Type erasure is just erasure of all things with type Type.
Proof erasure is more complicated. Let's assume that we have a Prop universe like in Coq, which is intended to be a universe of computationally irrelevant types. Here, we can use some p : Bool = Int proof to coerce Bool-s to Int. If the language is consistent, there is no closed proof of Bool = Int so closed program execution never encounters such coercion. Thus, closed program execution is safe even if we erase all coercions.
If the language is inconsistent, and the only way of proving contradiction is by an infinite loop, there is a diverging closed proof of Bool = Int. Now, closed program execution can actually hit a proof of falsehood; but we can still have type safety, by requiring that coercion must evaluate the proof argument. Then, the program loops whenever we coerce by falsehood, so execution never reaches the unsound parts of the program.
Probably the key point here is that A = B : Prop supports coercion, which eliminates into computationally relevant universe, but a parametric Type universe has no elimination principle at all and cannot influence computation.
Erasure can be generalized in several ways. For example, we may have any inductive data type with a single constructor (and no stored data which is not available from elsewhere, e.g. type indices), and try to erase every matching on that constructor. This is again sound if the language is total, and not otherwise. If we don't have a Prop universe, we can still do erasure like this. IIRC Idris does this a lot.
I just want to add a note that I believe is related to the question. Formality, a minimal proof language based on self-types, is non-terminating. I was involved in a Reddit discussion about whether Formality can segfault. One way that could happen is if you could prove Nat == String, then cast 42 :: Nat to 42 :: String and then print it as if it was a string, for example. But that's not the case. While you can prove String == Int in Formality:
nat_is_string: Nat == String
nat_is_string
And you can use it to cast a Nat to a String:
nat_str: String
42 :: rewrite x in x with nat_is_string
Any attempt to print nat_str, your program will not segfault, it will just hang. That's because you can't erase the equality evidence in Formality. To understand why, let's see the definition of Equal.rewrite (which is used to cast 42 to String):
Equal.rewrite<A: Type, a: A, b: A>(e: Equal(A,a,b))<P: A -> Type>(x: P(a)): P(b)
case e {
refl: x
} : P(e.b)
Once we erase the types, the normal form of rewrite becomes λe. λx. e(x). The e there is the equality evidence. In the example above, the normal form of nat_str is not 42, but nat_is_string(42). Since nat_is_string is an equality proof, then it has two options: either it will halt and become identity, in which case it will just return 42, or it will hang forever. In this case, it doesn't halt, so nat_is_string(42) will never return 42. As such, it can't be printed, and any attempt to use it will cause your entire program to hang, but not segfault.
So, in short, the insight is that self types allow us to encode the Equal, rewrite / subst, and erase all the type information, but not the equality evidence itself.
I have been searching for this all over the web, but everything is either talking about the ErrObject class, or the constant vbError returned by the VarType function. I want to know what that type actually is, e.g. an integer is something like 4, a string is something like "hello world", etc.
For a little background, here is a link to the official MSDN page about VarType, which shows all the constants it returns and what they represent. What is Error value?
If you are familiar with COM in C/C++, you should know that from that perspective VarType() is a simple function that essentially extracts the value of the vt member from the Variant passed in as a parameter. The possible values of the vt member are documented in many places, such as here.
If you check inside of the COM system headers (e.g. WTypes.h distributed as part of in the Windows SDK), you will see that the VbError value of 10 indeed maps to the C/C++ enum value of VT_ERROR.
enum VARENUM
{ VT_EMPTY = 0,
...
VT_ERROR = 10,
...
} ;
The MSDN link above describes the meaning of a vt that equals VT_ERROR as follows:
An SCODE was specified. The type of the error is specified in scode.
Generally, operations on error values should raise an exception or propagate the error to the return value, as appropriate.
So, here's basically what it means:
A Variant obviously supports storing many types of values, and among them is the obscure possibility of storing an "Error Code". More correctly, these codes are formally called scode's, because they can indicate many types of "success" as well as "failures". Most people refer to these codes as HRESULTs.
These codes are the same kind of "Error Codes" that you get from Err.Number in VB6. So, you can tell a Variant to distinguish an "Error Code" from just a plain number.
In reality, few programs or COM components - if any - will ever put error codes in Variants. Almost everybody just issues COM Exceptions to communicate errors (this mechanism is exposed in VB6 via the Err object). Even those components that return error codes outside of the COM Exception mechanism, would likely do so in typed variables (e.g. Long's).
Therefore, the reason this possible return value exists is for completeness. You will almost never see it in real life.
(Edit: Remove bit about not being able to create an "Error" variant. #Joe proved me wrong on that. You can use CVErr() to create one)
In VB6 and VBA, error values are created by calling the CVErr function.
This function returns a Variant whose VarType is vbError, and for which the IsError function returns True.
A typical use case for this is an Excel UDF that returns a Variant: if you return an error value, it will display as #VALUE!.
What happened to me while programming in Java:
String str
// want to call something(), but signature does not match
something(Foo foo)
// but I have this conversion function
Foo fooFrom(String)
// Obviously I am about to create another method overload.. sigh
something(String s) {
something(fooFrom(s));
}
But then I thought of the possibility of a "automatic type conversion" which just uses my defined conversion function fooFrom everytime a string is passed in where a Foo object is excepted.
My search brought me to the wikipedia page about type conversion with this Eiffel example:
class STRING_8
…
create
make_from_cil
…
convert
make_from_cil ({SYSTEM_STRING})
to_cil: {SYSTEM_STRING}
…
The methods after convert are called automatically if a STRING_8 is used as a SYSTEM_STRING and vice-versa.
Somehow surprising for me I could not find any other language supporting this.
So my question: are there any other languages supporting this feature?
If not, are there any reasons for that, since it seems quite useful to me?
Further I think it would not be difficult to implement it as a language add-on.
There is one minor point that may make the things a bit more complicated. At the moment Eiffel has a rule that conversion can be applied only when the source of reattachment is attached to an object, i.e. is not Void (not null in Java/C#).
Let's look at the original example:
something (str);
Suppose that str is null. Do we get a NullPointerException / InvalidArgumentException, because the code is transformed into
something (fooFrom (str));
and fooFrom does not expect null? Or is the compiler smart enough to transform this into
if (str == null)
something (null);
else
something (fooFrom (str));
?
The current Eiffel standard makes sure that such issues simply do not happen and str is not null if conversion is involved. However many other languages like Java or C# do not guarantee that and the additional complexity may be not worth the effort for them.
I believe that Eiffel is not the only language to support conversion routines, but I would say that it might be one of the very few that integrate this very nicely with the rest of the language definition.
In .NET, for example, you have both op_Explicit and op_Implicit routines that can be used for conversion for languages that support them. And I believe C# does.
Manu
Type coercion (implicit conversion) is a curse and a blessing--handy in some case, but it can also backfire.
For instance, Javascript has many weird coercion rules, that can leads to bug when coercings string to number, etc.
Scala has something called "implicit" which achieves something similar (at least to me) to what you describe in Eiffel. With little surprise, they can lead to certain gotchas. But they can be also very handy, see for instance the article Pimp My Library.
C++ has copy constructors and assignment operator.
EDIT: This question was misexpressed. What I've really wanted to ask was:
Is there anything what cant be written in OO languages (with support for closures) using continuation-passing style?
You can google what CPS does mean or just stick with definition of function/method never returning anything, always pushing data somewhere - using passed callback.
And after yers from original question, I can even answer myself - there's nothing like that. And moreover it's actually very good OO principle called Tell Dont Ask
function getName(){
return this.name;
}
console.log(xyz.getName())
vs.
function pushNameTo(callback){
callback(this.name);
}
xyz.pushNameTo(console.log)
good, but this time it was named after how it does the thing, lets name it after what it does and make it even more OO:
function renderOn(responseBuilder){
var b = responseBuilder;
//or just string, whatever, depending on your builder implementation
b.field("Name: ", this.name);
b.field("Age: ", this.age);
b.image("Profile photo", this.imageData);
}
person.renderOn(htmlBuilder);
the point here is - the object encapsulates not only its data but even behavior, the spirit, personality. Who else should be responsible for expressing person's representation rather than person itself?
Of course this does not necessarily means you should have html in your code, builder serves this purpose. It can even generate some xml or other data-format for actual UI-rendering layer. But its always push instead of pull.
Nothing, of course. Consider: if you have a program that is completely sequential, you could simply insert it into some kind of wrapper, like document.onload(). Then the sequential program would be started asynchronously.
Going the other way around, if all you have is a synchronous language, you can always write the asynchronous case by having a table of pieces to be executed, and an inner loop that looks to see what's been enabled, and takes it from the table to execute. in fact, this would look very much like the underlying runtime in whoich your javascript runs.
There are two types of programs -- imperative and functional.
Imperative programs are sequantial -- one step after another. C++, Java, etc. are examples.
Functional programs may not be sequential. Most async patterns use "continuation-style" programming, which is a type of functional programming with imperative overtones.
JavaScript is an imperative language which has first-class functions, i.e. it also enables certain functional programming paradigms.
What you described in your question is "continuation-style" async programming. Notice that the meaning of a "continuation" is "the rest of the program after this line". Therefore, theoretically, every imperative program can be rewritten in "continuation" style (i.e. the first line with a continuation of the rest of the program starting form the second line, and so on and so forth). For example:
Statement #1
Statement #2
Statement #3
can be rewritten as:
do(Statement #1, function{
do(Statement #2, function{
Statement #3
})
})
where the second parameter to do is the continuation of the statement.
Loops are more tricky though, but they can also be rewritten similarly -- essentially passing the loop body itsslef as the continuation.