I'm having problem understanding the usefulness of Rust enums after reading The Rust Programming Language.
In section 17.3, Implementing an Object-Oriented Design Pattern, we have this paragraph:
If we were to create an alternative implementation that didn’t use the state pattern, we might instead use match expressions in the methods on Post or even in the main code that checks the state of the post and changes behavior in those places. That would mean we would have to look in several places to understand all the implications of a post being in the published state! This would only increase the more states we added: each of those match expressions would need another arm.
I agree completely. It would be very bad to use enums in this case because of the reasons outlined. Yet, using enums was my first thought of a more idiomatic implementation. Later in the same section, the book introduces the concept of encoding the state of the objects using types, via variable shadowing.
It's my understanding that Rust enums can contain complex data structures, and different variants of the same enum can contain different types.
What is a real life example of a design in which enums are the better option? I can only find fake or very simple examples in other sources.
I understand that Rust uses enums for things like Result and Option, but those are very simple uses. I was thinking of some functionality with a more complex behavior.
This turned out to be a somewhat open ended question, but I could not find a useful response after searching Google. I'm free to change this question to a more closed version if someone could be so kind as to help me rephrase it.
A fundamental trade-off between these choices in a broad sense has a name: "the expression problem". You should find plenty on Google under that name, both in general and in the context of Rust.
In the context of the question, the "problem" is to write the code in such a way that both adding a new state and adding a new operation on states does not involve modifying existing implementations.
When using a trait object, it is easy to add a state, but not an operation. To add a state, one defines a new type and implements the trait. To add an operation, naively, one adds a method to the trait but has to intrusively update the trait implementations for all states.
When using an enum for state, it is easy to add a new operation, but not a new state. To add an operation, one defines a new function. To add a new state, naively, one must intrusively modify all the existing operations to handle the new state.
If I explained this well enough, hopefully it should be clear that both will have a place. They are in a way dual to one another.
With this lens, an enum would be a better fit when the operations on the enum are expected to change more than the alternatives. For example, suppose you were trying to represent an abstract syntax tree for C++, which changes every three years. The set of types of AST nodes may not change frequently relative to the set of operations you may want to perform on AST nodes.
With that said, there are solutions to the more difficult options in both cases, but they remain somewhat more difficult. And what code must be modified may not be the primary concern.
For example, could I implement a rule that would change every string that followed the pattern '1..4' into the array [1,2,3,4]? In JavaScript:
//here you create a rule that changes every string that matches /$([0-9]+)_([0-9]+)*/
//ever created into range($1,$2) (imagine a b are the results of the regexp)
var a = '1..4';
console.log(a);
>> output: [1,2,3,4];
Of course, I'm pretty confident that would be impossible in most languages. My question is: is there any language in which that would be possible? Or have anyone ever proposed something like that? Does this thing have a 'name' for which I can google to read more about?
Modifying the language from whithin itself falls under the umbrell of reflection and metaprogramming. It is referred as behavioral reflection. It differs from structural reflection that opperates at the level of the application (e.g. classes, methods) and not the language level. Support for behavioral reflection varies greatly across languages.
We can broadly categorize language changes in two categories:
changes that modify the semantics (i.e. the rules) of the language itself (e.g. redefine the method lookup algorithm),
changes that modify the syntax (e.g. your syntax '1..4' to create arrays).
For case 1, certain languages expose the structure of the application (structural reflection) and the inner working of their implementation (behavioral reflection) to the application itself via special object, called meta-objects. Meta-objects are reifications of otherwise implicit aspects, that become then explicitely manipulable: the application can modify the meta-objects to redefine part of its structure, or part of the language. When it comes to langauge changes, the focus is usually on modifiying message sending / method invocation since it is the core mechanism of object-oriented language. But the same idea could be applied to expose other aspects of the language, e.g. field accesses, synchronization primitives, foreach enumeration, etc. depending on the language.
For case 2, the program must be representated in a suitable data structure to be modified. For languages of the lisp family, the program manipulates lists, and the program can be itself represented as lists. This is called homoiconicity and is handy for metaprogramming, hence the flexibility of lisp-like languages. For other languages, their representation is usually an AST. Transforming the representation of the program, or rewriting it, is possible with macro, preprocessors, or hooks during compilation or class loading.
The line between 1 and 2 is however blurry. Syntactical changes can appear to modify the semantics of the language. For instance, I can rewrite all fields accesses with proper getter and setter and perform additional logic there, say to implement transactional memory. Did I perform a semantical change of what a field access is, or merely a syntax change?
Also, there are other constructs the fall bewten the lines. For instance, proxies and #doesNotUnderstand trap are popular techniques to simulate the reification of message sends to some extent.
Lisp and Smalltalk have been very influencial in the field of metaprogramming, and I think the two following projects/platform are interesting to look at for a representative of each of these:
Racket, a lisp-like language focused on growing languages from within the langauge
Helvetia, a Smalltalk extension to embed new languages into the host language by leveraging the AST of the host environment.
I hope you enjoyed this even if I did not really address your question ;)
Your desired change require modifying the way literals are created. This is AFAIK not usually exposed to the application. The closed work that I can think of is Virtual Values for Language Extension, that tackled Javascript.
Yes. Common Lisp (and certain other lisps) have "reader macros" which allow the user to reprogram (incrementally) the mapping between the input stream and the actual language construct as parsed.
See http://dorophone.blogspot.com/2008/03/common-lisp-reader-macros-simple.html
If you want to operate on the level of objects, you will want to use a debugging/memory management framework that keeps track of all objects, and processes the rules on each evaluation step (nasty). This seems like the kind of thing you might be able to shoehorn into smalltalk.
CLOS (Common Lisp Object System) allows redefinition of live objects.
Ultimately you need two things to implement this:
Access to the running system's AST (Abstract Syntax Tree), and
Access to the running system's objects.
You'll want to study meta-object protocols and the languages that use them, then the implementations of both the MOPs and the environment within which these programs are executed.
Image-based systems will be the easiest to modify (e.g., Lisp, potentially Smalltalk).
(Image-based systems store a snapshot of a running system, allowing complete shutdown and restarts, redefinitions, etc. of a complete environment, including existing objects, and their definitions.)
Ruby allows you to extend classes. For instance, this example adds functionality to the String class. But you can do more than add methods to classes. You can also overwrite methods, but defining a method that's already been defined. You may want to preserve access to the original method using alias_method.
Putting all this together, you can overload a constructor in Ruby, but in your case, there's a catch: It sounds like you want the constructor to return a different type. Constructors by definition return instances of their class. If you just want it to return the string "[1,2,3,4]", that's simple enough:
class string
alias_method :initialize :old_constructor
def initialize
old_constructor
# code that applies your transformation
end
end
But there's no way to make it return an Array if that's what you want.
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A couple of years ago I started writing an interpreter for a little Domain Specific Language which included programmer-defined functions.
At first I implemented variable scope using a simple stack of symbol-tables. But now I want to move to proper lexical scoping (with the option of closures). Can anyone explain the data-structure and algorithm behind lexical scope?
To get correct lexical scoping and closures in an interpreter, all you need to do is follow these rules:
In your interpreter, variables are always looked up in an environment table passed in by the caller or kept as a variable, not some global env-stack. The signature of your eval operation is like eval(expression, env) => value.
When interpreted code calls a function, the environment is NOT passed to that function. The signature of your function application operation is like apply(function, arguments) => value.
When an interpreted function is called, the environment its body is evaluated in is the environment in which the function definition was made, and has nothing whatsoever to do with the caller. So if you have a local function, then it is a closure, that is, a data structure containing fields {function definition, env-at-definition-time}.
To expand on that last bit in Python-ish syntax:
x = 1
return lambda y: x + y
should be executed as if it were
x = 1
return make_closure(<AST for "lambda y: x + y">, {"x": x})
where the second dict argument may be just the current-env rather than a data structure constructed at that time. (On the other hand, retaining the entire env rather than just the closed-over variables can mean programs have surprising memory leaks because closures are holding onto things the don't need. This is worth fixing in any 'practical' language implementation but not when you are just experimenting with language semantics.)
There are many different ways to implement lexical scoping. Here are some of my favorites:
If you don't need super-fast performance, use a purely functional data structure to implement your symbol tables, and represent a nested function by a pair containing a pointer to the code and a pointer to the symbol table.
If you need native-code speeds, my favorite technique is described in Making a Fast Curry by Simon Marlow and Simon Peyton Jones.
If you need native-code speeds, but curried functions are not that important, consider closure-passing style.
Read The implementation of Lua 5.0 for instance.
There is no single right way to do this. The important thing is to clearly state the semantics that you are looking to provide, and then the data structures and algorithms will follow.
Stroustrup implemented this in the first C++ compiler simply with one symbol table per scope, and a chaining rule that followed scopes outwards until a definition is found. How this works exactly depends on your precise semantics. Make sure you nail those down first.
Knuth in The Art of Computer Programming, Vol 1, gives an algorithm for a Cobol symbol table whereby scoping is done via links.
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I keep hearing this term tossed around in several different contexts. What is it?
Declarative programming is when you write your code in such a way that it describes what you want to do, and not how you want to do it. It is left up to the compiler to figure out the how.
Examples of declarative programming languages are SQL and Prolog.
The other answers already do a fantastic job explaining what declarative programming is, so I'm just going to provide some examples of why that might be useful.
Context Independence
Declarative Programs are context-independent. Because they only declare what the ultimate goal is, but not the intermediary steps to reach that goal, the same program can be used in different contexts. This is hard to do with imperative programs, because they often depend on the context (e.g. hidden state).
Take yacc as an example. It's a parser generator aka. compiler compiler, an external declarative DSL for describing the grammar of a language, so that a parser for that language can automatically be generated from the description. Because of its context independence, you can do many different things with such a grammar:
Generate a C parser for that grammar (the original use case for yacc)
Generate a C++ parser for that grammar
Generate a Java parser for that grammar (using Jay)
Generate a C# parser for that grammar (using GPPG)
Generate a Ruby parser for that grammar (using Racc)
Generate a tree visualization for that grammar (using GraphViz)
simply do some pretty-printing, fancy-formatting and syntax highlighting of the yacc source file itself and include it in your Reference Manual as a syntactic specification of your language
And many more …
Optimization
Because you don't prescribe the computer which steps to take and in what order, it can rearrange your program much more freely, maybe even execute some tasks in parallel. A good example is a query planner and query optimizer for a SQL database. Most SQL databases allow you to display the query that they are actually executing vs. the query that you asked them to execute. Often, those queries look nothing like each other. The query planner takes things into account that you wouldn't even have dreamed of: rotational latency of the disk platter, for example or the fact that some completely different application for a completely different user just executed a similar query and the table that you are joining with and that you worked so hard to avoid loading is already in memory anyway.
There is an interesting trade-off here: the machine has to work harder to figure out how to do something than it would in an imperative language, but when it does figure it out, it has much more freedom and much more information for the optimization stage.
Loosely:
Declarative programming tends towards:-
Sets of declarations, or declarative statements, each of which has meaning (often in the problem domain) and may be understood independently and in isolation.
Imperative programming tends towards:-
Sequences of commands, each of which perform some action; but which may or may not have meaning in the problem domain.
As a result, an imperative style helps the reader to understand the mechanics of what the system is actually doing, but may give little insight into the problem that it is intended to solve. On the other hand, a declarative style helps the reader to understand the problem domain and the approach that the system takes towards the solution of the problem, but is less informative on the matter of mechanics.
Real programs (even ones written in languages that favor the ends of the spectrum, such as ProLog or C) tend to have both styles present to various degrees at various points, to satisfy the varying complexities and communication needs of the piece. One style is not superior to the other; they just serve different purposes, and, as with many things in life, moderation is key.
Here's an example.
In CSS (used to style HTML pages), if you want an image element to be 100 pixels high and 100 pixels wide, you simply "declare" that that's what you want as follows:
#myImageId {
height: 100px;
width: 100px;
}
You can consider CSS a declarative "style sheet" language.
The browser engine that reads and interprets this CSS is free to make the image appear this tall and this wide however it wants. Different browser engines (e.g., the engine for IE, the engine for Chrome) will implement this task differently.
Their unique implementations are, of course, NOT written in a declarative language but in a procedural one like Assembly, C, C++, Java, JavaScript, or Python. That code is a bunch of steps to be carried out step by step (and might include function calls). It might do things like interpolate pixel values, and render on the screen.
I am sorry, but I must disagree with many of the other answers. I would like to stop this muddled misunderstanding of the definition of declarative programming.
Definition
Referential transparency (RT) of the sub-expressions is the only required attribute of a declarative programming expression, because it is the only attribute which is not shared with imperative programming.
Other cited attributes of declarative programming, derive from this RT. Please click the hyperlink above for the detailed explanation.
Spreadsheet example
Two answers mentioned spreadsheet programming. In the cases where the spreadsheet programming (a.k.a. formulas) does not access mutable global state, then it is declarative programming. This is because the mutable cell values are the monolithic input and output of the main() (the entire program). The new values are not written to the cells after each formula is executed, thus they are not mutable for the life of the declarative program (execution of all the formulas in the spreadsheet). Thus relative to each other, the formulas view these mutable cells as immutable. An RT function is allowed to access immutable global state (and also mutable local state).
Thus the ability to mutate the values in the cells when the program terminates (as an output from main()), does not make them mutable stored values in the context of the rules. The key distinction is the cell values are not updated after each spreadsheet formula is performed, thus the order of performing the formulas does not matter. The cell values are updated after all the declarative formulas have been performed.
Declarative programming is the picture, where imperative programming is instructions for painting that picture.
You're writing in a declarative style if you're "Telling it what it is", rather than describing the steps the computer should take to get to where you want it.
When you use XML to mark-up data, you're using declarative programming because you're saying "This is a person, that is a birthday, and over there is a street address".
Some examples of where declarative and imperative programming get combined for greater effect:
Windows Presentation Foundation uses declarative XML syntax to describe what a user interface looks like, and what the relationships (bindings) are between controls and underlying data structures.
Structured configuration files use declarative syntax (as simple as "key=value" pairs) to identify what a string or value of data means.
HTML marks up text with tags that describe what role each piece of text has in relation to the whole document.
Declarative Programming is programming with declarations, i.e. declarative sentences. Declarative sentences have a number of properties that distinguish them from imperative sentences. In particular, declarations are:
commutative (can be reordered)
associative (can be regrouped)
idempotent (can repeat without change in meaning)
monotonic (declarations don't subtract information)
A relevant point is that these are all structural properties and are orthogonal to subject matter. Declarative is not about "What vs. How". We can declare (represent and constrain) a "how" just as easily as we declare a "what". Declarative is about structure, not content. Declarative programming has a significant impact on how we abstract and refactor our code, and how we modularize it into subprograms, but not so much on the domain model.
Often, we can convert from imperative to declarative by adding context. E.g. from "Turn left. (... wait for it ...) Turn Right." to "Bob will turn left at intersection of Foo and Bar at 11:01. Bob will turn right at the intersection of Bar and Baz at 11:06." Note that in the latter case the sentences are idempotent and commutative, whereas in the former case rearranging or repeating the sentences would severely change the meaning of the program.
Regarding monotonic, declarations can add constraints which subtract possibilities. But constraints still add information (more precisely, constraints are information). If we need time-varying declarations, it is typical to model this with explicit temporal semantics - e.g. from "the ball is flat" to "the ball is flat at time T". If we have two contradictory declarations, we have an inconsistent declarative system, though this might be resolved by introducing soft constraints (priorities, probabilities, etc.) or leveraging a paraconsistent logic.
Describing to a computer what you want, not how to do something.
imagine an excel page. With columns populated with formulas to calculate you tax return.
All the logic is done declared in the cells, the order of the calculation is by determine by formula itself rather than procedurally.
That is sort of what declarative programming is all about. You declare the problem space and the solution rather than the flow of the program.
Prolog is the only declarative language I've use. It requires a different kind of thinking but it's good to learn if just to expose you to something other than the typical procedural programming language.
I have refined my understanding of declarative programming, since Dec 2011 when I provided an answer to this question. Here follows my current understanding.
The long version of my understanding (research) is detailed at this link, which you should read to gain a deep understanding of the summary I will provide below.
Imperative programming is where mutable state is stored and read, thus the ordering and/or duplication of program instructions can alter the behavior (semantics) of the program (and even cause a bug, i.e. unintended behavior).
In the most naive and extreme sense (which I asserted in my prior answer), declarative programming (DP) is avoiding all stored mutable state, thus the ordering and/or duplication of program instructions can NOT alter the behavior (semantics) of the program.
However, such an extreme definition would not be very useful in the real world, since nearly every program involves stored mutable state. The spreadsheet example conforms to this extreme definition of DP, because the entire program code is run to completion with one static copy of the input state, before the new states are stored. Then if any state is changed, this is repeated. But most real world programs can't be limited to such a monolithic model of state changes.
A more useful definition of DP is that the ordering and/or duplication of programming instructions do not alter any opaque semantics. In other words, there are not hidden random changes in semantics occurring-- any changes in program instruction order and/or duplication cause only intended and transparent changes to the program's behavior.
The next step would be to talk about which programming models or paradigms aid in DP, but that is not the question here.
It's a method of programming based around describing what something should do or be instead of describing how it should work.
In other words, you don't write algorithms made of expressions, you just layout how you want things to be. Two good examples are HTML and WPF.
This Wikipedia article is a good overview: http://en.wikipedia.org/wiki/Declarative_programming
Since I wrote my prior answer, I have formulated a new definition of the declarative property which is quoted below. I have also defined imperative programming as the dual property.
This definition is superior to the one I provided in my prior answer, because it is succinct and it is more general. But it may be more difficult to grok, because the implication of the incompleteness theorems applicable to programming and life in general are difficult for humans to wrap their mind around.
The quoted explanation of the definition discusses the role pure functional programming plays in declarative programming.
Declarative vs. Imperative
The declarative property is weird, obtuse, and difficult to capture in a technically precise definition that remains general and not ambiguous, because it is a naive notion that we can declare the meaning (a.k.a semantics) of the program without incurring unintended side effects. There is an inherent tension between expression of meaning and avoidance of unintended effects, and this tension actually derives from the incompleteness theorems of programming and our universe.
It is oversimplification, technically imprecise, and often ambiguous to define declarative as “what to do” and imperative as “how to do”. An ambiguous case is the “what” is the “how” in a program that outputs a program— a compiler.
Evidently the unbounded recursion that makes a language Turing complete, is also analogously in the semantics— not only in the syntactical structure of evaluation (a.k.a. operational semantics). This is logically an example analogous to Gödel's theorem— “any complete system of axioms is also inconsistent”. Ponder the contradictory weirdness of that quote! It is also an example that demonstrates how the expression of semantics does not have a provable bound, thus we can't prove2 that a program (and analogously its semantics) halt a.k.a. the Halting theorem.
The incompleteness theorems derive from the fundamental nature of our universe, which as stated in the Second Law of Thermodynamics is “the entropy (a.k.a. the # of independent possibilities) is trending to maximum forever”. The coding and design of a program is never finished— it's alive!— because it attempts to address a real world need, and the semantics of the real world are always changing and trending to more possibilities. Humans never stop discovering new things (including errors in programs ;-).
To precisely and technically capture this aforementioned desired notion within this weird universe that has no edge (ponder that! there is no “outside” of our universe), requires a terse but deceptively-not-simple definition which will sound incorrect until it is explained deeply.
Definition:
The declarative property is where there can exist only one possible set of statements that can express each specific modular semantic.
The imperative property3 is the dual, where semantics are inconsistent under composition and/or can be expressed with variations of sets of statements.
This definition of declarative is distinctively local in semantic scope, meaning that it requires that a modular semantic maintain its consistent meaning regardless where and how it's instantiated and employed in global scope. Thus each declarative modular semantic should be intrinsically orthogonal to all possible others— and not an impossible (due to incompleteness theorems) global algorithm or model for witnessing consistency, which is also the point of “More Is Not Always Better” by Robert Harper, Professor of Computer Science at Carnegie Mellon University, one of the designers of Standard ML.
Examples of these modular declarative semantics include category theory functors e.g. the Applicative, nominal typing, namespaces, named fields, and w.r.t. to operational level of semantics then pure functional programming.
Thus well designed declarative languages can more clearly express meaning, albeit with some loss of generality in what can be expressed, yet a gain in what can be expressed with intrinsic consistency.
An example of the aforementioned definition is the set of formulas in the cells of a spreadsheet program— which are not expected to give the same meaning when moved to different column and row cells, i.e. cell identifiers changed. The cell identifiers are part of and not superfluous to the intended meaning. So each spreadsheet result is unique w.r.t. to the cell identifiers in a set of formulas. The consistent modular semantic in this case is use of cell identifiers as the input and output of pure functions for cells formulas (see below).
Hyper Text Markup Language a.k.a. HTML— the language for static web pages— is an example of a highly (but not perfectly3) declarative language that (at least before HTML 5) had no capability to express dynamic behavior. HTML is perhaps the easiest language to learn. For dynamic behavior, an imperative scripting language such as JavaScript was usually combined with HTML. HTML without JavaScript fits the declarative definition because each nominal type (i.e. the tags) maintains its consistent meaning under composition within the rules of the syntax.
A competing definition for declarative is the commutative and idempotent properties of the semantic statements, i.e. that statements can be reordered and duplicated without changing the meaning. For example, statements assigning values to named fields can be reordered and duplicated without changed the meaning of the program, if those names are modular w.r.t. to any implied order. Names sometimes imply an order, e.g. cell identifiers include their column and row position— moving a total on spreadsheet changes its meaning. Otherwise, these properties implicitly require global consistency of semantics. It is generally impossible to design the semantics of statements so they remain consistent if randomly ordered or duplicated, because order and duplication are intrinsic to semantics. For example, the statements “Foo exists” (or construction) and “Foo does not exist” (and destruction). If one considers random inconsistency endemical of the intended semantics, then one accepts this definition as general enough for the declarative property. In essence this definition is vacuous as a generalized definition because it attempts to make consistency orthogonal to semantics, i.e. to defy the fact that the universe of semantics is dynamically unbounded and can't be captured in a global coherence paradigm.
Requiring the commutative and idempotent properties for the (structural evaluation order of the) lower-level operational semantics converts operational semantics to a declarative localized modular semantic, e.g. pure functional programming (including recursion instead of imperative loops). Then the operational order of the implementation details do not impact (i.e. spread globally into) the consistency of the higher-level semantics. For example, the order of evaluation of (and theoretically also the duplication of) the spreadsheet formulas doesn't matter because the outputs are not copied to the inputs until after all outputs have been computed, i.e. analogous to pure functions.
C, Java, C++, C#, PHP, and JavaScript aren't particularly declarative.
Copute's syntax and Python's syntax are more declaratively coupled to
intended results, i.e. consistent syntactical semantics that eliminate the extraneous so one can readily
comprehend code after they've forgotten it. Copute and Haskell enforce
determinism of the operational semantics and encourage “don't repeat
yourself” (DRY), because they only allow the pure functional paradigm.
2 Even where we can prove the semantics of a program, e.g. with the language Coq, this is limited to the semantics that are expressed in the typing, and typing can never capture all of the semantics of a program— not even for languages that are not Turing complete, e.g. with HTML+CSS it is possible to express inconsistent combinations which thus have undefined semantics.
3 Many explanations incorrectly claim that only imperative programming has syntactically ordered statements. I clarified this confusion between imperative and functional programming. For example, the order of HTML statements does not reduce the consistency of their meaning.
Edit: I posted the following comment to Robert Harper's blog:
in functional programming ... the range of variation of a variable is a type
Depending on how one distinguishes functional from imperative
programming, your ‘assignable’ in an imperative program also may have
a type placing a bound on its variability.
The only non-muddled definition I currently appreciate for functional
programming is a) functions as first-class objects and types, b)
preference for recursion over loops, and/or c) pure functions— i.e.
those functions which do not impact the desired semantics of the
program when memoized (thus perfectly pure functional
programming doesn't exist in a general purpose denotational semantics
due to impacts of operational semantics, e.g. memory
allocation).
The idempotent property of a pure function means the function call on
its variables can be substituted by its value, which is not generally
the case for the arguments of an imperative procedure. Pure functions
seem to be declarative w.r.t. to the uncomposed state transitions
between the input and result types.
But the composition of pure functions does not maintain any such
consistency, because it is possible to model a side-effect (global
state) imperative process in a pure functional programming language,
e.g. Haskell's IOMonad and moreover it is entirely impossible to
prevent doing such in any Turing complete pure functional programming
language.
As I wrote in 2012 which seems to the similar consensus of
comments in your recent blog, that declarative programming is an
attempt to capture the notion that the intended semantics are never
opaque. Examples of opaque semantics are dependence on order,
dependence on erasure of higher-level semantics at the operational
semantics layer (e.g. casts are not conversions and reified generics
limit higher-level semantics), and dependence on variable values
which can not be checked (proved correct) by the programming language.
Thus I have concluded that only non-Turing complete languages can be
declarative.
Thus one unambiguous and distinct attribute of a declarative language
could be that its output can be proven to obey some enumerable set of
generative rules. For example, for any specific HTML program (ignoring
differences in the ways interpreters diverge) that is not scripted
(i.e. is not Turing complete) then its output variability can be
enumerable. Or more succinctly an HTML program is a pure function of
its variability. Ditto a spreadsheet program is a pure function of its
input variables.
So it seems to me that declarative languages are the antithesis of
unbounded recursion, i.e. per Gödel's second incompleteness
theorem self-referential theorems can't be proven.
Lesie Lamport wrote a fairytale about how Euclid might have
worked around Gödel's incompleteness theorems applied to math proofs
in the programming language context by to congruence between types and
logic (Curry-Howard correspondence, etc).
Declarative programming is "the act of programming in languages that conform to the mental model of the developer rather than the operational model of the machine".
The difference between declarative and imperative programming is well
illustrated by the problem of parsing structured data.
An imperative program would use mutually recursive functions to consume input
and generate data. A declarative program would express a grammar that defines
the structure of the data so that it can then be parsed.
The difference between these two approaches is that the declarative program
creates a new language that is more closely mapped to the mental model of the
problem than is its host language.
It may sound odd, but I'd add Excel (or any spreadsheet really) to the list of declarative systems. A good example of this is given here.
I'd explain it as DP is a way to express
A goal expression, the conditions for - what we are searching for. Is there one, maybe or many?
Some known facts
Rules that extend the know facts
...and where there is a deduct engine usually working with a unification algorithm to find the goals.
As far as I can tell, it started being used to describe programming systems like Prolog, because prolog is (supposedly) about declaring things in an abstract way.
It increasingly means very little, as it has the definition given by the users above. It should be clear that there is a gulf between the declarative programming of Haskell, as against the declarative programming of HTML.
A couple other examples of declarative programming:
ASP.Net markup for databinding. It just says "fill this grid with this source", for example, and leaves it to the system for how that happens.
Linq expressions
Declarative programming is nice because it can help simplify your mental model* of code, and because it might eventually be more scalable.
For example, let's say you have a function that does something to each element in an array or list. Traditional code would look like this:
foreach (object item in MyList)
{
DoSomething(item);
}
No big deal there. But what if you use the more-declarative syntax and instead define DoSomething() as an Action? Then you can say it this way:
MyList.ForEach(DoSometing);
This is, of course, more concise. But I'm sure you have more concerns than just saving two lines of code here and there. Performance, for example. The old way, processing had to be done in sequence. What if the .ForEach() method had a way for you to signal that it could handle the processing in parallel, automatically? Now all of a sudden you've made your code multi-threaded in a very safe way and only changed one line of code. And, in fact, there's a an extension for .Net that lets you do just that.
If you follow that link, it takes you to a blog post by a friend of mine. The whole post is a little long, but you can scroll down to the heading titled "The Problem" _and pick it up there no problem.*
It depends on how you submit the answer to the text. Overall you can look at the programme at a certain view but it depends what angle you look at the problem. I will get you started with the programme:
Dim Bus, Car, Time, Height As Integr
Again it depends on what the problem is an overall. You might have to shorten it due to the programme. Hope this helps and need the feedback if it does not.
Thank You.