It seems easy to find general info on a specific 'operator' (method, syntactic sugar), but I can't seem to find anything that has a list of all, or even just most, of these goodies. As such, it makes it fairly difficult, or at least overly time consuming, to work through learning the language.
I have already looked over this question. While it has great information, and definitely shows you how to find any information you need, I was hoping for something like a 'pocket ref' that just had all the relevant info and was only dedicated to that.
So, my question is this:
Is there a such a list?
Am I getting ahead of myself by looking for such a reference early on in learning the language?
Thanks in advance.
Well, a list of all operators makes as much sense as a list of all methods in the library, regardless of the type. It isn't going to be particularly useful except for finding information about a specific operator.
However, if you do want one, at any ScalaDoc site (http://www.scala-lang.org/api/current/ for the standard library) there is an alphabetic index just under the search bar. The first link (#) lists all the non-alphabetic methods (i.e. "operators").
Many of these are rarely used, or only in specific circumstances.
Obviously, any other library can introduce its own operators, and you'll need to check its own documentation.
Related
When I return something of type Option, it seems useful to explain in the name of the function name that it is an option, not the thing itself. For example, seqs have reduceOption. Is there a standard naming convention? Things I have seen:
maybeFunctionName
functionNameOption
- neither seems to be all that great.
reduceOption and friends (headOption, etc.) are only named that way to distinguish them from their unsafe alternatives (which arguably shouldn't exist in the first place—i.e, there should just be a head that returns an Option[A]).
whateverOption isn't the usual practice in the standard library (or most other Scala libraries that I'm aware of), and in general you shouldn't need or want to use this kind of Hungarian notation in Scala.
Why would you want to make your function names longer? It doesn't contribute anything, as the fact that it returns an Option is obvious when looking at the function's type.
reduceOption is sort of a special case, since in most cases you really want to use reduce, except that it doesn't work on empty sequences.
Soooo...
Semigroups, Monoids, Monads, Functors, Lenses, Catamorphisms, Anamorphisms, Arrows... These all sound good, and after an exercise or two (or ten), you can grasp their essence. And with Scalaz, you get them for free...
However, in terms of real-world programming, I find myself struggling to find usages to these notions. Yes, of course I always find someone on the web using Monads for IO or Lenses in Scala, but... still...
What I am trying to find is something along the "prescriptive" lines of a pattern. Something like: "here, you are trying to solves this, and one good way to solve it is by using lenses this way!"
Suggestions?
Update: Something along these lines, with a book or two, would be great (thanks Paul): Examples of GoF Design Patterns in Java's core libraries
The key to functional programming is abstraction, and composability of abstractions. Monads, Arrows, Lenses, these are all abstractions which have proven themselves useful, mostly because they are composable. You've asked for a "prescriptive" answer, but I'm going to say no. Perhaps you're not convinced that functional programming matters?
I'm sure plenty of people on StackOverflow would be more than happy to try and help you solve a specific problem the FP way. Have a list of stuff and you want to traverse the list and build up some result? Use a fold. Want to parse XML? hxt uses arrows for that. And monads? Well, tons of data types turn out to be Monads, so learn about them and you'll discover a wealth of ways you can manipulate these data types. But its kind of hard to just pull examples out of thin air and say "lenses are the Right Way to do this", "monoids are the best way to do that", etc. How would you explain to a newbie what the use of a for loop is? If you want to [blank], then use a for loop [in this way]. It's so general; there are tons of ways to use a for loop. The same goes for these FP abstractions.
If you have many years of OOP experience, then don't forget you were once a newbie at OOP. It takes time to learn the FP way, and even more time to unlearn some OOP tendencies. Give it time and you will find plenty of uses for a Functional approach.
I gave a talk back in September focused on the practical application of monoids and applicative functors/monads via scalaz.Validation. I gave another version of the same talk at the scala Lift Off, where the emphasis was more on the validation. I would watch the first talk until I start on validations and then skip to the second talk (27 minutes in).
There's also a gist I wrote which shows how you might use Validation in a "practical" application. That is, if you are designing software for nightclub bouncers.
I think you can take the reverse approach and instead when writing a small piece of functionality, ask yourself whether any of those would apply: Semigroups, Monoids, Monads, Functors, Lenses, Catamorphisms, Anamorphisms, Arrows... A lots of those concepts can be used in a local way.
Once you start down that route, you may see usage everywhere. For me, I sort of get Semigroups, Monoids, Monads, Functors. So take the example of answering this question How do I populate a list of objects with new values. It's a real usage for the person asking the question (a self described noob). I am trying to answer in a simple way but I have to refrain myself from scratching the itch "there are monoids in here".
Scratching it now: using foldMap and the fact that Int and List are monoids and that the monoid property is preserved when dealing with tuple, maps and options:
// using scalaz
listVar.sliding(2).toList.foldMap{
case List(prev, i) => Some(Map(i -> (1, Some(List(math.abs(i - prev))))))
case List(i) => Some(Map(i -> (1, None)))
case _ => None
}.map(_.mapValues{ case (count, gaps) => (count, gaps.map(_.min)) })
But I don't come to that result by thinking I will use hard core functional programming. It comes more naturally by thinking this seems simpler if I compose those monoids combined with the fact that scalaz has utility methods like foldMap. Interestingly when looking at the resulting code it's not obvious that I'm totally thinking in terms of monoid.
You might like this talk by Chris Marshall. He covers a couple of Scalaz goodies - namely Monoid and Validation - with many practical examples. Ittay Dror has written a very accessible post on how Functor, Applicative Functor, and Monad can be useful in practice. Eric Torreborre and Debasish Gosh's blogs also have a bunch of posts covering use cases for categorical constructs.
This answer just lists a few links instead of providing some real substance here. (Too lazy to write.) Hope you find it helpful anyway.
I understand your situation, but you will find that to learn functional programming you will need to adjust your point of view to the documentation you find, instead of the other way around. Luckily in Scala you have the possibility of becoming a functional programmer gradually.
To answer your questions and explain the point-of-view difference, I need to distinguish between "type classes" (monoids, functors, arrows), mathematically called "structures", and generic operations or algorithms (catamorphisms or folds, anamorphisms or unfolds, etc.). These two often interact, since many generic operations are defined for specific classes of data types.
You look for prescriptive answers similar to design patterns: when does this concept apply? The truth is that you have surely seen the prescriptive answers, and they are simply the definitions of the different concepts. The problem (for you) is that those answers are intrinsically different from design patterns, but it is so for good reasons.
On the one hand, generic algorithms are not design patterns, which suggest a structure for the code you write; they are abstractions defined in the language which you can directly apply. They are general descriptions for common algorithms which you already implement today, but by hand. For instance, whenever you are computing the maximum element of a list by scanning it, you are hardcoding a fold; when you sum elements, you are doing the same; and so on. When you recognize that, you can declare the essence of the operation you are performing by calling the appropriate fold function. This way, you save code and bugs (no opportunity for off-by-one errors), and you save the reader the effort to read all the needed code.
On the other hand, structures concern not the goal you have in mind but properties of the entities you are modeling. They are more useful for bottom-up software construction, rather than top-down: when defining your data, you can declare that it is a e.g. a monoid. Later, when processing your data, you have the opportunity to use operations on e.g. monoids to implement your processing. In some cases it is useful to strive to express your algorithm in terms of the predefined ones. For instance, very often if you need to reduce a tree to a single value, a fold can do most or all of what you need. Of course, you can also declare that your data type is a monoid when you need a generic algorithm on monoids; but the earlier you notice that, the earlier you can start reusing generic algorithms for monoids.
Last advice is that probably most of the documentation you will find about these concepts concerns Haskell, because this language has been around for much more time and supports them in a quite elegant way. Quite recommended here are Learn you a Haskell for Great Good, a Haskell course for beginners, where among others chapters 11 to 14 focus on some type classes, and Typeclassopedia (which contains links to various articles with specific examples). EDIT: Finally, an example of applications of Monoids, taken from Typeclassopedia, is here: http://apfelmus.nfshost.com/articles/monoid-fingertree.html. I'm not saying there is little documentation for Scala, just that there is more in Haskell, and Haskell is where the application of these concepts to programming was born.
Most people agree that LISP helps to solve problems that are not well defined, or that are not fully understood at the beginning of the project.
"Not fully understood"" might indicate that we don't know what problem we are trying to solve, so the developer refines the problem domain continuously. But isn't this process language independent?
All this refinement does not take away the need for, say, developing algorithms/solutions for the final problem that does need to be solved. And that is the actual work.
So, I'm not sure what advantage LISP provides if the developer has no idea where he's going i.e. solving a problem that is not finalised yet.
Lisp (not "LISP") has a number of advantages when you're facing problems that are not well-defined. First of all, you have a REPL where you can quickly experiment with -- that helps in sketching out quick functions and trying to play with them, leading to a very rapid development cycle. Second, having a dynamically typed language is working well in this context too: with a statically typed language you need to "design more" before you begin, and changing the design leads to changing more code -- in contrast, with Lisps you just write the code and the data it operates on can change as needed. In addition to these, there's the usual benefits of a functional language -- one with first class lambda functions, etc (eg, garbage collection).
In general, these advantage have been finding their way into other languages. For example, Javascript has everything that I listed so far. But there is one more advantage for Lisps that is still not present in other languages -- macros. This is an important tool to use when your problem calls for a domain specific language. Basically, in Lisp you can extend the language with constructs that are specific to your problem -- even if these constructs lead to a completely different language.
Finally, you need to plan ahead for what happens when the code becomes more than a quick experiment. In this case you want your language to cope with "growing scripts into applications" -- for example, having a module system means that you can get a more "serious"
application. For example, in Racket you can get your solution separated into such modules, where each can be written in its own language -- it even has a statically typed language which makes it possible to start with a dynamically typed development cycle and once the code becomes more stable and/or big enough that maintenance becomes difficult, you can switch some modules into the static language and get the usual benefits from that. Racket is actually unique among Lisps and Schemes in this kind of support, but even with others the situation is still far more advanced than in non-Lisp languages.
In AI (Artificial Intelligence) historically Lisp was seen as the AI assembly language. It was used to build higher-level languages which help to work with the problem domain in a more direct way. Many of these domains need a lot of 'knowledge' for finding usable answers.
A typical example is an expert system for, say, oil exploration. The expert system gets as inputs (geological) observations and gives information about the chances to find oil, what kind of oil, in what depths, etc. To do that it needs 'expert knowledge' how to interpret the data. When you start such a project to develop such an expert system it is typically not clear what kind of inferences are needed, what kind of 'knowledge' experts can provide and how this 'knowledge' can be written down for a computer.
In this case one typically develops new languages on top of Lisp and you are not working with a fixed predefined language.
As an example see this old paper about Dipmeter Advisor, a Lisp-based expert system developed by Schlumberger in the 1980s.
So, Lisp does not solve any problems. But it was originally used to solve problems that are complex to program, by providing new language layers which should make it easier to express the domain 'knowledge', rules, constraints, etc. to find solutions which are not straight forward to compute.
The "big" win with a language that allows for incremental development is that you (typically) has a read-eval-print loop (or "listener" or "console") that you interact with, plus you tend to not need to lose state when you compile and load new code.
The ability to keep state around from test run to test run means that lengthy computations that are untouched by your changes can simply be kept around instead of being re-computed.
This allows you to experiment and iterate faster. Being able to iterate faster means that exploration is less of a hassle. Very useful for exploratory programming, something that is typical with dealing with less well-defined problems.
I'm going to be giving a short (30-40 mins) lunch-time talk on Scala to technical staff at my company. I'd like some suggestions for what would be the most appropriate content. Most people attending will have experience in Java and/or C# (plus various other languages).
What are the key things to cover? I'd like to give a brief introduction to the Scala syntax so that people don't feel lost when looking at code examples. I'll also cover some of the history behind the language and its designers. What would help people get the most out of the talk?
People are almost certainly coming to talk to get an answer to the question, "Why should I use Scala?" Anything you can provide to help them answer that will be valuable.
Keep the discussion of the history and the personalities behind Scala to a minimum.
A whirlwind tour of the syntax is useful, but keep it short.
Spend a good chunk of the talk demonstrating examples and comparisons to Java. Show cases where Scala shines. You should literally be running and executing code so that people get a real, hands-on feel for how things work.
Make sure to cover weaknesses, too! Provide an objective and balanced overview.
I gave a similar talk - mostly to those with a Java background. I felt that taking a piece of real Java (about 30 lines) and iteratively adding scala features worked pretty well. The 30 lines of Java eventually ended up as 6 (six!) of scala. The point being (of course) that 6 lines are more readable and maintainable than 30.
I converted the scala to line-by-line Java equivalent and then introduced:
Type inference
Option
Closures
Pattern-matching (on lists)
Type aliases
Tail recursion
I found that this segment took quite a long time because the audience were very interested in the minutiae of scala's syntax (especially around function-expressions). Before undertaking the pattern-matching bit, I had a slide explaining the various things you could use in a match.
Tough. One has to balance the new and the familiar. For instance:
Talk about traits, how they differ from interfaces and multiple inheritance. Note that most methods in all of Scala collections can actually be found on the trait Traversable, which has a single abstract method: foreach.
Speak of functions and partial functions, show map/filter/foreach, and how they make use of functions.
Talk about pattern matching -- show how unapply is used to enable representation independence, while at the same time case classes make the common case easy.
Above all AVOID any topic that might be difficult to understand quickly, or you may waste time on them. For example of great topics I wouldn't talk about: self types, variance, for-comprehensions.
Pick more topics than you have time for. Let the public steer the conversation towards the topcis they are more interested in. If anyone starts to boggle down a topic too much, say you'll be pleased to explain it in more details later, and ask if they would mind if you moved to another topic. On the other hand, if everyone seems to be picking up on one thing in particular, stay with it. Otherwise, it might feel like you want to hide something.
I gave a presentation on re-writing Java classes in Scala. It has lots of examples of Java -> Scala and (hopefully) makes the gains obvious. Feel free to borrow any content you want... presentation took 1hr 10minutes so you might want to cut some stuff out.
Presentation: http://www.colinhowe.co.uk/downloads/rewriting-java-in-scala.ppt
Video: http://skillsmatter.com/podcast/java-jee/re-writing-java-classes-in-scala-and-making-your-code-lovely
You could do worse than running through Jonas Bonér's presentation, Pragmatic Real-World Scala. Perhaps skip some advanced topics in there on different applications of traits and self-type annotations.
With the growth of dynamically typed languages, as they give us more flexibility, there is the very likely probability that people will write programs that go beyond what the specification allows.
My thinking was influenced by this question, when I read the answer by bobince:
A question about JavaScript's slice and splice methods
The basic thought is that splice, in Javascript, is specified to be used in only certain situations, but, it can be used in others, and there is nothing that the language can do to stop it, as the language is designed to be extremely flexible.
Unless someone reads through the specification, and decides to adhere to it, I am fairly certain that there are many such violations occuring.
Is this a problem, or a natural extension of writing such flexible languages? Or should we expect tools like JSLint to help be the specification police?
I liked one answer in this question, that the implementation of python is the specification. I am curious if that is actually closer to the truth for these types of languages, that basically, if the language allows you to do something then it is in the specification.
Is there a Python language specification?
UPDATE:
After reading a couple of comments, I thought I would check the splice method in the spec and this is what I found, at the bottom of pg 104, http://www.mozilla.org/js/language/E262-3.pdf, so it appears that I can use splice on the array of children without violating the spec. I just don't want people to get bogged down in my example, but hopefully to consider the question.
The splice function is intentionally generic; it does not require that its this value be an Array object.
Therefore it can be transferred to other kinds of objects for use as a method. Whether the splice function
can be applied successfully to a host object is implementation-dependent.
UPDATE 2:
I am not interested in this being about javascript, but language flexibility and specs. For example, I expect that the Java spec specifies you can't put code into an interface, but using AspectJ I do that frequently. This is probably a violation, but the writers didn't predict AOP and the tool was flexible enough to be bent for this use, just as the JVM is also flexible enough for Scala and Clojure.
Whether a language is statically or dynamically typed is really a tiny part of the issue here: a statically typed one may make it marginally easier for code to enforce its specs, but marginally is the key word here. Only "design by contract" -- a language letting you explicitly state preconditions, postconditions and invariants, and enforcing them -- can help ward you against users of your libraries empirically discovering what exactly the library will let them get away with, and taking advantage of those discoveries to go beyond your design intentions (possibly constraining your future freedom in changing the design or its implementation). And "design by contract" is not supported in mainstream languages -- Eiffel is the closest to that, and few would call it "mainstream" nowadays -- presumably because its costs (mostly, inevitably, at runtime) don't appear to be justified by its advantages. "Argument x must be a prime number", "method A must have been previously called before method B can be called", "method C cannot be called any more once method D has been called", and so on -- the typical kinds of constraints you'd like to state (and have enforced implicitly, without having to spend substantial programming time and energy checking for them yourself) just don't lend themselves well to be framed in the context of what little a statically typed language's compiler can enforce.
I think that this sort of flexibility is an advantage as long as your methods are designed around well defined interfaces rather than some artificial external "type" metadata. Most of the array functions only expect an object with a length property. The fact that they can all be applied generically to lots of different kinds of objects is a boon for code reuse.
The goal of any high level language design should be to reduce the amount of code that needs to be written in order to get stuff done- without harming readability too much. The more code that has to be written, the more bugs get introduced. Restrictive type systems can be, (if not well designed), a pervasive lie at worst, a premature optimisation at best. I don't think overly restrictive type systems aid in writing correct programs. The reason being that the type is merely an assertion, not necessarily based on evidence.
By contrast, the array methods examine their input values to determine whether they have what they need to perform their function. This is duck typing, and I believe that this is more scientific and "correct", and it results in more reusable code, which is what you want. You don't want a method rejecting your inputs because they don't have their papers in order. That's communism.
I do not think your question really has much to do with dynamic vs. static typing. Really, I can see two cases: on one hand, there are things like Duff's device that martin clayton mentioned; that usage is extremely surprising the first time you see it, but it is explicitly allowed by the semantics of the language. If there is a standard, that kind of idiom may appear in later editions of the standard as a specific example. There is nothing wrong with these; in fact, they can (unless overused) be a great productivity boost.
The other case is that of programming to the implementation. Such a case would be an actual abuse, coming from either ignorance of a standard, or lack of a standard, or having a single implementation, or multiple implementations that have varying semantics. The problem is that code written in this way is at best non-portable between implementations and at worst limits the future development of the language, for fear that adding an optimization or feature would break a major application.
It seems to me that the original question is a bit of a non-sequitor. If the specification explicitly allows a particular behavior (as MUST, MAY, SHALL or SHOULD) then anything compiler/interpreter that allows/implements the behavior is, by definition, compliant with the language. This would seem to be the situation proposed by the OP in the comments section - the JavaScript specification supposedly* says that the function in question MAY be used in different situations, and thus it is explicitly allowed.
If, on the other hand, a compiler/interpreter implements or allows behavior that is expressly forbidden by a specification, then the compiler/interpreter is, by definition, operating outside the specification.
There is yet a third scenario, and an associated, well defined, term for those situations where the specification does not define a behavior: undefined. If the specification does not actually specify a behavior given a particular situation, then the behavior is undefined, and may be handled either intentionally or unintentionally by the compiler/interpreter. It is then the responsibility of the developer to realize that the behavior is not part of the specification, and, should s/he choose to leverage the behavior, the developer's application is thereby dependent upon the particular implementation. The interpreter/compiler providing that implementation is under no obligation to maintain the officially undefined behavior beyond backwards compatibility and whatever commitments the producer may make. Furthermore, a later iteration of the language specification may define the previously undefined behavior, making the compiler/interpreter either (a) non-compliant with the new iteration, or (b) come out with a new patch/version to become compliant, thereby breaking older versions.
* "supposedly" because I have not seen the spec, myself. I go by the statements made, above.