The array we had in prepend is no longer used. In GC environments, it is marked as no longer being used, and eventually the garbage collector recycles the memory it is using. We may not be creating 3, stack frames, but we are creating three thousand new arrays and copying elements into each and every one of them.
Although the maximum amount of memory does not grow, the thrashing as we create short-lived arrays is very bad, and we do a lot of work copying elements from one array to another. Key Point : Our [first, The had a bit word, meaning that it was very fast to store and retrieve bit values. In broad terms, this means that a single bit word could store two separate bit values and it was very fast to save and retrieve pairs of values.
If you had two bit values and wished to write them to the register, the CONS macro would take the values and write them to a bit word. The bit values were used as pointers that could refer to a location in memory, so in effect, a cons cell was a little data structure with two pointers to other cons cells. Although the used core memory, it still used vacuum tubes for its logic.
Thus, the design of programming languages and algorithms was driven by what could be accomplished with limited memory and performance. We can make a list by calling cons repeatedly, and terminating it with null :. Notice that though JavaScript displays our list as if it is composed of arrays nested within each other like Russian Dolls, in reality the arrays refer to each other with references, so [1,[2,[3,[4,[5,null]]]]] is actually more like:. But it works the same way: If we want the head of a list, we call car on it:.
Getting one reference to a structure that already exists is faster than copying a bunch of elements. So now we understand that in Lisp, a lot of things use linked lists, and they do that in part because it was what the hardware made possible. Getting back to JavaScript now, when we write [first, That being said, it is easy to understand and helps us grasp how literals and destructuring works, and how recursive algorithms ought to mirror the self-similarity of the data structures they manipulate.
If [first, Well, linked lists are fast for a few things, like taking the front element off a list, and taking the remainder of a list. But not for iterating over a list: Pointer chasing through memory is quite a bit slower than incrementing an index. In addition to the extra fetches to dereference pointers, pointer chasing suffers from cache misses. And if you want an arbitrary item from a list, you have to iterate through the list element by element, whereas with the indexed array you just fetch it.
If we make any change other than cons-ing a new element to the front, we are changing both the new list and the old list. For these and other reasons, almost all languages today make it possible to use a fast array or vector type that is optimized for iteration, and even Lisp now has a variety of data structures that are optimized for specific use cases.
Although we showed how to use tail calls to map and fold over arrays with [first, But it is an extremely simple illustration of how recursion works when you have a self-similar means of constructing a data structure. Lists are obviously very handy for homogeneous collections of things, like a shopping list:.
Remembering that the name is the first item is error-prone, and being expected to look at user[0][1] and know that we are talking about a surname is unreasonable.
So back when lists were the only things available, programmers would introduce constants to make things easier on themselves:. Over time, this need to build heterogeneous data structures with access to members by name evolved into the Dictionary data type, a mapping from a unique set of objects to another set of objects. Dictionaries store key-value pairs, so instead of binding NAME to 0 and then storing a name in an array at index 0 , we can bind a name directly to name in a dictionary, and we let JavaScript sort out whether the implementation is a list of key-value pairs, a hashed collection, a tree of some sort, or anything else.
JavaScript has a literal syntax for creating objects. This object maps values to the keys year , month , and day :. Values contained within an object work just like values contained within an array, we access them by reference to the original:. Expressions can be used for keys as well. All containers can contain any value, including functions or other containers, like a fat arrow function:.
There are some other technical differences between binding a named function expression and using compact method syntax, but they are not relevant here. We will generally prefer compact method syntax whenever we can. Just as we saw with arrays, we can write destructuring assignments with literal object syntax.
So, we can write:. It is very common to write things like title: title when destructuring objects. So JavaScript supports a further syntactic optimization:. In essence, this simple implementation used functions to create an abstraction with named elements. We can then perform the equivalent of [first, What about mapping? As we saw above, and discussed in Garbage, Garbage Everywhere , it is fast to iterate forward through a linked list.
The problem here is that linked lists are constructed back-to-front, but we iterate over them front-to-back. So to copy a list, we have to save all the bits on the call stack and then construct the list from back-to-front as all the recursive calls return. Well, well, well. We have unwittingly reversed the list.
Our mapWith function takes twice as long as a straight iteration, because it iterates over the entire list twice, once to map, and once to reverse the list. Likewise, it takes twice as much memory, because it constructs a reverse of the desired result before throwing it away. Mind you, this is still much, much faster than making partial copies of arrays. For a list of length n , we created n superfluous nodes and copied n superfluous values. In JavaScript, almost every type of value can mutate.
Their identities stay the same, but not their structure. Specifically, arrays and objects can mutate. Recall that you can access a value from within an array or an object using []. Both halloween and allHallowsEve are bound to the same array value within the local environment. And also:. There are two nested environments, and each one binds a name to the exact same array value. In each of these examples, we have created two aliases for the same value.
Before we could reassign things, the most important point about this is that the identities were the same, because they were the same value. The outer value of allHallowsEve was not changed because all we did was rebind the name halloween within the inner environment. However, what happens if we mutate the value in the inner environment?
This is different. Mutation is a surprisingly complex subject. It is possible to compute anything without ever mutating an existing entity. In general, mutation makes some algorithms shorter to write and possibly faster, but harder to reason about. One pattern many people follow is to be liberal with mutation when constructing data, but conservative with mutation when consuming data. By this pattern, we would be happy to use mutation to construct the list while running mapWith. But after returning the new list, we then become conservative about mutation.
This also makes sense: Linked lists often use structure sharing. Whereas destructuring an array with [first, So back to avoiding mutation. As noted, one pattern is to be more liberal about mutation when building a data structure. Consider our copy algorithm. Without mutation, a copy of a linked list can be made in constant space by reversing a reverse of the list:.
If we want to make a copy of a linked list without iterating over it twice and making a copy we discard later, we can use mutation:. This algorithm makes copies of nodes as it goes, and mutates the last node in the list so that it can splice the next one on. Adding a node to an existing list is risky, as we saw when considering the fact that OneToFive and ThreeToFive share the same nodes.
Like some imperative programming languages, JavaScript allows you to re-assign the value bound to parameters. We saw this earlier in rebinding :.
Rebinding parameters is usually avoided, but what about rebinding names we declare within a function? What we want is a statement that works like const , but permits us to rebind variables. We took the time to carefully examine what happens with bindings in environments. The key is to understand that we are rebinding a different value to the same name in the same environment. Using let to bind 50 to age within the block does not change the binding of age in the outer environment because the binding of age in the block shadows the binding of age in the outer environment, just like const.
We go from:. Like evaluating variable labels, when a binding is rebound, JavaScript searches for the binding in the current environment and then each ancestor in turn until it finds one.
It then rebinds the name in that environment. Some programmers dislike deliberately shadowing variables. The suggestion is that shadowing a variable is confusing code. If you buy that argument, the way that shadowing works in JavaScript exists to protect us from accidentally shadowing a variable when we move code around. Shadowing a let with a const does not change our ability to rebind the variable in its original scope. Shadowing a const with a let does not permit it to be rebound in its original scope.
JavaScript has one more way to bind a name to a value, var. Declaring age twice does not cause an error! All var declarations behave as if they were hoisted to the top of the function, a little like function declarations.
But, again, it is unwise to expect consistency. A function declaration can appear anywhere within a function, but the declaration and the definition are hoisted. Note this example of a function that uses a helper:.
In that way, var is a little like const and let , we should always declare and bind names before using them. For nearly twenty years, variables were declared with var not counting parameters and function declarations, of course. However, its functional scope was a problem. It looks a lot like the for loop in C. Here it is with var :.
Hopefully, you can think of a faster way to calculate this sum. But is this ever a problem? What went wrong? The answer is that pesky var i. Now, at the time we created each function, i had a sensible value, like 0 , 1 , or 2. But at the time we call one of the functions, i has the value 3 , which is why the loop terminated.
So when the function is called, JavaScript looks i up in its enclosing environment its closure, obviously , and gets the value 3. This small error was a frequent cause of confusion, and in the days when there was no block-scoped let , programmers would need to know how to fake it, usually with an IIFE:.
This works, but let is so much simpler and cleaner that it was added to the language in the ECMAScript specification. In this book, we will use function declarations sparingly, and not use var at all. That does not mean that you should follow the exact same practice in your own code: The purpose of this book is to illustrate certain principles of programming.
The purpose of your own code is to get things done. The two goals are often, but not always, aligned. And therefore, modifications to the parent do not affect the child, and modifications to the child do not affect the parent. And therefore, modifications to the parent also modify the child, and modifications to the child also modify the parent. This is remarkably unsafe.
But how do we keep track of that? Add a bunch of bookkeeping to track references? So back to the problem of structure sharing. One strategy for avoiding problems is to be pessimistic. Whenever we take the rest of a list, make a copy. Thereafter, we can write to the parent or the copy of the child freely. As we expected, making a copy lets us modify the copy without interfering with the original. This is, however, expensive. Our mapWith function would be very expensive if we make a copy every time we call rest node.
Why are we copying? In case we modify a child list. Ok, what if we do this: Make the copy when we know we are modifying the list. When do we know that?
When we call set. But our new parent and child lists are copies that contain the desired modifications, without interfering with each other:.
And now functions like mapWith that make copies without modifying anything, work at full speed. Copy-on-write is the name given to the policy that whenever a task attempts to make a change to the shared information, it should first create a separate private copy of that information to prevent its changes from becoming visible to all the other tasks.
A good long while ago The First Age of Internet Startups , someone asked me one of those pet algorithm questions. I think I told him that I was trying to figure out if I could adapt a hashing algorithm such as XORing everything together. I went home and pondered the problem. I wanted to solve it. Eventually, I came up with something and tried it In Java! I sent him an email sharing my result, to demonstrate my ability to follow through.
I then forgot about it for a while. Some time later, I was told that the correct solution was:. You have two node references, and one traverses the list at twice the speed of the other. My first pass at it was clumsy, but it was roughly equivalent to this:. It seems to be faster under certain circumstances, depending on the size of the loop and the relative costs of certain operations. In tail-recursive style, it looks like this:.
As we saw earlier, this entangles the mechanism of traversing the array with the business of summing the bits. So we can separate them using fold :. The nice thing about this is that the definition for arraySum mostly concerns itself with summing, and not with traversing over a collection of data.
But it still relies on foldArrayWith , so it can only sum arrays. Well, we call arraySum with an array, and it has baked into it a method for traversing the array. Perhaps we could extract both of those things. Folding is a universal operation, and with care we can accomplish any task with folds that could be accomplished with that stalwart of structured programming, the for loop.
Nevertheless, there is some value in being able to express some algorithms as iteration. JavaScript has a particularly low-level version of for loop that mimics the semantics of the C language. Summing the elements of an array can be accomplished with:. Notice that buried inside our loop, we have bound the names done and value. With this code, we make a POJO that has done and value keys. All the summing code needs to know is to add eachIteration.
Now we can extract the ickiness into a separate function:. Now this is something else. The arrayIterator function takes an array and returns a function we can call repeatedly to obtain the elements of the array.
Iterators are functions. When they iterate over an array or linked list, they are traversing something that is already there. But they could just as easily manufacture the data as they go. A function that starts with a seed and expands it into a data structure is called an unfold. This business of going on forever has some drawbacks. We can start with take , an easy function that returns an iterator that only returns a fixed number of elements:. How about the squares of the first five odd numbers?
We can write that directly:. Mapping and filtering iterators allows us to compose the parts we already have, rather than writing a tricky bit of code with ifs and whiles and boundary conditions.
In Smalltalk, for example, they are known as collect , select , and detect. Or have we? Whereas if we wrote something like:. JavaScript would apply fn to every element. If array was very large, and fn very slow, this would consume a lot of unnecessary time. And if fn had some sort of side-effect, the program could be buggy.
Please note that unlike most of the other functions discussed in this book, iterators are stateful. There are some important implications of stateful functions. One is that while functions like take In our code so far, we have used arrays and objects to represent the structure of data, and we have extensively used the ternary operator to write algorithms that terminate when we reach a base case.
For example, this length function uses a functions to bind values to names, POJOs to structure nodes, and the ternary function to detect the base case, the empty list.
They searched for a radically simpler set of tools that could accomplish all of the same things. They established that arbitrary computations could be represented a small set of axiomatic components. We can model lists just using functions. The oscin. A constant function is a function that always returns the same thing, no matter what you give it. The kestrel, or K , is a function that makes constant functions.
You give it a value, and it returns a constant function that gives that value. The identity function is a function that evaluates to whatever parameter you pass it. Very simple, but useful. This is very interesting. Now, an interesting thing happens when we pass functions to each other. Consider K I.
Makes sense. Given two values, we can say that K always returns the first value, and given two values, K I always returns the second value. Our first and second functions are a little different than what most people are used to when we talk about functions that access data.
In both cases, the functions first and second know how the data is represented, whether it be an array or an object. You pass the data to these functions, and they extract it. You call them and pass them the bits, and they choose what to return. And instead of passing latin to first or second , we pass first or second to latin. In both cases, we take two parameters, and return the form of the data. Now what is this pair function?
So we can write:. It is known to most programmers as. Armed with nothing more than K , I , and V , we can make a little data structure that holds two values, the cons cell of Lisp and the node of a linked list. Without arrays, and without objects, just with functions. Presto, we can use pure functions to represent a linked list. And with care, we can do amazing things like use functions to represent numbers, build more complex data structures like trees, and in fact, anything that can be computed can be computed using just functions and nothing else.
This follows the philosophy we used with data structures: The function doing the work inspects the data structure. We can reverse this: Instead of asking a pair if it is empty and then deciding what to do, we can ask the pair to do it for us. We can write reverse and mapWith as well. There are lots of similar texts explaining how to construct complex semantics out of functions.
Functions are a fundamental building block of computation. However, that is not the interesting thing to note here. Practically speaking, languages like JavaScript already provide arrays with mapping and folding methods, choice operations, and other rich constructs. Knowing how to make a linked list out of functions is not really necessary for the working programmer. Knowing that it can be done, on the other hand, is very important to understanding computer science.
Knowing how to make a list out of just functions is a little like knowing that photons are the Gauge Bosons of the electromagnetic force. So what is interesting about this? To make pairs work, we did things backwards , we passed the first and rest functions to the pair, and the pair called our function. But we could have done something completely different.
We could have written a pair that stored its elements in an array, or a pair that stored its elements in a POJO. All we know is that we can pass the pair function a function of our own, at it will be called with the elements of the pair. The exact implementation of a pair is hidden from the code that uses a pair. We then ask list to do it, and provide a way for list to call the code we pass in. Or a column in a database. This is fundamentally not the same thing as this code for the length of a linked list:.
It presumes there is one canonical empty list value. Having a list know itself whether it is empty hides implementation information from the code that uses lists. This is a fundamental principle of good design. It is a tenet of Object-Oriented Programming, but it is not exclusive to OOP: We can and should design data structures to hide implementation information from the code that use them, whether we are working with functions, objects, or both.
There are many tools for hiding implementation information, and we have now seen two particularly powerful patterns:. In JavaScript, arrays have a. Map takes a function as an argument, and applies it to each of the elements of the array, then returns the results in another array. It reverses the arguments, taking the function first and the list second.
That means that you can pass a function to mapWith and get back a function that applies that mapping to any array. For example, we might need a function to return the squares of an array. Instead of writing a a wrapper around. Both patterns take us to the same destination: Composing functions out of common pieces, rather than building them entirely from scratch.
We could write our function something like this:. You can see that if we simplify it:. The first step is to give our parameters generic names:. We could make that into flip :. A small alteration gets the job done:. Both needs can be met with Object. You can copy an object by extending an empty object:. This is the canonical Y Combinator :. It enables you to make recursive functions without needing to bind a function to a name in an environment.
So again, why include the recipe? Well, besides all of the practical applications that combinators provide, there is this little thing called The joy of working things out. What is this something and how does it work? Another friendly tip: Change some of the fat arrow functions inside of it into named function expressions to help you decipher stack traces.
An expression is any valid unit of code that resolves to a value. Like most programming languages, JavaScript also has string literals, like 'fubar' or 'fizzbuzz'. Special characters can be included in a string literal by means of an escape sequence.
There are operators that can be used on strings. String manipulation is extremely common in programming. Writing is a big part of what makes us human, and strings are how JavaScript and most other languages represent writing. Quasi-literal strings are denoted with back quotes, and most strings that can be expressed as literals have the exact same meaning as quasi-literals, e.
Quasi-literals go much further. A quasi-literal can contain an expression to be evaluated. The expression is evaluated, and the result is coerced to a string, then inserted in the quasi-string. So the above expression could also be written:.
However, there is a big semantic difference between a quasi-literal and an expression. Quasi-literals are expressions that resemble their result. Like any other expression, quasi-literals are evaluated late , when that line or lines of code is evaluated. Thus, name is not bound to "Harry" , it is bound to 'Arthur Dent' , the value of the parameter when the function is invoked.
And we briefly touched upon the notion of mutating an object as part of building it. But we have avoided objects that are meant to be changed, objects that model state. OOP to me means only messaging, local retention and protection and hiding of state-process, and extreme late-binding of all things.
This is just plain encapsulation, 38 with a dash of information-hiding. In computer science, information hiding is the principle of segregation of the design decisions in a computer program that are most likely to change, thus protecting other parts of the program from extensive modification if the design decision is changed. The protection involves providing a stable interface which protects the remainder of the program from the implementation the details that are most likely to change.
Written another way, information hiding is the ability to prevent certain aspects of a class or software component from being accessible to its clients, using either programming language features like private variables or an explicit exporting policy. Consider a stack data structure.
There are three basic operations: Pushing a value onto the top push , popping a value off the top pop , and testing to see whether the stack is empty or not isEmpty. These three operations are the stable interface. Many stacks have an array for holding the contents of the stack. This is relatively stable. You could substitute a linked list, but in JavaScript, the array is highly efficient.
You might need an index, you might not. You could grow and shrink the array, or you could allocate a fixed size and use an index to keep track of how much of the array is in use. The design choices for keeping track of the head of the list are often driven by performance considerations.
If you expose the implementation detail such as whether there is an index, sooner or later some programmer is going to find an advantage in using the index directly. For example, she may need to know the size of a stack.
The ideal choice would be to add a size function that continues to hide the implementation. Now her code is coupled to the existence of an index, so if we wish to change the implementation to grow and shrink the array, we will break her code. The way to avoid this is to hide the array and index from other code and only expose the operations we have deemed stable. And given that their elements are mutable, they can clearly model state. Given an object that holds our state an array and an index 39 , we can easily implement our three operations as functions.
JavaScript objects can have elements of any type, including functions. To make our stack work, we need a way for our functions to refer to our stack. We can do that with an IIFE:.
The view taken in this book is that a function is a method of an object if it belongs to that object and interacts with that object in some way. So the functions implementing the operations on the stack are all absolutely methods of the stack. So how do we hide these? A deeply fundamental practice is to build components out of smaller components.
The choice of how to divide a component into smaller components is called factoring , after the operation in number theory The simplest and easiest way to build components out of smaller components in JavaScript is also the most obvious: Each component is a value, and the components can be put together into a single object or encapsulated with a closure. The techniques used for encapsulation work well with composition.
Another practice that many people consider fundamental is to extend an implementation. Meaning, they wish to define a new data structure in terms of adding new operations and semantics to an existing data structure. Now we wish to create a deque by adding pullTail and pushHead operations to our queue.
The design goals of encapsulation and extension are always going to exist in tension. Wait a second. We know that array values are references. So it probably copied a reference to the original array.
Even though we carefully made a copy of the array to prevent aliasing, it seems that our two queues behave like aliases of each other. The killer drawback, of course, is making sure we are always passing the correct queue in every time we invoke a function. Any time we must do the same repetitive thing over and over and over again, we industrial humans try to build a machine to do it for us.
JavaScript is one such machine. When we write a function expression using the compact method syntax or use the function keyword instead of the fat arrow , and then invoke that function using. Our AmnesiacQueue already uses compact method notation. Now we are relying on JavaScript to set the value of this whenever we invoke one of these functions using the. And indeed it does: Every time you invoke a function that is a member of an object, JavaScript binds that object to the name this in the environment of the function just as if it was an argument.
Now, does this solve our original problem? Can we make copies of an object? Now our functions refer to members of the object, and use this to ensure that they are referring to the object receiving a message. Presto, we now have a way to copy arrays.
By getting rid of the closure and taking advantage of this , we have functions that are more easily portable between objects, and the code is simpler as well. This is very important.
Being able to copy objects is an example of a larger concern: Being able to share functions between objects. Being able to share functions means being able to compose and reuse functionality. It returns the object when called, just as described. This is an important distinction. See full terms. If you buy a Leanpub book, you get free updates for as long as the author updates the book!
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Leanpub is a magical typewriter for authors: just write in plain text, and to publish your ebook, just click a button. It really is that easy. Haskell for FPGA Hardware Design: Use abstractions like monads and lenses to implement 's retro-computing devices like arcade machines and home computers. This is an intermediate textbook on Emacs Lisp. Learn how to write your own commands and make Emacs truly your editor, suited to your needs! A Functional Programming book from beginner to advanced without skipping a single step along the way.
In my 40 years of programming, I've felt that programming books always let me down, especially Functional Programming books.
So, I wrote the book I wish I had 5 years ago. At present, the E xperiential Learning series currently consists of four volumes. This first volume— Beginning —concerns getting started: starting using the experiential method, starting to design exercises, and getting a particular exercise off to a good start.
It should be particularly helpful for short classes—a day or two, or even an hour or two—though it could be for starting to use experiential parts of a longer workshop consisting of both short and long experiential pieces as well as more traditional learning models. A practical book aimed for those familiar with functional programming in Scala who are yet not confident about architecting an application from scratch.
Together, we will develop a purely functional application using the best libraries in the Cats ecosystem, while learning about design patterns and best practices. Learn how this impacts day-to-day programming, to benefit in practice, to combine new features, and to avoid all new traps.
This book not only tells you how ES6 works, it also tells you why it works the way it does. In order to understand this book, you should already know JavaScript. It is full of practical examples which will get you up and running quickly with the core tasks of JavaScript. In Ionic Succinctly , Ed Freitas takes readers through the creation of an app that searches the Succinctly series library and sorts the books into learning paths.
As applications move from the desktop to the browser, the need to learn well-structured JavaScript is vital. This book is for developers who want to learn JavaScript from scratch, or take their JavaScript skills to a new level of sophistication.
The JavaScript Way is primarily designed for beginners.
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