## A Pull of the Lever: Prefaces

“Café Allongé, also called Espresso Lungo, is a drink midway between an Espresso and Americano in strength. There are two different ways to make it. The first, and the one I prefer, is to add a small amount of hot water to a double or quadruple Espresso Ristretto. Like adding a splash of water to whiskey, the small dilution releases more of the complex flavours in the mouth.

“The second way is to pull an extra long double shot of Espresso. This achieves approximately the same ratio of oils to water as the dilution method, but also releases a different mix of flavours due to the longer extraction. Some complain that the long pull is more bitter and detracts from the best character of the coffee, others feel it releases even more complexity.

“The important thing is that neither method of preparation should use so much water as to result in a sickly, pale ghost of Espresso. Moderation in all things.”

JavaScript Allongé is a first and foremost, a book about programming with functions. It’s written in JavaScript, because JavaScript hits the perfect sweet spot of being both widely used, and of having proper first-class functions with lexical scope. If those terms seem unfamiliar, don’t worry: JavaScript Allongé takes great delight in explaining what they mean and why they matter.

JavaScript Allongé begins at the beginning, with values and expressions, and builds from there to discuss types, identity, functions, closures, scopes, collections, iterators, and many more subjects up to working with classes and instances.

It also provides recipes for using functions to write software that is simpler, cleaner, and less complicated than alternative approaches that are object-centric or code-centric. JavaScript idioms like function combinators and decorators leverage JavaScript’s power to make code easier to read, modify, debug and refactor.

JavaScript Allongé teaches you how to handle complex code, and it also teaches you how to simplify code without dumbing it down. As a result, JavaScript Allongé is a rich read releasing many of JavaScript’s subtleties, much like the Café Allongé beloved by coffee enthusiasts everywhere.

#### why the “six” edition?

ECMAScript 2015 (formerly called ECMAScript 6 or “ES6”), is ushering in a very large number of improvements to the way programmers can write small, powerful components and combine them into larger, fully featured programs. Features like destructuring, block-structured variables, iterables, generators, and the class keyword are poised to make JavaScript programming more expressive.

Prior to ECMAScript 2015, JavaScript did not include many features that programmers have discovered are vital to writing great software. For example, JavaScript did not include block-structured variables. Over time, programmers discovered ways to roll their own versions of important features.

For example, block-structured languages allow us to write:

And the variable i is scoped locally to the code within the braces. Prior to ECMAScript 2015, JavaScript did not support block-structuring, so programmers borrowed a trick from the Scheme programming language, and would write:

To create the same scoping with an Immediately Invoked Function Expression, or “IIFE.”

Likewise, many programming languages permit functions to have a variable number of arguments, and to collect the arguments into a single variable as an array. In Ruby, we can write:

Prior to ECMAScript 2015, JavaScript did not support collecting a variable number of arguments into a parameter, so programmers would take advantage of an awkward work-around and write things like:

The first edition of JavaScript Allongé explained these and many other patterns for writing flexible and composable programs in JavaScript, but the intention wasn’t to explain how to work around JavaScript’s missing features: The intention was to explain why the style of programming exemplified by the missing features is important.

Working around the missing features was a necessary evil.

But now, JavaScript is gaining many important features, in part because the governing body behind JavaScript has observed that programmers are constantly working around the same set of limitations. With ECMASCript 2015, we can write:

And i is scoped to the for loop. We can also write:

And presto, rest collects the rest of the arguments without a lot of malarky involving slicing arguments. Not having to work around these kinds of missing features makes JavaScript Allongé a better book, because it can focus on the why to do something and when to do it, instead of on the how to make it work

JavaScript Allongé, The “Six” Edition packs all the goodness of JavaScript Allongé into a new, updated package that is relevant for programmers working with (or planning to work with) the latest version of JavaScript.

#### that’s nice. is that the only reason?

Actually, no.

If it were just a matter of updating the syntax, the original version of JavaScript Allongé could have simply iterated, slowly replacing old syntax with new. It would have continued to say much the same things, only with new syntax.

But there’s more to it than that. The original JavaScript Allongé was not just written to teach JavaScript: It was written to describe certain ideas in programming: Working with small, independent entities that compose together to make bigger programs. Thus, the focus on things like writing decorators.

As noted above, JavaScript was chosen as the language for Allongé because it hit a sweet spot of having a large audience of programmers and having certain language features that happen to work well with this style of programming.

ECMAScript 2015 does more than simply update the language with some simpler syntax for a few things and help us avoid warts. It makes a number of interesting programming techniques easy to explain and easy to use. And these techniques dovetail nicely with Allongé’s focus on composing entities and working with functions.

Thus, the “six” edition introduces classes and mixins. It introduces the notion of implementing private properties with symbols. It introduces iterators and generators. But the common thread that runs through all these things is that since they are all simple objects and simple functions, we can use the same set of “programming with functions” techniques to build programs by composing small, flexible, and decoupled entities.

We just call some of those functions constructors, others decorators, others functional mixins, and yet others, policies.

Introducing so many new ideas did require a major rethink of the way the book was organized. And introducing these new ideas did add substantially to its bulk. But even so, in a way it is still explaining the exact same original idea that programs are built out of small, flexible functions composed together.

### What JavaScript Allongé is. And isn’t.

JavaScript Allongé is a book about programming with functions. From functions flow many ideas, from decorators to methods to delegation to mixins, and onwards in so many fruitful directions.

The focus in this book on the underlying ideas, what we might call the fundamentals, and how they combine to form new ideas. The intention is to improve the way we think about programs. That’s a good thing.

But while JavaScript Allongé attempts to be provocative, it is not prescriptive. There is absolutely no suggestion that any of the techniques shown here are the only way to do something, the best way, or even an acceptable way to write programs that are intended to be used, read, and maintained by others.

Software development is a complex field. Choices in development are often driven by social considerations. People often say that software should be written for people to read. Doesn’t that depend upon the people in question? Should code written by a small team of specialists use the same techniques and patterns as code maintained by a continuously changing cast of inexperienced interns?

Choices in software development are also often driven by requirements specific to the type of software being developed. For example, business software written in-house has a very different set of requirements than a library written to be publicly distributed as open-source.

Choices in software development must also consider the question of consistency. If a particular codebase is written with lots of helper functions that place the subject first, like this:

Then it can be jarring to add new helpers written that place the verb first, like this:

There are reasons why the second form is more flexible, especially when used in combination with partial application, but does that outweigh the benefit of having an entire codebase do everything consistently the first way or the second way?

Finally, choices in software development cannot ignore the tooling that is used to create and maintain software. The use of source-code control systems with integrated diffing rewards making certain types of focused changes. The use of linters makes checking for certain types of undesirable code very cheap. Debuggers encourage the use of functions with explicit or implicit names. Continuous integration encourages the creation of software in tandem with and factored to facilitate the creation of automated test suites.

JavaScript Allongé does not attempt to address the question of JavaScript best practices in the wider context of software development, because JavaScript Allongé isn’t a book about practicing, it’s a book about thinking.

#### how this book is organized

JavaScript Allongé introduces new aspects of programming with functions in each chapter, explaining exactly how JavaScript works. Code examples within each chapter are small and emphasize exposition rather than serving as patterns for everyday use.

Following some of the chapters are a series of recipes designed to show the application of the chapter’s ideas in practical form. While the content of each chapter builds naturally on what was discussed in the previous chapter, the recipes may draw upon any aspect of the JavaScript programming language.

### Foreword to the “Six” edition

ECMAScript 6 (short name: ES6; official name: ECMAScript 2015) was ratified as a standard on June 17. Getting there took a while – in a way, the origins of ES6 date back to the year 2000: After ECMAScript 3 was finished, TC39 (the committee evolving JavaScript) started to work on ECMAScript 4. That version was planned to have numerous new features (interfaces, namespaces, packages, multimethods, etc.), which would have turned JavaScript into a completely new language. After internal conflict, a settlement was reached in July 2008 and a new plan was made – to abandon ECMAScript 4 and to replace it with two upgrades:

• A smaller upgrade would bring a few minor enhancements to ECMAScript 3. This upgrade became ECMAScript 5.
• A larger upgrade would substantially improve JavaScript, but without being as radical as ECMAScript 4. This upgrade became ECMAScript 6 (some features that were initially discussed will show up later, in upcoming ECMAScript versions).

ECMAScript 6 has three major groups of features:

• Better syntax for features that already exist (e.g. via libraries). For example: classes and modules.
• New functionality in the standard library. For example:
• New methods for strings and arrays
• Promises (for asynchronous programming)
• Maps and sets
• Completely new features. For example: Generators, proxies and WeakMaps.

With ECMAScript 6, JavaScript has become much larger as a language. JavaScript Allongé, the “Six” Edition is both a comprehensive tour of its features and a rich collection of techniques for making better use of them. You will learn much about functional programming and object-oriented programming. And you’ll do so via ES6 code, handed to you in small, easily digestible pieces.

– Axel Rauschmayer Blogger, trainer and author of “Exploring ES6

### Forewords to the First Edition

#### michael fogus

As a life-long bibliophile and long-time follower of Reg’s online work, I was excited when he started writing books. However, I’m very conservative about books – let’s just say that if there was an aftershave scented to the essence of “Used Book Store” then I would be first in line to buy. So as you might imagine I was “skeptical” about the decision to release JavaScript Allongé as an ongoing ebook, with a pay-what-you-want model. However, Reg sent me a copy of his book and I was humbled. Not only was this a great book, but it was also a great way to write and distribute books. Having written books myself, I know the pain of soliciting and receiving feedback.

The act of writing is an iterative process with (very often) tight revision loops. However, the process of soliciting feedback, gathering responses, sending out copies, waiting for people to actually read it (if they ever do), receiving feedback and then ultimately making sense out of how to use it takes weeks and sometimes months. On more than one occasion I’ve found myself attempting to reify feedback with content that either no longer existed or was changed beyond recognition. However, with the Leanpub model the read-feedback-change process is extremely efficient, leaving in its wake a quality book that continues to get better as others likewise read and comment into infinitude.

In the case of JavaScript Allongé, you’ll find the Leanpub model a shining example of effectiveness. Reg has crafted (and continues to craft) not only an interesting book from the perspective of a connoisseur, but also an entertaining exploration into some of the most interesting aspects of his art. No matter how much of an expert you think you are, JavaScript Allongé has something to teach you… about coffee. I kid.

As a staunch advocate of functional programming, much of what Reg has written rings true to me. While not exclusively a book about functional programming, JavaScript Allongé will provide a solid foundation for functional techniques. However, you’ll not be beaten about the head and neck with dogma. Instead, every section is motivated by relevant dialog and fortified with compelling source examples. As an author of programming books I admire what Reg has managed to accomplish and I envy the fine reader who finds JavaScript Allongé via some darkened channel in the Internet sprawl and reads it for the first time.

Enjoy.

– Fogus, fogus.me

#### matthew knox

A different kind of language requires a different kind of book.

JavaScript holds surprising depths–its scoping rules are neither strictly lexical nor strictly dynamic, and it supports procedural, object-oriented (in several flavors!), and functional programming. Many books try to hide most of those capabilities away, giving you recipes for writing JavaScript in a way that approximates class-centric programming in other languages. Not JavaScript Allongé. It starts with the fundamentals of values, functions, and objects, and then guides you through JavaScript from the inside with exploratory bits of code that illustrate scoping, combinators, context, state, prototypes, and constructors.

Like JavaScript itself, this book gives you a gentle start before showing you its full depth, and like a Cafe Allongé, it’s over too soon. Enjoy!

–Matthew Knox, mattknox.com

## Prelude: Values and Expressions over Coffee

The following material is extremely basic, however like most stories, the best way to begin is to start at the very beginning.

#### operations on numbers

As we’ve seen, JavaScript has many common arithmetic operators. We can create expressions that look very much like mathematical expressions, for example we can write 1 + 1 or 2 * 3 or 42 - 34 or even 6 / 2. These can be combined to make more complex expressions, like 2 * 5 + 1.

In JavaScript, operators have an order of precedence designed to mimic the way humans typically parse written arithmetic. So:

JavaScript treats the expressions as if we had written (2 * 5) + 1 and 1 + (5 * 2), because the * operator has a higher precedence than the + operator. JavaScript has many more operators. In a sense, they behave like little functions. If we write 1 + 2, this is conceptually similar to writing plus(1, 2) (assuming we have a function that adds two numbers bound to the name plus, of course).

In addition to the common +, -, *, and /, JavaScript also supports modulus, %, and unary negation, -:

There are lots and lots more operators that can be used with numbers, including bitwise operators like | and & that allow you to operate directly on a number’s binary representation, and a number of other operators that perform assignment or logical comparison that we will look at later.

## The first sip: Basic Functions

### As Little As Possible About Functions, But No Less

In JavaScript, functions are values, but they are also much more than simple numbers, strings, or even complex data structures like trees or maps. Functions represent computations to be performed. Like numbers, strings, and arrays, they have a representation. Let’s start with the second simplest possible function.4 In JavaScript, it looks like this:

This is a function that is applied to no values and returns 0. Let’s verify that our function is a value like all others:

What!? Why didn’t it type back () => 0 for us? This seems to break our rule that if an expression is also a value, JavaScript will give the same value back to us. What’s going on? The simplest and easiest answer is that although the JavaScript interpreter does indeed return that value, displaying it on the screen is a slightly different matter. [Function] is a choice made by the people who wrote Node.js, the JavaScript environment that hosts the JavaScript REPL. If you try the same thing in a browser, you may see something else.

#### functions and identities

You recall that we have two types of values with respect to identity: Value types and reference types. Value types share the same identity if they have the same contents. Reference types do not.

Which kind are functions? Let’s try them out and see. For reasons of appeasing the JavaScript parser, we’ll enclose our functions in parentheses:

Like arrays, every time you evaluate an expression to produce a function, you get a new function that is not identical to any other function, even if you use the same expression to generate it. “Function” is a reference type.

#### applying functions

Let’s put functions to work. The way we use functions is to apply them to zero or more values called arguments. Just as 2 + 2 produces a value (in this case 4), applying a function to zero or more arguments produces a value as well.

Here’s how we apply a function to some values in JavaScript: Let’s say that fn_expr is an expression that when evaluated, produces a function. Let’s call the arguments args. Here’s how to apply a function to some arguments:

fn_expr(args)

Right now, we only know about one such expression: () => 0, so let’s use it. We’ll put it in parentheses5 to keep the parser happy, like we did above: (() => 0). Since we aren’t giving it any arguments, we’ll simply write () after the expression. So we write:

#### functions that return values and evaluate expressions

We’ve seen () => 0. We know that (() => 0)() returns 0, and this is unsurprising. Likewise, the following all ought to be obvious:

Well, the last one’s a doozy, but still, the general idea is this: We can make a function that returns a value by putting the value to the right of the arrow.

In the prelude, we looked at expressions. Values like 0 are expressions, as are things like 40 + 2. Can we put an expression to the right of the arrow?

Yes we can. We can put any expression to the right of the arrow. For example, (() => 0)() is an expression. Can we put it to the right of an arrow, like this: () => (() => 0)()?

Let’s try it:

Yes we can! Functions can return the value of evaluating another function.

When dealing with expressions that have a lot of the same characters (like parentheses), you may find it helpful to format the code to make things stand out. So we can also write:

It evaluates to the same thing, 0.

#### commas

The comma operator in JavaScript is interesting. It takes two arguments, evaluates them both, and itself evaluates to the value of the right-hand argument. In other words:

We can use commas with functions to create functions that evaluate multiple expressions:

This is useful when trying to do things that might involve side-effects, but we’ll get to that later. In most cases, JavaScript does not care whether things are separated by spaces, tabs, or line breaks. So we can also write:

Or even:

#### the simplest possible block

There’s another thing we can put to the right of an arrow, a block. A block has zero or more statements, separated by semicolons.6

So, this is a valid function:

It returns the result of evaluating a block that has no statements. What would that be? Let’s try it:

What is this undefined?

#### undefined

In JavaScript, the absence of a value is written undefined, and it means there is no value. It will crop up again. undefined is its own type of value, and it acts like a value type:

Like numbers, booleans and strings, JavaScript can print out the value undefined.

No matter how you evaluate undefined, you get an identical value back. undefined is a value that means “I don’t have a value.” But it’s still a value :-)

#### void

We’ve seen that JavaScript represents an undefined value by typing undefined, and we’ve generated undefined values in two ways:

1. By evaluating a function that doesn’t return a value (() => {})(), and;
2. By writing undefined ourselves.

There’s a third way, with JavaScript’s void operator. Behold:

void is an operator that takes any value and evaluates to undefined, always. So, when we deliberately want an undefined value, should we use the first, second, or third form?7 The answer is, use void. By convention, use void 0.

The first form works but it’s cumbersome. The second form works most of the time, but it is possible to break it by reassigning undefined to a different value, something we’ll discuss in Reassignment and Mutation. The third form is guaranteed to always work, so that’s what we will use.8

#### back on the block

Back to our function. We evaluated this:

We said that the function returns the result of evaluating a block, and we said that a block is a (possibly empty) list of JavaScript statements separated by semicolons.9

Something like: { statement1; statement2; statement3; ... ; statementn }

We haven’t discussed these statements. What’s a statement?

There are many kinds of JavaScript statements, but the first kind is one we’ve already met. An expression is a JavaScript statement. Although they aren’t very practical, these are valid JavaScript functions, and they return undefined when applied:

As we saw with commas above, we can rearrange these functions onto multiple lines when we feel its more readable that way:

But no matter how we arrange them, a block with one or more expressions still evaluates to undefined:

As you can see, a block with one expression does not behave like an expression, and a block with more than one expression does not behave like an expression constructed with the comma operator:

So how do we get a function that evaluates a block to return a value when applied? With the return keyword and any expression:

The return keyword creates a return statement that immediately terminates the function application and returns the result of evaluating its expression. For example:

And also:

The return statement is the first statement we’ve seen, and it behaves differently than an expression. For example, you can’t use one as the expression in a simple function, because it isn’t an expression:

Statements belong inside blocks and only inside blocks. Some languages simplify this by making everything an expression, but JavaScript maintains this distinction, so when learning JavaScript we also learn about statements like function declarations, for loops, if statements, and so forth. We’ll see a few more of these later.

#### functions that evaluate to functions

If an expression that evaluates to a function is, well, an expression, and if a return statement can have any expression on its right side… Can we put an expression that evaluates to a function on the right side of a function expression?

Yes:

That’s a function! It’s a function that when applied, evaluates to a function that when applied, evaluates to 0. So we have a function, that returns a function, that returns zero. Likewise:

That’s a function, that returns a function, that returns true:

We could, of course, do the same thing with a block if we wanted:

But we generally don’t.

Well. We’ve been very clever, but so far this all seems very abstract. Diffraction of a crystal is beautiful and interesting in its own right, but you can’t blame us for wanting to be shown a practical use for it, like being able to determine the composition of a star millions of light years away. So… In the next chapter, “I’d Like to Have an Argument, Please,” we’ll see how to make functions practical.

### Ah. I’d Like to Have an Argument, Please.10

Up to now, we’ve looked at functions without arguments. We haven’t even said what an argument is, only that our functions don’t have any.

Let’s make a function with an argument:

This function has one argument, room, and an empty body. Here’s a function with two arguments and an empty body:

I’m sure you are perfectly comfortable with the idea that this function has two arguments, room, and board. What does one do with the arguments? Use them in the body, of course. What do you think this is?

It’s a function for calculating the circumference of a circle given the diameter. I read that aloud as “When applied to a value representing the diameter, this function returns the diameter times 3.14159265.”

Remember that to apply a function with no arguments, we wrote (() => {})(). To apply a function with an argument (or arguments), we put the argument (or arguments) within the parentheses, like this:

You won’t be surprised to see how to write and apply a function to two arguments:

#### call by value

Like most contemporary programming languages, JavaScript uses the “call by value” evaluation strategy. That means that when you write some code that appears to apply a function to an expression or expressions, JavaScript evaluates all of those expressions and applies the functions to the resulting value(s).

So when you write:

What happened internally is that the expression 1 + 1 was evaluated first, resulting in 2. Then our circumference function was applied to 2.11

We’ll see below that while JavaScript always calls by value, the notion of a “value” has additional subtlety. But before we do, let’s look at variables.

#### variables and bindings

Right now everything looks simple and straightforward, and we can move on to talk about arguments in more detail. And we’re going to work our way up from (diameter) => diameter * 3.14159265 to functions like:

In order to talk about how this works, we should agree on a few terms (you may already know them, but let’s check-in together and “synchronize our dictionaries”). The first x, the one in (x) => ..., is an argument. The y in function (y) ... is another argument. The second x, the one in => x, is not an argument, it’s an expression referring to a variable. Arguments and variables work the same way whether we’re talking about (x) => (y) => x or just plain (x) => x.

Every time a function is invoked (“invoked” means “applied to zero or more arguments”), a new environment is created. An environment is a (possibly empty) dictionary that maps variables to values by name. The x in the expression that we call a “variable” is itself an expression that is evaluated by looking up the value in the environment.

How does the value get put in the environment? Well for arguments, that is very simple. When you apply the function to the arguments, an entry is placed in the dictionary for each argument. So when we write:

What happens is this:

1. JavaScript parses this whole thing as an expression made up of several sub-expressions.
2. It then starts evaluating the expression, including evaluating sub-expressions
3. One sub-expression, (x) => x evaluates to a function.
4. Another, 2, evaluates to the number 2.
5. JavaScript now evaluates applying the function to the argument 2. Here’s where it gets interesting…
6. An environment is created.
7. The value ‘2’ is bound to the name ‘x’ in the environment.
8. The expression ‘x’ (the right side of the function) is evaluated within the environment we just created.
9. The value of a variable when evaluated in an environment is the value bound to the variable’s name in that environment, which is ‘2’
10. And that’s our result.

When we talk about environments, we’ll use an unsurprising syntax for showing their bindings: {x: 2, ...}. meaning, that the environment is a dictionary, and that the value 2 is bound to the name x, and that there might be other stuff in that dictionary we aren’t discussing right now.

#### call by sharing

Earlier, we distinguished JavaScript’s value types from its reference types. At that time, we looked at how JavaScript distinguishes objects that are identical from objects that are not. Now it is time to take another look at the distinction between value and reference types.

There is a property that JavaScript strictly maintains: When a value–any value–is passed as an argument to a function, the value bound in the function’s environment must be identical to the original.

We said that JavaScript binds names to values, but we didn’t say what it means to bind a name to a value. Now we can elaborate: When JavaScript binds a value-type to a name, it makes a copy of the value and places the copy in the environment. As you recall, value types like strings and numbers are identical to each other if they have the same content. So JavaScript can make as many copies of strings, numbers, or booleans as it wishes.

What about reference types? JavaScript does not place copies of reference values in any environment. JavaScript places references to reference types in environments, and when the value needs to be used, JavaScript uses the reference to obtain the original.

Because many references can share the same value, and because JavaScript passes references as arguments, JavaScript can be said to implement “call by sharing” semantics. Call by sharing is generally understood to be a specialization of call by value, and it explains why some values are known as value types and other values are known as reference types.

And with that, we’re ready to look at closures. When we combine our knowledge of value types, reference types, arguments, and closures, we’ll understand why this function always evaluates to true no matter what argument12 you apply it to:

### Closures and Scope

It’s time to see how a function within a function works:

First off, let’s use what we learned above. Given (some function)(some argument), we know that we apply the function to the argument, create an environment, bind the value of the argument to the name, and evaluate the function’s expression. So we do that first with this code:

The environment belonging to the function with signature (x) => ... becomes {x: 1, ...}, and the result of applying the function is another function value. It makes sense that the result value is a function, because the expression for (x) => ...’s body is:

So now we have a value representing that function. Then we’re going to take the value of that function and apply it to the argument 2, something like this:

So we seem to get a new environment {y: 2, ...}. How is the expression x going to be evaluated in that function’s environment? There is no x in its environment, it must come from somewhere else.

#### if functions without free variables are pure, are closures impure?

The function (y) => x is interesting. It contains a free variable, x.13 A free variable is one that is not bound within the function. Up to now, we’ve only seen one way to “bind” a variable, namely by passing in an argument with the same name. Since the function (y) => x doesn’t have an argument named x, the variable x isn’t bound in this function, which makes it “free.”

Now that we know that variables used in a function are either bound or free, we can bifurcate functions into those with free variables and those without:

• Functions containing no free variables are called pure functions.
• Functions containing one or more free variables are called closures.

Pure functions are easiest to understand. They always mean the same thing wherever you use them. Here are some pure functions we’ve already seen:

The first function doesn’t have any variables, therefore doesn’t have any free variables. The second doesn’t have any free variables, because its only variable is bound. The third one is actually two functions, one inside the other. (y) => ... has a free variable, but the entire expression refers to (x) => ..., and it doesn’t have a free variable: The only variable anywhere in its body is x, which is certainly bound within (x) => ....

From this, we learn something: A pure function can contain a closure.

Pure functions always mean the same thing because all of their “inputs” are fully defined by their arguments. Not so with a closure. If I present to you this pure function (x, y) => x + y, we know exactly what it does with (2, 2). But what about this closure: (y) => x + y? We can’t say what it will do with argument (2) without understanding the magic for evaluating the free variable x.

#### it’s always the environment

To understand how closures are evaluated, we need to revisit environments. As we’ve said before, all functions are associated with an environment. We also hand-waved something when describing our environment. Remember that we said the environment for ((x) => (y) => x)(1) is {x: 1, ...} and that the environment for ((y) => x)(2) is {y: 2, ...}? Let’s fill in the blanks!

The environment for ((y) => x)(2) is actually {y: 2, '..': {x: 1, ...}}. '..' means something like “parent” or “enclosure” or “super-environment.” It’s (x) => ...’s environment, because the function (y) => x is within (x) => ...’s body. So whenever a function is applied to arguments, its environment always has a reference to its parent environment.

And now you can guess how we evaluate ((y) => x)(2) in the environment {y: 2, '..': {x: 1, ...}}. The variable x isn’t in (y) => ...’s immediate environment, but it is in its parent’s environment, so it evaluates to 1 and that’s what ((y) => x)(2) returns even though it ended up ignoring its own argument.

Functions can have grandparents too:

This function does much the same thing as:

Only you call it with (1)(2)(3) instead of (1, 2, 3). The other big difference is that you can call it with (1) and get a function back that you can later call with (2)(3).

An interesting thing happens when a variable has the same name as an ancestor environment’s variable. Consider:

The function (x, y) => x + y is a pure function, because its x is defined within its own environment. Although its parent also defines an x, it is ignored when evaluating x + y. JavaScript always searches for a binding starting with the functions own environment and then each parent in turn until it finds one. The same is true of:

When evaluating x + y + z, JavaScript will find x and y in the great-grandparent scope and z in the parent scope. The x in the great-great-grandparent scope is ignored, as are both ws. When a variable has the same name as an ancestor environment’s binding, it is said to shadow the ancestor.

This is often a good thing.

#### which came first, the chicken or the egg?

This behaviour of pure functions and closures has many, many consequences that can be exploited to write software. We are going to explore them in some detail as well as look at some of the other mechanisms JavaScript provides for working with variables and mutable state.

But before we do so, there’s one final question: Where does the ancestry start? If there’s no other code in a file, what is (x) => x’s parent environment?

JavaScript always has the notion of at least one environment we do not control: A global environment in which many useful things are bound such as libraries full of standard functions. So when you invoke ((x) => x)(1) in the REPL, its full environment is going to look like this: {x: 1, '..': global environment}.

Sometimes, programmers wish to avoid this. If you don’t want your code to operate directly within the global environment, what can you do? Create an environment for them, of course. Many programmers choose to write every JavaScript file like this:

The effect is to insert a new, empty environment in between the global environment and your own functions: {x: 1, '..': {'..': global environment}}. As we’ll see when we discuss mutable state, this helps to prevent programmers from accidentally changing the global state that is shared by all code in the program.

### That Constant Coffee Craving

Up to now, all we’ve really seen are anonymous functions, functions that don’t have a name. This feels very different from programming in most other languages, where the focus is on naming functions, methods, and procedures. Naming things is a critical part of programming, but all we’ve seen so far is how to name arguments.

There are other ways to name things in JavaScript, but before we learn some of those, let’s see how to use what we already have to name things. Let’s revisit a very simple example:

What is this “3.14159265” number? PI, obviously. We’d like to name it so that we can write something like:

In order to bind 3.14159265 to the name PI, we’ll need a function with a parameter of PI applied to an argument of 3.14159265. If we put our function expression in parentheses, we can apply it to the argument of 3.14159265:

What do we put inside our new function that binds 3.14159265 to the name PI when evaluated? Our circumference function, of course:

This expression, when evaluated, returns a function that calculates circumferences. That sounds bad, but when we think about it, (diameter) => diameter * 3.14159265 is also an expression, that when evaluated, returns a function that calculates circumferences. All of our “functions” are expressions. This one has a few more moving parts, that’s all. But we can use it just like (diameter) => diameter * 3.14159265.

Let’s test it:

That works! We can bind anything we want in an expression by wrapping it in a function that is immediately invoked with the value we want to bind.14

#### inside-out

There’s another way we can make a function that binds 3.14159265 to the name PI and then uses that in its expression. We can turn things inside-out by putting the binding inside our diameter calculating function, like this:

It produces the same result as our previous expressions for a diameter-calculating function:

Which one is better? Well, the first one seems simplest, but a half-century of experience has taught us that names matter. A “magic literal” like 3.14159265 is anathema to sustainable software development.

The third one is easiest for most people to read. It separates concerns nicely: The “outer” function describes its parameters:

Everything else is encapsulated in its body. That’s how it should be, naming PI is its concern, not ours. The other formulation:

“Exposes” naming PI first, and we have to look inside to find out why we care. So, should we always write this?

Well, the wrinkle with this is that typically, invoking functions is considerably more expensive than evaluating expressions. Every time we invoke the outer function, we’ll invoke the inner function. We could get around this by writing

But then we’ve obfuscated our code, and we don’t want to do that unless we absolutely have to.

What would be very nice is if the language gave us a way to bind names inside of blocks without incurring the cost of a function invocation. And JavaScript does.

#### const

Another way to write our “circumference” function would be to pass PI along with the diameter argument, something like this:

And we could use it like this:

This differs from our example above in that there is only one environment, rather than two. We have one binding in the environment representing our regular argument, and another our “constant.” That’s more efficient, and it’s almost what we wanted all along: A way to bind 3.14159265 to a readable name.

JavaScript gives us a way to do that, the const keyword. We’ll learn a lot more about const in future chapters, but here’s the most important thing we can do with const:

The const keyword introduces one or more bindings in the block that encloses it. It doesn’t incur the cost of a function invocation. That’s great. Even better, it puts the symbol (like PI) close to the value (3.14159265). That’s much better than what we were writing.

We use the const keyword in a const statement. const statements occur inside blocks, we can’t use them when we write a fat arrow that has an expression as its body.

It works just as we want. Instead of:

Or:

We write:

We can bind any expression. Functions are expressions, so we can bind helper functions:

Notice calc(d)? This underscores what we’ve said: if we have an expression that evaluates to a function, we apply it with (). A name that’s bound to a function is a valid expression evaluating to a function.15

We can bind more than one name-value pair by separating them with commas. For readability, most people put one binding per line:

#### nested blocks

Up to now, we’ve only ever seen blocks we use as the body of functions. But there are other kinds of blocks. One of the places you can find blocks is in an if statement. In JavaScript, an if statement looks like this:

And it works for fairly small numbers:

The if statement is a statement, not an expression (an unfortunate design choice), and its clauses are statements or blocks. So we could also write something like:

And this also works:

We’ve used a block as the else clause, and since it’s a block, we’ve placed a const statement inside it.

#### const and lexical scope

This seems very straightforward, but alas, there are some semantics of binding names that we need to understand if we’re to place const anywhere we like. The first thing to ask ourselves is, what happens if we use const to bind two different values to the “same” name?

Let’s back up and reconsider how closures work. What happens if we use parameters to bind two different values to the same name?

Here’s the second formulation of our diameter function, bound to a name using an IIFE:

It’s more than a bit convoluted, but it binds ((PI) => (diameter) => diameter * PI)(3.14159265) to diameter_fn and evaluates the expression that we’ve elided. We can use any expression in there, and that expression can invoke diameter_fn. For example:

We know this from the chapter on closures, but even though PI is not bound when we invoke diameter_fn by evaluating diameter_fn(2), PI is bound when we evaluated (diameter) => diameter * PI, and thus the expression diameter * PI is able to access values for PI and diameter when we evaluate diameter_fn.

This is called lexical scoping, because we can discover where a name is bound by looking at the source code for the program. We can see that PI is bound in an environment surrounding (diameter) => diameter * PI, we don’t need to know where diameter_fn is invoked.

We can test this by deliberately creating a “conflict:”

Although we have bound 3 to PI in the environment surrounding diameter_fn(2), the value that counts is 3.14159265, the value we bound to PI in the environment surrounding (diameter) ⇒ diameter * PI.

That much we can carefully work out from the way closures work. Does const work the same way? Let’s find out:

Yes. Binding values to names with const works just like binding values to names with parameter invocations, it uses lexical scope.

#### are consts also from a shadowy planet?

We just saw that values bound with const use lexical scope, just like values bound with parameters. They are looked up in the environment where they are declared. And we know that functions create environments. Parameters are declared when we create functions, so it makes sense that parameters are bound to environments created when we invoke functions.

But const statements can appear inside blocks, and we saw that blocks can appear inside of other blocks, including function bodies. So where are const variables bound? In the function environment? Or in an environment corresponding to the block?

We can test this by creating another conflict. But instead of binding two different variables to the same name in two different places, we’ll bind two different values to the same name, but one environment will be completely enclosed by the other.

Let’s start, as above, by doing this with parameters. We’ll start with:

And gratuitously wrap it in another IIFE so that we can bind PI to something else:

This still evaluates to a function that calculates diameters:

And we can see that our diameter * PI expression uses the binding for PI in the closest parent environment. but one question: Did binding 3.14159265 to PI somehow change the binding in the “outer” environment? Let’s rewrite things slightly differently:

Now we bind 3 to PI in an otherwise empty IIFE inside of our IIFE that binds 3.14159265 to PI. Does that binding “overwrite” the outer one? Will our function return 6 or 6.2831853? This is a book, you’ve already scanned ahead, so you know that the answer is no, the inner binding does not overwrite the outer binding:

We say that when we bind a variable using a parameter inside another binding, the inner binding shadows the outer binding. It has effect inside its own scope, but does not affect the binding in the enclosing scope.

So what about const. Does it work the same way?

Yes, names bound with const shadow enclosing bindings just like parameters. But wait! There’s more!!!

Parameters are only bound when we invoke a function. That’s why we made all these IIFEs. But const statements can appear inside blocks. What happens when we use a const inside of a block?

We’ll need a gratuitous block. We’ve seen if statements, what could be more gratuitous than:

Let’s try it:

Ah! const statements don’t just shadow values bound within the environments created by functions, they shadow values bound within environments created by blocks!

This is enormously important. Consider the alternative: What if const could be declared inside of a block, but it always bound the name in the function’s scope. In that case, we’d see things like this:

If const always bound its value to the name defined in the function’s environment, placing a const statement inside of a block would merely rebind the existing name, overwriting its old contents. That would be super-confusing. And this code would “work:”

Again, confusing. Typically, we want to bind our names as close to where we need them as possible. This design rule is called the Principle of Least Privilege, and it has both quality and security implications. Being able to bind a name inside of a block means that if the name is only needed in the block, we are not “leaking” its binding to other parts of the code that do not need to interact with it.

#### rebinding

By default, JavaScript permits us to rebind new values to names bound with a parameter. For example, we can write:

The line n = n - 2; rebinds a new value to the name n. We will discuss this at much greater length in Reassignment, but long before we do, let’s try a similar thing with a name bound using const. We’ve already bound evenStevens using const, let’s try rebinding it:

JavaScript does not permit us to rebind a name that has been bound with const. We can shadow it by using const to declare a new binding with a new function or block scope, but we cannot rebind a name that was bound with const in an existing scope.

This is valuable, as it greatly simplifies the analysis of programs to see at a glance that when something is bound with const, we need never worry that its value may change.

### Naming Functions

Let’s get right to it. This code does not name a function:

It doesn’t name the function “repeat” for the same reason that const answer = 42 doesn’t name the number 42. This syntax binds an anonymous function to a name in an environment, but the function itself remains anonymous.

#### the function keyword

JavaScript does have a syntax for naming a function, we use the function keyword. Until ECMAScript 2015 was created, function was the usual syntax for writing functions.

Here’s our repeat function written using a “fat arrow”

And here’s (almost) the exact same function written using the function keyword:

Let’s look at the obvious differences:

1. We introduce a function with the function keyword.
2. Something else we’re about to discuss is optional.
3. We have arguments in parentheses, just like fat arrow functions.
4. We do not have a fat arrow, we go directly to the body.
5. We always use a block, we cannot write function (str) str + str. This means that if we want our functions to return a value, we always need to use the return keyword

If we leave out the “something optional” that comes after the function keyword, we can translate all of the fat arrow functions that we’ve seen into function keyword functions, e.g.

Can be written as:

This still does not name a function, but as we noted above, functions written with the function keyword have an optional “something else.” Could that “something else” name a function? Yes, of course.16

Here are our example functions written with names:

Placing a name between the function keyword and the argument list names the function. Confusingly, the name of the function is not exactly the same thing as the name we may choose to bind to the value of the function. For example, we can write:

In this expression, double is the name in the environment, but repeat is the function’s actual name. This is a named function expression. That may seem confusing, but think of the binding names as properties of the environment, not of the function. While the name of the function is a property of the function, not of the environment.

And indeed the name is a property:

In this book we are not examining JavaScript’s tooling such as debuggers baked into browsers, but we will note that when you are navigating call stacks in all modern tools, the function’s binding name is ignored but its actual name is displayed, so naming functions is very useful even if they don’t get a formal binding, e.g.

Now, the function’s actual name has no effect on the environment in which it is used. To whit:

So “actualName” isn’t bound in the environment where we use the named function expression. Is it bound anywhere else? Yes it is. Here’s a function that determines whether a positive integer is even or not. We’ll use it in an IIFE so that we don’t have to bind it to a name with const:

Clearly, the name even is bound to the function within the function’s body. Is it bound to the function outside of the function’s body?

even is bound within the function itself, but not outside it. This is useful for making recursive functions as we see above, and it speaks to the principle of least privilege: If you don’t need to name it anywhere else, you needn’t.

#### function declarations

There is another syntax for naming and/or defining a function. It’s called a function declaration statement, and it looks a lot like a named function expression, only we use it as a statement:

This behaves a little like:

In that it binds a name in the environment to a named function. However, there are two important differences. First, function declarations are hoisted to the top of the function in which they occur.

Consider this example where we try to use the variable fizzbuzz as a function before we bind a function to it with const:

We haven’t actually bound a function to the name fizzbuzz before we try to use it, so we get an error. But a function declaration works differently:

Although fizzbuzz is declared later in the function, JavaScript behaves as if we’d written:

The definition of the fizzbuzz is “hoisted” to the top of its enclosing scope (an IIFE in this case). This behaviour is intentional on the part of JavaScript’s design to facilitate a certain style of programming where you put the main logic up front, and the “helper functions” at the bottom. It is not necessary to declare functions in this way in JavaScript, but understanding the syntax and its behaviour (especially the way it differs from const) is essential for working with production code.

#### function declaration caveats17

Function declarations are formally only supposed to be made at what we might call the “top level” of a function. Although some JavaScript environments permit the following code, this example is technically illegal and definitely a bad idea:

Function declarations are not supposed to occur inside of blocks. The big trouble with expressions like this is that they may work just fine in your test environment but work a different way in production. Or it may work one way today and a different way when the JavaScript engine is updated, say with a new optimization.

Another caveat is that a function declaration cannot exist inside of any expression, otherwise it’s a function expression. So this is a function declaration:

But this is not:

The parentheses make this an expression, not a function declaration.

### Combinators and Function Decorators

#### higher-order functions

As we’ve seen, JavaScript functions take values as arguments and return values. JavaScript functions are values, so JavaScript functions can take functions as arguments, return functions, or both. Generally speaking, a function that either takes functions as arguments, or returns a function, or both, is referred to as a “higher-order” function.

Here’s a very simple higher-order function that takes a function as an argument:

Higher-order functions dominate JavaScript Allongé. But before we go on, we’ll talk about some specific types of higher-order functions.

#### combinators

The word “combinator” has a precise technical meaning in mathematics:

“A combinator is a higher-order function that uses only function application and earlier defined combinators to define a result from its arguments.”–Wikipedia

If we were learning Combinatorial Logic, we’d start with the most basic combinators like S, K, and I, and work up from there to practical combinators. We’d learn that the fundamental combinators are named after birds following the example of Raymond Smullyan’s famous book To Mock a Mockingbird.

In this book, we will be using a looser definition of “combinator:” Higher-order pure functions that take only functions as arguments and return a function. We won’t be strict about using only previously defined combinators in their construction.

Let’s start with a useful combinator: Most programmers call it Compose, although the logicians call it the B combinator or “Bluebird.” Here is the typical18 programming implementation:

Let’s say we have:

With compose, anywhere you would write

You could also write:

This is, of course, just one example of many. You’ll find lots more perusing the recipes in this book. While some programmers believe “There Should Only Be One Way To Do It,” having combinators available as well as explicitly writing things out with lots of symbols and keywords has some advantages when used judiciously.

#### a balanced statement about combinators

Code that uses a lot of combinators tends to name the verbs and adverbs (like doubleOf, addOne, and compose) while avoiding language keywords and the names of nouns (like number). So one perspective is that combinators are useful when you want to emphasize what you’re doing and how it fits together, and more explicit code is useful when you want to emphasize what you’re working with.

#### function decorators

A function decorator is a higher-order function that takes one function as an argument, returns another function, and the returned function is a variation of the argument function. Here’s a ridiculously simple decorator:19

So instead of writing !someFunction(42), we can write not(someFunction)(42). Hardly progress. But like compose, we could write either:

And elsewhere, write:

Or we could write:

not is a function decorator because it modifies a function while remaining strongly related to the original function’s semantics. You’ll see other function decorators in the recipes, like once and maybe. Function decorators aren’t strict about being pure functions, so there’s more latitude for making decorators than combinators.

### Building Blocks

When you look at functions within functions in JavaScript, there’s a bit of a “spaghetti code” look to it. The strength of JavaScript is that you can do anything. The weakness is that you will. There are ifs, fors, returns, everything thrown higgledy piggledy together. Although you needn’t restrict yourself to a small number of simple patterns, it can be helpful to understand the patterns so that you can structure your code around some basic building blocks.

#### composition

One of the most basic of these building blocks is composition:

It’s really that simple: Whenever you are chaining two or more functions together, you’re composing them. You can compose them with explicit JavaScript code as we’ve just done. You can also generalize composition with the B Combinator or “compose” that we saw in Combinators and Decorators:

If that was all there was to it, composition wouldn’t matter much. But like many patterns, using it when it applies is only 20% of the benefit. The other 80% comes from organizing your code such that you can use it: Writing functions that can be composed in various ways.

In the recipes, we’ll look at a decorator called once: It ensures that a function can only be executed once. Thereafter, it does nothing. Once is useful for ensuring that certain side effects are not repeated. We’ll also look at maybe: It ensures that a function does nothing if it is given nothing (like null or undefined) as an argument.

Of course, you needn’t use combinators to implement either of these ideas, you can use if statements. But once and maybe compose, so you can chain them together as you see fit:

#### partial application

Another basic building block is partial application. When a function takes multiple arguments, we “apply” the function to the arguments by evaluating it with all of the arguments, producing a value. But what if we only supply some of the arguments? In that case, we can’t get the final value, but we can get a function that represents part of our application.

Code is easier than words for this. The Underscore library provides a higher-order function called map.20 It applies another function to each element of an array, like this:

We don’t want to fool around writing _., so we can use it by writing:21

This code implements a partial application of the map function by applying the function (n) => n * n as its second argument:

The resulting function–squareAll–is still the map function, it’s just that we’ve applied one of its two arguments already. squareAll is nice, but why write one function every time we want to partially apply a function to a map? We can abstract this one level higher. mapWith takes any function as an argument and returns a partially applied map function.

We’ll discuss mapWith again. The important thing to see is that partial application is orthogonal to composition, and that they both work together nicely:

We generalized composition with the compose combinator. Partial application also has a combinator, which we’ll see in the partial recipe.

### Magic Names

When a function is applied to arguments (or “called”), JavaScript binds the values of arguments to the function’s argument names in an environment created for the function’s execution. What we haven’t discussed so far is that JavaScript also binds values to some “magic” names in addition to any you put in the argument list.22

#### the function keyword

There are two separate rules for these “magic” names, one for when you invoke a function using the function keyword, and another for functions defined with “fat arrows.” We’ll begin with how things work for functions defined with the function keyword.

The first magic name is this, and it is bound to something called the function’s context. We will explore this in more detail when we start discussing objects and classes. The second magic name is very interesting, it’s called arguments, and the most interesting thing about it is that it contains a list of arguments passed to a function:

Although arguments looks like an array, it isn’t an array: It’s more like an object23 that happens to bind some values to properties with names that look like integers starting with zero:

arguments always contains all of the arguments passed to a function, regardless of how many are declared. Therefore, we can write plus like this:

When discussing objects, we’ll discuss properties in more depth. Here’s something interesting about arguments:

The most common use of the arguments binding is to build functions that can take a variable number of arguments. We’ll see it used in many of the recipes, starting off with partial application and ellipses.

#### magic names and fat arrows

The magic names this and arguments have a different behaviour when you invoke a function that was defined with a fat arrow: Instead of being bound when the function is invoked, the fat arrow function always acquires the bindings for this and arguments from its enclosing scope, just like any other binding.

For example, when this expression’s inner function is defined with function, arguments[0] refers to its only argument, "inner":

But if we use a fat arrow, arguments will be defined in the outer environment, the one defined with function. And thus arguments[0] will refer to "outer", not to "inner":

Although it seems quixotic for the two syntaxes to have different semantics, it makes sense when you consider the design goal: Fat arrow functions are designed to be very lightweight and are often used with constructs like mapping or callbacks to emulate syntax.

To give a contrived example, this function takes a number and returns an array representing a row in a hypothetical multiplication table. It uses mapWith, which we discussed in Building Blocks.24 We’ll use arguments just to show the difference between using a fat arrow and the function keyword:

This works just fine, because arguments[0] refers to the 3 we passed to the function row. Our “fat arrow” function (column) => column * arguments[0] doesn’t bind arguments when it’s invoked. But if we rewrite row to use the function keyword, it stops working:

Now our inner function binds arguments[0] every time it is invoked, so we get the same result as if we’d written function (column) { return column * column }.

Although this example is clearly unrealistic, there is a general design principle that deserves attention. Sometimes, a function is meant to be used as a Big-F function. It has a name, it is called by different pieces of code, it’s a first-class entity in the code.

But sometimes, a function is a small-f function. It’s a simple representation of an expression to be computed. In our example above, row is a Big-F function, but (column) => column * arguments[0] is a small-f function, it exists just to give mapWith something to apply.

Having magic variables apply to Big-F functions but not to small-f functions makes it much easier to use small-f functions as syntax, treating them as expressions or blocks that can be passed to functions like mapWith.

## Recipes with Basic Functions

Having looked at basic pure functions and closures, we’re going to see some practical recipes that focus on the premise of functions that return functions.

#### Disclaimer

The recipes are written for practicality, and their implementation may introduce JavaScript features that haven’t been discussed in the text to this point, such as methods and/or prototypes. The overall use of each recipe will fit within the spirit of the language discussed so far, even if the implementations may not.

### Partial Application

In Building Blocks, we discussed partial application, but we didn’t write a generalized recipe for it. This is such a common tool that many libraries provide some form of partial application. You’ll find examples in Lemonad from Michael Fogus, Functional JavaScript from Oliver Steele and the terse but handy node-ap from James Halliday.

These two recipes are for quickly and simply applying a single argument, either the leftmost or rightmost.25 If you want to bind more than one argument, or you want to leave a “hole” in the argument list, you will need to either use a generalized partial recipe, or you will need to repeatedly apply arguments. They are context-agnostic.

As noted above, our partial recipe allows us to create functions that are partial applications of functions that are context aware. We’d need a different recipe if we wish to create partial applications of object methods.

We take it a step further, and can use gathering and spreading to allow for partial application with more than one argument:

### Unary

“Unary” is a function decorator that modifies the number of arguments a function takes: Unary takes any function and turns it into a function taking exactly one argument.

The most common use case is to fix a problem. JavaScript has a .map method for arrays, and many libraries offer a map function with the same semantics. Here it is in action:

In that example, it looks exactly like the mapping function you’ll find in most languages: You pass it a function, and it calls the function with one argument, the element of the array. However, that’s not the whole story. JavaScript’s map actually calls each function with three arguments: The element, the index of the element in the array, and the array itself.

Let’s try it:

If you pass in a function taking only one argument, it simply ignores the additional arguments. But some functions have optional second or even third arguments. For example:

This doesn’t work because parseInt is defined as parseInt(string[, radix]). It takes an optional radix argument. And when you call parseInt with map, the index is interpreted as a radix. Not good! What we want is to convert parseInt into a function taking only one argument.

We could write ['1', '2', '3'].map((s) => parseInt(s)), or we could come up with a decorator to do the job for us:

And now we can write:

Presto!

### Tap

One of the most basic combinators is the “K Combinator,” nicknamed the “Kestrel:”

It has some surprising applications. One is when you want to do something with a value for side-effects, but keep the value around. Behold:

tap is a traditional name borrowed from various Unix shell commands. It takes a value and returns a function that always returns the value, but if you pass it a function, it executes the function for side-effects. Let’s see it in action as a poor-man’s debugger:

It’s easy to turn off:

Libraries like Underscore use a version of tap that is “uncurried:”

Let’s enhance our recipe so that it works both ways:

Now we can write:

Or:

And if we wish it to do nothing at all, We can write either tap('espresso')() or tap('espresso', null)

p.s. tap can do more than just act as a debugging aid. It’s also useful for working with object and instance methods.

### Maybe

A common problem in programming is checking for null or undefined (hereafter called “nothing,” while all other values including 0, [] and false will be called “something”). Languages like JavaScript do not strongly enforce the notion that a particular variable or particular property be something, so programs are often written to account for values that may be nothing.

This recipe concerns a pattern that is very common: A function fn takes a value as a parameter, and its behaviour by design is to do nothing if the parameter is nothing:

Alternately, the function may be intended to work with any value, but the code calling the function wishes to emulate the behaviour of doing nothing by design when given nothing:

Naturally, there’s a function decorator recipe for that, borrowed from Haskell’s maybe monad, Ruby’s andand, and CoffeeScript’s existential method invocation:

maybe reduces the logic of checking for nothing to a function call:

As a bonus, maybe plays very nicely with instance methods, we’ll discuss those later:

If some code ever tries to call model.setSomething with nothing, the operation will be skipped.

### Once

once is an extremely helpful combinator. It ensures that a function can only be called, well, once. Here’s the recipe:

Very simple! You pass it a function, and you get a function back. That function will call your function once, and thereafter will return undefined whenever it is called. Let’s try it:

It seems some people will only try blind dating once.

(Note: There are some subtleties with decorators like once that involve the intersection of state with methods. We’ll look at that again in stateful method decorators.)

A variadic function is a function that is designed to accept a variable number of arguments.26 In JavaScript, you can make a variadic function by gathering parameters. For example:

This can be useful when writing certain kinds of destructuring algorithms. For example, we might want to have a function that builds some kind of team record. It accepts a coach, a captain, and an arbitrary number of players. Easy in ECMAScript 2015:

But we can’t go the other way around:

ECMAScript 2015 only permits gathering parameters from the end of the parameter list. Not the beginning. What to do?

#### a history lesson

In “Ye Olde Days,”27 JavaScript could not gather parameters, and we had to either do backflips with arguments and .slice, or we wrote ourselves a variadic decorator that could gather arguments into the last declared parameter. Here it is in all of its ECMAScript-5 glory:

We don’t need rightVariadic any more, because instead of:

We now simply write:

This is a right-variadic function, meaning that it has one or more fixed arguments, and the rest are gathered into the rightmost argument.

#### overcoming limitations

It’s nice to have progress. But as noted above, we can’t write:

That’s a left-variadic function. All left-variadic functions have one or more fixed arguments, and the rest are gathered into the leftmost argument. JavaScript doesn’t do this. But if we wanted to write left-variadic functions, could we make ourselves a leftVariadic decorator to turn a function with one or more arguments into a left-variadic function?

We sure can, by using the techniques from rightVariadic. Mind you, we can take advantage of modern JavaScript to simplify the code:

Our leftVariadic function is a decorator that turns any function into a function that gathers parameters from the left, instead of from the right.

Gathering arguments for functions is one of the ways JavaScript can destructure arrays. Another way is when assigning variables, like this:

As with parameters, we can’t gather values from the left when destructuring an array:

We could use leftVariadic the hard way:

But we can write our own left-gathering function utility using the same principles without all the tedium:

With leftGather, we have to supply the length of the array we wish to use as the result, and it gathers excess arguments into it from the left, just like leftVariadic gathers excess parameters for a function.

### Compose and Pipeline

Here is the B Combinator, or compose that we saw in Combinators and Decorators:

As we saw before, given:

We could write:

If we wanted to implement a compose3, we could write:

Or observe that it is really:

Once we get to compose4, we ask ourselves if there is a better way. For example, if we had a variadic compose, we could write compose(a, b), compose(a, b, c), or compose(a, b, c, d).

We can implement a variadic compose recursively. The easiest way to reason about writing a recursive compose is to start with the smallest or degenerate case. If compose only took one argument, it would look like this:

The next thing is to have a way of breaking a piece off the problem. We can do this with a variadic function:

We can test whether we have the degenerate case:

If it is not the degenerate case, we need to combine what we have with the solution for the rest. In other words, we need to combine fn with compose(...rest). How do we do that? Well, consider compose(a, b). We know that compose(b) is the degenerate case, it’s just b. And we know that compose(a, b) is (c) => a(b(c)).

So let’s substitute compose(b) for b:

Now substitute ...rest for b:

This is our solution:

There are others, of course. compose can be implemented with iteration or with .reduce, like this:

But the principle behaviour is the same: To compose a series of functions together, creating a new one. And the value is the same: We can write smaller, single purpose functions and put them together in different ways.

#### the semantics of compose

With compose, we’re usually making a new function. Although it works perfectly well, we don’t need to write things like compose(double, addOne)(3) inline to get the result 8. It’s easier and clearer to write double(addOne(3)).

On the other hand, when working with something like method decorators, it can help to write:

This makes it clear that setter adds the behaviour of both fluent and maybe to each method it decorates, and it’s sometimes easier to read const setter = compose(fluent, maybe); than:

The take-away is that compose is helpful when we are defining a new function that combines the effects of existing functions.

#### pipeline

compose is extremely handy, but one thing it doesn’t communicate well is the order on operations. compose is written that way because it matches the way explicitly composing functions works in JavaScript and most other languages: When you write a(b(…)), a happens after b.

Sometimes it makes more sense to compose functions in data flow order, as in “The value flows through a and then through b.” For this, we can use the pipeline function:

Comparing pipeline to compose, pipeline says “add one to the number and then double it.” Compose says, “double the result of adding one to the number.” Both do the same job, but communicate their intention in opposite ways.

## Picking the Bean: Choice and Truthiness

We’ve seen operators that act on numeric values, like + and %. In addition to numbers, we often need to represent a much more basic idea of truth or falsehood. Is this array empty? Does this person have a middle name? Is this user logged in?

JavaScript does have “boolean” values, they’re written true and false:

true and false are value types. All values of true are === all other values of true. We can see that is the case by looking at some operators we can perform on boolean values, !, &&, and ||. To being with, ! is a unary prefix operator that negates its argument. So:

The && and || operators are binary infix operators that perform “logical and” and “logical or” respectively:

Now, note well: We have said what happens if you pass boolean values to !, &&, and ||, but we’ve said nothing about expressions or about passing other values. We’ll look at those presently.

#### truthiness and the ternary operator

In JavaScript, there is a notion of “truthiness.” Every value is either “truthy” or “falsy.” Obviously, false is falsy. So are null and undefined, values that semantically represent “no value.” NaN is falsy, a value representing the result of a calculation that is not a number.28 And there are more: 0 is falsy, a value representing “none of something.” The empty string, '' is falsy, a value representing having no characters.

Every other value in JavaScript is “truthy” except the aforementioned false, null, undefined, NaN, 0, and ''. (Many other languages that have a notion of truthiness consider zero and the empty string to be truthy, not falsy, so beware of blindly transliterating code from one language to another!)

The reason why truthiness matters is that the various logical operators (as well as the if statement) actually operate on truthiness, not on boolean values. This affects the way the !, &&, and || operators work. We’ll look at them in a moment, but first, we’ll look at one more operator.

JavaScript inherited an operator from the C family of languages, the ternary operator. It’s the only operator that takes three arguments. It looks like this: first ? second : third. It evaluates first, and if first is “truthy”, it evaluates second and that is its value. If first is not truthy, it evaluates third and that is its value.

This is a lot like the if statement, however it is an expression, not a statement, and that can be very valuable. It also doesn’t introduce braces, and that can be a help or a hindrance if we want to introduce a new scope or use statements.

Here’re some simple examples of the ternary operator:

The fact that either the second or the third (but not both) expressions are evaluated can have important repercussions. Consider this hypothetical example:

We certainly don’t want JavaScript trying to evaluate deleteRecord(currentRecord) unless isAuthorized(currentUser) returns true.

#### truthiness and operators

Our logical operators !, &&, and || are a little more subtle than our examples above implied. ! is the simplest. It always returns false if its argument is truthy, and true is its argument is not truthy:

Programmers often take advantage of this behaviour to observe that !!(someExpression) will always evaluate to true if someExpression is truthy, and to false if it is not. So in JavaScript (and other languages with similar semantics), when you see something like !!currentUser(), this is an idiom that means “true if currentUser is truthy.” Thus, a function like currentUser() is free to return null, or undefined, or false if there is no current user.

Thus, !! is the way we write “is truthy” in JavaScript. How about && and ||? What haven’t we discussed?

First, and unlike !, && and || do not necessarily evaluate to true or false. To be precise:

• && evaluates its left-hand expression.
• If its left-hand expression evaluates to something falsy, && returns the value of its left-hand expression without evaluating its right-hand expression.
• If its left-hand expression evaluates to something truthy, && evaluates its right-hand expression and returns the value of the right-hand expression.
• || evaluates its left-hand expression.
• If its left-hand expression evaluates to something truthy, || returns the value of its left-hand expression without evaluating its right-hand expression.
• If its left-hand expression evaluates to something false, || evaluates its right-hand expression and returns the value of the right-hand expression.

If we look at our examples above, we see that when we pass true and false to && and ||, we do indeed get true or false as a result. But when we pass other values, we no longer get true or false:

In JavaScript, && and || aren’t boolean logical operators in the logical sense. They don’t operate strictly on logical values, and they don’t commute: a || b is not always equal to b || a, and the same goes for &&.

This is not a subtle distinction.

#### || and && are control-flow operators

We’ve seen the ternary operator: It is a control-flow operator, not a logical operator. The same is true of && and ||. Consider this tail-recursive function that determines whether a positive integer is even:

For example:

If n === 0, JavaScript does not evaluate (n !== 1 && even(n - 2)). This is very important! Imagine that JavaScript evaluated both sides of the || operator before determining its value. n === 0 would be true. What about (n !== 1 && even(n - 2))? Well, it would evaluate even(n - 2), or even(-2)

This leads us to evaluate n === 0 || (n !== 1 && even(n - 2)) all over again, and this time we end up evaluating even(-4). And then even(-6). and so on and so forth until JavaScript throws up its hands and runs out of stack space.

But that’s not what happens. || and && have short-cut semantics. In this case, if n === 0, JavaScript does not evaluate (n !== 1 && even(n - 2)). Likewise, if n === 1, JavaScript evaluates n !== 1 && even(n - 2) as false without ever evaluating even(n - 2).

This is more than just an optimization. It’s best to think of || and && as control-flow operators. The expression on the left is always evaluated, and its value determines whether the expression on the right is evaluated or not.

#### function parameters are eager

In contrast to the behaviour of the ternary operator, ||, and &&, function parameters are always eagerly evaluated:

Now our expression or(n === 0, and(n !== 1, even(n - 2))) is calling functions, and JavaScript always evaluates the expressions for parameters before passing the values to a function to invoke. This leads to the infinite recursion we fear.

If we need to have functions with control-flow semantics, we can pass anonymous functions. We obviously don’t need anything like this for or and and, but to demonstrate the technique:

Here we’ve passed functions that contain the expressions we want to evaluate, and now we can write our own functions that can delay evaluation.

#### summary

• Logical operators are based on truthiness and falsiness, not the strict values true and false.
• ! is a logical operator, it always returns true or false.
• The ternary operator (?:), ||, and && are control flow operators, they do not always return true or false, and they have short-cut semantics.
• Function invocation uses eager evaluation, so if we need to roll our own control-flow semantics, we pass it functions, not expressions.

## Composing and Decomposing Data

Recursion is the root of computation since it trades description for time.—Alan Perlis, Epigrams in Programming

### Arrays and Destructuring Arguments

While we have mentioned arrays briefly, we haven’t had a close look at them. Arrays are JavaScript’s “native” representation of lists. Strings are important because they represent writing. Lists are important because they represent ordered collections of things, and ordered collections are a fundamental abstraction for making sense of reality.

#### array literals

JavaScript has a literal syntax for creating an array: The [ and ] characters. We can create an empty array:

We can create an array with one or more elements by placing them between the brackets and separating the items with commas. Whitespace is optional:

Any expression will work:

Including an expression denoting another array:

This is an array with one element that is an array with one element that is an array with one element that is an array with one element that is an empty array. Although that seems like something nobody would ever construct, many students have worked with almost the exact same thing when they explored various means of constructing arithmetic from Set Theory.

Any expression will do, including names:

Array literals are expressions, and arrays are reference types. We can see that each time an array literal is evaluated, we get a new, distinct array, even if it contains the exact same elements:

#### element references

Array elements can be extracted using [ and ] as postfix operators. We pass an integer as an index of the element to extract:

As we can see, JavaScript Arrays are zero-based.

We know that every array is its own unique entity, with its own unique reference. What about the contents of an array? Does it store references to the things we give it? Or copies of some kind?

#### destructuring arrays

There is another way to extract elements from arrays: Destructuring, a feature going back to Common Lisp, if not before. We saw how to construct an array literal using [, expressions, , and ]. Here’s an example of an array literal that uses a name:

Let’s expand it to use a block and an extra name:

The line const wrapped = [something]; is interesting. On the left hand is a name to be bound, and on the right hand is an array literal, a template for constructing an array, very much like a quasi-literal string.

In JavaScript, we can actually reverse the statement and place the template on the left and a value on the right:

The statement const [something] = wrapped; destructures the array represented by wrapped, binding the value of its single element to the name something. We can do the same thing with more than one element:

We could do the same thing with (name) => name[1], but destructuring is code that resembles the data it consumes, a valuable coding style.

Destructuring can nest:

#### gathering

Sometimes we need to extract arrays from arrays. Here is the most common pattern: Extracting the head and gathering everything but the head from an array:

car and cdr are archaic terms that go back to an implementation of Lisp running on the IBM 704 computer. Some other languages call them first and butFirst, or head and tail. We will use a common convention and call variables we gather rest, but refer to the ... operation as a “gather,” following Kyle Simpson’s example.29

Alas, the ... notation does not provide a universal patten-matching capability. For example, we cannot write

Now, when we introduced destructuring, we saw that it is kind-of-sort-of the reverse of array literals. So if

Then:

What is the reverse of gathering? We know that:

What is the reverse? It would be:

Let’s try it:

It works! We can use ... to place the elements of an array inside another array. We say that using ... to destructure is gathering, and using it in a literal to insert elements is called “spreading.”

#### destructuring is not pattern matching

Some other languages have something called pattern matching, where you can write something like a destructuring assignment, and the language decides whether the “patterns” matches at all. If it does, assignments are made where appropriate.

In such a language, if you wrote something like:

That match would fail because the array doesn’t have an element to assign to what. But this is not how JavaScript works. JavaScript tries its best to assign things, and if there isn’t something that fits, JavaScript binds undefined to the name. Therefore:

And if there aren’t any items to assign with ..., JavaScript assigns an empty array:

From its very inception, JavaScript has striven to avoid catastrophic errors. As a result, it often coerces values, passes undefined around, or does whatever it can to keep executing without failing. This often means that we must write our own code to detect failure conditions, as we cannot rely on the language to point out when we are doing semantically meaningless things.

#### destructuring and return values

Some languages support multiple return values: A function can return several things at once, like a value and an error code. This can easily be emulated in JavaScript with destructuring:

#### destructuring parameters

Consider the way we pass arguments to parameters:

It is very much like an array literal. And consider how we bind values to parameter names:

It looks like destructuring. It acts like destructuring. There is only one difference: We have not tried gathering. Let’s do that:

Gathering works with parameters! This is very useful indeed, and we’ll see more of it in a moment.30

### Self-Similarity

Recursion is the root of computation since it trades description for time.—Alan Perlis, Epigrams in Programming

In Arrays and Destructuring Arguments, we worked with the basic idea that putting an array together with a literal array expression was the reverse or opposite of taking it apart with a destructuring assignment.

We saw that the basic idea that putting an array together with a literal array expression was the reverse or opposite of taking it apart with a destructuring assignment.

Let’s be more specific. Some data structures, like lists, can obviously be seen as a collection of items. Some are empty, some have three items, some forty-two, some contain numbers, some contain strings, some a mixture of elements, there are all kinds of lists.

But we can also define a list by describing a rule for building lists. One of the simplest, and longest-standing in computer science, is to say that a list is:

1. Empty, or;
2. Consists of an element concatenated with a list .

Let’s convert our rules to array literals. The first rule is simple: [] is a list. How about the second rule? We can express that using a spread. Given an element e and a list list, [e, ...list] is a list. We can test this manually by building up a list:

Thanks to the parallel between array literals + spreads with destructuring + rests, we can also use the same rules to decompose lists:

For the purpose of this exploration, we will presume the following:31

Armed with our definition of an empty list and with what we’ve already learned, we can build a great many functions that operate on arrays. We know that we can get the length of an array using its .length. But as an exercise, how would we write a length function using just what we have already?

First, we pick what we call a terminal case. What is the length of an empty array? 0. So let’s start our function with the observation that if an array is empty, the length is 0:

We need something for when the array isn’t empty. If an array is not empty, and we break it into two pieces, first and rest, the length of our array is going to be length(first) + length(rest). Well, the length of first is 1, there’s just one element at the front. But we don’t know the length of rest. If only there was a function we could call… Like length!

Let’s try it!

Our length function is recursive, it calls itself. This makes sense because our definition of a list is recursive, and if a list is self-similar, it is natural to create an algorithm that is also self-similar.

#### linear recursion

When promising students are trying to choose between pure mathematics and applied engineering, they are given a two-part aptitude test. In the first part, they are led to a laboratory bench and told to follow the instructions printed on the card. They find a bunsen burner, a sparker, a tap, an empty beaker, a stand, and a card with the instructions “boil water.”

Of course, all the students know what to do: They fill the beaker with water, place the stand on the burner and the beaker on the stand, then they turn the burner on and use the sparker to ignite the flame. After a bit the water boils, and they turn off the burner and are lead to a second bench.

Once again, there is a card that reads, “boil water.” But this time, the beaker is on the stand over the burner, as left behind by the previous student. The engineers light the burner immediately. Whereas the mathematicians take the beaker off the stand and empty it, thus reducing the situation to a problem they have already solved.

There is more to recursive solutions that simply functions that invoke themselves. Recursive algorithms follow the “divide and conquer” strategy for solving a problem:

1. Divide the problem into smaller problems
2. If a smaller problem is solvable, solve the small problem
3. If a smaller problem is not solvable, divide and conquer that problem
4. When all small problems have been solved, compose the solutions into one big solution

The big elements of divide and conquer are a method for decomposing a problem into smaller problems, a test for the smallest possible problem, and a means of putting the pieces back together. Our solutions are a little simpler in that we don’t really break a problem down into multiple pieces, we break a piece off the problem that may or may not be solvable, and solve that before sticking it onto a solution for the rest of the problem.

This simpler form of “divide and conquer” is called linear recursion. It’s very useful and simple to understand. Let’s take another example. Sometimes we want to flatten an array, that is, an array of arrays needs to be turned into one array of elements that aren’t arrays.32

We already know how to divide arrays into smaller pieces. How do we decide whether a smaller problem is solvable? We need a test for the terminal case. Happily, there is something along these lines provided for us:

The usual “terminal case” will be that flattening an empty array will produce an empty array. The next terminal case is that if an element isn’t an array, we don’t flatten it, and can put it together with the rest of our solution directly. Whereas if an element is an array, we’ll flatten it and put it together with the rest of our solution.

So our first cut at a flatten function will look like this:

Once again, the solution directly displays the important elements: Dividing a problem into subproblems, detecting terminal cases, solving the terminal cases, and composing a solution from the solved portions.

#### mapping

Another common problem is applying a function to every element of an array. JavaScript has a built-in function for this, but let’s write our own using linear recursion.

If we want to square each number in a list, we could write:

And if we wanted to “truthify” each element in a list, we could write:

This specific case of linear recursion is called “mapping,” and it is not necessary to constantly write out the same pattern again and again. Functions can take functions as arguments, so let’s “extract” the thing to do to each element and separate it from the business of taking an array apart, doing the thing, and putting the array back together.

Given the signature:

We can write it out using a ternary operator. Even in this small function, we can identify the terminal condition, the piece being broken off, and recomposing the solution.

#### folding

With the exception of the length example at the beginning, our examples so far all involve rebuilding a solution using spreads. But they needn’t. A function to compute the sum of the squares of a list of numbers might look like this:

There are two differences between sumSquares and our maps above:

1. Given the terminal case of an empty list, we return a 0 instead of an empty list, and;
2. We catenate the square of each element to the result of applying sumSquares to the rest of the elements.

Let’s rewrite mapWith so that we can use it to sum squares.

And now we supply a function that does slightly more than our mapping functions:

Our foldWith function is a generalization of our mapWith function. We can represent a map as a fold, we just need to supply the array rebuilding code:

And if we like, we can write mapWith using foldWith:

And to return to our first example, our version of length can be written as a fold:

#### summary

Linear recursion is a basic building block of algorithms. Its basic form parallels the way linear data structures like lists are constructed: This helps make it understandable. Its specialized cases of mapping and folding are especially useful and can be used to build other functions. And finally, while folding is a special case of linear recursion, mapping is a special case of folding.

### Tail Calls (and Default Arguments)

The mapWith and foldWith functions we wrote in Self-Similarity are useful for illustrating the basic principles behind using recursion to work with self-similar data structures, but they are not “production-ready” implementations. One of the reasons they are not production-ready is that they consume memory proportional to the size of the array being folded.

Let’s look at how. Here’s our extremely simple mapWith function again:

Let’s step through its execution. First, mapWith((x) => x * x, [1, 2, 3, 4, 5]) is invoked. first is not undefined, so it evaluates [fn(first), …mapWith(fn, rest)]. To do that, it has to evaluate fn(first) and mapWith(fn, rest), then evaluate [fn(first), ...mapWith(fn, rest)].

This is roughly equivalent to writing:

Note that while evaluating mapWith(fn, rest), JavaScript must retain the value first or fn(first), plus some housekeeping information so it remembers what to do with mapWith(fn, rest) when it has a result. JavaScript cannot throw first away. So we know that JavaScript is going to hang on to 1.

Next, JavaScript invokes mapWith(fn, rest), which is semantically equivalent to mapWith((x) => x * x, [2, 3, 4, 5]). And the same thing happens: JavaScript has to hang on to 2 (or 4, or both, depending on the implementation), plus some housekeeping information so it remembers what to do with that value, while it calls the equivalent of mapWith((x) => x * x, [3, 4, 5]).

This keeps on happening, so that JavaScript collects the values 1, 2, 3, 4, and 5 plus housekeeping information by the time it calls mapWith((x) => x * x, []). It can start assembling the resulting array and start discarding the information it is saving.

That information is saved on a call stack, and it is quite expensive. Furthermore, doubling the length of an array will double the amount of space we need on the stack, plus double all the work required to set up and tear down the housekeeping data for each call (these are called call frames, and they include the place where the function was called, an environment, and so on).

In practice, using a method like this with more than about 50 items in an array may cause some implementations to run very slow, run out of memory and freeze, or cause an error.

Is there a better way? Yes. In fact, there are several better ways. Making algorithms faster is a very highly studied field of computer science. The one we’re going to look at here is called tail-call optimization, or “TCO.”

#### tail-call optimization

A “tail-call” occurs when a function’s last act is to invoke another function, and then return whatever the other function returns. For example, consider the maybe function decorator:

There are three places it returns. The first two don’t return anything, they don’t matter. But the third is fn.apply(this, args). This is a tail-call, because it invokes another function and returns its result. This is interesting, because after sorting out what to supply as arguments (this, args), JavaScript can throw away everything in its current stack frame. It isn’t going to do any more work, so it can throw its existing stack frame away.

And in fact, it does exactly that: It throws the stack frame away, and does not consume extra memory when making a maybe-wrapped call. This is a very important characteristic of JavaScript: If a function makes a call in tail position, JavaScript optimizes away the function call overhead and stack space.

That is excellent, but one wrapping is not a big deal. When would we really care? Consider this implementation of length:

The length function calls itself, but it is not a tail-call, because it returns 1 + length(rest), not length(rest).

The problem can be stated in such a way that the answer is obvious: length does not call itself in tail position, because it has to do two pieces of work, and while one of them is in the recursive call to length, the other happens after the recursive call.

The obvious solution?

#### converting non-tail-calls to tail-calls

The obvious solution is push the 1 + work into the call to length. Here’s our first cut:

This lengthDelaysWork function calls itself in tail position. The 1 + work is done before calling itself, and by the time it reaches the terminal position, it has the answer. Now that we’ve seen how it works, we can clean up the 0 + numberToBeAdded business. But while we’re doing that, it’s annoying to remember to call it with a zero. Let’s fix that:

Or we could use partial application:

This version of length calls uses lengthDelaysWork, and JavaScript optimizes that not to take up memory proportional to the length of the string. We can use this technique with mapWith:

We can use it with ridiculously large arrays:

Brilliant! We can map over large arrays without incurring all the memory and performance overhead of non-tail-calls. And this basic transformation from a recursive function that does not make a tail call, into a recursive function that calls itself in tail position, is a bread-and-butter pattern for programmers using a language that incorporates tail-call optimization.

#### factorials

Introductions to recursion often mention calculating factorials:

In mathematics, the factorial of a non-negative integer n, denoted by n!, is the product of all positive integers less than or equal to n. For example:

The naïve function for calcuating the factorial of a positive integer follows directly from the definition:

While this is mathematically elegant, it is computational filigree.

Once again, it is not tail-recursive, it needs to save the stack with each invocation so that it can take the result returned and compute n * factorial(n - 1). We can do the same conversion, pass in the work to be done:

Or we could use partial application:

As before, we wrote a factorialWithDelayedWork function, then used partial application (callLast) to make a factorial function that took just the one argument and supplied the initial work value.

#### default arguments

Our problem is that we can directly write:

But it is hideous to have to always add a 1 parameter, we’d be demanding that everyone using the factorial function know that we are using a tail-recursive implementation.

What we really want is this: We want to write something like factorial(6), and have JavaScript automatically know that we really mean factorial(6, 1). But when it calls itself, it will call factorial(5, 6) and that will not mean factorial(5, 1).

JavaScript provides this exact syntax, it’s called a default argument, and it looks like this:

By writing our parameter list as (n, work = 1) =>, we’re stating that if a second parameter is not provided, work is to be bound to 1. We can do similar things with our other tail-recursive functions:

Now we don’t need to use two functions. A default argument is concise and readable.

#### defaults and destructuring

We saw earlier that destructuring parameters works the same way as destructuring assignment. Now we learn that we can create a default parameter argument. Can we create a default destructuring assignment?

How very useful: defaults can be supplied for destructuring assignments, just like defaults for parameters.

### Garbage, Garbage Everywhere

We have now seen how to use Tail Calls to execute mapWith in constant space:

But when we try it on very large arrays, we discover that it is still very slow. Much slower than the built-in .map method for arrays. The right tool to discover why it’s still slow is a memory profiler, but a simple inspection of the program will reveal the following:

Every time we call mapWith, we’re calling [...prepend, fn(first)]. To do that, we take the array in prepend and push fn(first) onto the end, creating a new array that will be passed to the next invocation of mapWith.

Worse, the JavaScript Engine actually copies the elements from prepend into the new array one at a time. That is very laborious.33

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. Lather, rinse, repeat: Ever time we call mapWith, we’re creating a new array, copying all the elements from prepend into the new array, and then we no longer use prepend.

We may not be creating 3,000 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, ...rest] approach to recursion is slow because that it creates a lot of temporary arrays, and it spends an enormous amount of time copying elements into arrays that end up being discarded.

So here’s a question: If this is such a slow approach, why do some examples of “functional” algorithms work this exact way?

#### some history

Once upon a time, there was a programming language called Lisp, an acronym for LISt Processing.34 Lisp was one of the very first high-level languages, the very first implementation was written for the IBM 704 computer. (The very first FORTRAN implementation was also written for the 704).

The 704 had a 36-bit word, meaning that it was very fast to store and retrieve 36-bit values. The CPU’s instruction set featured two important macros: CAR would fetch 15 bits representing the Contents of the Address part of the Register, while CDR would fetch the Contents of the Decrement part of the Register.

In broad terms, this means that a single 36-bit word could store two separate 15-bit values and it was very fast to save and retrieve pairs of values. If you had two 15-bit values and wished to write them to the register, the CONS macro would take the values and write them to a 36-bit word.

Thus, CONS put two values together, CAR extracted one, and CDR extracted the other. Lisp’s basic data type is often said to be the list, but in actuality it was the “cons cell,” the term used to describe two 15-bit values stored in one word. The 15-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.

Lists were represented as linked lists of cons cells, with each cell’s head pointing to an element and the tail pointing to another cons cell.

Having these instructions be very fast was important to those early designers: They were working on one of the first high-level languages (COBOL and FORTRAN being the others), and computers in the late 1950s were extremely small and slow by today’s standards. Although the 704 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.

Here’s the scheme in JavaScript, using two-element arrays to represent cons cells:

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:

This is a Linked List, it’s just that those early Lispers used the names car and cdr after the hardware instructions, whereas today we use words like data and reference. But it works the same way: If we want the head of a list, we call car on it:

car is very fast, it simply extracts the first element of the cons cell.

But what about the rest of the list? cdr does the trick:

Again, it’s just extracting a reference from a cons cell, it’s very fast. In Lisp, it’s blazingly fast because it happens in hardware. There’s no making copies of arrays, the time to cdr a list with five elements is the same as the time to cdr a list with 5,000 elements, and no temporary arrays are needed. In JavaScript, it’s still much, much, much faster to get all the elements except the head from a linked list than from an array. 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, ...rest] to gather or spread arrays, we’re emulating the semantics of car and cdr, but not the implementation. We’re doing something laborious and memory-inefficient compared to using a linked list as Lisp did and as we can still do if we choose.

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. And so it is today that languages like JavaScript have arrays that are slow to split into the equivalent of a car/cdr pair, but instructional examples of recursive programs still have echoes of their Lisp origins.

We’ll look at linked lists again when we look at Plain Old JavaScript Objects.

#### so why arrays

If [first, ...rest] is so slow, why does JavaScript use arrays instead of making everything a linked list?

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.

We have avoided discussing rebinding and mutating values, but if we want to change elements of our lists, the naïve linked list implementation suffers as well: When we take the cdr of a linked list, we are sharing the elements. 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.

Arrays avoid this problem by pessimistically copying all the references whenever we extract an element or sequence of elements from them (We’ll see this explained later in Mutation).

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.

#### summary

Although we showed how to use tail calls to map and fold over arrays with [first, ...rest], in reality this is not how it ought to be done. But it is an extremely simple illustration of how recursion works when you have a self-similar means of constructing a data structure.

### Plain Old JavaScript Objects

Lists are not the only way to represent collections of things, but they are the “oldest” data structure in the history of high level languages, because they map very closely to the way the hardware is organized in a computer. Lists are obviously very handy for homogeneous collections of things, like a shopping list:

And they can be used to store heterogeneous things in various levels of structure:

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:

Now they could write user[NAME][LAST] or user[OCCUPATION][TITLE] instead of user[0][1] or user[1][0]. 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 dictionaries, and it calls them “objects.” The word “object” is loaded in programming circles, due to the widespread use of the term “object-oriented programming” that was coined by Alan Kay but has since come to mean many, many things to many different people.

In JavaScript, an object is a map from string keys to values.

#### literal object syntax

JavaScript has a literal syntax for creating objects. This object maps values to the keys year, month, and day:

Two objects created with separate evaluations have differing identities, just like arrays:

Objects use [] to access the values by name, using a string:

Values contained within an object work just like values contained within an array, we access them by reference to the original:

Names needn’t be alphanumeric strings. For anything else, enclose the label in quotes:

If the name is an alphanumeric string conforming to the same rules as names of variables, there’s a simplified syntax for accessing the values:

Expressions can be used for keys as well. The syntax is to enclose the key’s expression in [ and ]:

All containers can contain any value, including functions or other containers, like a fat arrow function:

Or proper functions:

Or named function expressions:

It is very common to associate named function expressions with keys in objects, and there is a “compact method syntax” for binding named function expressions to keywords:

(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.)

#### destructuring objects

Just as we saw with arrays, we can write destructuring assignments with literal object syntax. So, we can write:

And we can also write:

And of course, we destructure parameters:

Terrible grammar and capitalization, but let’s move on. It is very common to write things like title: title when destructuring objects. When the label is a valid variable name, it’s often the most obvious variable name as well. So JavaScript supports a further syntactic optimization:

And that same syntax works for literals:

Earlier, we used two-element arrays as nodes in a linked list:

In essence, this simple implementation used functions to create an abstraction with named elements. But now that we’ve looked at objects, we can use an object instead of a two-element array. While we’re at it, let’s use contemporary names. So our linked list nodes will be formed from { first, rest }

In that case, a linked list of the numbers 1, 2, and 3 will look like this: { first: 1, rest: { first: 2, rest: { first: 3, rest: EMPTY } } }.

We can then perform the equivalent of [first, ...rest] with direct property accessors:

Taking the length of a linked list is easy:

What about mapping? Well, let’s start with the simplest possible thing, making a copy of a list. As we saw above, and discussed in Garbage, Garbage Everywhere, it is fast to iterate forward through a linked list. What isn’t fast is naïvely copying a 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.

We could follow the strategy of delaying the work. Let’s write that naively:

Well, well, well. We have unwittingly reversed the list. This makes sense, if lists are constructed from back to front, and we make a linked list out of items as we iterate through it, we’re going to get a backwards copy of the list. This isn’t a bad thing by any stretch of the imagination. Let’s call it what it is:

And now, we can make a reversing map:

And a regular mapWith follows:

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. Whereas our naïve array algorithm created 2n superfluous arrays and copied n2 superfluous values.

### Mutation

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 []. You can reassign a value using [] =:

You can even add a value:

You can do the same thing with both syntaxes for accessing objects:

We have established that JavaScript’s semantics allow for two different bindings to refer to the same value. For example:

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. We haven’t rebound the inner name to a different variable, we’ve mutated the value that both bindings share. Now that we’ve finished with mutation and aliases, let’s have a look at it.

#### mutation and data structures

Mutation is a surprisingly complex subject. It is possible to compute anything without ever mutating an existing entity. Languages like Haskell don’t permit mutation at all. 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. Let’s recall linked lists from Plain Old JavaScript Objects. While we’re executing the mapWith function, we’re constructing a new linked list. 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. For example:

Changes made to ThreeToFive affect OneToFive, because they share the same structure. When we wrote ThreeToFive = OneToFive.rest.rest;, we weren’t making a brand new copy of {"first":3,"rest":{"first":4,"rest":{"first":5,"rest":{}}}}, we were getting a reference to the same chain of nodes.

Structure sharing like this is what makes linked lists so fast for taking everything but the first item of a list: We aren’t making a new list, we’re using some of the old list. Whereas destructuring an array with [first, ...rest] does make a copy, so:

The gathering operation [a, b, ...ThreeToFive] is slower, but “safer.”

So back to avoiding mutation. In general, it’s easier to reason about data that doesn’t change. We don’t have to remember to use copying operations when we pass it as a value to a function, or extract some data from it. We just use the data, and the less we mutate it, the fewer the times we have to think about whether making changes will be “safe.”

#### building with 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. But when we’re in the midst of creating a brand new list, we aren’t sharing any nodes with any other lists, and we can afford to be more liberal about using mutation to save space and/or time.

Armed with this basic copy implementation, we can write mapWith:

### Reassignment

Like some imperative programming languages, JavaScript allows you to re-assign the value bound to parameters. We saw this earlier in rebinding:

By default, JavaScript permits us to rebind new values to names bound with a parameter. For example, we can write:

The line n = n - 2; rebinds a new value to the name n. We will discuss this at much greater length in Reassignment, but long before we do, let’s try a similar thing with a name bound using const. We’ve already bound evenStevens using const, let’s try rebinding it:

JavaScript does not permit us to rebind a name that has been bound with const. We can shadow it by using const to declare a new binding with a new function or block scope, but we cannot rebind a name that was bound with const in an existing scope.

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. JavaScript has such a thing, it’s called let:

We took the time to carefully examine what happens with bindings in environments. Let’s take the time to explore what happens with reassigning values to variables. The key is to understand that we are rebinding a different value to the same name in the same environment.

So let’s consider what happens with a shadowed variable:

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:

To:

Then back to:

However, if we don’t shadow age with let, reassigning within the block changes the original:

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.

#### mixing let and const

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.

If you dislike deliberately shadowing variables, you’ll probably take an even more opprobrious view of mixing const and let semantics with a shadowed variable:

Shadowing a let with a const does not change our ability to rebind the variable in its original scope. And:

Shadowing a const with a let does not permit it to be rebound in its original scope.

#### var

JavaScript has one more way to bind a name to a value, var.35

var looks a lot like let:

But of course, it’s not exactly like let. It’s just different enough to present a source of confusion. First, var is not block scoped, it’s function scoped, just like function declarations:

Declaring age twice does not cause an error(!), and the inner declaration does not shadow the outer declaration. 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:

JavaScript interprets this code as if we had written:

JavaScript hoists the let and the assignment. But not so with var:

JavaScript hoists the declaration, but not the assignment. It is as if we’d written:

In that way, var is a little like const and let, we should always declare and bind names before using them. But it’s not like const and let in that it’s function scoped, not block scoped.

#### why const and let were invented

const and let are recent additions to JavaScript. For nearly twenty years, variables were declared with var (not counting parameters and function declarations, of course). However, its functional scope was a problem.

We haven’t looked at it yet, but JavaScript provides a for loop for your iterating pleasure and convenience. 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.36 And perhaps you have noticed that var i = 1 is tucked away instead of being at the top as we prefer. But is this ever a problem?

Yes. Consider this variation:

So far, so good. Hey, remember that functions in JavaScript are values? Let’s get fancy!

Again, so far, so good. Let’s try one of our functions:

What went wrong? Why didn’t it give us ‘Hello, Raganwald, my name is Friedrich’? The answer is that pesky var i. Remember that i is bound in the surrounding environment, so it’s as if we wrote:

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. That’s not what we want at all.

The error wouldn’t exist at all if we’d used let in the first place

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:

Now we’re creating a new inner parameter, i and binding it to the value of the outer i. This works, but let is so much simpler and cleaner that it was added to the language in the ECMAScript 2015 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.

### Copy on Write

We’ve seen how to build lists with arrays and with linked lists. We’ve touched on an important difference between them:

• When you take the rest of an array with destructuring ([first, ...rest]), you are given a copy of the elements of the array.
• When you take the rest of a linked list with its reference, you are given the exact same nodes of the elements of the original list.

The consequence of this is that if you have an array, and you take it’s “rest,” your “child” array is a copy of the elements of the parent array. And therefore, modifications to the parent do not affect the child, and modifications to the child do not affect the parent.

Whereas if you have a linked list, and you take it’s “rest,” your “child” list shares its nodes with the “parent” list. And therefore, modifications to the parent also modify the child, and modifications to the child also modify the parent.

Let’s confirm our understanding:

This is remarkably unsafe. If we know that a list doesn’t share any elements with another list, we can safely modify it. But how do we keep track of that? Add a bunch of bookkeeping to track references? We’ll end up reinventing reference counting and garbage collection.

#### a few utilities

before we go any further, let’s write a few naïve list utilities so that we can work at a slightly higher level of abstraction:

Our new at and set functions behave similarly to array[index] and array[index] = value. The main difference is that array[index] = value evaluates to value, while set(index, value, list) evaluates to the modified list.

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.

This strategy is called “copy-on-read”, because when we attempt the parent to “read” the value of a child of the list, we make a copy and read the copy of the child. 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. Sometimes we don’t need to make a copy because we won’t be modifying the list. Our mapWith function would be very expensive if we make a copy every time we call rest(node).

There’s also a bug: What happens when we modify the first element of a list? But before we fix that, let’s try being lazy about copying.

#### copy-on-write

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. We’ll restore our original definition for rest, but change set:

Our original parent and child lists remain unmodified:

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.

This strategy of waiting to copy until you are writing is called copy-on-write, or “COW:”

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.—Wikipedia

Like all strategies, it makes a tradeoff: It’s much cheaper than pessimistically copying structures when you make an infrequent number of small changes, but if you tend to make a lot of changes to some that you aren’t sharing, it’s more expensive.

Looking at the code again, you see that the copy function doesn’t copy on write: It follows the pattern that while constructing something, we own it and can be liberal with mutation. Once we’re done with it and give it to someone else, we need to be conservative and use a strategy like copy-on-read or copy-on-write.

### Tortoises, Hares, and Teleporting Turtles

A good long while ago (The First Age of Internet Startups), someone asked me one of those pet algorithm questions. It was, “Write an algorithm to detect a loop in a linked list, in constant space.”

I’m not particularly surprised that I couldn’t think up an answer in a few minutes at the time. And to the interviewer’s credit, he didn’t terminate the interview on the spot, he asked me to describe the kinds of things going through my head.

I think I told him that I was trying to figure out if I could adapt a hashing algorithm such as XORing everything together. This is the “trick answer” to a question about finding a missing integer from a list, so I was trying the old, “Transform this into a problem you’ve already solved” meta-algorithm. We moved on from there, and he didn’t reveal the “solution.”

I went home and pondered the problem. I wanted to solve it. Eventually, I came up with something and tried it (In Java!) on my home PC. 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:

This algorithm is called “The Tortoise and the Hare,” and was discovered by Robert Floyd in the 1960s. You have two node references, and one traverses the list at twice the speed of the other. No matter how large it is, you will eventually have the fast reference equal to the slow reference, and thus you’ll detect the loop.

At the time, I couldn’t think of any way to use hashing to solve the problem, so I gave up and tried to fit this into a powers-of-two algorithm. My first pass at it was clumsy, but it was roughly equivalent to this:

Years later, I came across a discussion of this algorithm, The Tale of the Teleporting Turtle. It seems to be faster under certain circumstances, depending on the size of the loop and the relative costs of certain operations.

What’s interesting about these two algorithms is that they both tangle two separate concerns: How to traverse a data structure, and what to do with the elements that you encounter. In Functional Iterators, we’ll investigate one pattern for separating these concerns.

### Functional Iterators

Let’s consider a remarkably simple problem: Finding the sum of the elements of an array. 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.

What happens when we want to sum a tree of numbers? Or a linked list of numbers?

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. Let’s rearrange our code a bit:

What we’ve done is turn an array into a function that folds an array with const foldArray = (array) => callRight(foldArrayWith, array);. The sumFoldable function doesn’t care what kind of data structure we have, as long as it’s foldable.

Here it is summing a tree of numbers:

We’ve found another way to express the principle of separating traversing a data structure from the operation we want to perform on that data structure, we’ve completely separated the knowledge of how to sum from the knowledge of how to fold an array or tree (or anything else, really).

#### iterating

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:

Once again, we’re mixing the code for iterating over an array with the code for calculating a sum. And worst of all, we’re getting really low-level with details like knowing that the elements of an array are indexed with consecutive integers that begin with 0.

We can write this a slightly different way, using a while loop:

Notice that buried inside our loop, we have bound the names done and value. We can put those into a POJO (a Plain Old JavaScript Object). It’ll be a little awkward, but we’ll be patient:

With this code, we make a POJO that has done and value keys. All the summing code needs to know is to add eachIteration.value. 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. The iteratorSum function iterates over the elements by calling the iterator function repeatedly until it returns { done: true }.

We can write a different iterator for a different data structure. Here’s one for linked lists:

#### unfolding and laziness

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. Let’s consider the simplest example:

And here’s another one:

A function that starts with a seed and expands it into a data structure is called an unfold. It’s the opposite of a fold. It’s possible to write a generic unfold mechanism, but let’s pass on to what we can do with unfolded iterators.

For starters, we can map an iterator, just like we map a collection:

This business of going on forever has some drawbacks. Let’s introduce an idea: A function that takes an iterator and returns another iterator. 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’ll need an iterator that produces odd numbers. We can write that directly:

We could also write a filter for iterators to accompany our mapping function:

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.

#### bonus

Many programmers coming to JavaScript from other languages are familiar with three “canonical” operations on collections: folding, filtering, and finding. In Smalltalk, for example, they are known as collect, select, and detect.

We haven’t written anything that finds the first element of an iteration that meets a certain criteria. Or have we?

This is interesting, because it is lazy: It doesn’t apply fn to every element in an iteration, just enough to find the first that passes the test. 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.

#### caveat

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(...) appear to create an entirely new iterator, in reality they return a decorated reference to the original iterator. So as you traverse the new decorator, you’re changing the state of the original!

For all intents and purposes, once you pass an iterator to a function, you can expect that you no longer “own” that iterator, and that its state either has changed or will change.

### Making Data Out Of Functions

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.

A very long time ago, mathematicians like Alonzo Church, Moses Schönfinkel, Alan Turning, and Haskell Curry and asked themselves if we really needed all these features to perform computations. 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. For example, we don’t need arrays to represent lists, or even POJOs to represent nodes in a linked list. We can model lists just using functions.

To Mock a Mockingbird established the metaphor of songbirds for the combinators, and ever since then logicians have called the K combinator a “kestrel,” the B combinator a “bluebird,” and so forth.

The oscin.es library contains code for all of the standard combinators and for experimenting using the standard notation.

Let’s start with some of the building blocks of combinatory logic, the K, I, and V combinators, nicknamed the “Kestrel”, the “Idiot Bird”, and the “Vireo:”

#### the kestrel and the idiot

A constant function is a function that always returns the same thing, no matter what you give it. For example, (x) => 42 is a constant function that always evaluates to 42. 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.

For example:

The identity function is a function that evaluates to whatever parameter you pass it. So I(42) => 42. Very simple, but useful. Now we’ll take it one more step forward: Passing a value to K gets a function back, and passing a value to that function gets us a value.

Like so:

This is very interesting. Given two values, we can say that K always returns the first value: K(x)(y) => x (that’s not valid JavaScript, but it’s essentially how it works).

Now, an interesting thing happens when we pass functions to each other. Consider K(I). From what we just wrote, K(x)(y) => x So K(I)(x) => I. Makes sense. Now let’s tack one more invocation on: What is K(I)(x)(y)? If K(I)(x) => I, then K(I)(x)(y) === I(y) which is y.

Therefore, K(I)(x)(y) => y:

Aha! Given two values, K(I) always returns the second value.

If we are not feeling particularly academic, we can name our functions:

This is very interesting. Given two values, we can say that K always returns the first value, and given two values, K(I) always returns the second value.

#### backwardness

Our first and second functions are a little different than what most people are used to when we talk about functions that access data. If we represented a pair of values as an array, we’d write them like this:

Or if we were using a POJO, we’d write them like this:

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.

But the first and second we built out of K and I don’t work that way. You call them and pass them the bits, and they choose what to return. So if we wanted to use them with a two-element array, we’d need to have a piece of code that calls some code.

Here’s the first cut:

Our latin data structure is no longer a dumb data structure, it’s a function. And instead of passing latin to first or second, we pass first or second to latin. It’s exactly backwards of the way we write functions that operate on data.

#### the vireo

Given that our latin data is represented as the function (selector) => selector("primus")("secundus"), our obvious next step is to make a function that makes data. For arrays, we’d write cons = (first, second) => [first, second]. For objects we’d write: cons = (first, second) => {first, second}. In both cases, we take two parameters, and return the form of the data.

For “data” we access with K and K(I), our “structure” is the function (selector) => selector("primus")("secundus"). Let’s extract those into parameters:

For consistency with the way combinators are written as functions taking just one parameter, we’ll curry the function:

Let’s try it, we’ll use the word pair for the function that makes data (When we need to refer to a specific pair, we’ll use the name aPair by default):

It works! Now what is this pair function? If we change the names to x, y, and z, we get: (x) => (y) => (z) => z(x)(y). That’s the V combinator, the Vireo! So we can write:

As an aside, the Vireo is a little like JavaScript’s .apply function. It says, “take these two values and apply them to this function.” There are other, similar combinators that apply values to functions. One notable example is the “thrush” or T combinator: It takes one value and applies it to a function. It is known to most programmers as .tap.

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. We’d better try it out to check.

#### lists with functions as data

Here’s another look at linked lists using POJOs. We use the term rest instead of second, but it’s otherwise identical to what we have above:

We can write length and mapWith functions over it:

Can we do the same with the linked lists we build out of functions? Yes:

We write them in a backwards way, but they seem to work. How about length?

And mapWith?

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.

But without building our way up to something insane like writing a JavaScript interpreter using JavaScript functions and no other data structures, let’s take things another step in a slightly different direction.

We used functions to replace arrays and POJOs, but we still use JavaScript’s built-in operators to test for equality (===) and to branch ?:.

We keep using the same pattern in our functions: aPair === EMPTY ? doSomething : doSomethingElse. 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. Here’s length again:

Let’s presume we are working with a slightly higher abstraction, we’ll call it a list. Instead of writing length(list) and examining a list, we’ll write something like:

Now we’ll need to write first and rest functions for a list, and those names will collide with the first and rest we wrote for pairs. So let’s disambiguate our names:

We’ll also write a handy list printer:

How would all this work? Let’s start with the obvious. What is an empty list?

And what is a node of a list?

Let’s try it:

We can write reverse and mapWith as well. We aren’t being super-strict about emulating combinatory logic, we’ll use default parameters:

We have managed to provide the exact same functionality that === and ?: provided, but using functions and nothing else.

#### functions are not the real point

There are lots of similar texts explaining how to construct complex semantics out of functions. You can establish that K and K(I) can represent true and false, model magnitudes with Church Numerals or Surreal Numbers, and build your way up to printing FizzBuzz.

The superficial conclusion reads something like this:

Functions are a fundamental building block of computation. They are “axioms” of combinatory logic, and can be used to compute anything that JavaScript can compute.

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. It’s the QED of physics that underpins the Maxwell’s Equations of programming. Deeply important, but not practical when you’re building a bridge.

So what is interesting about this? What nags at our brain as we’re falling asleep after working our way through 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. As it happened, the pair was composed by the vireo (or V combinator): (x) => (y) => (z) => z(x)(y).

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. Here, we’ll prove it:

This is a little gratuitous, but it makes the point: The code that uses the data doesn’t reach in and touch it: The code that uses the data provides some code and asks the data to do something with it.

The same thing happens with our lists. Here’s length for lists:

We’re passing list what we want done with an empty list, and what we want done with a list that has at least one element. We then ask list to do it, and provide a way for list to call the code we pass in.

We won’t bother here, but it’s easy to see how to swap our functions out and replace them with an array. Or a column in a database. This is fundamentally not the same thing as this code for the length of a linked list:

The line node === EMPTY presumes a lot of things. It presumes there is one canonical empty list value. It presumes you can compare these things with the === operator. We can fix this with an isEmpty function, but now we’re pushing even more knowledge about the structure of lists into the code that uses them.

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:

• Instead of directly manipulating part of an entity, pass it a function and have it call our function with the part we want.
• And instead of testing some property of an entity and making a choice of our own with ?: (or if), pass the entity the work we want done for each case and let it test itself.

## Recipes with Data

#### Disclaimer

The recipes are written for practicality, and their implementation may introduce JavaScript features that haven’t been discussed in the text to this point, such as methods and/or prototypes. The overall use of each recipe will fit within the spirit of the language discussed so far, even if the implementations may not.

### mapWith

In JavaScript, arrays have a .map method. 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. For example:

We could write a function that behaves like the .map method if we wanted:

This recipe isn’t for map: It’s for mapWith, a function that wraps around map and turns any other function into a mapper. mapWith is very simple:37

mapWith differs from map in two ways. It reverses the arguments, taking the function first and the list second. It also “curries” the function: Instead of taking two arguments, it takes one argument and returns a function that takes another argument.

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 .map:

We can call mapWith in one step:

If we didn’t use mapWith, we’d could have also used callRight with map to accomplish the same result:

Both patterns take us to the same destination: Composing functions out of common pieces, rather than building them entirely from scratch. mapWith is a very convenient abstraction for a very common pattern.

mapWith was suggested by ludicast

### Flip

We wrote mapWith like this:

Let’s consider the case whether we have a map function of our own, perhaps from the allong.es library, perhaps from Underscore. We could write our function something like this:

Looking at this, we see we’re conflating two separate transformations. First, we’re reversing the order of arguments. You can see that if we simplify it:

Second, we’re “currying” the function so that instead of defining a function that takes two arguments, it returns a function that takes the first argument and returns a function that takes the second argument and applies them both, like this:

Let’s return to the implementation of mapWith that relies on a map function rather than a method:

We’re going to extract these two operations by refactoring our function to paramaterize map. The first step is to give our parameters generic names:

Then we wrap the entire thing in a function and extract map

What we have now is a function that takes a function and “flips” the order of arguments around, then curries it. So let’s call it flipAndCurry:

Sometimes you want to flip, but not curry:

This is gold. Consider how we define mapWith now:

Much nicer!

#### self-currying flip

Sometimes we’ll want to flip a function, but retain the flexibility to call it in its curried form (pass one parameter) or non-curried form (pass both). We could make that into flip:

Now if we write mapWith = flip(map), we can call mapWith(fn, list) or mapWith(fn)(list), our choice.

#### flipping methods

When we learn about context and methods, we’ll see that flip throws the current context away, so it can’t be used to flip methods. A small alteration gets the job done:

### Object.assign

It’s very common to want to “extend” an object by assigning properties to it:

It’s also common to want to assign the properties of one object to another:

Both needs can be met with Object.assign, a standard function. You can copy an object by extending an empty object:

You can extend one object with another:

And when we discuss prototypes, we will use Object.assign to turn this:

Into this:

Assigning properties from one object to another (also called “cloning” or “shallow copying”) is a basic building block that we will later use to implement more advanced paradigms like mixins.

### Why?

This is the canonical Y Combinator:

You use it like this:

Why? It enables you to make recursive functions without needing to bind a function to a name in an environment. This has little practical utility in JavaScript, but in combinatory logic it’s essential: With fixed-point combinators it’s possible to compute everything computable without binding names.

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.

There are many explanations of the Y Combinator’s mechanism on the internet, but resist the temptation to read any of them: Work it out for yourself. Use it as an excuse to get familiar with your environment’s debugging facility.

One tip is to use JavaScript to name things. For example, you could start by writing:

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.

Work things out for yourself!

## A Warm Cup: Basic Strings and Quasi-Literals

An expression is any valid unit of code that resolves to a value.—Mozilla Development Network: Expressions and operators

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. For example, the escape sequence \n inserts a newline character in a string literal, like this: 'first line\nsecond line'.

There are operators that can be used on strings. The most common is +, it concatenates:

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-literals

JavaScript supports quasi-literal strings, a/k/a “Template Strings” or “String Interpolation Expressions.” A quasi-literal string is something that looks like a string literal, but is actually an expression. 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.g.

Quasi-literals go much further. A quasi-literal can contain an expression to be evaluated. Old-school lispers call this “unquoting,” the more contemporary term is “interpolation.” An unquoted expression is inserted in a quasi-literal with \${expression}. The expression is evaluated, and the result is coerced to a string, then inserted in the quasi-string.

For example:

A quasi-literal is computationally equivalent to an expression using +. 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. They’re easier to read and it’s easier to avoid errors like the following:

#### evaluation time

Like any other expression, quasi-literals are evaluated late, when that line or lines of code is evaluated.

So for example,

JavaScript evaluates the quasi-literal when the function is invoked and the quasi-literal inside the function’s body 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.

This is exactly what we’d expect if we’d written it like this:

## Stir the Allongé: Objects and State

So far, we have discussed what many call “pure functional” programming, where every expression is necessarily idempotent, because we have no way of changing state within a program using the tools we have examined.

We’ve also explored functions that rebind names within themselves as part of performing their calculations. 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.

It’s time to change everything.

### Encapsulating State with Closures

OOP to me means only messaging, local retention and protection and hiding of state-process, and extreme late-binding of all things.–Alan Kay

We’re going to look at encapsulation using JavaScript’s functions and objects. We’re not going to call it object-oriented programming, mind you, because that would start a long debate. This is just plain encapsulation,38 with a dash of information-hiding.

#### what is hiding of state-process, and why does it matter?

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. But she’s in a hurry, so she reads the index directly. 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. If and when someone needs to know the size of the stack, we’ll add a size function and expose it as well.

Hiding information (or “state”) is the design principle that allows us to limit the coupling between components of software.

#### how do we hide state using javascript?

We’ve been introduced to JavaScript’s objects, and it’s fairly easy to see that objects can be used to model what other programming languages call (variously) records, structs, frames, or what-have-you. And given that their elements are mutable, they can clearly model state.

Given an object that holds our state (an array and an index39), we can easily implement our three operations as functions. Bundling the functions with the state does not require any special “magic” features. 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’ll do that by making sure it has a name. We can do that with an IIFE:

#### method-ology

In this text, we lurch from talking about “functions that belong to an object” to “methods.” Other languages may separate methods from functions very strictly, but in JavaScript every method is a function, but not all functions are methods.

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.

But these two wouldn’t be methods. Although they “belong” to an object, they don’t interact with it:

#### hiding state

Our stack does bundle functions with data, but it doesn’t hide its state. “Foreign” code could interfere with its array or index. So how do we hide these? We already have a closure, let’s use it:

We don’t want to repeat this code every time we want a stack, so let’s make ourselves a “stack maker.” The temptation is to wrap what we have above in a function:

But there’s an easier way :-)

Now we can make stacks freely, and we’ve hidden their internal data elements. We have methods and encapsulation, and we’ve built them out of JavaScript’s fundamental functions and objects. In Constructors and Classes, we’ll look at JavaScript’s support for class-oriented programming and some of the idioms that functions bring to the party.

### Composition and Extension

#### composition

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 40.

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.

Here’s an abstract “model” that supports undo and redo composed from a pair of stacks (see Encapsulating State), and a Plain Old JavaScript Object:

We can set and get attributes on a model

The techniques used for encapsulation work well with composition. In this case, we have a “model” that hides its attribute store as well as its implementation that is composed of an undo stack and redo stack.

#### extension

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.

Consider a queue:

Now we wish to create a deque by adding pullTail and pushHead operations to our queue.41 Unfortunately, encapsulation prevents us from adding operations that interact with the hidden data structures.

This isn’t really surprising: The entire point of encapsulation is to create an opaque data structure that can only be manipulated through its public interface. The design goals of encapsulation and extension are always going to exist in tension.

Let’s “de-encapsulate” our queue:

Now we can extend a queue into a deque:

Presto, we have reuse through extension, at the cost of encapsulation.

### This and That

Let’s take another look at extensible objects. Here’s a Queue:

Let’s make a copy of our queue using Object.assign:

Wait a second. We know that array values are references. So it probably copied a reference to the original array. Let’s make a copy of the array as well:

Now let’s pull the head off the original:

If we’ve copied everything properly, we should get the exact same result when we pull the head off the copy:

What!? 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 problem is that while we’ve carefully copied our array and other elements over, the closures all share the same environment, and therefore the functions in copyOfQueue all operate on the first queue’s private data, not on the copies.

Let’s take an impossibly optimistic flight of fancy:

The AmnesiacQueue makes queues with amnesia: They don’t know who they are, so every time we invoke one of their functions, we have to tell them who they are. You can work out the implications for copying queues as a thought experiment: We don’t have to worry about environments, because every function operates on the queue you pass in.

The killer drawback, of course, is making sure we are always passing the correct queue in every time we invoke a function. What to do?

#### what’s all this?

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 . notation, JavaScript binds the “receiver” of a “method invocation” to the special name this.

Our AmnesiacQueue already uses compact method notation. So, we’ll remove myself from the parameter list, and rename it to this within the body of each function:

Now we are relying on JavaScript to set the value of this whenever we invoke one of these functions using the . or [ and ] operators.

In other words, when we write:

We expect that JavaScript will invoke the functions we’ve bound to pushTail and pullHead, and automatically bind betterQueue to the name this within them. 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.42

Now, does this solve our original problem? Can we make copies of an object? Recall that the problem was that when we used a closure for private data, copying references to an object’s functions meant that we were using functions that still referred to the original closure, and therefore shared the same private data.

Now our functions refer to members of the object, and use this to ensure that they are referring to the object receiving a message. Let’s see if this does, indeed, allow us to copy objects:

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. That’s how classes work. That’s how extending objects works. Being able to share functions means being able to compose and reuse functionality.

There is more to this than we’ve discussed here. We’ll explore things in more detail later, in What Context Applies When We Call a Function?.

### What Context Applies When We Call a Function?

In This and That, we learned that when a function is denoted using the function keyword, and is called as an object method, the name this is bound in its environment to the object acting as a “receiver.” For example:

We’ve constructed a method that returns whatever value is bound to this when it is called. It returns the object when called, just as described.

#### it’s all about the way the function is called

JavaScript programmers talk about functions having a “context” when being called. this is bound to the context.43 The important thing to understand is that the context for a function being called is set by the way the function is called, not the function itself.

This is an important distinction. Consider closures: As we discussed in Closures and Scope, a function’s free variables are resolved by looking them up in their enclosing functions’ environments. You can always determine the functions that define free variables by examining the source code of a JavaScript program, which is why this scheme is known as Lexical Scope.

A function’s context cannot be determined by examining the source code of a JavaScript program. Let’s look at our example again:

What is the context of the function someObject.someFunction? Don’t say someObject! Watch this:

It gets weirder:

So it amounts to this: The exact same function can be called in two different ways, and you end up with two different contexts. If you call it using someObject.someFunction() syntax, the context is set to the receiver. If you call it using any other expression for resolving the function’s value (such as someFunction()), you get something else.

Let’s investigate:

Interesting!

It seems that whether you use a.b() or a['b']() or a[n]() or (a.b)(), you get context a.

And if you don’t use a.b() or a['b']() or a[n]() or (a.b)(), you get the global environment for a context, not the context of whatever function is doing the calling. To simplify things, when you call a function with . or [] access, you get an object as context, otherwise you get the global environment.

There are actually two other ways to set the context of a function. And once again, both are determined by the caller. At the very end of objects everywhere?, we’ll see that everything in JavaScript behaves like an object, including functions. We’ll learn that functions have methods themselves, and one of them is call.

Here’s call in action:

When You call a function with call, you set the context by passing it in as the first parameter. Other arguments are passed to the function in the normal manner. Much hilarity can result from call shenanigans like this:

But now we thoroughly understand what a.b() really means: It’s synonymous with a.b.call(a). Whereas in a browser, c() is synonymous with c.call(window).

#### arguments

JavaScript has another automagic binding in every function’s environment. arguments is a special object that behaves a little like an array.44

For example:

Gathering arguments with ... accomplishes most of the use cases people have for using the arguments special binding, and in addition, gathering works with both fat arrows and with the function keyword, whereas arguments only works with the function keyword.

There are a few things that arguments can do that gathering cannot do, for example if you declare a function with function (a, b, c) { ... }, arguments holds the arguments passed to the function even though you haven’t declared a parameter to be gathered. It works alongside the declared parameters.

But by and large, we will gather parameters in this book.

#### application and contextualization

Hold that thought for a moment. JavaScript also provides a fourth way to set the context for a function. apply is a method implemented by every function that takes a context as its first argument, and it takes an array or array-like thing of arguments as its second argument. That’s a mouthful, let’s look at an example:

Now let’s put the two together. Here’s another travesty:

We get the result of concatenating [4,5] onto an array containing the global environment. Not what we want! Behold:

Our contextualize function returns a new function that calls a function with a fixed context. It can be used to fix some of the unexpected results we had above. Consider:

Both are true because we are accessing them with aFourthObject. Now we write:

When we call these functions without using aFourthObject., only the contextualized version maintains the context of aFourthObject.

We’ll return to contextualizing methods later, in Binding. But before we dive too deeply into special handling for methods, we need to spend a little more time looking at how functions and methods work.

### Method Decorators

In function decorators, we learned that a decorator takes a function as an argument, returns a function, and there’s a semantic relationship between the two. If a function is a verb, a decorator is an adverb.

Decorators can be used to decorate methods provided that they carefully preserve the function’s context. For example, here is a naïve version of maybe for one argument:

We use it like this:

This version doesn’t preserve the context, so it can’t be used as a method decorator. Instead, we have to convert the decoration from a fat arrow to a function function:

And then use .call to preserve this:

Now that we have a “proper function,” we can also handle variadic functions and methods. This variation only invokes the decorated function if none of the arguments are null or undefined:

But back to basics. As long as we are correctly preserving this by one, using a function, and two, invoking the decorated function with .call(this, ...) or .apply(this, ...), we can decorate methods as well as functions.

Now we can write things like:

And this is correctly set:

Using .call or .apply and arguments is substantially slower than writing function decorators that don’t set the context, so it might be right to sometimes write function decorators that aren’t usable as method decorators. However, in practice you’re far more likely to introduce a defect by failing to pass the context through a decorator than by introducing a performance pessimization, so the default choice should be to write all function decorators in such a way that they are “context agnostic.”

In some cases, there are other considerations to writing a method decorator. If the decorator introduces state of any kind (such as once and memoize do), this must be carefully managed for the case when several objects share the same method through the mechanism of the prototype or through sharing references to the same function.

## Recipes with Objects, Mutations, and State

#### Disclaimer

The recipes are written for practicality, and their implementation may introduce JavaScript features that haven’t been discussed in the text to this point, such as methods and/or prototypes. The overall use of each recipe will fit within the spirit of the language discussed so far, even if the implementations may not.

### Memoize

Consider that age-old interview quiz, writing a recursive fibonacci function (there are other ways to derive a fibonacci number, of course). Here’s an implementation that doesn’t use a named function expression. The reason for that omission will be explained later:

We’ll time it:

Why is it so slow? Well, it has a nasty habit of recalculating the same results over and over and over again. We could rearrange the computation to avoid this, but let’s be lazy and trade space for time. What we want to do is use a lookup table. Whenever we want a result, we look it up. If we don’t have it, we calculate it and write the result in the table to use in the future. If we do have it, we return the result without recalculating it.

Here’s our recipe:

We can apply memoized to a function and we will get back a new function that “memoizes” its results so that it never has to recalculate the same value twice. It only works for functions that are “idempotent,” meaning functions that always return the same result given the same argument(s). Like fibonacci:

Let’s try it:

We get the result back instantly. It works! You can use memoize with all sorts of “idempotent” pure functions. by default, it works with any function that takes arguments which can be transformed into JSON using JavaScript’s standard library function for this purpose.

If you have another strategy for turning the arguments into a string key, we’ll need to make a version that allows you to supply an optional keymaker function:

#### memoizing recursive functions

We deliberately picked a recursive function to memoize, because it demonstrates a pitfall when combining decorators with named functional expressions. Consider this implementation that uses a named functional expression:

If we try to memoize it, we don’t get the expected speedup:

That’s because the function bound to the name fibonacci in the outer environment has been memoized, but the named functional expression binds the name fibonacci inside the unmemoized function, so none of the recursive calls to fibonacci are ever memoized. Therefore we must write:

If we need to prevent a rebinding from breaking the function, we’ll need to use the module pattern.

### getWith

getWith is a very simple function. It takes the name of an attribute and returns a function that extracts the value of that attribute from an object:

You can use it like this:

This isn’t much of a recipe yet. But let’s combine it with mapWith:

That’s nicer than writing things out “longhand:”

getWith plays nicely with maybe as well. Consider a sparse array. You can use:

To get the orange count from all the non-null inventories in a list.

#### what’s in a name?

Why is this called getWith? Consider this function that is common in languages that have functions and dictionaries but not methods:

You might ask, “Why use a function instead of just using []?” The answer is, we can manipulate functions in ways that we can’t manipulate syntax. For example, do you remember from flip that we can define mapWith from map?

We can do the same thing with getWith, and that’s why it’s named in this fashion:

### pluckWith

This pattern of combining mapWith and getWith is very frequent in JavaScript code. So much so, that we can take it up another level:

Or even better:

And now we can write:

Libraries like Underscore provide pluck, the flipped version of pluckWith:

Our recipe is terser when you want to name a function:

vs.

And of course, if we have pluck we can use flip to derive pluckWith:

### Deep Mapping

mapWith is an excellent tool, but from time to time you will find yourself working with arrays that represent trees rather than lists. For example, here is a partial list of sales extracted from a report of some kind. It’s grouped in some mysterious way, and we need to operate on each item in the report.

We could nest some mapWiths, but we humans are tool users. If we can use a stick to extract tasty ants from a hole to eat, we can automate working with arrays:

And now we can use deepMapWith on a tree the way we use mapWith on a flat array:

We’ll have another look at trees of data when we look at TreeIterators for Collections.

## The Coffee Factory: “Object-Oriented Programming”

Programming with objects and classes began in Norway in the late 1960s with the Simula programming language. Its creators, Ole-Johan Dahl and Kristen Nygaard, did not use those words to describe what would eventually become the dominant paradigm in computing.

A decade later, Dr. Alan Kay coined the phrase “Object-Oriented Programming” along with co-creating the Smalltalk programming language. He has famously said that to him, “OOP” was objects communicating with each other using messages, and that other languages copied the things that didn’t matter from Smalltalk, and ignored the things he thought did matter.

Since that time, languages have either bolted object-ish ideas on top of their existing paradigms (like Object Pascal and OCaml), baked them in alongside other paradigms (like JavaScript), or embraced objects wholeheartedly.

That being said, there really is no one definition of “object-oriented.” For one thing, there is no one definition of “object.”

#### objects

Some languages, like Smalltalk and Ruby, treat an object as a fully encapsulated entity. There is no access to an object’s private state, all you can do is invoke one of its methods. Other languages, like Java, permit objects to access each other’s state.

Some languages (again, like Java) have very rigid objects and classes, it is impossible or awkward to add new methods or properties to objects at run time. Some are flexible about adding methods and properties at run time. And yet other languages treat objects as dictionaries, where properties and even methods can be added, modified, or removed with abandon.

So we can see that the concept of “object” is flexible across languages.

#### classes

The concept of “class” is also flexible across languages. Object-oriented languages do not uniformly agree on whether classes are necessary, much less how they work. For example, The Common Lisp Object System defines behaviour with classes, and it also defines behaviour with generic functions. The Self and NewtonScript languages have prototypes instead of classes.

So some “OO” languages have objects, but not classes.

C++ has classes, but they are not “first-class entities.” You can’t assign a class to a variable or pass it to a function. You can, however, manipulate the constructors for classes, the functions that make new objects. But you can’t manipulate those constructors to change the behaviour of objects that have already been constructed, instance behaviour is early-bound by default.

Ruby has classes, and they’re first-class entities. You can ask an object for its class, you can put a class in a variable, pass it to a method, or return it from a method, just like every other entity in the language. Classes in Ruby and Smalltalk even have their own class, they are instances of Class!45 Instance behaviour is late-bound and open for extension.46

#### constructors

Some languages allow programs to construct objects independently, others (notably those that are heavily class-centric) require that objects always be constructed by their classes. Some languages allow any function or method to be used as a constructor, others require a special syntax or declaration for constructors.

#### prototypes are not classes

Prototypical languages like Self and NewtonScript eschew classes altogether, using prototypes to define common behaviour for a set of objects. The difference between a prototype and a class is similar to the difference between a model home and a blueprint for a home.

You can say to a builder, “make me a home just like that model home,” and the builder makes you a home that has a lot in common with the model home. You then decorate your home with additional personalization. But the model home is, itself, a home. Although you may choose to keep it empty, you could in principle move a family into it. This is different than asking a builder to make you a home based on a blueprint. The blueprint may specify the features of the home, but it isn’t a home. It could never be used as a home.

Prototypes are like model homes, and classes are like blueprints. Classes are not like the objects they describe.47

#### “object-oriented programming” can mean almost anything

From this whirlwind tour of “object-oriented programming,” we can see that the ideas behind “object-oriented programming” have some common roots in the history of programming languages, but each language implements its own particular flavour in its own particular way.

Thus, when we talk about “objects” and “prototypes” and “classes” in JavaScript, we’re talking about objects, prototypes, and classes as implemented in JavaScript. And we must keep in mind that other languages can have a radically different take on these ideas.

#### the javascript approach

JavaScript has objects, and by default, those objects are dictionaries. By default, objects directly manipulate each other’s state. Methods can be added to, or removed from objects at run time.

JavaScript has optional prototypes. Prototypes are objects in the same sense that model homes are homes.

In JavaScript, object and array literals construct objects that delegate behaviour to the standard library’s object prototype and array prototype, respectively. JavaScript also supports using Object.create to construct objects with or without a prototype, and new to construct objects using a constructor function.

Using prototypes and constructor functions, JavaScript programs can emulate many of the features of classes in other languages. JavaScript also has a class keyword that provides syntactic sugar for writing constructor functions and prototypes in a declarative fashion.

By default, a JavaScript class is a constructor composed with an object as its associated prototype. This can be denoted with the class keyword, by working with a function’s default .prototype property, or by composing functions and objects independently.

JavaScript classes are constructors, but they are more than C++ constructors, in that manipulation of their prototype extends or modifies the behaviour of the instances they create. JavaScript classes take a minimalist approach to OO in the same sense that JavaScript objects take a minimal approach to OO. For example, behaviour can be mixed into an object, a prototype, or a class using the exact same mechanism, because objects, prototypes, and a constructor’s prototype are all objects that are open to extension.

In sum, JavaScript is not exactly like any other object-oriented programming language, and its classes aren’t like any other language that features classes, but then again, neither is any other object-oriented programming language, and neither are any other classes.

## Served by the Pot: Collections

### Iteration and Iterables

Many objects in JavaScript can model collections of things. A collection is like a box containing stuff. Sometimes you just want to move the box around. But sometimes you want to open it up and do things with its contents.

Things like “put a label on every bag of coffee in this box,” Or, “Open the box, take out the bags of decaf, and make a new box with just the decaf.” Or, “go through the bags in this box, and take out the first one marked ‘Espresso’ that contains at least 454 grams of beans.”

All of these actions involve going through the contents one by one. Acting on the elements of a collection one at a time is called iterating over the contents, and JavaScript has a standard way to iterate over the contents of collections.

#### a look back at functional iterators

When discussing functions, we looked at the benefits of writing Functional Iterators. We can do the same thing for objects. Here’s a stack that has its own functional iterator method:

The way we’ve written .iterator as a method, each object knows how to return an iterator for itself.

And here’s a sum function implemented as a fold over a functional iterator:

We can use it with our stack:

We could save a step and write collectionSum, a function that folds over any object, provided that the object implements an .iterator method:

If we write a program with the presumption that “everything is an object,” we can write maps, folds, and filters that work on objects. We just ask the object for an iterator, and work on the iterator. Our functions don’t need to know anything about how an object implements iteration, and we get the benefit of lazily traversing our objects.

This is a good thing.

#### iterator objects

Iteration for functions and objects has been around for many, many decades. For simple linear collections like arrays, linked lists, stacks, and queues, functional iterators are the simplest and easiest way to implement iterators.

In programs involving large collections of objects, it can be handy to implement iterators as objects, rather than functions. The mechanics of iterating can then be factored using the same tools that are used to factor the mechanics of all other objects in the system.

Fortunately, an iterator object is almost as simple as an iterator function. Instead of having a function that you call to get the next element, you have an object with a .next() method.

Like this:

Now our .iterator() method is returning an iterator object. When working with objects, we do things the object way. But having started by building functional iterators, we understand what is happening underneath the object’s scaffolding.

#### iterables

People have been writing iterators since JavaScript was first released in the late 1990s. Since there was no particular standard way to do it, people used all sorts of methods, and their methods returned all sorts of things: Objects with various interfaces, functional iterators, you name it.

So, when a standard way to write iterators was added to the JavaScript language, it didn’t make sense to use a method like .iterator() for it: That would conflict with existing code. Instead, the language encourages new code to be written with a different name for the method that a collection object uses to return its iterator.

To ensure that the method would not conflict with any existing code, JavaScript provides a symbol. Symbols are unique constants that are guaranteed not to conflict with existing strings. Symbols are a longstanding technique in programming going back to Lisp, where the GENSYM function generated… You guessed it… Symbols.48

The expression Symbol.iterator evaluates to a special symbol representing the name of the method that objects should use if they return an iterator object.

Our stack does, so instead of binding the existing iterator method to the name iterator, we bind it to the Symbol.iterator. We’ll do that using the [ ] syntax for using an expression as an object literal key:

Using [Symbol.iterator] instead of .iterator seems like adding an extra moving part for nothing. Do we get anything in return?

Indeed we do. Behold the for...of loop:

The for...of loop works directly with any object that is iterable, meaning it works with any object that has a Symbol.iterator method that returns an object iterator. Here’s another linked list, this one is iterable:

As we can see, we can use for...of with linked lists just as easily as with stacks. And there’s one more thing: You recall that the spread operator (...) can spread the elements of an array in an array literal or as parameters in a function invocation.

Now is the time to note that we can spread any iterable. So we can spread the elements of an iterable into an array literal:

And we can also spread the elements of an array literal into parameters:

This can be extremely useful.

One caveat of spreading iterables: JavaScript creates an array out of the elements of the iterable. That might be very wasteful for extremely large collections. For example, if we spread a large collection just to find an element in the collection, it might have been wiser to iterate over the element using its iterator directly.

And if we have an infinite collection, spreading is going to fail outright as we’re about to see.

#### iterables out to infinity

Iterables needn’t represent finite collections:

There are useful things we can do with iterables representing an infinitely large collection. But let’s point out what we can’t do with them:

Attempting to spread an infinite iterable into an array is always going to fail.

#### ordered collections

The iterables we’re discussing represent ordered collections. One of the semantic properties of an ordered collection is that every time you iterate over it, you get its elements in order, from the beginning. For example:

This is accomplished with our own collections by returning a brand new iterator every time we call [Symbol.iterator], and ensuring that our iterators start at the beginning and work forward.

Iterables needn’t represent ordered collections. We could make an infinite iterable representing random numbers:

Whether you work with the same iterator over and over, or get a fresh iterable every time, you are always going to get fresh random numbers. Therefore, RandomNumbers is not an ordered collection.

Right now, we’re just looking at ordered collections. To reiterate (hah), an ordered collection represents a (possibly infinite) collection of elements that are in some order. Every time we get an iterator from an ordered collection, we start iterating from the beginning.

#### operations on ordered collections

Let’s define some operations on ordered collections. Here’s mapWith, it takes an ordered collection, and returns another ordered collection representing a mapping over the original:49

This illustrates the general pattern of working with ordered collections: We make them iterables, meaning that they have a [Symbol.iterator] method, that returns an iterator. An iterator is also an object, but with a .next() method that is invoked repeatedly to obtain the elements in order.

Many operations on ordered collections return another ordered collection. They do so by taking care to iterate over a result freshly every time we get an iterator for them. Consider this example for mapWith:

Numbers is an ordered collection. We invoke mapWith((x) => 2 * x, Numbers) and get Evens. Evens works just as if we’d written this:

Every time we write for (const i of Evens), JavaScript calls Evens[Symbol.iterator](). That in turns means it executes const iterator = Numbers[Symbol.iterator](); every time we write for (const i of Evens), and that means that iterator starts at the beginning of Numbers.

So, Evens is also an ordered collection, because it starts at the beginning each time we get a fresh iterator over it. Thus, mapWith has the property of preserving the collection semantics of the iterable we give it. So we call it a collection operation.

Mind you, we can also map non-collection iterables, like RandomNumbers:

mapWith can get a new iterator from RandomNumbers each time we iterate over ZeroesToNines, but if RandomNumbers doesn’t behave like an ordered collection, that’s not mapWith’s fault. RandomNumbers is a stream, not an ordered collection, and thus mapWith returns another iterable behaving like a stream.

Here are two more operations on ordered collections, filterWith and untilWith:

Like mapWith, they preserve the ordered collection semantics of whatever you give them.

And here’s a computation performed using operations on ordered collections: We’ll create an ordered collection of square numbers that end in one and are less than 1,000:

As we expect from an ordered collection, each time we iterate over UpTo1000, we begin at the beginning.

For completeness, here are two more handy iterable functions. first returns the first element of an iterable (if it has one), and rest returns an iterable that iterates over all but the first element of an iterable. They are equivalent to destructuring arrays with [first, ...rest]:

like our other operations, rest preserves the ordered collection semantics of its argument.

#### from

Having iterated over a collection, are we limited to for..do and/or gathering the elements in an array literal and/or gathering the elements into the parameters of a function? No, of course not, we can do anything we like with them.

One useful thing is to write a .from function that gathers an iterable into a particular collection type. JavaScript’s built-in Array class already has one:

We can do the same with our own collections. As you recall, functions are mutable objects. And we can assign properties to functions with a . or even [ and ]. And if we assign a function to a property, we’ve created a method.

So let’s do that:

Now we can go “end to end,” If we want to map a linked list of numbers to a linked list of the squares of some numbers, we can do that:

#### summary

Iterators are a JavaScript feature that allow us to separate the concerns of how to iterate over a collection from what we want to do with the elements of a collection. Iterable ordered collections can be iterated over or gathered into another collection.

Separating concerns with iterators speaks to JavaScript’s fundamental nature: It’s a language that wants to compose functionality out of small, singe-responsibility pieces, whether those pieces are functions or objects built out of functions.

### Generating Iterables

Iterables look cool, but then again, everything looks amazing when you’re given cherry-picked examples. What is there they don’t do well?

Let’s consider how they work. Whether it’s a simple functional iterator, or an iterable object with a .next() method, an iterator is something we call repeatedly until it tells us that it’s done.

Iterators have to arrange their own state such that when you call them, they compute and return the next item. This seems blindingly obvious and simple. If, for example, you want numbers, you write:

The Numbers iterable returns an object that updates a mutable variable, n, to deliver number after number. How hard can this be?

Well, we’ve written our iterator as a server. It waits until given a request, and then it returns exactly one item. Then it waits for the next request. There is no concept of pushing numbers out from the iterator, just waiting until a number is pulled out of the iterator by whatever code consumes numbers.

Of course, when we have some code that makes a bunch of something, we don’t usually write it like that. We usually just write something like:

And magically, the numbers would pour forth. We would generate numbers. Let’s put that beside the code for the iterator, minus the iterable scaffolding:

They’re of approximately equal complexity. So why bring up generation? Well, there are some collections that are much easier to generate than to iterate over. Let’s look at one:

#### recursive iterators

Iterators maintain state, that’s what they do. Generators have to manage the exact same amount of state, but sometimes, it’s much easier to manage that state in a generator. One of those cases is when we have to recursively enumerate something.

For example, iterating over a tree. Given an array that might contain arrays, let’s say we want to generate all the “leaf” elements, i.e. elements that are not, themselves, iterable.

Very simple. Now for the iteration version. We’ll write a functional iterator to keep things simple, but it’s easy to see the shape of the basic problem:

If you peel off isIterable and ignore the way that the iteration version uses [Symbol.iterator] and .next, we’re left with the fact that the generating version calls itself recursively, and the iteration version maintains an explicit stack. In essence, both the generation and iteration implementations have stacks, but the generation version’s stack is implicit, while the iteration version’s stack is explicit.

A less kind way to put it is that the iteration version is greenspunning something built into our programming language: We’re reinventing the use of a stack to manage recursion, because writing our code to respond to a function call makes us turn a simple recursive algorithm inside-out.

#### state machines

Some iterables can be modelled as state machines. Let’s revisit the Fibonacci sequence. Again. One way to define it is:

• The first element of the fibonacci sequence is zero.
• The second element of the fibonacci sequence is one.
• Every subsequent element of the fibonacci sequence is the sum of the previous two elements.

Let’s write a generator:

The thing to note here is that our fibonacci generator has three states: generating 0, generating 1, and generating everything after that. This isn’t a good fit for an iterator, because iterators have one functional entry point and therefore, we’d have to represent our three states explicitly, perhaps using a state pattern:

We’ll keep it simple:

Again, this is not particularly horrendous, but like the recursive example, we’re explicitly greenspunning the natural linear state. In a generator, we write “do this, then this, then this.” In an iterator, we have to wrap that up and explicitly keep track of what step we’re on.

So we see the same thing: The generation version has state, but it’s implicit in JavaScript’s linear control flow. Whereas the iteration version must make that state explicit.

#### javascript’s generators

It would be very nice if we could sometimes write iterators as a .next() method that gets called, and sometimes write out a generator. Given the title of this chapter, it is not a surprise that JavaScript makes this possible.

We can write an iterator, but use a generation style of programming. An iterator written in a generation style is called a generator. To write a generator, we write a function, but we make two changes:

1. We declare the function using the function * syntax. Not a fat arrow. Not a plain function.
2. We don’t return values or output them to console.log. We “yield” values using the yield keyword.

When we invoke the function, we get an iterator object back. Let’s start with the degenerate example, the empty iterator:50

When we invoke empty, we get an iterator with no elements. This makes sense, because empty never yields anything. We call its .next() method, but it’s done immediately.

Generator functions can take an argument. Let’s use that to illustrate yield:

Invoking only("you") returns an iterator that we can call with .next(), and it yields "you". Invoking only more than once gives us fresh iterators each time:

We can invoke the same iterator twice:

It yields the value of something, and then it’s done.

#### generators are coroutines

Here’s a generator that yields three numbers:

This is where generators behave very, very differently from ordinary functions. What happens semantically?

1. We call oneTwoThree() and get an iterator.
2. The iterator is in a nascent or “newborn” state.
3. When we call interator.next(), the body of our generator begins to be evaluated.
4. The body of our generator runs until it returns, ends, or encounters a yield statement, which is yield 1;.
• The iterator suspends its execution.
• The iterator wraps 1 in {done: false, value: 1} and returns that from the call to .next().
• The rest of the program continues along its way until it makes another call to iterator.next().
• The iterator resumes execution from the point where it yielded the last value.
5. The body of our generator runs until it returns, ends, or encounters the next yield statement, which is yield 2;.
• The iterator suspends its execution.
• The iterator wraps 2 in {done: false, value: 2} and returns that from the call to .next().
• The rest of the program continues along its way until it makes another call to iterator.next().
• The iterator resumes execution from the point where it yielded the last value.
6. The body of our generator runs until it returns, ends, or encounters the next yield statement, which is yield 3;.
• The iterator suspends its execution.
• The iterator wraps 3 in {done: false, value: 3} and returns that from the call to .next().
• The rest of the program continues along its way until it makes another call to iterator.next().
• The iterator resumes execution from the point where it yielded the last value.
7. The body of our generator runs until it returns, ends, or encounters the next yield statement. There are no more lines of code, so it ends.
• The iterator returns {done: true} from the call to .next(), and every call to this iterator’s .next() method will return {done: true} from now on.

This behaviour is not unique to JavaScript, generators are called coroutines in other languages:

Coroutines are computer program components that generalize subroutines for nonpreemptive multitasking, by allowing multiple entry points for suspending and resuming execution at certain locations. Coroutines are well-suited for implementing more familiar program components such as cooperative tasks, exceptions, event loop, iterators, infinite lists and pipes.

Instead of thinking of there being one execution context, we can imagine that there are two execution contexts. With an iterator, we can call them the producer and the consumer. The iterator is the producer, and the code that iterates over it is the consumer. When the consumer calls .next(), it “suspends” and the producer starts running. When the producer yields a value, the producer suspends and the consumer starts running, taking the value from the result of calling .next().

Of course, generators need not be implemented exactly as coroutines. For example, a “transpiler” might implement oneTwoThree as a state machine, a little like this (there is more to generators, but we’ll see that later):

But no matter how JavaScript implements it, our mental model is that a generator function returns an iterator, and that when we call .next(), it runs until it returns, ends, or yields. If it yields, it suspends its own execution and the consuming code resumes execution, until .next() is called again, at which point the iterator resumes its own execution from the point where it yielded.

#### generators and iterables

Our generator function oneTwoThree is not an iterator. It’s a function that returns an iterator when we invoke it. We write the function to yield values instead of return a single value, and JavaScript takes care of turning this into an object with a .next() function we can call.

If we call our generator function more than once, we get new iterators. As we saw above, we called oneTwoThree three times, and each time we got an iterator that begins at 1 and counts to 3. Recalling the way we wrote ordered collections, we could make a collection that uses a generator function:

Now we can use it in a for...of loop, spread it into an array literal, or spread it into a function invocation, because we have written an iterable that uses a generator to return an iterator from its [Symbol.iterator] method.

This pattern is encouraged, so much so that JavaScript provides a concise syntax for writing generator methods for objects:

This object declares a [Symbol.iterator] function that makes it iterable. Because it’s declared *[Symbol.iterator], it’s a generator instead of an iterator.

So to summarize, ThreeNumbers is an object that we’ve made iterable, by way of writing a generator method for [Symbol.iterator].

#### more generators

Generators can produce infinite streams of values:

Our OneTwoThree example used implicit state to output the numbers in sequence. Recall that we wrote Fibonacci using explicit state:

And here is the Fibonacci ordered collection, implemented with a generator method:

We’ve writing a function that returns an iterator, but we used a generator to do it. And the generator’s syntax allows us to use JavaScript’s natural management of state instead of constantly rolling our own.

Of course, we could just as easily write a generator function for Fibonacci numbers:

#### yielding iterables

Here’s a first crack at a function that returns an iterable object for iterating over trees:

We’ve gone with the full iterable here, a TreeIterable(iterable) returns an iterable that treats iterable as a tree. It works, but as we’ve just seen, a function that returns an iterable can often be written much more simply as a generator, rather than a function that returns an iterable object:51

We take advantage of the for...of loop in a plain and direct way: For each element e, if it is iterable, treat it as a tree and iterate over it, yielding each of its elements. If e is not an iterable, yield e.

JavaScript handles the recursion for us using its own execution stack. This is clearly simpler than trying to maintain our own stack and remembering whether we are shifting and unshifting, or pushing and popping.

But while we’re here, let’s look at one bit of this code:

These three lines say, in essence, “yield all the elements of TreeIterable(e), in order.” This comes up quite often when we have collections that are compounds, collections made from other collections.

Consider this operation on iterables:

append iterates over a collection of iterables, one element at a time. Things like arrays can be easily catenated, but append iterates lazily, so there’s no need to construct intermediary results.

Tucked inside of it is the same three-line idiom for yielding each element of an iterable. There is an abbreviation for this, we can use yield * to yield all the elements of an iterable:

yield * yields all of the elements of an iterable, in order. We can use it in tree, too:

yield* is handy when writing generator functions that operate on or create iterables.

#### rewriting iterable operations

Now that we know about iterables, we can rewrite our iterable operations as generators. Instead of:

We can write:

No need to explicitly construct an object that has a [Symbol.iterator] method. No need to return an object with a .next() method. No need to fool around with {done} or {value}, just yield values until we’re done.

We can do the same thing with our other operations like filterWith and untilWith. Here’re our iterable methods rewritten as generators:

first works directly with iterators and remains unchanged, but rest can be rewritten as a generator:

#### Summary

A generator is a function that is defined with function * and uses yield (or yield *) to generate values. Using a generator instead of writing an iterator object that has a .next() method allows us to write code that can be much simpler for cases like recursive iterations or state patterns. And we don’t need to worry about wrapping our values in an object with .done and .value properties.

This is especially useful for making iterables.

### Lazy and Eager Collections

The operations on iterables are tremendously valuable, but let’s reiterate why we care: In JavaScript, we build single-responsibility objects, and single-responsibility functions, and we compose these together to build more full-featured objects and algorithms.

Composing an iterable with a mapIterable method cleaves the responsibility for knowing how to map from the fiddly bits of how a linked list differs from a stack

in the older style of object-oriented programming, we built “fat” objects. Each collection knew how to map itself (.map), how to fold itself (.reduce), how to filter itself (.filter) and how to find one element within itself (.find). If we wanted to flatten collections to arrays, we wrote a .toArray method for each type of collection.

Over time, this informal “interface” for collections grows by accretion. Some methods are only added to a few collections, some are added to all. But our objects grow fatter and fatter. We tell ourselves that, well, a collection ought to know how to map itself.

But we end up recreating the same bits of code in each .map method we create, in each .reduce method we create, in each .filter method we create, and in each .find method. Each one has its own variation, but the overall form is identical. That’s a sign that we should work at a higher level of abstraction, and working with iterables is that higher level of abstraction.

This “fat object” style springs from a misunderstanding: When we say a collection should know how to perform a map over itself, we don’t need for the collection to handle every single detail. That would be like saying that when we ask a bank teller for some cash, they personally print every bank note.

#### implementing methods with iteration

Object-oriented collections should definitely have methods for mapping, reducing, filtering, and finding. And they should know how to accomplish the desired result, but they should do so by delegating as much of the work as possible to operations like mapWith.

Composing an iterable with a mapIterable method cleaves the responsibility for knowing how to map from the fiddly bits of how a linked list differs from a stack. And if we want to create convenience methods, we can reuse common pieces.

Here is LazyCollection, a mixin we can use with any ordered collection that is also an iterable:

To use LazyCollection, we mix it into an any iterable object. For simplicity, we’ll show how to mix it into Numbers and Pair. But it can also be mixed into prototypes (a/k/a “classes”), traits, or other OO constructs:

#### lazy collection operations

“Laziness” is a very pejorative word when applied to people. But it can be an excellent strategy for efficiency in algorithms. Let’s be precise: Laziness is the characteristic of not doing any work until you know you need the result of the work.

Here’s an example. Compare these two:

Both expressions evaluate to 220. And the array is faster in practice, because it is a built-in data type that performs its work in the engine, while the linked list does its work in JavaScript.

But it’s still illustrative to dissect something important: Array’s .map and .filter methods gather their results into new arrays. Thus, calling .map.filter.reduce produces two temporary arrays that are discarded when .reduce performs its final computation.

Whereas the .map and .filter methods on Pair work with iterators. They produce small iterable objects that refer back to the original iteration. This reduces the memory footprint. When working with very large collections and many operations, this can be important.

The effect is even more pronounced when we use methods like first, until, or take:

This expression begins with a stack containing 30 elements. The top two are 29 and 28. It maps to the squares of all 30 numbers, but our code for mapping an iteration returns an iterable that can iterate over the squares of our numbers, not an array or stack of the squares. Same with .filter, we get an iterable that can iterate over the even squares, but not an actual stack or array.

Finally, we take the first element of that filtered, squared iterable and now JavaScript actually iterates over the stack’s elements, and it only needs to square two of those elements, 29 and 28, to return the answer.

We can confirm this:

If we write the almost identical thing with an array, we get a different behaviour:

Arrays copy-on-read, so every time we perform a map or filter, we get a new array and perform all the computations. This might be expensive.

You recall we briefly touched on the idea of infinite collections? Let’s make iterable numbers. They have to be lazy, otherwise we couldn’t write things like:

Balanced against their flexibility, our “lazy collections” use structure sharing. If we mutate a collection after taking an iterable, we might get an unexpected result. This is why “pure” functional languages like Haskell combine lazy semantics with immutable collections, and why even “impure” languages like Clojure emphasize the use of immutable collections.

#### eager collections

An eager collection, like an array, returns a collection of its own type from each of the methods. We can make an eager collection out of any collection that is gatherable, meaning it has a .from method:

Here is our Pair implementation. Pair is gatherable, because it implements .from(). We mix EagerCollection(Pair) into it, and this gives it all of our collection methods, which each method returning a new list of pairs: