There are few things I have learned in my programming career that have paid off like higher order functions. Map , Reduce and Filter with their cousins, along with the concept of passing functions as data in general make code easier to reason about, easier to write, easier to test. I find myself evangelizing these concepts often, so I thought I would try to do my best to give an introduction of them, along with some real world examples of how they can improve your everyday programming life. These examples are in JavaScript, but the concepts are universal.

Terminology

Let’s go over just a bit of terminology before we begin to make sure everyone is on the same page. If you read this article and there are any other terms you don’t understand, or need a further/better explanation for, let me know in the comments and I will try to either expand on them here or link to better resources.

First Class Function

A language that has first class functions is one where you can assign a function to a variable and pass that function around like data. Without first class functions you cannot have higher order functions - usually if you have one you have the other as well.

Higher Order Functions

A higher order function is a function that either takes another function as an argument or returns a function. That’s pretty much it - it’s all about functions as data. Once you have a reference to a function in a variable and can pass it around or return it, many things become possible. Map and Reduce and the other higher order functions we discuss are certainly not the only ones - you can create your own higher order functions. These are just common examples of them that are also commonly baked into the standard libraries of many languages. These functions we will discuss are generally not special either, you could write your own version of them without too much trouble - they are notable because they are solutions to common problems in everyday programming, no matter what the domain.

Pure Functions

A pure function is one that given the same inputs will always return the same output, with no side effects. An example might be (a, b) => a + b; . No matter what you provide as a and b , if you give them again you will get the same answer. The function doesn’t do anything else either - no side effects means no writing to a database, or logging, or mutating state, just takes inputs, gives outputs, and what goes in deterministically directs what comes out.

There are many benefits to pure functions, most of which I will not go into, but some include cacheability, portability (reuse), self-documenting, testabilty. Pure functions are a unit tester’s dream! You can’t make an (interesting / useful) system with only pure functions, it wouldn’t really do anything - but you should strive to use pure functions wherever possible - sequester your state manipulation in as few places as possible.

Higher Order Functions do not require you to use pure functions, but that is what you will use most of the time and you should always try to use pure functions when possible.

Map

Map is one of the most common and most useful higher order functions, so let’s start there. Let’s say I have a collection of data, and I need to perform some sort of transformation on it. Common examples might be taking complex data and formatting it for output, or plucking individual parts of data out of a more complicated structure. Let’s take a look at an example.

var data = [ { id : 1 , firstName : "Ryan" , lastName : "Guill" , email : [email protected]" }, { id : 2 , firstName : "John" , lastName : "Doe" , email : [email protected]" }, { id : 3 , firstName : "Mary" , lastName : "Smith" , email : [email protected]" } ];

Let’s say we wanted to get a list of everyone to put in the “to” field for an email, something like lastName, firstName <email> . Traditionally we might write a loop:

Pretty standard stuff, but let’s take a look at how we might write the same thing with map :

The first thing to notice is that we don’t have the loop machinery, no chance to get any of those details wrong, no need to keep track of another variable for the index. The code is much closer to just the code that matters.

But let’s change it a bit to illustrate another point.

This makes it clearer that Map takes a function, one that has the following argument signature: function (element [, index] [, collection]) . We won’t go into detail with the other parameters, you generally don’t have to use them. Index can be useful in some cases, but generally speaking if you are using the collection argument in a map call, you’re probably doing it wrong.

Back to the example, toFieldFormat is just a function (a pure one) that takes an input of an object and returns a string as output. That’s it. We could call it directly:

and it would work just fine. We could also write our original loop to use this function:

var output = []; for ( var i = 0 ; i < data . length ; i ++ ) { output . push ( toFieldFormat ( data [ i ])); }

But now you can see that we have abstracted out the real logic that matters and all that’s left is the plumbing. I don’t know about you but im tired of writing loops for most things. The loop is not what is interesting about this code. When I come back to it in 6 months, I don’t want to read the loop to understand what is happening - I want to get to the meat of what the author is trying to achieve.

Also, toFieldFormat is easily testable! It is a pure function, and abstracted out we can easily pass in a variety of inputs and deterministically check their outputs.

Let’s look at another example - let’s say we have our array of data, but we want to get just an array of id’s. That’s easy with map:

var pluckId = function ( item ) { return item . id ; }; data . map ( pluckId ); //[1,2,3]

So let’s talk a bit more about the intrinsic properties of a mapping operation.

Map translates data from one type to another. Doesn’t necessarily mean that the data type changes - but you are changing from one form to another.

Map will always return a collection the same size and order as its input. If you map an array of 3 things, you will get an array of 3 elements back, and the first item in the input will always correspond with the first item of the output.

The result of map is always a new collection - the input collection is not modified.

Map operations are inherently chainable. data.map(...).map(...).map(...) is a common pattern.

is a common pattern. Mathematically, if using pure functions, data.map(a).map(b) is the same as data.map(b ⋅ a); . Don’t worry about this now, (and this is not valid JavaScript syntax, it’s mathematical) but what it means is that certain libraries can speed up your code by performing optimizations, knowing that they can transform your functions for performance and not change the meaning of your code.

We are only scratching the surface of the power and usefulness of map . If you take away nothing else from this article, learn and embrace at least this one function. There are more complicated but elegant abilities built on the foundations of map that are outside the scope of this particular article, but that I hope to revisit soon.

Editorial Note: some languages use collect or transform instead of map .

Filter

After map , filter is probably one of the most commonly used higher order functions. You give it some data and a function to use as a “test” for your data - if the element passes it is included in the resulting collection, if not it is excluded. Let’s look at an example:

var data = [ { userID : 1 , name : "Ryan" , groups : [ "dev" , "ops" , "qa" , "employee" ]}, { userID : 2 , name : "Bob" , groups : [ "ops" , "employee" ]}, { userID : 3 , name : "John" , groups : [ "qa" , "employee" ]}, { userID : 4 , name : "Paul" , groups : [ "dev" , "qa" , "employee" ]} ]; var isDev = function ( user ) { return user . groups . includes ( "dev" ); }; data . filter ( isDev ); //[{userID: 1, name: "Ryan", groups: ["dev", "ops", "qa"]}, {userID: 4, name: "Paul", groups: ["dev", "qa"]}]

isDev is our “test” - it returns true or false, does the user’s group contain “dev”. That’s all there is to it - your test function needs to always return a boolean (undefined and null in js are considered false, but don’t do that). Your filter function could be much more complicated if you wanted though - as long as it returns a boolean.

var meetsArbitraryRestriction = function ( user ) { return user . groups . includes ( "qa" ) && user . name . charAt ( 0 ). toLowerCase () === "p" ; }; data . filter ( meetsArbitraryRestriction ); //{userID: 4, name: "Paul", groups: ["dev", "qa"]}

Again, the testing function is a pure function, which means easy to test, and the function is easy to use in other parts of your application’s logic.

So properties of filter :

Filter will always return the same type of collection as the input, with fewer than or equal to the number of elements in the input.

Filter is used to remove items from a collection that fail the testing function.

The result of filter is always a new collection - the input collection is not modified.

Filter operations are also inherently chainable - you can do data.filter(...).filter(...) as much as you want, or, more commonly, data.filter(...).map(...)

Some and Every

A few languages use any instead of some , but some and every are aggregate functions, in that they take a collection, but they return a true or false. We aren’t going to spend much time on these, but let’s take a look:

var data = [ { userID : 1 , name : "Ryan" , groups : [ "dev" , "ops" , "qa" , "employee" ]}, { userID : 2 , name : "Bob" , groups : [ "ops" , "employee" ]}, { userID : 3 , name : "John" , groups : [ "qa" , "employee" ]}, { userID : 4 , name : "Paul" , groups : [ "dev" , "qa" , "employee" ]} ]; var isDev = function ( user ) { return user . groups . includes ( "dev" ); }; var isEmployee = function ( user ) { return user . groups . includes ( "employee" ); }; var isManager = function ( user ) { return user . groups . includes ( "manager" ); }; data . every ( isDev ); //false, not everyone is a dev data . every ( isEmployee ); //true, everyone is an employee data . some ( isDev ); //true, at least one user is a dev data . some ( isManager ); //false, no user is a manager

Hopefully that is pretty straight forward. The only other thing I will mention is that these functions take advantage of their logic to generally return without having to look at every item in the collection - every can return as soon as it hits the first false result, some can return as soon as it hits the first true result.

Reduce

So now we get to the big one, reduce . For some reason reduce seems more opaque to the average developer than the others, and I think that stems from the fact that it is a bit of a swiss army knife of higher order functions. So let’s start high level - what is a reduction? All it really means is that it will iterate over the collection, building up a result that will be returned at the end. That’s it in a nutshell. But as we will see, that generic definition lends itself to many uses.

If you have ever used an aggregate function in SQL, you used a reduction. If you have ever used join() you’ve used a reduction.

Let’s take a look at what is probably the standard example to get our feet wet:

var data = [ 1 , 2 , 3 , 4 , 5 ]; data . reduce ( function ( prev , item ) { return prev + item ; }, 0 ); //15

We just totaled up all the items in our array. You’ll notice that reduce takes an extra argument, and that the function given to reduce also takes an extra argument. Here is the definition:

reduce(function(previousValue, item, index, collection) {...}, startingValue);

Reduce will iterate over the collection element by element, taking the result of the previous iteration and passing it to the next iteration. The first element will get the startingValue . This doesn’t mean that we can’t provide it with useful, reusable pure functions though. Consider this version of the same example:

var data = [ 1 , 2 , 3 , 4 , 5 ]; var sum = function ( a , b ) { return a + b ; }; data . reduce ( sum , 0 ); //15

Our sum function is perfectly generic, testible, reusable - pure.

Remember I said that join is a type of reduction? Let’s take a look at what a generic join might look like using reduce :

var data = [ 1 , 2 , 3 , 4 , 5 ]; var join = function ( a , b ) { if ( typeof a === 'undefined' ) return b ; return a + ',' + b ; }; data . reduce ( join ); //"1,2,3,4,5"

In this case, we don’t pass a starting value, and have our join handle it appropriately. Now, the native string.prototype.join is much better at its job than this, but hopefully this was useful as an illustration.

Let’s look at another example, this time let’s see if we can find the smallest number out of a set.

var data = [ 100 , 250 , 50 , 300 , 450 ]; var min = function ( a , b ) { return Math . min ( a , b ); }; data . reduce ( min ); //50

So all of our examples so far have used reduce as an aggregation, getting down to a simpler value from an input collection - but reduce can return any kind of value - including a collection. In fact, it could even return a larger collection than the input.

Let’s say that we want to take our users array from earlier examples, and create an element for each user / group combination.

var data = [ { userID : 1 , name : "Ryan" , groups : [ "dev" , "ops" , "qa" , "employee" ]}, { userID : 2 , name : "Bob" , groups : [ "ops" , "employee" ]} ]; //just creating this for clarity later function arrayMerge ( arrayOne , arrayTwo ) { Array . prototype . push . apply ( arrayOne , arrayTwo ); return arrayOne ; } data . reduce ( function ( acc , user ) { return arrayMerge ( acc , user . groups . map ( function ( group ) { return { name : user . name , group : group }; })); }, []); /* [{name:"Ryan",group:"dev"},{name:"Ryan",group:"ops"},{name:"Ryan",group:"qa"},{name:"Ryan",group:"employee"},{name:"Bob",group:"ops"},{name:"Bob",group:"employee"}] */

Hopefully this is still clear - as you can see we use map to take our array of groups and return a new array of the same size, but transforming them into objects with the name of the user and the group. Then we just merge our mapped groups into the array we are building user by user until we end up with the complete result.

So to further bolster the swiss-army-knife analogy, let me show you how all the other higher order functions we have talked about are actually just specialized reductions. Map , Filter , Some , Every - all of these can be achieved with reduce .

Here is our first Map example implemented using Reduce :

var data = [ { id : 1 , firstName : "Ryan" , lastName : "Guill" , email : [email protected]" }, { id : 2 , firstName : "John" , lastName : "Doe" , email : [email protected]" }, { id : 3 , firstName : "Mary" , lastName : "Smith" , email : [email protected]" } ]; var toFieldFormat = function ( item ) { return item . firstName + " " + item . lastName + " <" + item . email + ">" ; }; data . reduce ( function ( arr , item ) { arr . push ( toFieldFormat ( item )); return arr ; }, []). join ( ", " );

Here is our Filter example:

var data = [ { userID : 1 , name : "Ryan" , groups : [ "dev" , "ops" , "qa" , "employee" ]}, { userID : 2 , name : "Bob" , groups : [ "ops" , "employee" ]}, { userID : 3 , name : "John" , groups : [ "qa" , "employee" ]}, { userID : 4 , name : "Paul" , groups : [ "dev" , "qa" , "employee" ]} ]; var isDev = function ( user ) { return user . groups . includes ( "dev" ); }; data . reduce ( function ( arr , user ){ if ( isDev ( user )) { arr . push ( user ); } return arr ; }, []); //[{userID: 1, name: "Ryan", groups: ["dev", "ops", "qa"]}, {userID: 4, name: "Paul", groups: ["dev", "qa"]}]

And here is our Some and Every examples rewritten as reductions:

var data = [ { userID : 1 , name : "Ryan" , groups : [ "dev" , "ops" , "qa" , "employee" ]}, { userID : 2 , name : "Bob" , groups : [ "ops" , "employee" ]}, { userID : 3 , name : "John" , groups : [ "qa" , "employee" ]}, { userID : 4 , name : "Paul" , groups : [ "dev" , "qa" , "employee" ]} ]; var isDev = function ( user ) { return user . groups . includes ( "dev" ); }; var isEmployee = function ( user ) { return user . groups . includes ( "employee" ); }; var isManager = function ( user ) { return user . groups . includes ( "manager" ); }; //every isDev data . reduce ( function ( result , user ) { return result && isDev ( user ); }, true ); //false, not everyone is a dev //every isEmployee data . reduce ( function ( result , user ) { return result && isEmployee ( user ); }, true ); //true, everyone is an employee //some isDev data . reduce ( function ( result , user ) { return result || isDev ( user ); }, false ); //true, at least one user is a dev //some isManager data . reduce ( function ( result , user ) { return result || isManager ( user ); }, false ); //false, no user is a manager

Now, do I recommend then that you just use reduce for everything and ignore the other built-in functions? Absolutely not - as you can see from the examples, the reduce versions are more complicated in every case. Not bad - but definitely not as streamlined. The API of the other functions help readability too - when you see map or filter you know exactly what is going on. Plus the specialized versions of these functions can take care of other optimizations behind the scenes.

Where reduce shines is when you either don’t have a more specialized function to use, or when you might need to combine multiple actions together, like in our user / group combination example.

You should definitely learn reduce and how it works, as well as how to read it. Get familiar with how the accumulation argument is passed from one iteration to the next - it’s not difficult with very little practice.

Editorial Note: reduce can be called by many names in different languages. reduce is by far the most common, but other languages might use fold or aggregate . These are different words for the same concept. There are also right and left versions of reduce , in JavaScript reduce is the left version. Left and right just refer to if the reduction happens from left to right or right to left (ascending vs descending, forwards or backwards). A right reduce is the same thing as doing a reverse of the data and then a left reduce.

Notes

In most languages, using these higher order functions instead of a loop is slower. Almost always will be - the overhead of the function calls, plus the ability to use things like break or continue in certain examples means that you can optimize those loops easier. But the speed comes at the cost of maintainability and readability. In any language that provides these functions I will always reach for these where I can and only begrudgingly go back and rewrite performance critical code into the loop version once testing shows it necessary.

You might notice I didn’t talk about forEach - that is because while it is a higher order function, it doesn’t generally take pure functions. If you are passing a pure function to forEach you probably should be using map or something else. forEach (some languages just call it each ) is for side effects - referencing outside variables (such as through closure) or writing output to the screen or talking to a database, etc. There is nothing wrong with using it, but minimize it and consider first where you could use one of the other functions first and then use that output with your side-effecting code - it will provide much more testable and maintainable code.

Conclusion

I hope these explanations and examples have whet your appetite and shown how you can do your everyday programming by passing around functions, leading to easier to read and test code. But, even though these are common software patterns, the goal shouldn’t be to use them. These are just tools that you should add to your repertoire and you should learn when they are and aren’t appropriate.

That said, I believe that if you learn these patterns not only will you find ways to utilize them in your code, once you internalize the idea of first class functions and higher order functions you will start thinking of code very differently. Be careful though, higher order functions are gateway drugs. Soon you will be learing about currying, memoization and before you know it you’re talking about monads, functors and tagging Hindley-Milner notation on train cars - I’ve seen it a thousand times. ;)

If you find any errors in this article, or have any other clarifications or insights to any of this, please leave a comment below, I would love to see them.

Update: Thanks to Dan L, Mingo Hagen, Paul Turner and Adam Cameron from the cfml-slack for proofreading :)