Let’s return to functions and study them more indepth.
Our first topic will be recursion.
If you are not new to programming, then it is probably familiar and you could skip this chapter.
Recursion is a programming pattern that is useful in situations when a task can be naturally split into several tasks of the same kind, but simpler. Or when a task can be simplified into an easy action plus a simpler variant of the same task. Or, as we’ll see soon, to deal with certain data structures.
When a function solves a task, in the process it can call many other functions. A partial case of this is when a function calls itself. That’s called recursion.
Two ways of thinking
For something simple to start with – let’s write a function pow(x, n)
that raises x
to a natural power of n
. In other words, multiplies x
by itself n
times.
pow(2, 2) = 4
pow(2, 3) = 8
pow(2, 4) = 16
There are two ways to implement it.

Iterative thinking: the
for
loop:function pow(x, n) { let result = 1; // multiply result by x n times in the loop for(let i = 0; i < n; i++) { result *= x; } return result; } alert( pow(2, 3) ); // 8

Recursive thinking: simplify the task and call self:
function pow(x, n) { if (n == 1) { return x; } else { return x * pow(x, n  1); } } alert( pow(2, 3) ); // 8
Please note how the recursive variant is fundamentally different.
When pow(x, n)
is called, the execution splits into two branches:
if n==1 = x
/
pow(x, n) =
\
else = x * pow(x, n  1)
 If
n==1
, then everything is trivial. It is called the base of recursion, because it immediately produces the obvious result:pow(x, 1)
equalsx
.  Otherwise, we can represent
pow(x, n)
asx * pow(x, n1)
. In maths, one would writex^{n} = x * x^{n1}
. This is called a recursive step: we transform the task into a simpler action (multiplication byx
) and a simpler call of the same task (pow
with lowern
). Next steps simplify it further and further untilln
reaches1
.
We can also say that pow
recursively calls itself till n == 1
.
For example, to calculate pow(2, 4)
the recursive variant does these steps:
pow(2, 4) = 2 * pow(2, 3)
pow(2, 3) = 2 * pow(2, 2)
pow(2, 2) = 2 * pow(2, 1)
pow(2, 1) = 2
So, the recursion reduces a function call to a simpler one, and then – to even more simpler, and so on, until the result becomes obvious.
A recursive solution is usually shorter than an iterative one.
Here we can rewrite the same using the ternary ?
operator instead of if
to make pow(x, n)
more terse and still very readable:
function pow(x, n) {
return (n == 1) ? x : (x * pow(x, n1));
}
The maximal number of nested calls (including the first one) is called recursion depth. In our case, it will be exactly n
.
The maximal recursion depth is limited by JavaScript engine. We can make sure about 10000, some engines allow more, but 100000 is probably out of limit for the majority of them. There are automatic optimizations that help alleviate this (“tail calls optimizations”), but they are not yet supported everywhere and work only in simple cases.
That limits the application of recursion, but it still remains very wide. There are many tasks where recursive way of thinking gives simpler code, easier to maintain.
The execution stack
Now let’s examine how recursive calls work. For that we’ll look under the hood of functions.
The information about a function run is stored in its execution context.
The execution context is an internal data structure that contains details about the execution of a function: where the control flow is now, the current variables, the value of this
(we don’t use it here) and few other internal details.
One function call has exactly one execution context associated with it.
When a function makes a nested call, the following happens:
 The current function is paused.
 The execution context associated with it is remembered in a special data structure called execution context stack.
 The nested call executes.
 After it ends, the old execution context is retrieved from the stack, and the outer function is resumed from where it stopped.
Let’s see what happens during the pow(2, 3)
call.
pow(2, 3)
In the beginning of the call pow(2, 3)
the execution context will store variables: x = 2, n = 3
, the execution flow is at line 1
of the function.
We can sketch it as:
 Context: { x: 2, n: 3, at line 1 } pow(2, 3)
That’s when the function starts to execute. The condition n == 1
is falsy, so the flow continues into the second branch of if
:
function pow(x, n) {
if (n == 1) {
return x;
} else {
return x * pow(x, n  1);
}
}
alert( pow(2, 3) );
The variables are same, but the line changes, so the context is now:
 Context: { x: 2, n: 3, at line 5 } pow(2, 3)
To calculate x*pow(x, n1)
, we need to make a subcall of pow
with new arguments pow(2, 2)
.
pow(2, 2)
To do a nested call, JavaScript remembers the current execution context in the execution context stack.
Here we call the same function pow
, but it absolutely doesn’t matter. The process is the same for all functions:
 The current context is “remembered” on top of the stack.
 The new context is created for the subcall.
 When the subcall is finished – the previous context is popped from the stack, and its execution continues.
Here’s the context stack when we entered the subcall pow(2, 2)
:
 Context: { x: 2, n: 2, at line 1 } pow(2, 2)
 Context: { x: 2, n: 3, at line 5 } pow(2, 3)
The new current execution context is on top (and bold), and previous remembered contexts are below.
When we finish the subcall – it is easy to resume the previous context, because it keeps both variables and the exact place of the code where it stopped. Here in the picture we use the word “line”, but of course it’s more precise.
pow(2, 1)
The process repeats: a new subcall is made at line 5
, now with arguments x=2
, n=1
.
A new execution context is created, the previous one is pushed on top of the stack:
 Context: { x: 2, n: 1, at line 1 } pow(2, 1)
 Context: { x: 2, n: 2, at line 5 } pow(2, 2)
 Context: { x: 2, n: 3, at line 5 } pow(2, 3)
There are 2 old contexts now and 1 currently running for pow(2, 1)
.
The exit
During the execution of pow(2, 1)
, unlike before, the condition n == 1
is truthy, so the first branch of if
works:
function pow(x, n) {
if (n == 1) {
return x;
} else {
return x * pow(x, n  1);
}
}
There are no more nested calls, so the function finishes, returning 2
.
As the function finishes, its execution context is not needed any more, so it’s removed from the memory. The previous one is restored off the top of the stack:
 Context: { x: 2, n: 2, at line 5 } pow(2, 2)
 Context: { x: 2, n: 3, at line 5 } pow(2, 3)
The execution of pow(2, 2)
is resumed. It has the result of the subcall pow(2, 1)
, so it also can finish the evaluation of x * pow(x, n1)
, returning 4
.
Then the previous context is restored:
 Context: { x: 2, n: 3, at line 5 } pow(2, 3)
When it finishes, we have a result of pow(2, 3) = 8
.
The recursion depth in this case was: 3.
As we can see from the illustrations above, recursion depth equals the maximal number of context in the stack.
Note the memory requirements. Contexts take memory. In our case, raising to the power of n
actually requires the memory for n
contexts, for all lower values of n
.
A loopbased algorithm is more memorysaving:
function pow(x, n) {
let result = 1;
for(let i = 0; i < n; i++) {
result *= x;
}
return result;
}
The iterative pow
uses a single context changing i
and result
in the process. Its memory requirements are small, fixed and do not depend on n
.
Any recursion can be rewritten as a loop. The loop variant usually can be made more effective.
…But sometimes the rewrite is nontrivial, especially when function uses different recursive subcalls depending on conditions and merges their results or when the branching is more intricate. And the optimization may be unneeded and totally not worth the efforts.
Recursion can give a shorter code, easier to understand and support. Optimizations are not required in every place, mostly we need a good code, that’s why it’s used.
Recursive traversals
Another great application of the recursion is a recursive traversal.
Imagine, we have a company. The staff structure can be presented as an object:
let company = {
sales: [{
name: 'John',
salary: 1000
}, {
name: 'Alice',
salary: 600
}],
development: {
sites: [{
name: 'Peter',
salary: 2000
}, {
name: 'Alex',
salary: 1800
}],
internals: [{
name: 'Jack',
salary: 1300
}]
}
};
In other words, a company has departments.

A department may have an array of staff. For instance,
sales
department has 2 employees: John and Alice. 
Or a department may split into subdepartments, like
development
has two branches:sites
andinternals
. Each of them has the own staff. 
It is also possible that when a subdepartment grows, it divides into subsubdepartments (or teams).
For instance, the
sites
department in the future may be split into teams forsiteA
andsiteB
. And they, potentially, can split even more. That’s not on the picture, just something to have in mind.
Now let’s say we want a function to get the sum of all salaries. How can we do that?
An iterative approach is not easy, because the structure is not simple. The first idea may be to make a for
loop over company
with nested subloop over 1st level departments. But then we need more nested subloops to iterate over the staff in 2nd level departments like sites
. …And then another subloop inside those for 3rd level departments that might appear in the future? Should we stop on level 3 or make 4 levels of loops? If we put 34 nested subloops in the code to traverse a single object, it becomes rather ugly.
Let’s try recursion.
As we can see, when our function gets a department to sum, there are two possible cases:
 Either it’s a “simple” department with an array of people – then we can sum the salaries in a simple loop.
 Or it’s an object with
N
subdepartments – then we can makeN
recursive calls to get the sum for each of the subdeps and combine the results.
The (1) is the base of recursion, the trivial case.
The (2) is the recursive step. A complex task is split into subtasks for smaller departments. They may in turn split again, but sooner or later the split will finish at (1).
The algorithm is probably even easier to read from the code:
let company = { // the same object, compressed for brevity
sales: [{name: 'John', salary: 1000}, {name: 'Alice', salary: 600 }],
development: {
sites: [{name: 'Peter', salary: 2000}, {name: 'Alex', salary: 1800 }],
internals: [{name: 'Jack', salary: 1300}]
}
};
// The function to do the job
function sumSalaries(department) {
if (Array.isArray(department)) { // case (1)
return department.reduce((prev, current) => prev + current.salary, 0); // sum the array
} else { // case (2)
let sum = 0;
for(let subdep of Object.values(department)) {
sum += sumSalaries(subdep); // recursively call for subdepartments, sum the results
}
return sum;
}
}
alert(sumSalaries(company)); // 6700
The code is short and easy to understand (hopefully?). That’s the power of recursion. It also works for any level of subdepartment nesting.
Here’s the diagram of calls:
We can easily see the principle: for an object {...}
subcalls are made, while arrays [...]
are the “leaves” of the recursion tree, they give immediate result.
Note that the code uses smart features that we’ve covered before:
 Method
arr.reduce
explained in the chapter Array methods to get the sum of the array.  Loop
for(val of Object.values(obj))
to iterate over object values:Object.values
returns an array of them.
Recursive structures
A recursive (recursivelydefined) data structure is a structure that replicates itself in parts.
We’ve just seen it in the example of a company structure above.
A company department is:
 Either an array of people.
 Or an object with departments.
For webdevelopers there are much better known examples: HTML and XML documents.
In the HTML document, an HTMLtag may contain a list of:
 Text pieces.
 HTMLcomments.
 Other HTMLtags (that in turn may contain text pieces/comments or other tags etc).
That’s once again a recursive definition.
For better understanding, we’ll cover one more recursive structure named “Linked list” that might be a better alternative for arrays in some cases.
Linked list
Imagine, we want to store an ordered list of objects.
The natural choice would be an array:
let arr = [obj1, obj2, obj3];
…But there’s a problem with arrays. The “delete element” and “insert element” operations are expensive. For instance, arr.unshift(obj)
operation has to renumber all elements to make room for a new obj
, and if the array is big, it takes time. Same with arr.shift()
.
The only structural modifications that do not require massrenumbering are those that operate with the end of array: arr.push/pop
. So an array can be quite slow for big queues.
Alternatively, if we really need fast insertion/deletion, we can choose another data structure called a linked list.
The linked list element is recursively defined as an object with:
value
.next
property referencing the next linked list element ornull
if that’s the end.
For instance:
let list = {
value: 1,
next: {
value: 2,
next: {
value: 3,
next: {
value: 4,
next: null
}
}
}
};
Graphical representation of the list:
An alternative code for creation:
let list = { value: 1 };
list.next = { value: 2 };
list.next.next = { value: 3 };
list.next.next.next = { value: 4 };
Here we can even more clearer see that there are multiple objects, each one has the value
and next
pointing to the neighbour. The list
variable is the first object in the chain, so following next
pointers from it we can reach any element.
The list can be easily split into multiple parts and later joined back:
let secondList = list.next.next;
list.next.next = null;
To join:
list.next.next = secondList;
And surely we can insert or remove items in any place.
For instance, to prepend a new value, we need to update the head of the list:
let list = { value: 1 };
list.next = { value: 2 };
list.next.next = { value: 3 };
list.next.next.next = { value: 4 };
// prepend the new value to the list
list = { value: "new item", next: list };
To remove a value from the middle, change next
of the previous one:
list.next = list.next.next;
We made list.next
jump over 1
to value 2
. The value 1
is now excluded from the chain. If it’s not stored anywhere else, it will be automatically removed from the memory.
Unlike arrays, there’s no massrenumbering, we can easily rearrange elements.
Naturally, lists are not always better than arrays. Otherwise everyone would use only lists.
The main drawback is that we can’t easily access an element by its number. In an array that’s easy: arr[n]
is a direct reference. But in the list we need to start from the first item and go next
N
times to get the Nth element.
…But we don’t always need such operations. For instance, when we need a queue or even a deque – the ordered structure that must allow very fast adding/removing elements from both ends.
Sometimes it’s worth to add another variable named tail
to track the last element of the list (and update it when adding/removing elements from the end). For large sets of elements the speed difference versus arrays is huge.
Summary
Terms:

Recursion is a programming term that means a “selfcalling” function. Such functions can be used to solve certain tasks in elegant ways.
When a function calls itself, that’s called a recursion step. The basis of recursion is function arguments that make the task so simple that the function does not make further calls.

A recursivelydefined data structure is a data structure that can be defined using itself.
For instance, the linked list can be defined as a data structure consisting of an object referencing a list (or null).
list = { value, next > list }
Trees like HTML elements tree or the department tree from this chapter are also naturally recursive: they branch and every branch can have other branches.
Recursive functions can be used to walk them as we’ve seen in the
sumSalary
example.
Any recursive function can be rewritten into an iterative one. And that’s sometimes required to optimize stuff. But for many tasks a recursive solution is fast enough and easier to write and support.
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