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Data structures in Go: Stacks and queues

·11 mins

In a previous post1, we took a look at linked lists and how we can apply them in a hypothetical use-case. In this post, we will look at two similar but powerful data structures.

People queueing

Modelling actions and history #

Think about Excel or Google docs. You know, the most ubiquitous applications for composing documents that humanity has invented. We’ve all used them in some capacity. As you might know, these apps come with various actions one can apply to a text. Like adding colour to the text, underline, various fonts and sizes or organising the content in tables. The list is long, but there’s one ubiquitous functionality that we expect from these tools - the ability to “Undo” and “Redo” the actions we have applied.

Have you ever considered how you would program such functionality if given the opportunity? Let’s explore a data structure that can help us with such a task.

Let’s try to think how we would model the actions each of these applications have. Also, later on, we will see how we can keep the history of the actions and “Undo” them.

A simple Action struct would look something like:

type Action struct {
	name string
	metadata Meta
	// Probably some other data here...

We will store only the name at the moment, while the real-deal would have much more options compared to our example. So now, when the user of the editor applies a function to a bunch of text, we want to store that action in some sort of a collection so we can “Undo” it later.

type ActionHistory struct {
  top *Action
  size int

Looking at this ActionHistory data structure, we store a pointer to the Action at the top of the stack and the size of the stack. Once an action is applied, we will link it to the top item. So, when applying an action to the document, this is what could go under the hood.

func (history *ActionHistory) Apply(newAction *Action) {
	if != nil {
		oldTop := = oldTop
	} = newAction

An Add function would add the latest Action to the top of the ActionHistory. If the history struct has an action present at the top, it would push it down the history by linking it to the new action. Otherwise, it will attach the new action to the top of the list. Now, if you know about linked lists (or read my last post about linked lists in Go) you might see the resemblance here. So far, what we use here is essentially a linked list, none the less.

What about undoing an action? This is what an Undo function would look like:

func (history *ActionHistory) Undo() *Action {
	topAction :=
	if topAction != nil { =
	} else if == nil { = nil
	return topAction

If you are paying close attention you can notice that this is a bit different than just removing a node from a linked list. Due to the nature of an ActionHistory, we want the last applied action to be the one that will be Undoed first. Which makes sense, right?

This is the default behaviour of a stack. A stack is a data structure where you can only insert or delete items at the top of the stack. Think of it like a stack of papers, or stack of plates in your kitchen drawer. If you want to take the bottom plate from a stack you are going to have a hard time. But taking the one at the top is plain simple. Stacks are also considered LIFO structures - meaning Last In First Out, because of the behaviour we explained above.

That is basically what our Undo function does. If the stack (a.k.a. ActionHistory) has more than one Action in it, it will set the top link for the second item. Otherwise, it will empty up the ActionHistory by setting the top element to nil.

From a big-O perspective, searching through a stack has O(n) complexity, but insertion and deletion from a stack are super quick - O(1). This is because traversing the whole stack, in worst case scenario, will still take going all n items in it, while inserting and removing items is constant time because we always insert and remove from the top of the stack.

You can play with the working version of this code here.

Person in subway

Luggage control #

Most of us have travelled by plane and know all of the security checks that a person has to go through to get on the plane. Sure, it’s for own our safety but sometimes it’s unnecessarily stressful to get through all of the scans, checks and tests.

One of the usual sights at airports security checkpoints are the long queues of people and luggage being put on the strip of the X-ray machines while people walk through metal detector gates. Probably there’s more to it that we even don’t know about it, but let’s focus on the X-ray machines that scan our bags.

Have you thought how would you model the interactions that happen on that machine? Of course, at least the visible ones. Let’s explore this idea for a moment. We would have to somehow represent the luggage on the strip as a collection of items, and the x-ray machine that scans the luggage one piece at a time.

The Luggage would be simple, for now:

type Luggage struct {
	weight int
	passenger string

We can also add a simple constructor function for the Luggage type as well:

func NewLuggage(weight int, passenger string) *Luggage {
	l := Luggage{
		weight:    weight,
		passenger: passenger, // just as an identifier
	return &l

Then, we can create a Belt where the Luggage is put on and taken to the x-ray scanner:

type Belt []*Luggage

Not what you were expecting? Well, what we create is a Belt type that is actually a slice of Luggage pointers. Which is what exactly the machine belt is - just a collection of bags that get scanned one by one.

So now we need to add a function that knows how to add Luggage to the Belt:

func (belt *Belt) Add(newLuggage *Luggage) {
	*belt = append(*belt, newLuggage)

Since Belt is actually a slice, we can use the built-in append function which will add the newLuggage to the Belt. The cool part about this implementation the time complexity - because we use append, which is a built-in method, we get an amortised O(1) for insertion. Of course, this comes at space cost, due to the way slices in Go work.

As the Belt moves and takes Luggage to the x-ray machine, we will need to somehow take this luggage off and load it into the machine for inspection. But because of the nature of the Belt, the first item that’s put on it is the first item to be scanned. And the last one that is added will be the last one to be scanned. So, we can say that the Belt is a FIFO (First in, first out) data structure.

This is what the function Take would look like, keeping in mind the details above:

func (belt *Belt) Take() *Luggage {
	first, rest := (*belt)[0], (*belt)[1:]
	*belt = rest
	return first

It will take the first item and return it, and it will assign everything else in the collection to the beginning of it, so the second item becomes first and so on. If you were wondering, this has a O(1) time complexity since it always takes the first item from the queue.

Using our new types and functions can be done as such:

func main() {
	belt := &Belt{}
	belt.Add(NewLuggage(3, "Elmer Fudd"))
	belt.Add(NewLuggage(5, "Sylvester"))
	belt.Add(NewLuggage(2, "Yosemite Sam"))
	belt.Add(NewLuggage(10, "Daffy Duck"))
	belt.Add(NewLuggage(1, "Bugs Bunny"))

	fmt.Println("Belt:", belt, "Length:", len(*belt))
	first := belt.Take()
	fmt.Println("First luggage:", first)
	fmt.Println("Belt:", belt, "Length:", len(*belt))

The output of the main function will be something like:

Belt: &[0x1040a0c0 0x1040a0d0 0x1040a0e0 0x1040a100 0x1040a110] Length: 5
First luggage: &{3 Elmer Fudd}
Belt: &[0x1040a0d0 0x1040a0e0 0x1040a100 0x1040a110] Length: 4

Basically what happens is we add five different Luggage structs on the Belt and we take the first one off, which is the one printed on the second line of the output.

You can play with the code from this example here.

Person talking on phone carrying hand luggage

What about first-class passengers? #

Yeah, what about them? I mean, they’ve paid so much money for their ticket that it just doesn’t make sense for them to wait in queues as the economy ticket holders. So, how can we prioritise these passengers? What if their luggage has some sort of priority, where the higher the priority is the faster they get through the queue?

Let’s modify our Luggage struct to enable this:

type Luggage struct {
	weight    int
	priority  int
	passenger string

Also, all Luggage created in the NewLuggage function will have to take the priority level as an argument:

func NewLuggage(weight int, priority int, passenger string) *Luggage {
	l := Luggage{
		weight:    weight,
		priority:  priority,
		passenger: passenger,
	return &l

Let’s think this through again. Basically, when a new Luggage is put on the Belt, we need to detect it’s priority and put it as close to the beginning of the Belt as possible based on the detected priority.

Let’s modify our Add function:

func (belt *Belt) Add(newLuggage *Luggage) {
	if len(*belt) == 0 {
		*belt = append(*belt, newLuggage)
	} else {
		added := false
		for i, placedLuggage := range *belt {
			if newLuggage.priority > placedLuggage.priority {
				*belt = append((*belt)[:i], append(Belt{newLuggage}, (*belt)[i:]...)...)
				added = true
		if !added {
			*belt = append(*belt, newLuggage)

Compared to the previous implementation, this is rather complicated. There are a couple of things going on here and the first case is quite simple. If the belt is empty, we just put the new luggage on the belt and we are done. The Belt will have only one item on it and that one will be the first that can be taken off of it.

The second case, when there’s more than one item on the Belt, loops through all of the luggage on the Belt and compares their priority with the new one coming on. Then it finds a Luggage that has a smaller priority it bypasses it and puts the new luggage in front of it. This means, the higher the priority the Luggage has the further to the beginning of the Belt it will be placed.

Of course, if the loop doesn’t find such a Luggage it will just append it to the end of the Belt. Our new Add function has a time complexity of O(n) because in worst case scenario it will have to traverse the whole slice before it inserts the new Luggage struct. Inherently, searching and accessing any item in our queue will cost us the same - O(n).

To demonstrate the new Add functionality, we can run the following code:

func main() {
	belt := make(Belt, 0)
	belt.Add(NewLuggage(3, 1, "Elmer Fudd"))
	belt.Add(NewLuggage(3, 1, "Sylvester"))
	belt.Add(NewLuggage(3, 1, "Yosemite Sam"))

	belt.Add(NewLuggage(3, 2, "Daffy Duck"))

	belt.Add(NewLuggage(3, 3, "Bugs Bunny"))

	belt.Add(NewLuggage(100, 2, "Wile E. Coyote"))

First, it will create a Belt with three Luggage structs on it, each of them with a priority of 1:

0. &{3 1 Elmer Fudd}
1. &{3 1 Sylvester}
2. &{3 1 Yosemite Sam}

Then, it will add a new Luggage with a priority of 2:

0. &{3 2 Daffy Duck}
1. &{3 1 Elmer Fudd}
2. &{3 1 Sylvester}
3. &{3 1 Yosemite Sam}

You see, the new luggage with the highest priority got promoted to the first place in the Belt. Next, we add a new one with even higher priority (3):

0. &{3 3 Bugs Bunny}
1. &{3 2 Daffy Duck}
2. &{3 1 Elmer Fudd}
3. &{3 1 Sylvester}
4. &{3 1 Yosemite Sam}

As expected, the one with the highest priority was put on the first spot in the Belt. Lastly, we add another Luggage, this time with priority 2:

0. &{3 3 Bugs Bunny}
1. &{3 2 Daffy Duck}
2. &{100 2 Wile E. Coyote}
3. &{3 1 Elmer Fudd}
4. &{3 1 Sylvester}
5. &{3 1 Yosemite Sam}

The new Luggage was added right after the Luggage with the same priority as the new one and of course, not at the beginning of the Belt. Basically, our Belt gets implicitly sorted by the priority as we add new Luggage to it.

If you are knowledgeable about queues you might think that these are not the most performant ways of implementing priority queues and you are quite right. Implementing priority queues can be done more performant using heaps, that we’ll take a look at in another post.

There’s more interesting knowledge around priority queues that we can explore. Until then you can take a look at priority queues’ Wiki page. If you are knowledgeable about queues you might think that these are not the most performant ways of implementing priority queues and you are quite right. Implementing priority queues can be done more performant using heaps, that we’ll take a look at in another post.), especially the “Implementation” section.

You can see and play with the code from this example here.

Closing thoughts #

In this post, we took a look at what stacks and queues are and we tried to mimic some interesting real-world application to them. I strongly believe that when we learn a topic (especially in computer science) it’s much better to do it through examples and application to a problem and I hope I managed to do exactly that.

In further posts we will look at some more priority queues (using heaps), but before we get there we will take a look at some a bit simpler data structures.

Acknowledgements: Big thanks to [Vincent Boisard]( for reviewing a final draft of this post and his constructive criticism on it.