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Data Structures in Go: Tree (BST)

dorin profile image Dorin Originally published at fodor.org ・3 min read

Introduction

A Tree is a non-linear data structure which consists of one or more nodes where a node can have zero or more references to other nodes which will form a sub-tree.

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There are multiple types of trees: ordered, un-ordered, balanced, non-balanced, with two children, with more than two children, etc.

For the purpose of this article I have chosen to implement a non-balanced Binary Search Tree, which is an ordered tree with two child nodes. The one on the left will always contain a smaller value and the one on the right a greater value.

Implementation

To start with, we are going to create a struct which is going to represent a single node of our tree. There will always be a node at the top, to which everything else is going to be added.

// Node : represents a single node of a Tree data structure
type Node struct {
    Left  *Node
    Right *Node
    Data  int
}

Next we add a constructor function New which will take the value of the first node and return an instance of a tree node.

// New : constructor function for a Tree node
func New(data int) *Node {
    return &Node{
        Left:  nil,
        Right: nil,
        Data:  data,
    }
}

Now we are going to add a few methods to our tree node struct. These are going to be: Insert, Contains and Traverse.

The Insert method is going to add a node with the value passed to it, to the tree. We are going to recursively look into all the nodes until we find a suitable location with a nil reference where we can append the new node.

// Insert : adds a node to the tree
func (t *Node) Insert(data int) *Node {
    if data < t.Data {
        if t.Left != nil {
            t.Left.Insert(data)
        } else {
            t.Left = New(data)
        }
    } else {
        if t.Right != nil {
            t.Right.Insert(data)
        } else {
            t.Right = New(data)
        }
    }
    return t
}

Then we add the Contains method which is going to start traversing the tree and look for a node with a specific value. It will return a boolean with the success status.

// Contains : checks if the tree contains a node with a given value
func (t *Node) Contains(data int) bool {
    if t.Data == data {
        return true
    }
    if data < t.Data {
        if t.Left != nil {
            t.Left.Contains(data)
        } else {
            return false
        }
    } else {
        if t.Right != nil {
            t.Right.Contains(data)
        } else {
            return false
        }
    }
    return false
}

And lastly we add the Traverse method which is going to traverse the entirety of the tree and print all its values to the standard output.

// Traverse : prints all elements of the tree
func (t *Node) Traverse() {
    if t.Left != nil {
        t.Left.Traverse()
    }
    fmt.Println(t.Data)
    if t.Right != nil {
        t.Right.Traverse()
    }
}

Conclusion

We ended up with a basic Binary Search Tree which will let us add any element to it with a complexity of O(n) and search, in the worst case also with O(n). This is not ideal and it is possible to improve the tree by making sure it is balanced. A balanced tree would let us search at O(log(n)).

Stay tuned for new posts on how to balance our tree!

Source code with tests

GitHub logo dorin131 / go-data-structures

A collection of data structures implemented in Go

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