# Definition

A binary search tree is a binary tree whose keys satisfy the **binary search tree** property:

Let *x* be a node in the BST.

If *y* is a node in the left subtree of *x*, then *key[y] <= key[x]*.

If *y* is a node in the right subtree of *x* then *key[y] >= key[x]*.

Basic operations on a binary search tree (BST) take time proportional to the height of the tree. For a complete binary tree with *n* nodes, such operations run in

*O(lg n) *worst-case time.

If the tree is a linear chain of *n* nodes, the same operations take *O(n)* worst case time. The expected height of a randomly built BST is *O(lg n)*, so that basic dynamic set operations on such a tree take *θ*(lg n) time on average. There are variations of BSTs whose worst-case performance can be guaranteed to be good – red-black and B-trees are 2 such examples.

Basic operations on BSTs include:

`SEARCH, MINIMUM, MAXIMUM, PREDECESSOR, SUCCESSOR, INSERT & DELETE`

.

The BST property allows us to print out all the keys in 3 different ways:

- InOrder Tree Walk – the key of the root of a subtree is printed between the values in its left subtree and those in its right subtree – LPR
- PreOrder Tree walk – root is printed before values in either subtree
- PostOrder Treee walk – root is printed after values in its subtrees.

It takes *θ(n)* time to walk an *n* -node binary tree.

## Querying a binary search tree

### Searching

We wishto search for a node with a given key in a BST. Given a pointer to the root of the tree and a key *k*, `TREE_SEARCH`

returns a pointer to a node with key k if one exists, else it returns NULL. The search begins at the root and moves downward in the tree. For each node *x* it encounters, it compares the key *k* with *key[x]*. if the 2 keys are equal, the search terminates. If *k* < *key[x]* the search continues in the left subtree, else it continues in the right subtree. The running time of `TREE_SEARCH`

is *O(h)*, where *h* is the height of the tree.

TREE_SEARCH(x,k) 1 if x==NULL or k=key[x] 2 then return x 3 if k < key[x] 4 then return TREE_SEARCH(left[x],k) 5 else return TREE_SEARCH(right[x],k) ITERATIVE_TREE_SEARCH(x,k) 1 while x!=NULL && k != key[x] 2 do if k < key[x] 3 then x <-- left[x] 4 else x <-- right[x] 5 return x

### Maximum and Minimum

A minimum can always be found by following left child pointers down from the rroot until a NULL is reached :

TREE_MINIMUM(x) 1 while left[x] != NULL 2 do x <--- left[x] 3 return x

The pseudo-code for `TREE_MAXIMUM`

is symmetric:

TREE_MAXIMUM(x) 1 while right[x] != NULL 2 do x <--- right[x] 3 return x

Both of these procedures run in *O(h)* time.

### SuCcessor and predecessoR

It is sometimes important to be able to find the successor to a node in the sorted order determined by an inorder tree walk. There are 2 cases:

- If the right subtree of node
*x*is nonempty, then the successor of*x*is the leftmost node in the right subtree, given by`TREE_MINIMUM(right[x])`

. - If the right subtree of
*x*is empty, and*x*has a successor*y*, then*y*is the lowest ancestor of*x*whose left child is also an ancestor of*x*.

TREE_SUCCESSOR(x) 1 if right[x] != NULL 2 then return TREE_MINIMUM(right[x]) 3 y <-- p[x] 4 while y!=NULL && x=right[y] 5 do x <--- y 6 y <--- p[y] 7 return y

`TREE_PREDECESSOR`

is symmetric to `TREE_SUCCESSOR`

and both run in time *O(h)*,

### Insertion and Deletion

The `TREE_INSERT`

procedure works as follows – walk down the tree from the root, choosing the left or right child until we find an appropriate leaf node from which to hang the new node. Hence if our new node is *z*, for each node *x* if *key[z] < key [x]*, we know that *z* must hang somewhere off the left subtree of *x*, and set *x* as *left[x]* and as *right[x]* if NOT.

When we reach a leaf node we have found the node *y* to hang off and we assign this node *y* as parent[z]. We then determine if *key[z] < key[y]*. If so, then *z* becomes *left[y]* and if not, it becomes *right[y]*.

TREE_INSERT(T,z) 1 y <--- NULL 2 x <--- root[T] 3 while x !=NULL 4 do y <-- x 5 if key[z] < key[x] 6 then x <--- left[x] 7 else x <--- right[x] 8 parent[z] <--- y //At this point y=parent[x] from loop above 9 if y == NULL 10 then root[T] <--- z 11 else if key[z] < key[y] 12 then left[y] <--- z 13 else right[y] <--- z

**Deletion**

There are 3 cases to consider when deleting a node *z* from a BST.

- Node
*z*is a leaf node and has no children In this case we modify parent[z] to replace*z*as its child with NULL. - Node
*z*has only 1 child. In this case we “splice out”*z*by making a new link between*child[z]*and*parent[z]*. - Node
*z*has 2 children. In this case we splice out*z*‘s successor*y*, which has no left child (else it wouldn’t be the successor) and replace*z*‘s key and satellite data with*y*‘s key and satellite data.

TREE_DELETE(T,z) 1 if left[z]==NULL or right[z]==NULL // Cases 1 && 2 2 then y <--- z // Cases 1 && 2 3 else y <--- TREE_SUCCESSOR(z) // Case 3 4 if left[y] != NULL // Lines 4-6 set x to non-NULL child of y, or NULL 5 then x <--- left[y] // if y has no children 6 else x <--- right[y] 7 if x != NULL // Line 7-13 splice out y 8 then parent[x] <--- parent[y] 9 if parent[y]==NULL // y is root,splice it out and set its child as 10 then root[T] <--- x // root 11 else if y==left[parent[y]] // If y is the left child of its parent, 12 then left[parent[y]] <--- x // splice it out and replace it with it's child x 13 else right[parent[y]] <--- x // else replace the right child of y's parent 14 if y != z // In lines 14-16, if the successor of z y was the 15 then key[z] <--- key[y] // node spliced out, replace z's key and data with 16 copy y's satellite data into z // that of y 17 return y

Both insertion and deletion procedures run in *O(h)* time on a tree of height *h*.

**References** :

http://algs4.cs.princeton.edu/32bst