Python Data Structure and Algorithm Tutorial – B-tree


In this tutorial, you will learn what a B-tree is. Also, you will find working examples of search operation on a B-tree in Python.

B-tree is a special type of self-balancing search tree in which each node can contain more than one key and can have more than two children. It is a generalized form of the binary search tree.

It is also known as a height-balanced m-way tree.

B-tree example

Why B-tree?

The need for B-tree arose with the rise in the need for lesser time in accessing the physical storage media like a hard disk. The secondary storage devices are slower with a larger capacity. There was a need for such types of data structures that minimize the disk accesses.

Other data structures such as a binary search tree, avl tree, red-black tree, etc can store only one key in one node. If you have to store a large number of keys, then the height of such trees becomes very large and the access time increases.

However, B-tree can store many keys in a single node and can have multiple child nodes. This decreases the height significantly allowing faster disk accesses.

B-tree Properties

  1. For each node x, the keys are stored in increasing order.
  2. In each node, there is a boolean value x.leaf which is true if x is a leaf.
  3. If n is the order of the tree, each internal node can contain at most n – 1 keys along with a pointer to each child.
  4. Each node except root can have at most n children and at least n/2 children.
  5. All leaves have the same depth (i.e. height-h of the tree).
  6. The root has at least 2 children and contains a minimum of 1 key.
  7. If n ≥ 1, then for any n-key B-tree of height h and minimum degree t ≥ 2h ≥ logt (n+1)/2.




Searching for an element in a B-tree is the generalized form of searching an element in a Binary Search Tree. The following steps are followed.

  1. Starting from the root node, compare k with the first key of the node.
    If k = the first key of the node, return the node and the index.
  2. If k.leaf = true, return NULL (i.e. not found).
  3. If k < the first key of the root node, search the left child of this key recursively.
  4. If there is more than one key in the current node and k > the first key, compare k with the next key in the node.
    If k < next key, search the left child of this key (ie. k lies in between the first and the second keys).
    Else, search the right child of the key.
  5. Repeat steps 1 to 4 until the leaf is reached.

Searching Example

  1. Let us search key k = 17 in the tree below of degree 3.
  2. k is not found in the root so, compare it with the root key.
    Not found on the root node
    k is not found on the root node
  3. Since k > 11, go to the right child of the root node.
    Go to the right subtree
    Go to the right subtree
  4. Compare k with 16. Since k > 16, compare k with the next key 18.
    Compare with the keys from left to right
    Compare with the keys from left to right
  5. Since k < 18, k lies between 16 and 18. Search in the right child of 16 or the left child of 18.
    k lies in between 16 and 18
    k lies in between 16 and 18
  6. k is found.
    k is found
    k is found

Algorithm for Searching an Element

BtreeSearch(x, k)
 i = 1
 while i ≤ n[x] and k ≥ keyi[x]        // n[x] means number of keys in x node
    do i = i + 1
if i  n[x] and k = keyi[x]
    then return (x, i)
if leaf [x]
    then return NIL
    return BtreeSearch(ci[x], k)

To learn more about different B-tree operations, please visit

  • Insertion on B-tree
  • Deletion on B-tree

Python Examples

/* Searching a key on a B-tree in Python */

/* Create node */
class BTreeNode:
    def __init__(self, leaf=False):
        self.leaf = leaf
        self.keys = []
        self.child = []

class BTree:
    def __init__(self, t):
        self.root = BTreeNode(True)
        self.t = t

    /* Print the tree */
    def print_tree(self, x, l=0):
        print("Level ", l, " ", len(x.keys), end=":")
        for i in x.keys:
            print(i, end=" ")
        l += 1
        if len(x.child) > 0:
            for i in x.child:
                self.print_tree(i, l)

    /* Search key */
    def search_key(self, k, x=None):
        if x is not None:
            i = 0
            while i < len(x.keys) and k > x.keys[i][0]:
                i += 1
            if i < len(x.keys) and k == x.keys[i][0]:
                return (x, i)
            elif x.leaf:
                return None
                return self.search_key(k, x.child[i])
            return self.search_key(k, self.root)

    /* Insert the key */
    def insert_key(self, k):
        root = self.root
        if len(root.keys) == (2 * self.t) - 1:
            temp = BTreeNode()
            self.root = temp
            temp.child.insert_key(0, root)
            self.split(temp, 0)
            self.insert_non_full(temp, k)
            self.insert_non_full(root, k)

    /* Insert non full condition */
    def insert_non_full(self, x, k):
        i = len(x.keys) - 1
        if x.leaf:
            x.keys.append((None, None))
            while i >= 0 and k[0] < x.keys[i][0]:
                x.keys[i + 1] = x.keys[i]
                i -= 1
            x.keys[i + 1] = k
            while i >= 0 and k[0] < x.keys[i][0]:
                i -= 1
            i += 1
            if len(x.child[i].keys) == (2 * self.t) - 1:
                self.split(x, i)
                if k[0] > x.keys[i][0]:
                    i += 1
            self.insert_non_full(x.child[i], k)

    /* Split */
    def split(self, x, i):
        t = self.t
        y = x.child[i]
        z = BTreeNode(y.leaf)
        x.child.insert_key(i + 1, z)
        x.keys.insert_key(i, y.keys[t - 1])
        z.keys = y.keys[t: (2 * t) - 1]
        y.keys = y.keys[0: t - 1]
        if not y.leaf:
            z.child = y.child[t: 2 * t]
            y.child = y.child[0: t - 1]

def main():
    B = BTree(3)

    for i in range(10):
        B.insert_key((i, 2 * i))


    if B.search_key(8) is not None:
        print("nNot found")

if __name__ == '__main__':

Searching Complexity on B Tree

Worst case Time complexity: Θ(log n)

Average case Time complexity: Θ(log n)

Best case Time complexity: Θ(log n)

Average case Space complexity: Θ(n)

Worst case Space complexity: Θ(n)


B Tree Applications

  • databases and file systems
  • to store blocks of data (secondary storage media)
  • multilevel indexing


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