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# B+ Tree

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

A B+ tree is an advanced form of a self-balancing tree in which all the values are present in the leaf level.

An important concept to be understood before learning B+ tree is multilevel indexing. In multilevel indexing, the index of indices is created as in figure below. It makes accessing the data easier and faster. Multilevel Indexing using B+ tree

## Properties of a B+ Tree

1. All leaves are at the same level.
2. The root has at least two children.
3. Each node except root can have a maximum of m children and at least m/2 children.
4. Each node can contain a maximum of m – 1 keys and a minimum of ⌈m/2⌉ – 1 keys.

## Comparison between a B-tree and a B+ Tree B-tree B+ tree

The data pointers are present only at the leaf nodes on a B+ tree whereas the data pointers are present in the internal, leaf or root nodes on a B-tree.

The leaves are not connected with each other on a B-tree whereas they are connected on a B+ tree.

Operations on a B+ tree are faster than on a B-tree.

The following steps are followed to search for data in a B+ Tree of order m. Let the data to be searched be k.

1. Start from the root node. Compare k with the keys at the root node [k1, k2, k3,……km – 1].
2. If k < k1, go to the left child of the root node.
3. Else if k == k1, compare k2. If `k < k``2`, k lies between k1 and k2. So, search in the left child of k2.
4. If k > k2, go for k3, k4,…km-1 as in steps 2 and 3.
5. Repeat the above steps until a leaf node is reached.
6. If k exists in the leaf node, return true else return false.

## Searching Example on a B+ Tree

Let us search k = 45 on the following B+ tree. B+ tree
1. Compare k with the root node. k is not found at the root
2. Since k > 25, go to the right child. Go to right of the root
3. Compare k with 35. Since k > 30, compare k with 45. k not found
4. Since k ≥ 45, so go to the right child. go to the right
5. k is found. k is found

## Python Examples

``````/* B+ tee in python */

import math

/* Node creation */
class Node:
def __init__(self, order):
self.order = order
self.values = []
self.keys = []
self.nextKey = None
self.parent = None
self.check_leaf = False

/* Insert at the leaf */
def insert_at_leaf(self, leaf, value, key):
if (self.values):
temp1 = self.values
for i in range(len(temp1)):
if (value == temp1[i]):
self.keys[i].append(key)
break
elif (value < temp1[i]):
self.values = self.values[:i] + [value] + self.values[i:]
self.keys = self.keys[:i] + [[key]] + self.keys[i:]
break
elif (i + 1 == len(temp1)):
self.values.append(value)
self.keys.append([key])
break
else:
self.values = [value]
self.keys = [[key]]

/* B plus tree */
class BplusTree:
def __init__(self, order):
self.root = Node(order)
self.root.check_leaf = True

/* Insert operation */
def insert(self, value, key):
value = str(value)
old_node = self.search(value)
old_node.insert_at_leaf(old_node, value, key)

if (len(old_node.values) == old_node.order):
node1 = Node(old_node.order)
node1.check_leaf = True
node1.parent = old_node.parent
mid = int(math.ceil(old_node.order / 2)) - 1
node1.values = old_node.values[mid + 1:]
node1.keys = old_node.keys[mid + 1:]
node1.nextKey = old_node.nextKey
old_node.values = old_node.values[:mid + 1]
old_node.keys = old_node.keys[:mid + 1]
old_node.nextKey = node1
self.insert_in_parent(old_node, node1.values, node1)

/* Search operation for different operations */
def search(self, value):
current_node = self.root
while(current_node.check_leaf == False):
temp2 = current_node.values
for i in range(len(temp2)):
if (value == temp2[i]):
current_node = current_node.keys[i + 1]
break
elif (value < temp2[i]):
current_node = current_node.keys[i]
break
elif (i + 1 == len(current_node.values)):
current_node = current_node.keys[i + 1]
break
return current_node

/* Find the node */
def find(self, value, key):
l = self.search(value)
for i, item in enumerate(l.values):
if item == value:
if key in l.keys[i]:
return True
else:
return False
return False

/* Inserting at the parent */
def insert_in_parent(self, n, value, ndash):
if (self.root == n):
rootNode = Node(n.order)
rootNode.values = [value]
rootNode.keys = [n, ndash]
self.root = rootNode
n.parent = rootNode
ndash.parent = rootNode
return

parentNode = n.parent
temp3 = parentNode.keys
for i in range(len(temp3)):
if (temp3[i] == n):
parentNode.values = parentNode.values[:i] +
[value] + parentNode.values[i:]
parentNode.keys = parentNode.keys[:i +
1] + [ndash] + parentNode.keys[i + 1:]
if (len(parentNode.keys) > parentNode.order):
parentdash = Node(parentNode.order)
parentdash.parent = parentNode.parent
mid = int(math.ceil(parentNode.order / 2)) - 1
parentdash.values = parentNode.values[mid + 1:]
parentdash.keys = parentNode.keys[mid + 1:]
value_ = parentNode.values[mid]
if (mid == 0):
parentNode.values = parentNode.values[:mid + 1]
else:
parentNode.values = parentNode.values[:mid]
parentNode.keys = parentNode.keys[:mid + 1]
for j in parentNode.keys:
j.parent = parentNode
for j in parentdash.keys:
j.parent = parentdash
self.insert_in_parent(parentNode, value_, parentdash)

/* Delete a node */
def delete(self, value, key):
node_ = self.search(value)

temp = 0
for i, item in enumerate(node_.values):
if item == value:
temp = 1

if key in node_.keys[i]:
if len(node_.keys[i]) > 1:
node_.keys[i].pop(node_.keys[i].index(key))
elif node_ == self.root:
node_.values.pop(i)
node_.keys.pop(i)
else:
node_.keys[i].pop(node_.keys[i].index(key))
del node_.keys[i]
node_.values.pop(node_.values.index(value))
self.deleteEntry(node_, value, key)
else:
print("Value not in Key")
return
if temp == 0:
print("Value not in Tree")
return

/* Delete an entry */
def deleteEntry(self, node_, value, key):

if not node_.check_leaf:
for i, item in enumerate(node_.keys):
if item == key:
node_.keys.pop(i)
break
for i, item in enumerate(node_.values):
if item == value:
node_.values.pop(i)
break

if self.root == node_ and len(node_.keys) == 1:
self.root = node_.keys
node_.keys.parent = None
del node_
return
elif (len(node_.keys) < int(math.ceil(node_.order / 2)) and node_.check_leaf == False) or (len(node_.values) < int(math.ceil((node_.order - 1) / 2)) and node_.check_leaf == True):

is_predecessor = 0
parentNode = node_.parent
PrevNode = -1
NextNode = -1
PrevK = -1
PostK = -1
for i, item in enumerate(parentNode.keys):

if item == node_:
if i > 0:
PrevNode = parentNode.keys[i - 1]
PrevK = parentNode.values[i - 1]

if i < len(parentNode.keys) - 1:
NextNode = parentNode.keys[i + 1]
PostK = parentNode.values[i]

if PrevNode == -1:
ndash = NextNode
value_ = PostK
elif NextNode == -1:
is_predecessor = 1
ndash = PrevNode
value_ = PrevK
else:
if len(node_.values) + len(NextNode.values) < node_.order:
ndash = NextNode
value_ = PostK
else:
is_predecessor = 1
ndash = PrevNode
value_ = PrevK

if len(node_.values) + len(ndash.values) < node_.order:
if is_predecessor == 0:
node_, ndash = ndash, node_
ndash.keys += node_.keys
if not node_.check_leaf:
ndash.values.append(value_)
else:
ndash.nextKey = node_.nextKey
ndash.values += node_.values

if not ndash.check_leaf:
for j in ndash.keys:
j.parent = ndash

self.deleteEntry(node_.parent, value_, node_)
del node_
else:
if is_predecessor == 1:
if not node_.check_leaf:
ndashpm = ndash.keys.pop(-1)
ndashkm_1 = ndash.values.pop(-1)
node_.keys = [ndashpm] + node_.keys
node_.values = [value_] + node_.values
parentNode = node_.parent
for i, item in enumerate(parentNode.values):
if item == value_:
p.values[i] = ndashkm_1
break
else:
ndashpm = ndash.keys.pop(-1)
ndashkm = ndash.values.pop(-1)
node_.keys = [ndashpm] + node_.keys
node_.values = [ndashkm] + node_.values
parentNode = node_.parent
for i, item in enumerate(p.values):
if item == value_:
parentNode.values[i] = ndashkm
break
else:
if not node_.check_leaf:
ndashp0 = ndash.keys.pop(0)
ndashk0 = ndash.values.pop(0)
node_.keys = node_.keys + [ndashp0]
node_.values = node_.values + [value_]
parentNode = node_.parent
for i, item in enumerate(parentNode.values):
if item == value_:
parentNode.values[i] = ndashk0
break
else:
ndashp0 = ndash.keys.pop(0)
ndashk0 = ndash.values.pop(0)
node_.keys = node_.keys + [ndashp0]
node_.values = node_.values + [ndashk0]
parentNode = node_.parent
for i, item in enumerate(parentNode.values):
if item == value_:
parentNode.values[i] = ndash.values
break

if not ndash.check_leaf:
for j in ndash.keys:
j.parent = ndash
if not node_.check_leaf:
for j in node_.keys:
j.parent = node_
if not parentNode.check_leaf:
for j in parentNode.keys:
j.parent = parentNode

/* Print the tree */
def printTree(tree):
lst = [tree.root]
level = 
leaf = None
flag = 0
lev_leaf = 0

node1 = Node(str(level) + str(tree.root.values))

while (len(lst) != 0):
x = lst.pop(0)
lev = level.pop(0)
if (x.check_leaf == False):
for i, item in enumerate(x.keys):
print(item.values)
else:
for i, item in enumerate(x.keys):
print(item.values)
if (flag == 0):
lev_leaf = lev
leaf = x
flag = 1

record_len = 3
bplustree = BplusTree(record_len)
bplustree.insert('5', '33')
bplustree.insert('15', '21')
bplustree.insert('25', '31')
bplustree.insert('35', '41')
bplustree.insert('45', '10')

printTree(bplustree)

if(bplustree.find('5', '34')):
print("Found")
else:
print("Not found")```

```

## Search Complexity

### Time Complexity

If linear search is implemented inside a node, then total complexity is Θ(logt n).

If binary search is used, then total complexity is Θ(log2t.logt n).

## B+ Tree Applications

• Multilevel Indexing
• Faster operations on the tree (insertion, deletion, search)
• Database indexing

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