search
HomeDatabaseMysql TutorialDetailed explanation of the principle of B+ tree and implementation of Python code

B trees are an advanced form of self-balancing trees where all values ​​exist at the leaf level. All leaves of the B-tree are at the same level, and the number of child nodes of each node is ≥ 2. The difference between B-tree and B-tree is that the nodes are not connected to each other on B-tree, but are connected to each other on B-tree.

B Tree multi-level index structure diagram

B+树原理 Python实现B+树详细代码

B Tree search rules

1. Start from the root node. Compare k with the key of the root node [k1,k2,k3,...k(m-1)]

2. If k

3. If k==k1, compare with K2. If k

4. If k>k2, continue to compare with k3, k4,...k(m-1), repeat steps 2 and 3

5 until k exists in the node, then return true, otherwise return false.

Python implements B-tree

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

    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]]

class BplusTree:
    def __init__(self, order):
        self.root = Node(order)
        self.root.check_leaf = True

    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[0], node1)

    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

    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

    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)

    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

    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[0]
            node_.keys[0].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[0]
                                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

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

    node1 = Node(str(level[0]) + 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(&#x27;5&#x27;, &#x27;33&#x27;)
bplustree.insert(&#x27;15&#x27;, &#x27;21&#x27;)
bplustree.insert(&#x27;25&#x27;, &#x27;31&#x27;)
bplustree.insert(&#x27;35&#x27;, &#x27;41&#x27;)
bplustree.insert(&#x27;45&#x27;, &#x27;10&#x27;)

printTree(bplustree)

if(bplustree.find(&#x27;5&#x27;, &#x27;34&#x27;)):
    print("Found")
else:
    print("Not found")

The above is the detailed content of Detailed explanation of the principle of B+ tree and implementation of Python code. For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:网易伏羲. If there is any infringement, please contact admin@php.cn delete
Explain the InnoDB Buffer Pool and its importance for performance.Explain the InnoDB Buffer Pool and its importance for performance.Apr 19, 2025 am 12:24 AM

InnoDBBufferPool reduces disk I/O by caching data and indexing pages, improving database performance. Its working principle includes: 1. Data reading: Read data from BufferPool; 2. Data writing: After modifying the data, write to BufferPool and refresh it to disk regularly; 3. Cache management: Use the LRU algorithm to manage cache pages; 4. Reading mechanism: Load adjacent data pages in advance. By sizing the BufferPool and using multiple instances, database performance can be optimized.

MySQL vs. Other Programming Languages: A ComparisonMySQL vs. Other Programming Languages: A ComparisonApr 19, 2025 am 12:22 AM

Compared with other programming languages, MySQL is mainly used to store and manage data, while other languages ​​such as Python, Java, and C are used for logical processing and application development. MySQL is known for its high performance, scalability and cross-platform support, suitable for data management needs, while other languages ​​have advantages in their respective fields such as data analytics, enterprise applications, and system programming.

Learning MySQL: A Step-by-Step Guide for New UsersLearning MySQL: A Step-by-Step Guide for New UsersApr 19, 2025 am 12:19 AM

MySQL is worth learning because it is a powerful open source database management system suitable for data storage, management and analysis. 1) MySQL is a relational database that uses SQL to operate data and is suitable for structured data management. 2) The SQL language is the key to interacting with MySQL and supports CRUD operations. 3) The working principle of MySQL includes client/server architecture, storage engine and query optimizer. 4) Basic usage includes creating databases and tables, and advanced usage involves joining tables using JOIN. 5) Common errors include syntax errors and permission issues, and debugging skills include checking syntax and using EXPLAIN commands. 6) Performance optimization involves the use of indexes, optimization of SQL statements and regular maintenance of databases.

MySQL: Essential Skills for Beginners to MasterMySQL: Essential Skills for Beginners to MasterApr 18, 2025 am 12:24 AM

MySQL is suitable for beginners to learn database skills. 1. Install MySQL server and client tools. 2. Understand basic SQL queries, such as SELECT. 3. Master data operations: create tables, insert, update, and delete data. 4. Learn advanced skills: subquery and window functions. 5. Debugging and optimization: Check syntax, use indexes, avoid SELECT*, and use LIMIT.

MySQL: Structured Data and Relational DatabasesMySQL: Structured Data and Relational DatabasesApr 18, 2025 am 12:22 AM

MySQL efficiently manages structured data through table structure and SQL query, and implements inter-table relationships through foreign keys. 1. Define the data format and type when creating a table. 2. Use foreign keys to establish relationships between tables. 3. Improve performance through indexing and query optimization. 4. Regularly backup and monitor databases to ensure data security and performance optimization.

MySQL: Key Features and Capabilities ExplainedMySQL: Key Features and Capabilities ExplainedApr 18, 2025 am 12:17 AM

MySQL is an open source relational database management system that is widely used in Web development. Its key features include: 1. Supports multiple storage engines, such as InnoDB and MyISAM, suitable for different scenarios; 2. Provides master-slave replication functions to facilitate load balancing and data backup; 3. Improve query efficiency through query optimization and index use.

The Purpose of SQL: Interacting with MySQL DatabasesThe Purpose of SQL: Interacting with MySQL DatabasesApr 18, 2025 am 12:12 AM

SQL is used to interact with MySQL database to realize data addition, deletion, modification, inspection and database design. 1) SQL performs data operations through SELECT, INSERT, UPDATE, DELETE statements; 2) Use CREATE, ALTER, DROP statements for database design and management; 3) Complex queries and data analysis are implemented through SQL to improve business decision-making efficiency.

MySQL for Beginners: Getting Started with Database ManagementMySQL for Beginners: Getting Started with Database ManagementApr 18, 2025 am 12:10 AM

The basic operations of MySQL include creating databases, tables, and using SQL to perform CRUD operations on data. 1. Create a database: CREATEDATABASEmy_first_db; 2. Create a table: CREATETABLEbooks(idINTAUTO_INCREMENTPRIMARYKEY, titleVARCHAR(100)NOTNULL, authorVARCHAR(100)NOTNULL, published_yearINT); 3. Insert data: INSERTINTObooks(title, author, published_year)VA

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Tools

SecLists

SecLists

SecLists is the ultimate security tester's companion. It is a collection of various types of lists that are frequently used during security assessments, all in one place. SecLists helps make security testing more efficient and productive by conveniently providing all the lists a security tester might need. List types include usernames, passwords, URLs, fuzzing payloads, sensitive data patterns, web shells, and more. The tester can simply pull this repository onto a new test machine and he will have access to every type of list he needs.

WebStorm Mac version

WebStorm Mac version

Useful JavaScript development tools

ZendStudio 13.5.1 Mac

ZendStudio 13.5.1 Mac

Powerful PHP integrated development environment

Safe Exam Browser

Safe Exam Browser

Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.