Redis中的跳表和B树结构（redis 跳表 b 树）

Redis中的跳表和B树结构

Redis是一种高性能的键值存储系统，它使用了多种数据结构来实现不同的数据存储需求。跳表和B树是两种常用的数据结构，在Redis中它们都被广泛应用于存储和查找操作中。

跳表

跳表（Skip List）是一种有序的数据结构，它的基本思路是在单向链表上增加多级索引。每级索引都跨度比前一级大，对于查找、插入和删除等操作，使用索引可以加速访问链表中的元素，提高了效率。以下是一个简单的跳表实现：

“`python

import random

class Node:

def __init__(self, value=None, level=0):

self.value = value

self.level = level

self.forward = [None]*(level+1)

class SkipList:

def __init__(self):

self.head = Node()

self.max_level = 0

def random_level(self):

level = 0

while random.random()

level += 1

return level

def insert(self, value):

level = self.random_level()

node = Node(value, level)

update = [None]*(level+1)

curr = self.head

for i in range(self.max_level, -1, -1):

while curr.forward[i] and curr.forward[i].value

curr = curr.forward[i]

update[i] = curr

for i in range(level+1):

node.forward[i] = update[i].forward[i]

update[i].forward[i] = node

if level > self.max_level:

self.max_level = level

def search(self, value):

curr = self.head

for i in range(self.max_level, -1, -1):

while curr.forward[i] and curr.forward[i].value

curr = curr.forward[i]

if curr.forward[0] and curr.forward[0].value == value:

return curr.forward[0]

return None

def delete(self, value):

update = [None]*(self.max_level+1)

curr = self.head

for i in range(self.max_level, -1, -1):

while curr.forward[i] and curr.forward[i].value

curr = curr.forward[i]

update[i] = curr

if curr.forward[0] and curr.forward[0].value == value:

for i in range(self.max_level+1):

if update[i].forward[i] != curr.forward[i]:

break

update[i].forward[i] = curr.forward[i]

while self.max_level > 0 and self.head.forward[self.max_level] is None:

self.max_level -= 1

def display(self):

for i in range(self.max_level, -1, -1):

print(“Level {}: “.format(i), end=” “)

node = self.head

while node.forward[i] is not None:

print(node.forward[i].value, end=” “)

node = node.forward[i]

print(“”)

if __name__ == ‘__mn__’:

sl = SkipList()

sl.insert(1)

sl.insert(2)

sl.insert(3)

sl.insert(4)

sl.insert(5)

sl.display()

sl.delete(3)

sl.display()


在这个实现中，索引的数量是随机的，但是随着元素的增多，索引的数量也会增加。跳表的时间复杂度为O(logn)，与二分查找相同。跳表可以解决一些常见的数据存储问题，比如有序链表的查找、插入和删除操作，但是它的空间复杂度较高，因为需要维护多级索引。

B树

B树也是一种多级索引的数据结构，它与跳表的不同之处在于，它是一个平衡树，每个节点可以包含多个元素，而不仅仅是一个。B树的每个节点都包含了一个元素数组和一个指向子节点的指针数组，每个节点中的元素都按照从小到大的顺序排列。以下是一个简单的B树实现：

```python
class Node:
    def __init__(self, t):
        self.t = t
        self.keys = []
        self.children = []
    def traverse(self):
        n = len(self.keys)
        for i in range(n):
            if i 
                self.children[i].traverse()
            print(self.keys[i], end=" ")
        if len(self.children) > n:
            self.children[n].traverse()
    def search(self, key):
        i = 0
        n = len(self.keys)
        while i  self.keys[i]:
            i += 1
        if i 
            return self
        if len(self.children) > i:
            return self.children[i].search(key)
        return None
    def insert(self, key):
        n = len(self.keys)
        i = n - 1
        if self.is_leaf():
            while i >= 0 and self.keys[i] > key:
                self.keys[i+1] = self.keys[i]
                i -= 1
            self.keys[i+1] = key
        else:
            while i >= 0 and self.keys[i] > key:
                i -= 1
            if len(self.children[i+1].keys) == (2*self.t - 1):
                self.split_child(i+1, self.children[i+1])
                if self.keys[i+1] 
                    i += 1
            self.children[i+1].insert(key)
    def split_child(self, i, y):
        z = Node(y.t)
        self.children.insert(i+1, z)
        self.keys.insert(i, y.keys[y.t-1])
        z.keys = y.keys[y.t:(2*y.t-1)]
        y.keys = y.keys[0:(y.t-1)]
        if not y.is_leaf():
            z.children = y.children[y.t:(2*y.t)]
            y.children = y.children[0:(y.t-1)]

    def is_leaf(self):
        return len(self.children) == 0
class BTree:
    def __init__(self, t):
        self.t = t
        self.root = Node(t)
    def traverse(self):
        if self.root is not None:
            self.root.traverse()

    def search(self, key):
        return None if self.root is None else self.root.search(key)
    def insert(self, key):
        if self.root is None:
            self.root = Node(self.t)
            self.root.keys.append(key)
        else:
            if len(self.root.keys) == (2*self.t - 1):
                s = Node(self.t)
                s.children.append(self.root)
                s.split_child(0, self.root)
                i = 0 if s.keys[0] 
                s.children[i].insert(key)
                self.root = s
            else:
                self.root.insert(key)
if __name__ == '__mn__':
    t = int(input("Enter value of t: "))
    btree = BTree(t)
    btree.insert(1)
    btree.insert(3)
    btree.insert(7)
    btree.insert(10)
    btree.insert(11)
    btree.insert(13)
    btree.insert(14)
    btree.insert(15)
    btree.insert(18)
    btree.insert(16)
    btree.insert(19)
    print("Traversal of the constructed tree:")
    btree.traverse()