LRU 算法 All In One

LRU (最近最少使用) 缓存机制

least recently used cache algorithm

/**
 * @param {number} capacity
 */
var LRUCache = function(capacity) {
  this.capacity = capacity;
  this.map = new Map();
};

/** 
 * @param {number} key
 * @return {number}
 */
LRUCache.prototype.get = function(key) {
  if(!this.map.has(key)) {
    return -1;
  }
  const value = this.map.get(key);
  // 更新
  this.put(key, value);
  return value;
};

/** 
 * @param {number} key 
 * @param {number} value
 * @return {void}
 */
LRUCache.prototype.put = function(key, value) {
  if(this.map.has(key)) {
    this.map.delete(key)
  }
  if(this.map.size === this.capacity) {
    // 空间满了，删除第一个 `.next()` ✅
    const oldKey = this.map.keys().next.value;
    this.map.delete(key)
  }
  this.map.set(key, value);
};

/**
 * Your LRUCache object will be instantiated and called as such:
 * var obj = new LRUCache(capacity)
 * var param_1 = obj.get(key)
 * obj.put(key,value)
 */

.next() iterators

leetcode

https://leetcode.com/problems/lru-cache/

https://leetcode-cn.com/problems/lru-cache/

/**
 * @param {number} capacity
 */
var LRUCache = function(capacity) {
  this.capacity = capacity;
  this.map = new Map();
  // 换成数组， 使用索引从后往前找到第一个不是 last obj？
  // this.last = -1;
};

/** 
 * @param {number} key
 * @return {number}
 */
LRUCache.prototype.get = function(key) {
  // ✅ 优先处理代码少的逻辑，错误优先
  if(!this.map.has(key)) {
    return -1;
  } else {
    const value = this.map.get(key)
    // 读取后，更新顺序
    this.put(key, value);
    return value;
  }
};

/** 
 * @param {number} key 
 * @param {number} value
 * @return {void}
 */
LRUCache.prototype.put = function(key, value) {
  // 替换式更新，先删除
  if(this.map.has(key)) {
    this.map.delete(key);
  }
  // 达到容量限制，先删除最近最少使用的 map 第一个元素
  // if(this.map.size === this.capacity) {
  //   const oldKey = this.map.keys().next().value;
  //   this.map.delete(oldKey);
  // }
  // 再重新添加
  this.map.set(key, value);
  // 先添加，再判断是否达到容量限制
  if(this.map.size > this.capacity) {
    // 删除 old key （删除最近最少使用的 map 第一个元素）
    this.map.delete(this.map.keys().next().value);
  }
};

/** 
 * Your LRUCache object will be instantiated and called as such:
 * var obj = new LRUCache(capacity)
 * var param_1 = obj.get(key)
 * obj.put(key,value)
 */

// 最近最少使用 (LRU) 缓存

Double LinkedList / 双向链表

Cache replacement policies

缓存替换策略

https://en.wikipedia.org/wiki/Cache_replacement_policies

https://zh.wikipedia.org/wiki/快取文件置換機制

CPU cache

L1 D Cache L1 I Cache, 分别是 L1 Data Cache 和 L1 Instruction Cache

CPU Caches and Pipelining

A modern CPU has a lot of different CPU caches, which are fast and quite small compared to our large main memory (RAM).
The following picture gives you a high level overview about the various caches that are part of a modern CPU.

https://www.sqlpassion.at/archive/2018/01/06/understanding-the-meltdown-exploit-in-my-own-simple-words/