LRU 算法

 LRU算法

很多Cache都支持LRU（Least Recently Used）算法，LRU算法的设计原则是：如果一个数据在最近一段时间没有被访问到，那么在将来它被访问的可能性也很小。也就是说，当限定的空间已存满数据时，应当把最久没有被访问到的数据淘汰。

LRU Cache一般支持两个操作：

• get(key)，如果key在cache中，则返回对应的value值，否则返回-1；
• set(key,value)，如果key在cache中，则重置value的值；如果key不在cache中，则将该(key,value)插入cache中（注意，如果cache已满，则必须把最近最久未使用的元素从cache中删除）；

而用什么数据结构来实现LRU算法呢？最常见的实现是使用一个链表保存缓存数据，如下图：

 

 算法如下：

1. 新数据插入到链表头部；
2. 每当缓存命中（即缓存数据被访问），则将数据移到链表头部；
3. 当链表满的时候，将链表尾部的数据丢弃。

这种链表结构实现简单，但效率不高，每次请求时都需要遍历链表，需要O(N)的复杂度；下面考虑一种更复杂的实现方式。

使用Hash表+双向链表的实现：hash表保证get操作在O(1)时间复杂度完成，双向链表保证增加/删除操作在O(1)时间完成；

实现原理：

get方法：

• 如果hash表不存在，直接返回；
• 若存在，则将这个key从双链表移动到头部；

set方法：

• 如果hash表不存在，写入hash表，并写入双链表头部；
• 若存在，则将这个key从双链表移动到头部；

一个C++实现版本

#include <algorithm>
#include <cstdint>
#include <list>
#include <mutex>
#include <stdexcept>
#include <thread>
#include <unordered_map>

/*
 * a noop lockable concept that can be used in place of std::mutex
 */
class NullLock {
 public:
  void lock() {}
  void unlock() {}
  bool try_lock() { return true; }
};

/**
 * error raised when a key not in cache is passed to get()
 */
class KeyNotFound : public std::invalid_argument {
 public:
  KeyNotFound() : std::invalid_argument("key_not_found") {}
};

template <typename K, typename V>
struct KeyValuePair {
 public:
  K key;
  V value;

  KeyValuePair(const K& k, const V& v) : key(k), value(v) {}
};

/**
 *  The LRUCache class templated by
 *      Key - key type
 *      Value - value type
 *      MapType - an associative container like std::unordered_map
 *      LockType - a lock type derived from the Lock class (default:
 *NullLock = no synchronization)
 *
 *  The default NullLock based template is not thread-safe, however passing
 *Lock=std::mutex will make it
 *  thread-safe
 */
template <class Key, class Value, class Lock = NullLock,
          class Map = std::unordered_map<
              Key, typename std::list<KeyValuePair<Key, Value>>::iterator>>
class LRUCache {
 public:
  typedef KeyValuePair<Key, Value> node_type;
  typedef std::list<KeyValuePair<Key, Value>> list_type;
  typedef Map map_type;
  typedef Lock lock_type;
  using Guard = std::lock_guard<lock_type>;
  /**
   * the maxSize is the soft limit of keys and (maxSize + elasticity) is the
   * hard limit
   * the cache is allowed to grow till (maxSize + elasticity) and is pruned back
   * to maxSize keys
   * set maxSize = 0 for an unbounded cache (but in that case, you're better off
   * using a std::unordered_map
   * directly anyway! :)
   */
  explicit LRUCache(size_t maxSize = 1024, size_t elasticity = 10)
      : maxSize_(maxSize), elasticity_(elasticity) {
    hit_count_ = 0;
    miss_count_ = 0;
  }
  virtual ~LRUCache() = default;
  size_t size() const {
    Guard g(lock_);
    return cache_.size();
  }
  bool empty() const {
    Guard g(lock_);
    return cache_.empty();
  }
void clear() {
    Guard g(lock_);
    cache_.clear();
    keys_.clear();
  }
  void insert(const Key& k, const Value& v) {
    Guard g(lock_);
    const auto iter = cache_.find(k);
    if (iter != cache_.end()) {
      iter->second->value = v;
      keys_.splice(keys_.begin(), keys_, iter->second);
      return;
    }

    keys_.emplace_front(k, v);
    cache_[k] = keys_.begin();
    prune();
  }
  bool tryGet(const Key& kIn, Value& vOut) {
    Guard g(lock_);
    const auto iter = cache_.find(kIn);
    if (iter == cache_.end()) {
      miss_count_++;
      return false;
    }
    keys_.splice(keys_.begin(), keys_, iter->second);
    vOut = iter->second->value;
    hit_count_++;
    return true;
  }
  /**
   *    The const reference returned here is only
   *    guaranteed to be valid till the next insert/delete
   */
  const Value& get(const Key& k) {
    Guard g(lock_);
    const auto iter = cache_.find(k);
    if (iter == cache_.end()) {
      miss_count_++;
      throw KeyNotFound();
    }
    keys_.splice(keys_.begin(), keys_, iter->second);
    hit_count_++;
    return iter->second->value;
  }
  /**
 * returns a copy of the stored object (if found)
   */
  Value getCopy(const Key& k) {
   return get(k);
  }
  bool remove(const Key& k) {
    Guard g(lock_);
    auto iter = cache_.find(k);
    if (iter == cache_.end()) {
      return false;
    }
    keys_.erase(iter->second);
    cache_.erase(iter);
    return true;
  }
  bool contains(const Key& k) const {
    Guard g(lock_);
    return cache_.find(k) != cache_.end();
  }

  void setMaxSize(size_t maxSize) { maxSize_ = maxSize; }
  size_t getMaxSize() const { return maxSize_; }
  size_t getElasticity() const { return elasticity_; }
  size_t getMaxAllowedSize() const { return maxSize_ + elasticity_; }
  template <typename F>
  void cwalk(F& f) const {
    Guard g(lock_);
    std::for_each(keys_.begin(), keys_.end(), f);
  }

 protected:
  size_t prune() {
    size_t maxAllowed = maxSize_ + elasticity_;
    if (maxSize_ == 0 || cache_.size() < maxAllowed) {
      return 0;
    }
    size_t count = 0;
    while (cache_.size() > maxSize_) {
      cache_.erase(keys_.back().key);
      keys_.pop_back();
      ++count;
    }
    return count;
  }

private:
  // Dissallow copying.
  LRUCache(const LRUCache&) = delete;
  LRUCache& operator=(const LRUCache&) = delete;

  mutable Lock lock_;
  Map cache_;
  list_type keys_;
  size_t maxSize_;
  size_t elasticity_;
  uint64_t hit_count_;
  uint64_t miss_count_;
};

一种改进的有预读的LRU：

https://mp.weixin.qq.com/s/nA6UHBh87U774vu4VvGhyw