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drygrass
2025-11-18 23:41:04 +08:00
commit 89351183c3
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28
.gitignore vendored Normal file
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# CMake 生成目录
build/
CMakeFiles/
CMakeCache.txt
.cache/
.vscode/
.test
# CMake 生成的可执行文件和库
*.exe
*.out
*.a
*.so
*.dylib
*.lib
*.dll
# CMake 临时文件
*.cmake
Makefile
CMakeScripts/
# 其他常见生成文件
*.ninja
.ninja_deps
.ninja_log
compile_commands.json
install_manifest.txt

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CMakeLists.txt Normal file
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# 设置最低 CMake 版本要求
cmake_minimum_required(VERSION 3.10)
# 设置项目名称
project(KCacheSystem)
# 设置 C++ 标准
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED True)
# 指定源文件目录下的所有 .cpp 文件
file(GLOB SOURCES "*.cpp")
# 设置目标可执行文件
add_executable(main ${SOURCES})
# 清理中间的 .o 文件
set_target_properties(main PROPERTIES CLEAN_DIRECT_OUTPUT 1)
# 额外的编译选项（可根据需要启用）
# target_compile_options(main PRIVATE -Wall -Wextra -O2)

92
KArcCache/KArcCache.h Normal file
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#pragma once
#include "../KICachePolicy.h"
#include "KArcLruPart.h"
#include "KArcLfuPart.h"
#include <memory>
namespace KamaCache
{
template<typename Key, typename Value>
class KArcCache : public KICachePolicy<Key, Value>
{
public:
explicit KArcCache(size_t capacity = 10, size_t transformThreshold = 2)
: capacity_(capacity)
, transformThreshold_(transformThreshold)
, lruPart_(std::make_unique<ArcLruPart<Key, Value>>(capacity, transformThreshold))
, lfuPart_(std::make_unique<ArcLfuPart<Key, Value>>(capacity, transformThreshold))
{}
~KArcCache() override = default;
void put(Key key, Value value) override
{
checkGhostCaches(key);
// 检查 LFU 部分是否存在该键
bool inLfu = lfuPart_->contain(key);
// 更新 LRU 部分缓存
lruPart_->put(key, value);
// 如果 LFU 部分存在该键，则更新 LFU 部分
if (inLfu)
{
lfuPart_->put(key, value);
}
}
bool get(Key key, Value& value) override
{
checkGhostCaches(key);
bool shouldTransform = false;
if (lruPart_->get(key, value, shouldTransform))
{
if (shouldTransform)
{
lfuPart_->put(key, value);
}
return true;
}
return lfuPart_->get(key, value);
}
Value get(Key key) override
{
Value value{};
get(key, value);
return value;
}
private:
bool checkGhostCaches(Key key)
{
bool inGhost = false;
if (lruPart_->checkGhost(key))
{
if (lfuPart_->decreaseCapacity())
{
lruPart_->increaseCapacity();
}
inGhost = true;
}
else if (lfuPart_->checkGhost(key))
{
if (lruPart_->decreaseCapacity())
{
lfuPart_->increaseCapacity();
}
inGhost = true;
}
return inGhost;
}
private:
size_t capacity_;
size_t transformThreshold_;
std::unique_ptr<ArcLruPart<Key, Value>> lruPart_;
std::unique_ptr<ArcLfuPart<Key, Value>> lfuPart_;
};
} // namespace KamaCache

41
KArcCache/KArcCacheNode.h Normal file
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#pragma once
#include <memory>
namespace KamaCache
{
template<typename Key, typename Value>
class ArcNode
{
private:
Key key_;
Value value_;
size_t accessCount_;
std::weak_ptr<ArcNode> prev_;
std::shared_ptr<ArcNode> next_;
public:
ArcNode() : accessCount_(1), next_(nullptr) {}
ArcNode(Key key, Value value)
: key_(key)
, value_(value)
, accessCount_(1)
, next_(nullptr)
{}
// Getters
Key getKey() const { return key_; }
Value getValue() const { return value_; }
size_t getAccessCount() const { return accessCount_; }
// Setters
void setValue(const Value& value) { value_ = value; }
void incrementAccessCount() { ++accessCount_; }
template<typename K, typename V> friend class ArcLruPart;
template<typename K, typename V> friend class ArcLfuPart;
};
} // namespace KamaCache

231
KArcCache/KArcLfuPart.h Normal file
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#pragma once
#include "KArcCacheNode.h"
#include <unordered_map>
#include <map>
#include <mutex>
namespace KamaCache
{
template<typename Key, typename Value>
class ArcLfuPart
{
public:
using NodeType = ArcNode<Key, Value>;
using NodePtr = std::shared_ptr<NodeType>;
using NodeMap = std::unordered_map<Key, NodePtr>;
using FreqMap = std::map<size_t, std::list<NodePtr>>;
explicit ArcLfuPart(size_t capacity, size_t transformThreshold)
: capacity_(capacity)
, ghostCapacity_(capacity)
, transformThreshold_(transformThreshold)
, minFreq_(0)
{
initializeLists();
}
bool put(Key key, Value value)
{
if (capacity_ == 0)
return false;
std::lock_guard<std::mutex> lock(mutex_);
auto it = mainCache_.find(key);
if (it != mainCache_.end())
{
return updateExistingNode(it->second, value);
}
return addNewNode(key, value);
}
bool get(Key key, Value& value)
{
std::lock_guard<std::mutex> lock(mutex_);
auto it = mainCache_.find(key);
if (it != mainCache_.end())
{
updateNodeFrequency(it->second);
value = it->second->getValue();
return true;
}
return false;
}
bool contain(Key key)
{
return mainCache_.find(key) != mainCache_.end();
}
bool checkGhost(Key key)
{
auto it = ghostCache_.find(key);
if (it != ghostCache_.end())
{
removeFromGhost(it->second);
ghostCache_.erase(it);
return true;
}
return false;
}
void increaseCapacity() { ++capacity_; }
bool decreaseCapacity()
{
if (capacity_ <= 0) return false;
if (mainCache_.size() == capacity_)
{
evictLeastFrequent();
}
--capacity_;
return true;
}
private:
void initializeLists()
{
ghostHead_ = std::make_shared<NodeType>();
ghostTail_ = std::make_shared<NodeType>();
ghostHead_->next_ = ghostTail_;
ghostTail_->prev_ = ghostHead_;
}
bool updateExistingNode(NodePtr node, const Value& value)
{
node->setValue(value);
updateNodeFrequency(node);
return true;
}
bool addNewNode(const Key& key, const Value& value)
{
if (mainCache_.size() >= capacity_)
{
evictLeastFrequent();
}
NodePtr newNode = std::make_shared<NodeType>(key, value);
mainCache_[key] = newNode;
// 将新节点添加到频率为1的列表中
if (freqMap_.find(1) == freqMap_.end())
{
freqMap_[1] = std::list<NodePtr>();
}
freqMap_[1].push_back(newNode);
minFreq_ = 1;
return true;
}
void updateNodeFrequency(NodePtr node)
{
size_t oldFreq = node->getAccessCount();
node->incrementAccessCount();
size_t newFreq = node->getAccessCount();
// 从旧频率列表中移除
auto& oldList = freqMap_[oldFreq];
oldList.remove(node);
if (oldList.empty())
{
freqMap_.erase(oldFreq);
if (oldFreq == minFreq_)
{
minFreq_ = newFreq;
}
}
// 添加到新频率列表
if (freqMap_.find(newFreq) == freqMap_.end())
{
freqMap_[newFreq] = std::list<NodePtr>();
}
freqMap_[newFreq].push_back(node);
}
void evictLeastFrequent()
{
if (freqMap_.empty())
return;
// 获取最小频率的列表
auto& minFreqList = freqMap_[minFreq_];
if (minFreqList.empty())
return;
// 移除最少使用的节点
NodePtr leastNode = minFreqList.front();
minFreqList.pop_front();
// 如果该频率的列表为空，则删除该频率项
if (minFreqList.empty())
{
freqMap_.erase(minFreq_);
// 更新最小频率
if (!freqMap_.empty())
{
minFreq_ = freqMap_.begin()->first;
}
}
// 将节点移到幽灵缓存
if (ghostCache_.size() >= ghostCapacity_)
{
removeOldestGhost();
}
addToGhost(leastNode);
// 从主缓存中移除
mainCache_.erase(leastNode->getKey());
}
void removeFromGhost(NodePtr node)
{
if (!node->prev_.expired() && node->next_) {
auto prev = node->prev_.lock();
prev->next_ = node->next_;
node->next_->prev_ = node->prev_;
node->next_ = nullptr; // 清空指针，防止悬垂引用
}
}
void addToGhost(NodePtr node)
{
node->next_ = ghostTail_;
node->prev_ = ghostTail_->prev_;
if (!ghostTail_->prev_.expired()) {
ghostTail_->prev_.lock()->next_ = node;
}
ghostTail_->prev_ = node;
ghostCache_[node->getKey()] = node;
}
void removeOldestGhost()
{
NodePtr oldestGhost = ghostHead_->next_;
if (oldestGhost != ghostTail_)
{
removeFromGhost(oldestGhost);
ghostCache_.erase(oldestGhost->getKey());
}
}
private:
size_t capacity_;
size_t ghostCapacity_;
size_t transformThreshold_;
size_t minFreq_;
std::mutex mutex_;
NodeMap mainCache_;
NodeMap ghostCache_;
FreqMap freqMap_;
NodePtr ghostHead_;
NodePtr ghostTail_;
};
} // namespace KamaCache

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KArcCache/KArcLruPart.h Normal file
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#pragma once
#include "KArcCacheNode.h"
#include <unordered_map>
#include <mutex>
namespace KamaCache
{
template<typename Key, typename Value>
class ArcLruPart
{
public:
using NodeType = ArcNode<Key, Value>;
using NodePtr = std::shared_ptr<NodeType>;
using NodeMap = std::unordered_map<Key, NodePtr>;
explicit ArcLruPart(size_t capacity, size_t transformThreshold)
: capacity_(capacity)
, ghostCapacity_(capacity)
, transformThreshold_(transformThreshold)
{
initializeLists();
}
bool put(Key key, Value value)
{
if (capacity_ == 0) return false;
std::lock_guard<std::mutex> lock(mutex_);
auto it = mainCache_.find(key);
if (it != mainCache_.end())
{
return updateExistingNode(it->second, value);
}
return addNewNode(key, value);
}
bool get(Key key, Value& value, bool& shouldTransform)
{
std::lock_guard<std::mutex> lock(mutex_);
auto it = mainCache_.find(key);
if (it != mainCache_.end())
{
shouldTransform = updateNodeAccess(it->second);
value = it->second->getValue();
return true;
}
return false;
}
bool checkGhost(Key key)
{
auto it = ghostCache_.find(key);
if (it != ghostCache_.end()) {
removeFromGhost(it->second);
ghostCache_.erase(it);
return true;
}
return false;
}
void increaseCapacity() { ++capacity_; }
bool decreaseCapacity()
{
if (capacity_ <= 0) return false;
if (mainCache_.size() == capacity_) {
evictLeastRecent();
}
--capacity_;
return true;
}
private:
void initializeLists()
{
mainHead_ = std::make_shared<NodeType>();
mainTail_ = std::make_shared<NodeType>();
mainHead_->next_ = mainTail_;
mainTail_->prev_ = mainHead_;
ghostHead_ = std::make_shared<NodeType>();
ghostTail_ = std::make_shared<NodeType>();
ghostHead_->next_ = ghostTail_;
ghostTail_->prev_ = ghostHead_;
}
bool updateExistingNode(NodePtr node, const Value& value)
{
node->setValue(value);
moveToFront(node);
return true;
}
bool addNewNode(const Key& key, const Value& value)
{
if (mainCache_.size() >= capacity_)
{
evictLeastRecent(); // 驱逐最近最少访问
}
NodePtr newNode = std::make_shared<NodeType>(key, value);
mainCache_[key] = newNode;
addToFront(newNode);
return true;
}
bool updateNodeAccess(NodePtr node)
{
moveToFront(node);
node->incrementAccessCount();
return node->getAccessCount() >= transformThreshold_;
}
void moveToFront(NodePtr node)
{
// 先从当前位置移除
if (!node->prev_.expired() && node->next_) {
auto prev = node->prev_.lock();
prev->next_ = node->next_;
node->next_->prev_ = node->prev_;
node->next_ = nullptr; // 清空指针，防止悬垂引用
}
// 添加到头部
addToFront(node);
}
void addToFront(NodePtr node)
{
node->next_ = mainHead_->next_;
node->prev_ = mainHead_;
mainHead_->next_->prev_ = node;
mainHead_->next_ = node;
}
void evictLeastRecent()
{
NodePtr leastRecent = mainTail_->prev_.lock();
if (!leastRecent || leastRecent == mainHead_)
return;
// 从主链表中移除
removeFromMain(leastRecent);
// 添加到幽灵缓存
if (ghostCache_.size() >= ghostCapacity_)
{
removeOldestGhost();
}
addToGhost(leastRecent);
// 从主缓存映射中移除
mainCache_.erase(leastRecent->getKey());
}
void removeFromMain(NodePtr node)
{
if (!node->prev_.expired() && node->next_) {
auto prev = node->prev_.lock();
prev->next_ = node->next_;
node->next_->prev_ = node->prev_;
node->next_ = nullptr; // 清空指针，防止悬垂引用
}
}
void removeFromGhost(NodePtr node)
{
if (!node->prev_.expired() && node->next_) {
auto prev = node->prev_.lock();
prev->next_ = node->next_;
node->next_->prev_ = node->prev_;
node->next_ = nullptr; // 清空指针，防止悬垂引用
}
}
void addToGhost(NodePtr node)
{
// 重置节点的访问计数
node->accessCount_ = 1;
// 添加到幽灵缓存的头部
node->next_ = ghostHead_->next_;
node->prev_ = ghostHead_;
ghostHead_->next_->prev_ = node;
ghostHead_->next_ = node;
// 添加到幽灵缓存映射
ghostCache_[node->getKey()] = node;
}
void removeOldestGhost()
{
// 使用lock()方法，并添加null检查
NodePtr oldestGhost = ghostTail_->prev_.lock();
if (!oldestGhost || oldestGhost == ghostHead_)
return;
removeFromGhost(oldestGhost);
ghostCache_.erase(oldestGhost->getKey());
}
private:
size_t capacity_;
size_t ghostCapacity_;
size_t transformThreshold_; // 转换门槛值
std::mutex mutex_;
NodeMap mainCache_; // key -> ArcNode
NodeMap ghostCache_;
// 主链表
NodePtr mainHead_;
NodePtr mainTail_;
// 淘汰链表
NodePtr ghostHead_;
NodePtr ghostTail_;
};
} // namespace KamaCache

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KICachePolicy.h Normal file
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#pragma once
namespace KamaCache
{
template <typename Key, typename Value>
class KICachePolicy
{
public:
virtual ~KICachePolicy() {};
// 添加缓存接口
virtual void put(Key key, Value value) = 0;
// key是传入参数 访问到的值以传出参数的形式返回 | 访问成功返回true
virtual bool get(Key key, Value& value) = 0;
// 如果缓存中能找到key，则直接返回value
virtual Value get(Key key) = 0;
};
} // namespace KamaCache

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KLfuCache.h Normal file
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#pragma once
#include <cmath>
#include <memory>
#include <mutex>
#include <thread>
#include <unordered_map>
#include <vector>
#include "KICachePolicy.h"
namespace KamaCache
{
template<typename Key, typename Value> class KLfuCache;
template<typename Key, typename Value>
class FreqList
{
private:
struct Node
{
int freq; // 访问频次
Key key;
Value value;
std::weak_ptr<Node> pre; // 上一结点改为weak_ptr打破循环引用
std::shared_ptr<Node> next;
Node()
: freq(1), next(nullptr) {}
Node(Key key, Value value)
: freq(1), key(key), value(value), next(nullptr) {}
};
using NodePtr = std::shared_ptr<Node>;
int freq_; // 访问频率
NodePtr head_; // 假头结点
NodePtr tail_; // 假尾结点
public:
explicit FreqList(int n)
: freq_(n)
{
head_ = std::make_shared<Node>();
tail_ = std::make_shared<Node>();
head_->next = tail_;
tail_->pre = head_;
}
bool isEmpty() const
{
return head_->next == tail_;
}
// 提那家结点管理方法
void addNode(NodePtr node)
{
if (!node || !head_ || !tail_)
return;
node->pre = tail_->pre;
node->next = tail_;
tail_->pre.lock()->next = node; // 使用lock()获取shared_ptr
tail_->pre = node;
}
void removeNode(NodePtr node)
{
if (!node || !head_ || !tail_)
return;
if (node->pre.expired() || !node->next)
return;
auto pre = node->pre.lock(); // 使用lock()获取shared_ptr
pre->next = node->next;
node->next->pre = pre;
node->next = nullptr; // 确保显式置空next指针，彻底断开节点与链表的连接
}
NodePtr getFirstNode() const { return head_->next; }
friend class KLfuCache<Key, Value>;
};
template <typename Key, typename Value>
class KLfuCache : public KICachePolicy<Key, Value>
{
public:
using Node = typename FreqList<Key, Value>::Node;
using NodePtr = std::shared_ptr<Node>;
using NodeMap = std::unordered_map<Key, NodePtr>;
KLfuCache(int capacity, int maxAverageNum = 1000000)
: capacity_(capacity), minFreq_(INT8_MAX), maxAverageNum_(maxAverageNum),
curAverageNum_(0), curTotalNum_(0)
{}
~KLfuCache() override = default;
void put(Key key, Value value) override
{
if (capacity_ == 0)
return;
std::lock_guard<std::mutex> lock(mutex_);
auto it = nodeMap_.find(key);
if (it != nodeMap_.end())
{
// 重置其value值
it->second->value = value;
// 找到了直接调整就好了，不用再去get中再找一遍，但其实影响不大
getInternal(it->second, value);
return;
}
putInternal(key, value);
}
// value值为传出参数
bool get(Key key, Value& value) override
{
std::lock_guard<std::mutex> lock(mutex_);
auto it = nodeMap_.find(key);
if (it != nodeMap_.end())
{
getInternal(it->second, value);
return true;
}
return false;
}
Value get(Key key) override
{
Value value;
get(key, value);
return value;
}
// 清空缓存,回收资源
void purge()
{
nodeMap_.clear();
freqToFreqList_.clear();
}
private:
void putInternal(Key key, Value value); // 添加缓存
void getInternal(NodePtr node, Value& value); // 获取缓存
void kickOut(); // 移除缓存中的过期数据
void removeFromFreqList(NodePtr node); // 从频率列表中移除节点
void addToFreqList(NodePtr node); // 添加到频率列表
void addFreqNum(); // 增加平均访问等频率
void decreaseFreqNum(int num); // 减少平均访问等频率
void handleOverMaxAverageNum(); // 处理当前平均访问频率超过上限的情况
void updateMinFreq();
private:
int capacity_; // 缓存容量
int minFreq_; // 最小访问频次(用于找到最小访问频次结点)
int maxAverageNum_; // 最大平均访问频次
int curAverageNum_; // 当前平均访问频次
int curTotalNum_; // 当前访问所有缓存次数总数
std::mutex mutex_; // 互斥锁
NodeMap nodeMap_; // key 到 缓存节点的映射
std::unordered_map<int, FreqList<Key, Value>*> freqToFreqList_;// 访问频次到该频次链表的映射
};
template<typename Key, typename Value>
void KLfuCache<Key, Value>::getInternal(NodePtr node, Value& value)
{
// 找到之后需要将其从低访问频次的链表中删除，并且添加到+1的访问频次链表中，
// 访问频次+1, 然后把value值返回
value = node->value;
// 从原有访问频次的链表中删除节点
removeFromFreqList(node);
node->freq++;
addToFreqList(node);
// 如果当前node的访问频次如果等于minFreq+1，并且其前驱链表为空，则说明
// freqToFreqList_[node->freq - 1]链表因node的迁移已经空了，需要更新最小访问频次
if (node->freq - 1 == minFreq_ && freqToFreqList_[node->freq - 1]->isEmpty())
minFreq_++;
// 总访问频次和当前平均访问频次都随之增加
addFreqNum();
}
template<typename Key, typename Value>
void KLfuCache<Key, Value>::putInternal(Key key, Value value)
{
// 如果不在缓存中，则需要判断缓存是否已满
if (nodeMap_.size() == capacity_)
{
// 缓存已满，删除最不常访问的结点，更新当前平均访问频次和总访问频次
kickOut();
}
// 创建新结点，将新结点添加进入，更新最小访问频次
NodePtr node = std::make_shared<Node>(key, value);
nodeMap_[key] = node;
addToFreqList(node);
addFreqNum();
minFreq_ = std::min(minFreq_, 1);
}
template<typename Key, typename Value>
void KLfuCache<Key, Value>::kickOut()
{
NodePtr node = freqToFreqList_[minFreq_]->getFirstNode();
removeFromFreqList(node);
nodeMap_.erase(node->key);
decreaseFreqNum(node->freq);
}
template<typename Key, typename Value>
void KLfuCache<Key, Value>::removeFromFreqList(NodePtr node)
{
// 检查结点是否为空
if (!node)
return;
auto freq = node->freq;
freqToFreqList_[freq]->removeNode(node);
}
template<typename Key, typename Value>
void KLfuCache<Key, Value>::addToFreqList(NodePtr node)
{
// 检查结点是否为空
if (!node)
return;
// 添加进入相应的频次链表前需要判断该频次链表是否存在
auto freq = node->freq;
if (freqToFreqList_.find(node->freq) == freqToFreqList_.end())
{
// 不存在则创建
freqToFreqList_[node->freq] = new FreqList<Key, Value>(node->freq);
}
freqToFreqList_[freq]->addNode(node);
}
template<typename Key, typename Value>
void KLfuCache<Key, Value>::addFreqNum()
{
curTotalNum_++;
if (nodeMap_.empty())
curAverageNum_ = 0;
else
curAverageNum_ = curTotalNum_ / nodeMap_.size();
if (curAverageNum_ > maxAverageNum_)
{
handleOverMaxAverageNum();
}
}
template<typename Key, typename Value>
void KLfuCache<Key, Value>::decreaseFreqNum(int num)
{
// 减少平均访问频次和总访问频次
curTotalNum_ -= num;
if (nodeMap_.empty())
curAverageNum_ = 0;
else
curAverageNum_ = curTotalNum_ / nodeMap_.size();
}
template<typename Key, typename Value>
void KLfuCache<Key, Value>::handleOverMaxAverageNum()
{
if (nodeMap_.empty())
return;
// 当前平均访问频次已经超过了最大平均访问频次，所有结点的访问频次- (maxAverageNum_ / 2)
for (auto it = nodeMap_.begin(); it != nodeMap_.end(); ++it)
{
// 检查结点是否为空
if (!it->second)
continue;
NodePtr node = it->second;
// 先从当前频率列表中移除
removeFromFreqList(node);
// 减少频率
node->freq -= maxAverageNum_ / 2;
if (node->freq < 1) node->freq = 1;
// 添加到新的频率列表
addToFreqList(node);
}
// 更新最小频率
updateMinFreq();
}
template<typename Key, typename Value>
void KLfuCache<Key, Value>::updateMinFreq()
{
minFreq_ = INT8_MAX;
for (const auto& pair : freqToFreqList_)
{
if (pair.second && !pair.second->isEmpty())
{
minFreq_ = std::min(minFreq_, pair.first);
}
}
if (minFreq_ == INT8_MAX)
minFreq_ = 1;
}
// 并没有牺牲空间换时间，他是把原有缓存大小进行了分片。
template<typename Key, typename Value>
class KHashLfuCache
{
public:
KHashLfuCache(size_t capacity, int sliceNum, int maxAverageNum = 10)
: sliceNum_(sliceNum > 0 ? sliceNum : std::thread::hardware_concurrency())
, capacity_(capacity)
{
size_t sliceSize = std::ceil(capacity_ / static_cast<double>(sliceNum_)); // 每个lfu分片的容量
for (int i = 0; i < sliceNum_; ++i)
{
lfuSliceCaches_.emplace_back(new KLfuCache<Key, Value>(sliceSize, maxAverageNum));
}
}
void put(Key key, Value value)
{
// 根据key找出对应的lfu分片
size_t sliceIndex = Hash(key) % sliceNum_;
lfuSliceCaches_[sliceIndex]->put(key, value);
}
bool get(Key key, Value& value)
{
// 根据key找出对应的lfu分片
size_t sliceIndex = Hash(key) % sliceNum_;
return lfuSliceCaches_[sliceIndex]->get(key, value);
}
Value get(Key key)
{
Value value;
get(key, value);
return value;
}
// 清除缓存
void purge()
{
for (auto& lfuSliceCache : lfuSliceCaches_)
{
lfuSliceCache->purge();
}
}
private:
// 将key计算成对应哈希值
size_t Hash(Key key)
{
std::hash<Key> hashFunc;
return hashFunc(key);
}
private:
size_t capacity_; // 缓存总容量
int sliceNum_; // 缓存分片数量
std::vector<std::unique_ptr<KLfuCache<Key, Value>>> lfuSliceCaches_; // 缓存lfu分片容器
};
} // namespace KamaCache

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#pragma once
#include <cstring>
#include <list>
#include <memory>
#include <mutex>
#include <unordered_map>
#include "KICachePolicy.h"
namespace KamaCache
{
// 前向声明
template<typename Key, typename Value> class KLruCache;
template<typename Key, typename Value>
class LruNode
{
private:
Key key_;
Value value_;
size_t accessCount_; // 访问次数
std::weak_ptr<LruNode<Key, Value>> prev_; // 改为weak_ptr打破循环引用
std::shared_ptr<LruNode<Key, Value>> next_;
public:
LruNode(Key key, Value value)
: key_(key)
, value_(value)
, accessCount_(1)
{}
// 提供必要的访问器
Key getKey() const { return key_; }
Value getValue() const { return value_; }
void setValue(const Value& value) { value_ = value; }
size_t getAccessCount() const { return accessCount_; }
void incrementAccessCount() { ++accessCount_; }
friend class KLruCache<Key, Value>;
};
template<typename Key, typename Value>
class KLruCache : public KICachePolicy<Key, Value>
{
public:
using LruNodeType = LruNode<Key, Value>;
using NodePtr = std::shared_ptr<LruNodeType>;
using NodeMap = std::unordered_map<Key, NodePtr>;
KLruCache(int capacity)
: capacity_(capacity)
{
initializeList();
}
~KLruCache() override = default;
// 添加缓存
void put(Key key, Value value) override
{
if (capacity_ <= 0)
return;
std::lock_guard<std::mutex> lock(mutex_);
auto it = nodeMap_.find(key);
if (it != nodeMap_.end())
{
// 如果在当前容器中,则更新value,并调用get方法，代表该数据刚被访问
updateExistingNode(it->second, value);
return ;
}
addNewNode(key, value);
}
bool get(Key key, Value& value) override
{
std::lock_guard<std::mutex> lock(mutex_);
auto it = nodeMap_.find(key);
if (it != nodeMap_.end())
{
moveToMostRecent(it->second);
value = it->second->getValue();
return true;
}
return false;
}
Value get(Key key) override
{
Value value{};
// memset(&value, 0, sizeof(value)); // memset 是按字节设置内存的，对于复杂类型（如 string）使用 memset 可能会破坏对象的内部结构
get(key, value);
return value;
}
// 删除指定元素
void remove(Key key)
{
std::lock_guard<std::mutex> lock(mutex_);
auto it = nodeMap_.find(key);
if (it != nodeMap_.end())
{
removeNode(it->second);
nodeMap_.erase(it);
}
}
private:
void initializeList()
{
// 创建首尾虚拟节点
dummyHead_ = std::make_shared<LruNodeType>(Key(), Value());
dummyTail_ = std::make_shared<LruNodeType>(Key(), Value());
dummyHead_->next_ = dummyTail_;
dummyTail_->prev_ = dummyHead_;
}
void updateExistingNode(NodePtr node, const Value& value)
{
node->setValue(value);
moveToMostRecent(node);
}
void addNewNode(const Key& key, const Value& value)
{
if (nodeMap_.size() >= capacity_)
{
evictLeastRecent();
}
NodePtr newNode = std::make_shared<LruNodeType>(key, value);
insertNode(newNode);
nodeMap_[key] = newNode;
}
// 将该节点移动到最新的位置
void moveToMostRecent(NodePtr node)
{
removeNode(node);
insertNode(node);
}
void removeNode(NodePtr node)
{
if(!node->prev_.expired() && node->next_)
{
auto prev = node->prev_.lock(); // 使用lock()获取shared_ptr
prev->next_ = node->next_;
node->next_->prev_ = prev;
node->next_ = nullptr; // 清空next_指针，彻底断开节点与链表的连接
}
}
// 从尾部插入结点
void insertNode(NodePtr node)
{
node->next_ = dummyTail_;
node->prev_ = dummyTail_->prev_;
dummyTail_->prev_.lock()->next_ = node; // 使用lock()获取shared_ptr
dummyTail_->prev_ = node;
}
// 驱逐最近最少访问
void evictLeastRecent()
{
NodePtr leastRecent = dummyHead_->next_;
removeNode(leastRecent);
nodeMap_.erase(leastRecent->getKey());
}
private:
int capacity_; // 缓存容量
NodeMap nodeMap_; // key -> Node
std::mutex mutex_;
NodePtr dummyHead_; // 虚拟头结点
NodePtr dummyTail_;
};
// LRU优化：Lru-k版本。 通过继承的方式进行再优化
template<typename Key, typename Value>
class KLruKCache : public KLruCache<Key, Value>
{
public:
KLruKCache(int capacity, int historyCapacity, int k)
: KLruCache<Key, Value>(capacity) // 调用基类构造
, historyList_(std::make_unique<KLruCache<Key, size_t>>(historyCapacity))
, k_(k)
{}
Value get(Key key)
{
// 首先尝试从主缓存获取数据
Value value{};
bool inMainCache = KLruCache<Key, Value>::get(key, value);
// 获取并更新访问历史计数
size_t historyCount = historyList_->get(key);
historyCount++;
historyList_->put(key, historyCount);
// 如果数据在主缓存中，直接返回
if (inMainCache)
{
return value;
}
// 如果数据不在主缓存，但访问次数达到了k次
if (historyCount >= k_)
{
// 检查是否有历史值记录
auto it = historyValueMap_.find(key);
if (it != historyValueMap_.end())
{
// 有历史值，将其添加到主缓存
Value storedValue = it->second;
// 从历史记录移除
historyList_->remove(key);
historyValueMap_.erase(it);
// 添加到主缓存
KLruCache<Key, Value>::put(key, storedValue);
return storedValue;
}
// 没有历史值记录，无法添加到缓存，返回默认值
}
// 数据不在主缓存且不满足添加条件，返回默认值
return value;
}
void put(Key key, Value value)
{
// 检查是否已在主缓存
Value existingValue{};
bool inMainCache = KLruCache<Key, Value>::get(key, existingValue);
if (inMainCache)
{
// 已在主缓存，直接更新
KLruCache<Key, Value>::put(key, value);
return;
}
// 获取并更新访问历史
size_t historyCount = historyList_->get(key);
historyCount++;
historyList_->put(key, historyCount);
// 保存值到历史记录映射，供后续get操作使用
historyValueMap_[key] = value;
// 检查是否达到k次访问阈值
if (historyCount >= k_)
{
// 达到阈值，添加到主缓存
historyList_->remove(key);
historyValueMap_.erase(key);
KLruCache<Key, Value>::put(key, value);
}
}
private:
int k_; // 进入缓存队列的评判标准
std::unique_ptr<KLruCache<Key, size_t>> historyList_; // 访问数据历史记录(value为访问次数)
std::unordered_map<Key, Value> historyValueMap_; // 存储未达到k次访问的数据值
};
// lru优化：对lru进行分片，提高高并发使用的性能
template<typename Key, typename Value>
class KHashLruCaches
{
public:
KHashLruCaches(size_t capacity, int sliceNum)
: capacity_(capacity)
, sliceNum_(sliceNum > 0 ? sliceNum : std::thread::hardware_concurrency())
{
size_t sliceSize = std::ceil(capacity / static_cast<double>(sliceNum_)); // 获取每个分片的大小
for (int i = 0; i < sliceNum_; ++i)
{
lruSliceCaches_.emplace_back(new KLruCache<Key, Value>(sliceSize));
}
}
void put(Key key, Value value)
{
// 获取key的hash值，并计算出对应的分片索引
size_t sliceIndex = Hash(key) % sliceNum_;
lruSliceCaches_[sliceIndex]->put(key, value);
}
bool get(Key key, Value& value)
{
// 获取key的hash值，并计算出对应的分片索引
size_t sliceIndex = Hash(key) % sliceNum_;
return lruSliceCaches_[sliceIndex]->get(key, value);
}
Value get(Key key)
{
Value value;
memset(&value, 0, sizeof(value));
get(key, value);
return value;
}
private:
// 将key转换为对应hash值
size_t Hash(Key key)
{
std::hash<Key> hashFunc;
return hashFunc(key);
}
private:
size_t capacity_; // 总容量
int sliceNum_; // 切片数量
std::vector<std::unique_ptr<KLruCache<Key, Value>>> lruSliceCaches_; // 切片LRU缓存
};
} // namespace KamaCache

674
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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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# KamaCache
> ⭐️ 本项目为[【代码随想录知识星球】](https://programmercarl.com/other/kstar.html) 教学项目
> ⭐️ 在 [缓存项目文档](https://www.programmercarl.com/other/project_huancun.html) 里详细讲解：**项目前置知识 + 项目细节 + 代码解读 + 项目难点 + 面试题与回答 + 简历写法 + 项目拓展**。 全面帮助你用这个项目求职面试！
## 项目介绍
本项目使用多个页面替换策略实现一个线程安全的缓存：
- LRU：最近最久未使用
- LFU：最近不经常使用
- ARC：自适应替换
对于LRU和LFU策略，我在其基础的缓存策略上进行了相应的优化，例如：
- LRU优化：
- LRU分片：对多线程下的高并发访问有性能上的优化
- LRU-k：一定程度上防止热点数据被冷数据挤出容器而造成缓存污染等问题
- LFU优化：
- LFU分片：对多线程下的高并发访问有性能上的优化
- 引入最大平均访问频次：解决过去的热点数据最近一直没被访问，却仍占用缓存等问题
## 系统环境
```
Ubuntu 22.04 LTS
```
## 编译
创建一个build文件夹并进入
```
mkdir build && cd build
```
生成构建文件
```
cmake ..
```
构建项目
```
make
```
如果要清理生成的可执行文件
```
make clean
```
## 运行
```
./main
```
## 测试结果
不同缓存策略缓存命中率测试对比结果如下：
（ps: 该测试代码只是尽可能地模拟真实的访问场景，但是跟真实的场景仍存在一定差距，测试结果仅供参考。）
![alt text](images/hitTest.jpg)

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#include <iostream>
#include <string>
#include <chrono>
#include <vector>
#include <iomanip>
#include <random>
#include <algorithm>
#include <array>
#include "KICachePolicy.h"
#include "KLfuCache.h"
#include "KLruCache.h"
#include "KArcCache/KArcCache.h"
class Timer {
public:
Timer() : start_(std::chrono::high_resolution_clock::now()) {}
double elapsed() {
auto now = std::chrono::high_resolution_clock::now();
return std::chrono::duration_cast<std::chrono::milliseconds>(now - start_).count();
}
private:
std::chrono::time_point<std::chrono::high_resolution_clock> start_;
};
// 辅助函数：打印结果
void printResults(const std::string& testName, int capacity,
const std::vector<int>& get_operations,
const std::vector<int>& hits) {
std::cout << "=== " << testName << " 结果汇总 ===" << std::endl;
std::cout << "缓存大小: " << capacity << std::endl;
// 假设对应的算法名称已在测试函数中定义
std::vector<std::string> names;
if (hits.size() == 3) {
names = {"LRU", "LFU", "ARC"};
} else if (hits.size() == 4) {
names = {"LRU", "LFU", "ARC", "LRU-K"};
} else if (hits.size() == 5) {
names = {"LRU", "LFU", "ARC", "LRU-K", "LFU-Aging"};
}
for (size_t i = 0; i < hits.size(); ++i) {
double hitRate = 100.0 * hits[i] / get_operations[i];
std::cout << (i < names.size() ? names[i] : "Algorithm " + std::to_string(i+1))
<< " - 命中率: " << std::fixed << std::setprecision(2)
<< hitRate << "% ";
// 添加具体命中次数和总操作次数
std::cout << "(" << hits[i] << "/" << get_operations[i] << ")" << std::endl;
}
std::cout << std::endl; // 添加空行，使输出更清晰
}
void testHotDataAccess() {
std::cout << "\n=== 测试场景1：热点数据访问测试 ===" << std::endl;
const int CAPACITY = 20; // 缓存容量
const int OPERATIONS = 500000; // 总操作次数
const int HOT_KEYS = 20; // 热点数据数量
const int COLD_KEYS = 5000; // 冷数据数量
KamaCache::KLruCache<int, std::string> lru(CAPACITY);
KamaCache::KLfuCache<int, std::string> lfu(CAPACITY);
KamaCache::KArcCache<int, std::string> arc(CAPACITY);
// 为LRU-K设置合适的参数：
// - 主缓存容量与其他算法相同
// - 历史记录容量设为可能访问的所有键数量
// - k=2表示数据被访问2次后才会进入缓存，适合区分热点和冷数据
KamaCache::KLruKCache<int, std::string> lruk(CAPACITY, HOT_KEYS + COLD_KEYS, 2);
KamaCache::KLfuCache<int, std::string> lfuAging(CAPACITY, 20000);
std::random_device rd;
std::mt19937 gen(rd());
// 基类指针指向派生类对象，添加LFU-Aging
std::array<KamaCache::KICachePolicy<int, std::string>*, 5> caches = {&lru, &lfu, &arc, &lruk, &lfuAging};
std::vector<int> hits(5, 0);
std::vector<int> get_operations(5, 0);
std::vector<std::string> names = {"LRU", "LFU", "ARC", "LRU-K", "LFU-Aging"};
// 为所有的缓存对象进行相同的操作序列测试
for (int i = 0; i < caches.size(); ++i) {
// 先预热缓存，插入一些数据
for (int key = 0; key < HOT_KEYS; ++key) {
std::string value = "value" + std::to_string(key);
caches[i]->put(key, value);
}
// 交替进行put和get操作，模拟真实场景
for (int op = 0; op < OPERATIONS; ++op) {
// 大多数缓存系统中读操作比写操作频繁
// 所以设置30%概率进行写操作
bool isPut = (gen() % 100 < 30);
int key;
// 70%概率访问热点数据，30%概率访问冷数据
if (gen() % 100 < 70) {
key = gen() % HOT_KEYS; // 热点数据
} else {
key = HOT_KEYS + (gen() % COLD_KEYS); // 冷数据
}
if (isPut) {
// 执行put操作
std::string value = "value" + std::to_string(key) + "_v" + std::to_string(op % 100);
caches[i]->put(key, value);
} else {
// 执行get操作并记录命中情况
std::string result;
get_operations[i]++;
if (caches[i]->get(key, result)) {
hits[i]++;
}
}
}
}
// 打印测试结果
printResults("热点数据访问测试", CAPACITY, get_operations, hits);
}
void testLoopPattern() {
std::cout << "\n=== 测试场景2：循环扫描测试 ===" << std::endl;
const int CAPACITY = 50; // 缓存容量
const int LOOP_SIZE = 500; // 循环范围大小
const int OPERATIONS = 200000; // 总操作次数
KamaCache::KLruCache<int, std::string> lru(CAPACITY);
KamaCache::KLfuCache<int, std::string> lfu(CAPACITY);
KamaCache::KArcCache<int, std::string> arc(CAPACITY);
// 为LRU-K设置合适的参数：
// - 历史记录容量设为总循环大小的两倍，覆盖范围内和范围外的数据
// - k=2，对于循环访问，这是一个合理的阈值
KamaCache::KLruKCache<int, std::string> lruk(CAPACITY, LOOP_SIZE * 2, 2);
KamaCache::KLfuCache<int, std::string> lfuAging(CAPACITY, 3000);
std::array<KamaCache::KICachePolicy<int, std::string>*, 5> caches = {&lru, &lfu, &arc, &lruk, &lfuAging};
std::vector<int> hits(5, 0);
std::vector<int> get_operations(5, 0);
std::vector<std::string> names = {"LRU", "LFU", "ARC", "LRU-K", "LFU-Aging"};
std::random_device rd;
std::mt19937 gen(rd());
// 为每种缓存算法运行相同的测试
for (int i = 0; i < caches.size(); ++i) {
// 先预热一部分数据（只加载20%的数据）
for (int key = 0; key < LOOP_SIZE / 5; ++key) {
std::string value = "loop" + std::to_string(key);
caches[i]->put(key, value);
}
// 设置循环扫描的当前位置
int current_pos = 0;
// 交替进行读写操作，模拟真实场景
for (int op = 0; op < OPERATIONS; ++op) {
// 20%概率是写操作，80%概率是读操作
bool isPut = (gen() % 100 < 20);
int key;
// 按照不同模式选择键
if (op % 100 < 60) { // 60%顺序扫描
key = current_pos;
current_pos = (current_pos + 1) % LOOP_SIZE;
} else if (op % 100 < 90) { // 30%随机跳跃
key = gen() % LOOP_SIZE;
} else { // 10%访问范围外数据
key = LOOP_SIZE + (gen() % LOOP_SIZE);
}
if (isPut) {
// 执行put操作，更新数据
std::string value = "loop" + std::to_string(key) + "_v" + std::to_string(op % 100);
caches[i]->put(key, value);
} else {
// 执行get操作并记录命中情况
std::string result;
get_operations[i]++;
if (caches[i]->get(key, result)) {
hits[i]++;
}
}
}
}
printResults("循环扫描测试", CAPACITY, get_operations, hits);
}
void testWorkloadShift() {
std::cout << "\n=== 测试场景3：工作负载剧烈变化测试 ===" << std::endl;
const int CAPACITY = 30; // 缓存容量
const int OPERATIONS = 80000; // 总操作次数
const int PHASE_LENGTH = OPERATIONS / 5; // 每个阶段的长度
KamaCache::KLruCache<int, std::string> lru(CAPACITY);
KamaCache::KLfuCache<int, std::string> lfu(CAPACITY);
KamaCache::KArcCache<int, std::string> arc(CAPACITY);
KamaCache::KLruKCache<int, std::string> lruk(CAPACITY, 500, 2);
KamaCache::KLfuCache<int, std::string> lfuAging(CAPACITY, 10000);
std::random_device rd;
std::mt19937 gen(rd());
std::array<KamaCache::KICachePolicy<int, std::string>*, 5> caches = {&lru, &lfu, &arc, &lruk, &lfuAging};
std::vector<int> hits(5, 0);
std::vector<int> get_operations(5, 0);
std::vector<std::string> names = {"LRU", "LFU", "ARC", "LRU-K", "LFU-Aging"};
// 为每种缓存算法运行相同的测试
for (int i = 0; i < caches.size(); ++i) {
// 先预热缓存，只插入少量初始数据
for (int key = 0; key < 30; ++key) {
std::string value = "init" + std::to_string(key);
caches[i]->put(key, value);
}
// 进行多阶段测试，每个阶段有不同的访问模式
for (int op = 0; op < OPERATIONS; ++op) {
// 确定当前阶段
int phase = op / PHASE_LENGTH;
// 每个阶段的读写比例不同
int putProbability;
switch (phase) {
case 0: putProbability = 15; break; // 阶段1: 热点访问，15%写入更合理
case 1: putProbability = 30; break; // 阶段2: 大范围随机，写比例为30%
case 2: putProbability = 10; break; // 阶段3: 顺序扫描，10%写入保持不变
case 3: putProbability = 25; break; // 阶段4: 局部性随机，微调为25%
case 4: putProbability = 20; break; // 阶段5: 混合访问，调整为20%
default: putProbability = 20;
}
// 确定是读还是写操作
bool isPut = (gen() % 100 < putProbability);
// 根据不同阶段选择不同的访问模式生成key - 优化后的访问范围
int key;
if (op < PHASE_LENGTH) { // 阶段1: 热点访问 - 热点数量5，使热点更集中
key = gen() % 5;
} else if (op < PHASE_LENGTH * 2) { // 阶段2: 大范围随机 - 范围400，更适合30大小的缓存
key = gen() % 400;
} else if (op < PHASE_LENGTH * 3) { // 阶段3: 顺序扫描 - 保持100个键
key = (op - PHASE_LENGTH * 2) % 100;
} else if (op < PHASE_LENGTH * 4) { // 阶段4: 局部性随机 - 优化局部性区域大小
// 产生5个局部区域，每个区域大小为15个键，与缓存大小20接近但略小
int locality = (op / 800) % 5; // 调整为5个局部区域
key = locality * 15 + (gen() % 15); // 每区域15个键
} else { // 阶段5: 混合访问 - 增加热点访问比例
int r = gen() % 100;
if (r < 40) { // 40%概率访问热点（从30%增加）
key = gen() % 5; // 5个热点键
} else if (r < 70) { // 30%概率访问中等范围
key = 5 + (gen() % 45); // 缩小中等范围为50个键
} else { // 30%概率访问大范围（从40%减少）
key = 50 + (gen() % 350); // 大范围也相应缩小
}
}
if (isPut) {
// 执行写操作
std::string value = "value" + std::to_string(key) + "_p" + std::to_string(phase);
caches[i]->put(key, value);
} else {
// 执行读操作并记录命中情况
std::string result;
get_operations[i]++;
if (caches[i]->get(key, result)) {
hits[i]++;
}
}
}
}
printResults("工作负载剧烈变化测试", CAPACITY, get_operations, hits);
}
int main() {
testHotDataAccess();
testLoopPattern();
testWorkloadShift();
return 0;
}