Cache目的
为了加快查找速度，leveldb采用Cache的方式
基本原理
leveldb中默认实现的Cache是LRU Cache(最近最少使用)。
LRU Cache
- 新数据插入到链表头部；
- 每当缓存命中（即缓存数据被访问），则将数据移到链表头部；
- 当链表满的时候，将链表尾部的数据丢弃。
具体实现
先上代码
// A single shard of sharded cache.
class LRUCache {
public:
LRUCache();
~LRUCache();
// Separate from constructor so caller can easily make an array of LRUCache
void SetCapacity(size_t capacity) { capacity_ = capacity; }
// Like Cache methods, but with an extra "hash" parameter.
Cache::Handle* Insert(const Slice& key, uint32_t hash,
void* value, size_t charge,
void (*deleter)(const Slice& key, void* value));
Cache::Handle* Lookup(const Slice& key, uint32_t hash);
void Release(Cache::Handle* handle);
void Erase(const Slice& key, uint32_t hash);
void Prune();
size_t TotalCharge() const {
MutexLock l(&mutex_);
return usage_;
}
private:
void LRU_Remove(LRUHandle* e);
void LRU_Append(LRUHandle*list, LRUHandle* e);
void Ref(LRUHandle* e);
void Unref(LRUHandle* e);
bool FinishErase(LRUHandle* e);
// Initialized before use.
size_t capacity_;
// mutex_ protects the following state.
mutable port::Mutex mutex_;
size_t usage_;
// Dummy head of LRU list.
// lru.prev is newest entry, lru.next is oldest entry.
// Entries have refs==1 and in_cache==true.
LRUHandle lru_;
// Dummy head of in-use list.
// Entries are in use by clients, and have refs >= 2 and in_cache==true.
LRUHandle in_use_;
HandleTable table_;
};
这里需要注意的几点是，leveldb对基本方法进行了一些改进，采用了两个链表，
lru_：包含哪些不是正在被使用的数据（已按照最近使用时间排序）
in_use_：包含正在被使用的数据（未排序）
数据的结构
// An entry is a variable length heap-allocated structure. Entries
// are kept in a circular doubly linked list ordered by access time.
struct LRUHandle {
void* value;
void (*deleter)(const Slice&, void* value);
LRUHandle* next_hash;
LRUHandle* next;
LRUHandle* prev;
size_t charge; // TODO(opt): Only allow uint32_t?
size_t key_length;
bool in_cache; // Whether entry is in the cache.
uint32_t refs; // References, including cache reference, if present.
uint32_t hash; // Hash of key(); used for fast sharding and comparisons
char key_data[1]; // Beginning of key
Slice key() const {
// For cheaper lookups, we allow a temporary Handle object
// to store a pointer to a key in "value".
if (next == this) {
return *(reinterpret_cast<Slice*>(value));
} else {
return Slice(key_data, key_length);
}
}
};
数据自身带有一个refs标志，当其值为0，表示不在cache中，值为1，表示在lru_链表中，当值大于1时，表示在in_use_的链表中，且其值减一表示正在使用这个数据的cliens的个数。
- 当新数据进入Cache时，首先初始化数据
Cache::Handle* LRUCache::Insert(
const Slice& key, uint32_t hash, void* value, size_t charge,
void (*deleter)(const Slice& key, void* value)) {
MutexLock l(&mutex_);
LRUHandle* e = reinterpret_cast<LRUHandle*>(
malloc(sizeof(LRUHandle)-1 + key.size()));
e->value = value;
e->deleter = deleter;
e->charge = charge;
e->key_length = key.size();
e->hash = hash;
e->in_cache = false;
e->refs = 1; // for the returned handle.
memcpy(e->key_data, key.data(), key.size());
将数据加入in_use_的链表中
if (capacity_ > 0) {
e->refs++; // for the cache's reference.
e->in_cache = true;
LRU_Append(&in_use_, e);
usage_ += charge;
FinishErase(table_.Insert(e));
} // else don't cache. (Tests use capacity_==0 to turn off caching.)
如果Cache满了则将lru_中的老数据移除
while (usage_ > capacity_ && lru_.next != &lru_) {
LRUHandle* old = lru_.next;
assert(old->refs == 1);
bool erased = FinishErase(table_.Remove(old->key(), old->hash));
if (!erased) { // to avoid unused variable when compiled NDEBUG
assert(erased);
}
}
return reinterpret_cast<Cache::Handle*>(e);
}
其余函数基本就是一些简单的链表操作，没什么好说的，其实注释很详细的😄
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