【7.3】146. LRU caching mechanism

Method ： Hashtable + Double linked list

• keyword ：key,value Yes
• pattern recognition ： Once a key value pair appears , Think of hash table
• Change the access time of data ：1 Need to be able to access randomly ,2 You need to be able to insert the data into the head or tail

test ：

• Test insert , Including spillage
• Test update , Check for correct updates
• Test read , Check whether it can be read correctly

hashmap+ Double linked list , Complexity analysis ：

• Time complexity ：O1, Hash table location O1, List operation O1
• Spatial complexity ：Oc

Code ：

struct DLinkedNode {
    
    int key, value;
    DLinkedNode* prev;
    DLinkedNode* next;
    DLinkedNode(): key(0), value(0), prev(nullptr), next(nullptr) {
    }
    DLinkedNode(int _key, int _value): key(_key), value(_value), prev(nullptr), next(nullptr) {
    }
};

class LRUCache {
    
private:
    unordered_map<int, DLinkedNode*> cache;
    DLinkedNode* head;
    DLinkedNode* tail;
    int size;
    int capacity;

public:
    LRUCache(int _capacity): capacity(_capacity), size(0) {
      While initializing the data structure in the code , initialization capacity and size Two parameters 
        //  Using pseudo head and pseudo tail nodes 
        head = new DLinkedNode();  Create a head node 
        tail = new DLinkedNode();  Create tail node 
        head->next = tail;  Head -》 The tail 
        tail->prev = head;  Head 《- The tail 
    }
    
    int get(int key) {
    
        if (!cache.count(key)) {
      If not, return -1
            return -1;
        }
        //  If  key  There is , First, locate through the hash table , And then to the head 
        DLinkedNode* node = cache[key];  Locate the node through the hash table and then operate on it 
        moveToHead(node);  Here, the header is represented as the most frequently used element 
        return node->value;
    }
    
    void put(int key, int value) {
    
        if (!cache.count(key)) {
    
            //  If  key  non-existent , Create a new node 
            DLinkedNode* node = new DLinkedNode(key, value);
            //  Add to hash table 
            cache[key] = node;
            //  Add to the head of the bidirectional list 
            addToHead(node);
            ++size;
            if (size > capacity) {
    
                //  If the capacity is exceeded , Delete the tail node of the bidirectional linked list 
                DLinkedNode* removed = removeTail();
                //  Delete the corresponding entry in the hash table 
                cache.erase(removed->key);
                //  Prevent memory leaks 
                delete removed;
                --size;
            }
        }
        else {
    
            //  If  key  There is , First, locate through the hash table , Revise  value, And move to the head 
            DLinkedNode* node = cache[key];
            node->value = value;
            moveToHead(node);
        }
    }

    void addToHead(DLinkedNode* node) {
    
        node->prev = head;
        node->next = head->next;
        head->next->prev = node;
        head->next = node;
    }
    
    void removeNode(DLinkedNode* node) {
    
        node->prev->next = node->next;
        node->next->prev = node->prev;
    }

    void moveToHead(DLinkedNode* node) {
    
        removeNode(node);
        addToHead(node);
    }

    DLinkedNode* removeTail() {
      Here we extract the nodes that have not been used recently , It will not affect the operation of deleting him in the hash table , Because he didn't disappear 
        DLinkedNode* node = tail->prev;
        removeNode(node);
        return node;
    }
};