LFU Cache
Problem Statement - link #
Design and implement a data structure for a Least Frequently Used (LFU) cache.
Implement the LFUCache
class:
LFUCache(int capacity)
Initializes the object with thecapacity
of the data structure.int get(int key)
Gets the value of thekey
if thekey
exists in the cache. Otherwise, returns-1
.void put(int key, int value)
Update the value of thekey
if present, or inserts thekey
if not already present. When the cache reaches itscapacity
, it should invalidate and remove the least frequently used key before inserting a new item. For this problem, when there is a tie (i.e., two or more keys with the same frequency), the least recently usedkey
would be invalidated.
To determine the least frequently used key, a use counter is maintained for each key in the cache. The key with the smallest use counter is the least frequently used key.
When a key is first inserted into the cache, its use counter is set to 1
(due to the put
operation). The use counter for a key in the cache is incremented either a get
or put
operation is called on it.
The functions get
and put
must each run in O(1)
average time complexity.
Examples #
Example 1:
Input
["LFUCache", "put", "put", "get", "put", "get", "get", "put", "get", "get", "get"]
[[2], [1, 1], [2, 2], [1], [3, 3], [2], [3], [4, 4], [1], [3], [4]]
Output
[null, null, null, 1, null, -1, 3, null, -1, 3, 4]
Explanation
// cnt(x) = the use counter for key x
// cache=[] will show the last used order for tiebreakers (leftmost element is most recent)
LFUCache lfu = new LFUCache(2);
lfu.put(1, 1); // cache=[1,_], cnt(1)=1
lfu.put(2, 2); // cache=[2,1], cnt(2)=1, cnt(1)=1
lfu.get(1); // return 1
// cache=[1,2], cnt(2)=1, cnt(1)=2
lfu.put(3, 3); // 2 is the LFU key because cnt(2)=1 is the smallest, invalidate 2.
// cache=[3,1], cnt(3)=1, cnt(1)=2
lfu.get(2); // return -1 (not found)
lfu.get(3); // return 3
// cache=[3,1], cnt(3)=2, cnt(1)=2
lfu.put(4, 4); // Both 1 and 3 have the same cnt, but 1 is LRU, invalidate 1.
// cache=[4,3], cnt(4)=1, cnt(3)=2
lfu.get(1); // return -1 (not found)
lfu.get(3); // return 3
// cache=[3,4], cnt(4)=1, cnt(3)=3
lfu.get(4); // return 4
// cache=[4,3], cnt(4)=2, cnt(3)=3
Constraints #
0 <= capacity <= 10^4
0 <= key <= 10^5
0 <= value <= 10^9
- At most
2 * 10^5
calls will be made toget
andput
.
Solutions #
class LFUCache {
public:
unordered_map <int,list<pair<int,int>>> lst_map; //will store frequency as key and list of all <key,value> with that frequency
unordered_map <int,list<pair<int,int>>::iterator> key_map; //will store key and it's respective address
unordered_map <int,int> frequency_map; //will store key and it's frequency
int cap=0,size=0,min_frequency=0;
LFUCache(int capacity)
{
cap=capacity;
}
int get(int key)
{
auto found= key_map.find(key);
if(found==key_map.end())
return -1;
else
{ auto temp = found->second->second;
put(key,temp);
return temp;
}
};
void put(int key, int value)
{
auto found = key_map.find(key);
if(found== key_map.end())
{ if(cap!=0)
{
if(size>=cap)
{ //removing page
auto temp = lst_map[min_frequency].back(); //making copy of popped element
lst_map[min_frequency].pop_back(); //popping it out
key_map.erase(temp.first); //popping from key_map
frequency_map.erase(temp.first); //popping from frequency_map;
// done removing
}
else
size++;
lst_map[1].emplace_front(make_pair(key,value)); // adding node in list
min_frequency=1;
key_map[key]= lst_map[1].begin(); // adding new key iterator pair in key map
frequency_map[key]=1;
}
}
else
{
auto frequency = frequency_map[key];
lst_map[frequency].erase(found->second); //removed from old list
if(lst_map[frequency].size()==0) //in case list becomes empty it might change min_frequency
{
if(min_frequency ==frequency)
min_frequency++;
}
lst_map[++frequency].emplace_front(key,value); //added in new list
key_map[key]=lst_map[frequency].begin(); //updated in keymap
frequency_map[key]=frequency;
}
}
};