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LRU Cache implementation using Double Linked Lists
Caching is a technique to improve a computer's performance by storing frequently accessed data in a cache. The cache is a high-speed storage area in a computer. In this, data can be quickly retrieved from the cache rather than from slower main memory or disc storage whenever needed. Caching can be accomplished in a number of ways. This includes the use of a hash table, an array, or a linked list. In this article, we will explore the LRU Cache implementation using Double Linked Lists in detail.
What is LRU Cache implementation?
The Least Recently Used (LRU) algorithm is a popular caching algorithm that removes the least recently used item from the cache. The LRU removes the least recently used item as soon as it detects that the cache is full. The LRU algorithm accomplishes this by keeping track of the order of access of items in the cache.
When a new item is introduced to the memory cache; the LRU technique shifts it to the highest position of the list, indicating that it has been used most recently. The least recently utilized item is usually near the bottom of the list. If the cookie is full and an additional item needs to be added, the least recently used product is removed.
A double-linked list is able to be utilized to keep apprised of the arrangement of items in the cache while establishing the LRU algorithm. Each of the items in the list is denoted by a node with a key-value pair. The node in question also contains points to the nodes in the list that come before and after it. A hash map can also be used to offer quick access to cache items. The hash map associates keys with their corresponding linked list nodes.
Advantages of LRU Cache
The use of a doubly linked list to implement an LRU cache has several advantages over other caching algorithms −
Fast Access − The doubly linked list allows for constant-time access to the list's beginning and end, making it simple to retrieve the most recently used or least recently used item. This allows for quick access to data in the cache as well as quick eviction of the least recently used item.
Efficient Eviction − By removing the node at the end of the list, the doubly linked list allows for efficient eviction of the least recently used item. This means that when the cache reaches its maximum capacity, the algorithm can quickly free up space.
Flexible Capacity − The LRU Cache's capacity can be easily adjusted by adjusting the maximum number of items that can be stored in the cache. This makes optimizing the cache for different use cases and workloads simple.
Low Overhead − Due to its low overhead, the implementation is suitable for use in memory-constrained environments. The doubly linked list and hash map require only a small amount of extra memory to be stored.
Customizability − LRU cache can be easily modified to meet a variety of needs and use cases. According to the requirements of the application, for instance, the cache capacity, eviction rules, and other parameters can be changed to optimize the cache behavior.
Disadvantages of LRU Cache
While using a doubly linked list to implement an LRU cache has several advantages, there are some drawbacks to consider −
Memory Overhead &minus When compared to other caching algorithms, the use of a doubly linked list and a hash map to implement the cache requires more memory overhead. In memory-constrained environments or when caching large amounts of data, this can be a problem.
Complexity &minus The implementation of an LRU Cache using a double-linked list might be more complex than it looks. Though it is comparatively simpler to implement than other caching algorithms, prior knowledge of hash maps and linked lists is a must. This makes it complex to implement correctly and, without which might lead to bugs.
Limited Adaptability &minus The LRU Cache algorithm ideally, should perform well in situations with a limited set of keys and a fixed capacity. But, in case there are a large number of unique keys or the capacity must be frequently changed, it may not perform as well.
Inefficient for Larger Datasets &minus The LRU Cache algorithm may not be as efficient for very large data sets. But other caching algorithms, like Bloom Filter or a Trie, may be more appropriate in the case of larger datasets.
Removal of Frequently Used Items &minus Sometimes, The LRU Cache algorithm may remove items that are still frequently used as well. This might result in a decrease in the cache hit rate and overall performance of the algorithm. We can mitigate this by increasing cache capacity or by using a different caching algorithm that keeps the frequency of use in consideration.
A double-linked list can be used to implement an LRU cache in a quick and effective way. This is used to add caching capabilities to a software system. In this article, we learned that the combining the use of a double-linked list with a hash map enables powerful features like effective memory management, quick data retrieval, and predictable data access behavior. Further, this allows for customization in terms of cache size and removal rules. This makes it applicable to a number of use cases.
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