Explain LRU caching and its purpose.

Explain LRU caching and its purpose.

• A. Evict the least recently used item when the cache is full.
• B. Evict the most recently used item.
• C. Evict a random item.
• D. Never evict items; cache grows indefinitely.

Answer: (A)

LRU caching centers on keeping the items you’ve used most recently because they’re most likely to be used again soon. When the cache runs out of space, it removes the item that hasn’t been touched for the longest time, making room for newer data while preserving the ones you’ve just accessed. This approach capitalizes on temporal locality: recently accessed data tends to be accessed again in the near future. In practice, caches implement this with a data structure that lets them quickly find items and also quickly update their recency. A common setup uses a hashmap for O(1) lookups and a doubly linked list to track order of use. Each time you access an item, it’s moved to the front; when eviction is needed, the item at the back (the least recently used) is removed. This keeps the most relevant data close at hand. For intuition, think of recently opened webpages or files you’ve worked with. Those are likely to be needed again soon, so LRU aims to keep them in the cache. Other options don’t fit the goal: evicting the most recently used would throw away items you just used, harming performance; evicting randomly adds unpredictability and potential waste; letting nothing ever be evicted causes the cache to grow without bound, which is impractical.

LRU caching centers on keeping the items you’ve used most recently because they’re most likely to be used again soon. When the cache runs out of space, it removes the item that hasn’t been touched for the longest time, making room for newer data while preserving the ones you’ve just accessed. This approach capitalizes on temporal locality: recently accessed data tends to be accessed again in the near future.

In practice, caches implement this with a data structure that lets them quickly find items and also quickly update their recency. A common setup uses a hashmap for O(1) lookups and a doubly linked list to track order of use. Each time you access an item, it’s moved to the front; when eviction is needed, the item at the back (the least recently used) is removed. This keeps the most relevant data close at hand.

For intuition, think of recently opened webpages or files you’ve worked with. Those are likely to be needed again soon, so LRU aims to keep them in the cache.

Other options don’t fit the goal: evicting the most recently used would throw away items you just used, harming performance; evicting randomly adds unpredictability and potential waste; letting nothing ever be evicted causes the cache to grow without bound, which is impractical.