Recently, I had the pleasure of interviewing with Rubrik, and I must say, it was an incredibly friendly and understanding experience. The interviewer was genuinely interested in understanding my thought process and problem-solving abilities. In this blog post, I'll walk you through the LRU Cache question they asked me and how I approached it. I'll also discuss the follow-up questions regarding my choice of data structure and other relevant aspects of the interview.
The LRU Cache Question:
The interview started with a classic software engineering problem: designing an LRU (Least Recently Used) Cache. I was tasked with implementing a data structure that can efficiently store a limited number of items and quickly identify and evict the least recently used item when the cache reaches its capacity.
My Approach:
Firstly, I thanked the interviewer for the question and clarified some of the details to ensure I fully understood the requirements. Then, I began discussing my thought process aloud. I mentioned that I would use a combination of a doubly linked list and a hash map to create the LRU Cache.
-
Doubly Linked List: I explained that the doubly linked list would help maintain the order of item access. The most recently used item would be placed at the front of the list, while the least recently used item would be at the end.
-
Hash Map: I then talked about how the hash map would provide fast access to the nodes in the doubly linked list. The keys in the hash map would be the cache keys, and the values would be the corresponding nodes in the linked list, allowing O(1) access time.
Next, I discussed the operations needed for the LRU Cache:
-
Get(key): When a get operation is performed, I would check if the key exists in the hash map. If it does, I would move the corresponding node to the front of the linked list (representing its most recently used status) and return the associated value.
-
Put(key, value): When a put operation is performed, I would first check if the key already exists in the hash map. If it does, I would update the value and move the corresponding node to the front of the linked list. If the cache is full, I would remove the last node (least recently used) from the linked list and the hash map. Then, I would create a new node for the new key-value pair and add it to both the linked list's front and the hash map.
The Follow-up Questions:
After successfully implementing the LRU Cache, the interviewer seemed impressed with my approach and asked a few follow-up questions to better understand my reasoning:
-
"Why did you choose a doubly linked list for the LRU Cache implementation?"
I explained that a doubly linked list would allow constant time (O(1)) removal and insertion operations. When an item is accessed or updated, I only need to change the pointers to move it to the front of the list. The doubly linked list provides a more efficient way to maintain the order of recently used items compared to a regular list or array. -
"Could you optimize the cache's space complexity further?"
In response, I discussed the trade-off between space and time complexity. To optimize space, I could use a single-linked list, which would reduce the node size by half. However, this would make removing a node from the middle of the list more cumbersome and time-consuming (O(n)). I explained that the chosen data structure provided an excellent balance between time and space efficiency for an LRU Cache.
My interview experience with Rubrik was truly exceptional. The friendly and understanding approach of the interviewer allowed me to showcase my problem-solving skills and discuss my thought process openly. I enjoyed designing the LRU Cache and explaining my choice of data structure to manage it effectively. Regardless of the outcome, I left the interview with a positive impression of Rubrik as a company that values both technical proficiency and a collaborative work environment. I'm eagerly looking forward to future opportunities and challenges in my software engineering journey.
