Design a Simple LRU Cache using Python/Java built-ins
Implement an LRU cache using language-specific ordered dictionary structures (e.g., Python's `OrderedDict` or Java's `LinkedHashMap`) to maintain the $O(1)$ complexity requirement.
Why Interviewers Ask This
Google asks this to verify if candidates understand that standard library abstractions are valid engineering tools when they meet complexity constraints. They evaluate your ability to recognize existing optimized structures like OrderedDict or LinkedHashMap, avoiding reinventing the wheel while still articulating the underlying logic of eviction policies and access ordering.
How to Answer This Question
1. Clarify requirements immediately: confirm capacity limits and whether operations must be strictly O(1). 2. Propose using the language's built-in ordered dictionary rather than a manual doubly-linked list, explaining why this satisfies performance needs. 3. Walk through the logic for 'get' and 'put' operations: explain how accessing a key moves it to the end (most recently used) and how insertion triggers removal of the first item (least recently used) when full. 4. Provide code snippets demonstrating the specific API calls, such as move_to_end in Python or removeEldestEntry in Java. 5. Conclude by analyzing time and space complexity to prove the solution meets the O(1) requirement for both read and write operations.
Key Points to Cover
- Explicitly stating the use of built-in structures saves development time and reduces bugs
- Confirming that the chosen data structure naturally supports O(1) access and updates
- Explaining the mechanism of moving accessed items to the 'end' of the order
- Describing the eviction strategy where the 'head' or 'first' element is removed
- Demonstrating knowledge of specific API methods like move_to_end or removeEldestEntry
Sample Answer
To design an LRU cache with O(1) complexity, I would leverage the built-in ordered dictionary provided by the language runtime. In Python, I'd use collections.OrderedDict, and in Java, java.util.LinkedHashMap. The interviewer likely wants to see that I know these structures maintain insertion order automatically. For the get operation, I check if the key exists. If it does, I retrieve the value and then update its position to mark it as most recently used. In Python, this is done via move_to_end(key), and in Java, we override getOrDefault or handle it within the constructor's accessOrder parameter. For the put operation, if the key already exists, we update the value and refresh its position. If it's new, we insert it at the end. Crucially, before insertion, if the cache exceeds capacity, we must evict the least recently used item. Since our structure keeps items ordered, the first item is always the oldest. We pop that item off. This ensures every operation remains O(1) because hash map lookups are constant time, and moving or removing from the ends of the linked list within the dictionary is also constant time. This approach avoids the overhead of manually implementing a doubly-linked list, reducing potential bugs while meeting Google's high standards for efficient, production-ready code.
Common Mistakes to Avoid
- Implementing a custom doubly-linked list when a built-in ordered map was sufficient and expected
- Failing to mention how the specific library method handles the reordering of keys during access
- Neglecting to explicitly state the time complexity analysis for both get and put operations
- Confusing the order of elements and attempting to remove from the wrong end of the structure
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