Design HashMap
Design a HashMap without using any built-in hash table libraries. You need to implement the `put`, `get`, and `remove` methods. Discuss collision resolution.
Why Interviewers Ask This
Google asks this to evaluate a candidate's deep understanding of data structures, specifically hash table mechanics and collision resolution strategies. They assess whether you can translate theoretical concepts into efficient code without relying on built-in libraries, testing your ability to handle edge cases like resizing, load factors, and bucket management under pressure.
How to Answer This Question
1. Clarify Requirements: Confirm constraints like key/value types, expected size, and whether the map must be thread-safe or support specific operations. 2. Define Data Structure: Propose using an array of buckets (linked lists or trees) to handle collisions. Mention the initial capacity and load factor threshold. 3. Design Hash Function: Explain how to convert keys to indices, emphasizing handling negative numbers and ensuring uniform distribution. 4. Implement Logic: Walk through 'put' (handle collisions), 'get' (traverse bucket), and 'remove' (unlink node). 5. Discuss Optimization: Address rehashing when load factor exceeds limits and compare chaining vs. open addressing trade-offs for Google's high-scale environments.
Key Points to Cover
- Explicitly explaining the choice between chaining and open addressing
- Demonstrating a clear strategy for handling negative hash codes
- Defining a specific load factor threshold for triggering rehashing
- Calculating time complexity for both average and worst-case scenarios
- Mentioning potential optimizations like tree-based buckets for long chains
Sample Answer
To design a HashMap from scratch, I would start by defining a fixed-size array of buckets, where each bucket holds a linked list of entries. For the hash function, I'd use a standard polynomial rolling hash or Java's Integer.hashCode() logic, ensuring the result is non-negative by applying a bitwise AND with the array length minus one. This handles the modulo operation efficiently.
For collision resolution, I will implement separate chaining. When inserting a key-value pair, I calculate the index. If the bucket is empty, I create a new node. If it contains nodes, I traverse the list; if the key exists, I update the value; otherwise, I append a new node. This approach keeps average time complexity at O(1).
However, to maintain performance as the map grows, I need a resizing strategy. I'll track the number of elements and the load factor. Once the ratio of elements to bucket count exceeds a threshold, say 0.75, I will double the array size and rehash all existing entries into the new buckets. This prevents clustering and keeps lookups fast. While traversing linked lists is O(n) in worst-case scenarios, Google's systems often require robustness against adversarial inputs, so I might mention that switching to balanced binary search trees within buckets (like Java 8+) could improve worst-case performance to O(log n) if chains become too long.
Common Mistakes to Avoid
- Failing to handle negative hash values which causes out-of-bounds array access
- Ignoring the need for resizing when the load factor becomes too high
- Assuming the hash function always produces unique indices without collisions
- Not discussing the trade-off between memory usage and lookup speed
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