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I went with separate chaining because open addressing under pressure felt like a recipe for off-by-one disasters.
Start by clarifying requirements and constraints, then describe the core design: an array of buckets, each holding a linked list (or tree) for collision resolution. Explain the hash function, load factor, and resizing strategy, and finally walk through the implementation of put, get, remove, and contains with average O(1) time complexity.
Pro tip: Mention that you would use a balanced tree (e.g., red-black tree) for buckets when collisions are high, as Java's HashMap does, to guarantee O(log n) worst-case performance. Also, discuss the trade-offs between different collision resolution techniques (chaining vs. open addressing) and hash functions.
Ask about expected key/value types, thread-safety, and performance guarantees. Confirm that average O(1) is acceptable and that we can assume a good hash function.
Propose an array of buckets, each bucket being a linked list (or tree) of key-value pairs. Define the initial capacity, load factor threshold, and the hash function (e.g., use key's hashCode and apply supplemental hash).
For put: compute hash, find bucket, check for existing key, update or add. For get/contains: compute hash, search bucket. For remove: compute hash, find and remove entry. Handle resizing when load factor exceeds threshold.
When size/capacity > load factor, double capacity and rehash all entries. For collisions, use separate chaining; optionally convert long chains to balanced trees for efficiency.
Explain that average O(1) is achieved with good hash distribution and low load factor. Discuss worst-case O(n) or O(log n) with trees, and trade-offs between memory and speed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, then systematically present locking strategies from coarse-grained to fine-grained, explaining the trade-offs between throughput and consistency for each. Conclude with a recommendation based on the expected workload and a discussion of potential optimizations like lock striping or lock-free approaches.
Pro tip: Demonstrate awareness of real-world implementations like Java's ConcurrentHashMap and mention how they evolved (e.g., from segment locking to CAS+synchronized) to balance performance and consistency. This shows you understand practical trade-offs beyond textbook theory.
Ask about expected read/write ratio, consistency requirements (e.g., strong vs. eventual), and performance goals. This ensures your solution aligns with the interviewer's expectations.
Describe using a single lock for the entire HashMap. Explain that this provides strong consistency but severely limits throughput due to contention.
Discuss lock striping (e.g., per-bucket or per-segment locks) to allow concurrent access to different parts. Highlight improved throughput but increased complexity and potential for deadlocks.
Mention read-write locks, optimistic concurrency (e.g., CAS operations), and lock-free data structures. Explain how they can offer higher throughput but may weaken consistency or increase implementation complexity.
Compare strategies on throughput, consistency, scalability, and complexity. Recommend a strategy based on the clarified requirements, and mention real-world examples like ConcurrentHashMap.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.