I'd done the simpler version before so I felt okay starting out.
Use a dynamic array to store all elements (including duplicates) and a hash map from value to a set of indices in the array. For insert, append to the array and add the index to the map; for remove, swap the target element with the last element, update the map, and pop from the array; for getRandom, pick a random index from the array. This ensures average O(1) time for all operations and naturally handles duplicates with proportional probability.
Pro tip: Emphasize that the swap-with-last technique is key to O(1) removal, and that using a set of indices (or a list with lazy deletion) efficiently handles duplicates. Also, mention that getRandom's proportionality is achieved because each occurrence occupies a separate array slot.
Confirm that duplicates are allowed, getRandom should return values with probability proportional to their frequency, and all operations must be average O(1). Ask about potential constraints like memory or thread safety if relevant.
Choose a dynamic array (e.g., ArrayList in Java, list in Python) to store all elements, and a hash map from value to a collection of indices (e.g., set or list) to track positions. This supports O(1) average insert, remove, and random access.
Append the new value to the array, then add its index to the map's collection for that value. If the value is new, create a new entry in the map.
Retrieve an index of the value to remove from the map. Swap the element at that index with the last element in the array, update the map for the swapped element, then remove the last element from the array and the index from the map. If the map entry becomes empty, remove it.
Generate a random index between 0 and array size - 1, and return the element at that index. This gives each occurrence equal probability, so values are returned proportionally to their frequency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data structure, its operations, and the scale requirements (e.g., data size, read/write ratio, latency SLAs). Then propose a partitioning strategy (e.g., sharding by key) and describe how each operation works across nodes, including replication and consistency mechanisms. Finally, discuss trade-offs such as consistency vs. availability, latency vs. throughput, and complexity vs. performance.
Pro tip: Explicitly state your assumptions about the workload (e.g., read-heavy, write-heavy, uniform vs. skewed access) and tie your design choices to those assumptions—this shows you understand that distributed design is context-dependent and avoids over-engineering.
Ask about the data structure's operations, expected scale (data volume, QPS), latency/consistency requirements, and failure tolerance. This ensures your solution addresses the actual problem.
Decide how to split the data across servers (e.g., range, hash, or consistent hashing) based on access patterns. Explain how this affects load balancing and scalability.
Determine the replication factor and consistency model (e.g., strong vs. eventual). Describe how writes propagate and how reads are served (e.g., quorum reads/writes).
For each operation (e.g., insert, lookup, delete), explain the steps: routing to the correct shard, coordinating replicas, handling failures, and returning results. Include how metadata (e.g., shard map) is managed.
Analyze the trade-offs of your design (e.g., consistency vs. latency, complexity vs. scalability) and mention alternative approaches (e.g., CRDTs, gossip protocols) with their pros and cons.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.