The basic CRUD layer felt straightforward and I got through it fine.
Start by clarifying requirements and constraints, then propose a data model using a hash map of records, where each record is itself a hash map of field-value pairs. Discuss operations, edge cases, and complexity, and consider optimizations like lazy deletion or maintaining a field count.
Pro tip: Demonstrate awareness of real-world concerns such as thread safety and memory management, and mention how you would test the implementation thoroughly, including edge cases like deleting non-existent records.
Ask questions to understand expected operations, data sizes, concurrency needs, and persistence requirements. Confirm that deleting the last field removes the record.
Propose a nested hash map structure: an outer map from record keys to inner maps, where inner maps store field-value pairs. Discuss alternatives like using a single map with composite keys.
Specify the behavior of set, get, and delete. For set, update or insert the field; for get, retrieve the value or indicate absence; for delete, remove the field and if the record becomes empty, remove the record.
Discuss time and space complexity for each operation. Cover edge cases: deleting non-existent fields/records, setting fields on non-existent records, and handling empty records.
Mention potential optimizations like lazy deletion, maintaining field counts, or using concurrent data structures for thread safety. Discuss how to extend to support additional operations or persistence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what operations exist, how keys are referenced, and the expected scale. Then propose a data structure that tracks reference counts efficiently, such as a hash map from key to count, and discuss how to maintain a sorted order for topN queries. Finally, outline the algorithm for topN, including tie-breaking, and analyze time and space complexity.
Pro tip: Mention that you would use a min-heap of size N to find top N in O(M log N) time, where M is the number of unique keys, and that tie-breaking alphabetically can be handled by including the key in the heap comparison. Also, discuss whether the counts need to be persisted or can be in-memory, and how to handle concurrent updates if needed.
Ask about the operations that reference keys, the expected number of unique keys, the frequency of topN calls, and whether the data needs to be persisted or can be in-memory. Also clarify tie-breaking rules and if N is fixed or variable.
Propose a hash map (dictionary) mapping each key to its reference count. Discuss how to increment counts on each operation, and consider if additional structures are needed for efficient topN queries.
Describe an algorithm to retrieve the N most-accessed keys. For example, iterate through the hash map and maintain a min-heap of size N based on count, with ties broken alphabetically. Alternatively, sort the keys by count descending and key ascending, then take the first N.
For the heap approach, time complexity is O(M log N) per topN call, where M is the number of unique keys. Space complexity is O(M) for the hash map and O(N) for the heap. If topN is called frequently, consider maintaining a sorted structure or caching results.
Compare the heap approach with sorting (O(M log M)) and with maintaining a balanced BST or skip list for O(log M) updates and O(N) retrieval. Mention concurrency, persistence, and scalability considerations for a production system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and assumptions, then design a data model that stores lock ownership (e.g., a lock_owner field per key) and define the API semantics for set, delete, lock, and unlock. Walk through the operations with clear rules for authorization, error handling, and concurrency, and discuss trade-offs like atomicity, consistency, and scalability.
Pro tip: Emphasize atomicity and race conditions: locking must be atomic to prevent two users from locking simultaneously, and operations should be idempotent where possible. Also, consider how to handle lock expiration or stale locks to avoid deadlocks in a production system.
Ask about concurrency expectations, persistence, and whether locks should expire. Confirm that lock ownership is per key and that only the owner can modify or unlock.
Define a key-value store where each key has a value and an optional lock owner. Specify the API signatures for set, delete, lock, and unlock, including parameters and return values.
For each operation, specify the rules: set/delete allowed if unlocked or if requester is owner; lock allowed only if unlocked; unlock allowed only by owner. Include error responses for unauthorized attempts.
Explain how to ensure atomic check-and-set for lock and modify operations, using transactions, compare-and-swap, or distributed locks. Discuss race conditions and how to prevent them.
Talk about consistency vs availability, lock expiration, scalability, and potential optimizations like caching or sharding. Mention monitoring and failure recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.